National Wildlife Health Center

Enhanced Surveillance Strategies for 

Detecting and Monitoring Chronic Wasting Disease 

in Free-Ranging Cervids

Open-File Report 2012–1036

U.S. Department of the Interior

U.S. Geological Survey
Front and back cover photographs by Dr. Daniel P. Walsh
Enhanced Surveillance Strategies for 

Detecting and Monitoring Chronic Wasting 

Disease in Free-Ranging Cervids

Edited by Dr. Daniel P. Walsh

Chapter 1

Risk Factors for Chronic Wasting Disease: A Deer Is Not a Deer 

Is Not a Deer

By Daniel P. Walsh, Ph.D., and Michael D. Samuel, Ph.D.

Chapter 2

Disease Surveillance: Incorporating Available Information to Enhance 

Disease-Detection Efforts

By Daniel P. Walsh, Ph.D., and David L. Otis, Ph.D.

Chapter 3

Monitoring Surveillance: Disease Investigations in Chronic Wasting 

Disease Positive Areas

By Julie A. Langenberg, VMD, Daniel J. O’Brien, DVM, Ph.D., Daniel P. Walsh, Ph.D., 

Lynn H. Creekmore, DVM, Jenny G. Powers, DVM, Ph.D., and Michael W. Miller, DVM, Ph.D.

National Wildlife Health Center

Open-File Report 2012–1036

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012

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reproduce any copyrighted materials contained within this report.

Suggested citation:

Walsh, D.P.,ed., 2012, Enhanced surveillance strategies for detecting and monitoring chronic wasting disease in freeranging cervids: U.S. Geological Survey Open-File Report 2012–1036. 42 p.
iii

Preface

In 2003, Surveillance Strategies for Detecting CWD in Free-ranging Deer and Elk was made 

available to wildlife managers as they developed or enhanced their chronic wasting disease 

(CWD) programs. The document was the product of a 2002 meeting hosted by the U.S. Geological Survey-National Wildlife Health Center (USGS-NWHC). Although the guidelines were stateof-the-art when published, wildlife managers more recently have expressed the need for more 

efficient surveillance strategies based on scientific information developed since 2003. 

At the request of the Association of Fish and Wildlife Agencies (AFWA), Bryan Richards of the 

USGS-NWHC led a 2008 meeting of wildlife health professionals, statisticians, and biometricians to update CWD surveillance strategies. The following chapters on CWD risk factors, 

surveillance, and monitoring were drafted by meeting participants and others in 2008. The 

intent of these chapters is to provide wildlife professionals with knowledge regarding recent 

developments in the arena of CWD surveillance, as well as present the collective experience of 

meeting participants in constructing, managing, and researching CWD surveillance techniques 

and programs. It is hoped that this information will aid managers in implementing more efficient 

surveillance and monitoring programs.

In 2010, AFWA formed a working group charged with providing members with updated surveillance strategies in response to further erosion of public support and financial support for CWD 

surveillance and monitoring. The AFWA CWD Working Group reviewed the three chapters, found 

them to be an excellent resource for wildlife managers.

v

Executive Summary

The purpose of this document is to provide wildlife management agencies with the foundation 

upon which they can build scientifically rigorous and cost-effective surveillance and monitoring 

programs for chronic wasting disease (CWD) or refine their existing programs. The first chapter 

provides an overview of potential demographic and spatial risk factors of susceptible wildlife 

populations that may be exploited for CWD surveillance and monitoring. The information contained in this chapter explores historic as well as recent developments in our understanding of 

CWD disease dynamics. It also contains many literature references for readers who may desire 

a more thorough review of the topics or CWD in general. The second chapter examines methods 

for enhancing efforts to detect CWD on the landscape where it is not presently known to exist 

and focuses on the efficiency and cost-effectiveness of the surveillance program. Specifically, it 

describes the means of exploiting current knowledge of demographic and spatial risk factors, as 

described in the first chapter, through a two-stage surveillance scheme that utilizes traditional 

design-based sampling approaches and novel statistical methods to incorporate information 

about the attributes of the landscape, environment, populations and individual animals into 

CWD surveillance activities. By accounting for these attributes, efficiencies can be gained and 

cost-savings can be realized. The final chapter is unique in relation to the first two chapters. 

Its focus is on designing programs to monitor CWD once it is discovered within a jurisdiction. 

Unlike the prior chapters that are more detailed or prescriptive, this chapter by design is considerably more general because providing comprehensive direction for creating monitoring programs for jurisdictions without consideration of their monitoring goals, sociopolitical constraints, 

or their biological systems, is not possible. Therefore, the authors draw upon their collective 

experiences implementing disease-monitoring programs to present the important questions 

to consider, potential tools, and various strategies for those wildlife management agencies 

endeavoring to create or maintain a CWD monitoring program. Its intent is to aid readers in 

creating efficient and cost-effective monitoring programs, while avoiding potential pitfalls. It is 

hoped that these three chapters will be useful tools for wildlife managers struggling to implement efficient and effective CWD disease management programs.

vii

Contents

Preface ...........................................................................................................................................................iii

Executive Summary.......................................................................................................................................v

Chapter 1

Risk Factors for Chronic Wasting Disease: A Deer Is Not a Deer Is Not a Deer

Introduction.....................................................................................................................................................1

Spatial Risk Factors.......................................................................................................................................1

Demographic Risk Factors ...........................................................................................................................4

Conclusion.......................................................................................................................................................5

Literature Cited...............................................................................................................................................6

Chapter 2

Disease Surveillance: Incorporating Available Information to Enhance 

Disease-Detection Efforts

Introduction...................................................................................................................................................11

Weighted Surveillance System .................................................................................................................12

Probabilistic Sampling Designs for Selection of Sampling Units—Clustering and 

Stratification...........................................................................................................................12

Unequal Probability Sampling and Risk Factors............................................................................13

Simulation Studies—Spatial Distribution of Samples ..................................................................13

Selection of Population Elements from within Sampling Units—No Risk Data .......................16

Selection of Population Elements from within Sampling Units–Risk Data Available..............17

Sample Size..........................................................................................................................................17

Temporal Scale of Disease Surveillance.................................................................................................21

When Is Enough...Enough?.......................................................................................................................21

Conclusion.....................................................................................................................................................22

Acknowledgments.......................................................................................................................................22

Literature Cited.............................................................................................................................................22

Chapter 3

Monitoring Surveillance: Disease Investigations in Chronic Wasting Disease-Positive Areas

Introduction...................................................................................................................................................25

Initial Assessment of a New Chronic Wasting Disease Focus: Evaluating Intensity 

and Spatial Extent .........................................................................................................................26

Monitoring Trends in Intensity of Disease...............................................................................................27

Relevant Questions.............................................................................................................................27

Metrics for Monitoring Trends in Intensity.....................................................................................27

Number of Chronic Wasting Disease-Positive Animals......................................................28

Prevalence..................................................................................................................................28

Yearling Prevalence...................................................................................................................28

Force of Infection.......................................................................................................................28

Incidence.....................................................................................................................................29

Strategies and Approaches for Intensity Monitoring...................................................................29

Where and How Many to Sample for Intensity Monitoring.........................................................31
viii

Chapter 3

Monitoring Surveillance: Disease Investigations in Chronic Wasting 

Disease-Positive Areas—Continued

Challenges with Intensity Monitoring .............................................................................................32

Monitoring Trends in Spatial Distribution................................................................................................32

Relevant Questions.............................................................................................................................32

Considerations in Monitoring Spatial Changes .............................................................................33

Metrics for Monitoring Trends in Spatial Distribution..................................................................33

Strategies and Approaches for Monitoring Surveillance............................................................34

Challenges with Spatial Monitoring ................................................................................................34

Research and Chronic Wasting Disease Monitoring Surveillance.....................................................36

Research That Could Improve Planning of Monitoring Surveillance ........................................36

Research Ideas to Consider Incorporating With a Monitoring Plan..........................................36

Conclusion.....................................................................................................................................................36

Literature Cited.............................................................................................................................................37

Appendix 1. Periodic Sampling Simulation ...........................................................................................39

Figures

1. Diagram showing landscape used in sampling simulations ...............................................14

2. Graphs showing A, Examples of distributions of sampling locations 

used in the simulations. B,The probability of detecting chronic wasting 

disease on a landscape as a function of sample size and dispersion of samples ..........15

3. Map showing sampling regions for detecting changes in 

spatial distribution of chronic wasting disease in Wisconsin during 2006–7...................35

 1–1. Graphs showing comparison of mean chronic wasting disease prevalence 

estimates and their measures of precision between A, Scenario A with 

continuous disease monitoring and B, Scenario B with periodic sampling 

generated during 10,000 simulation repetitions.....................................................................40

 1–2. Graphs showing comparison of proportion of 10,000 simulation 

repetitions where the correct underlying disease model was selected 

based on Akaike’s Information Criterion (AIC) between a continuous 

sampling regime (Scenario A) and a periodic sampling scheme (Scenario B) ...............41

 1–3. Graphs showing comparison of mean trend estimates and mean, 

standard 95-percent confidence bounds for models where the correct 

disease model was selected by using Akaike’s Information Criterion (AIC) 

between A, Scenario A with continuous disease monitoring and 

B, Scenario B with periodic sampling (generated during 10,000 

simulation repetitions) ...............................................................................................................42
ix

Tables

1. Estimated mean hazard ratios, associated standard deviations, 

Markov Chain Monte Carlo (MCMC) errors, and quantiles for various 

demographic groups of mule deer (Odocoileus hemionus) based on 

samples collected from chronic wasting disease-positive areas 

in Colorado during 2003–6 .........................................................................................................18

2. Estimated mean hazard ratios, associated standard deviations, 

Markov Chain Monte Carlo (MCMC) errors, and quantiles for various 

demographic groups of elk (Cervus elaphus) based on samples collected 

from chronic wasting disease-positive areas in Colorado during 2003–6 ........................19

3. The “real weights” that account for uncertainty in the estimates of the 

hazard ratios and associated number of samples (N) needed to reach a 

95-percent confidence that an area of interest is “disease-free” if the 

prevalence is less than or equal to 0.01 for mule deer (Odocoileus hemionus) 

from chronic wasting disease-positive areas in Colorado during 2003–6 ........................19

4. The “real weights” that account for uncertainty in the estimates of the 

hazard ratios and associated number of samples (N) needed to reach a 

95-percent confidence that an area of interest is “disease-free” if the 

prevalence is less than or equal to 0.01 for elk (Cervus elaphus) from 

chronic wasting disease-positive areas in Colorado during 2003–6 .................................19

Chapter 1

Risk Factors for Chronic Wasting Disease: 

A Deer Is Not a Deer Is Not a Deer

By Daniel P. Walsh, Ph.D.1, and Michael D. Samuel, Ph.D.2

Introduction

Risk factors are attributes of the landscape, environment, 

populations, and individual animals associated with a greater 

probability of chronic wasting disease (CWD) occurring in a 

target region or target population. Identifying and understanding the importance of various risk factors is a critical step in 

designing both CWD surveillance and monitoring strategies. 

This is particularly critical for CWD, because most areas 

have low disease prevalence that is difficult to detect, and the 

disease is distributed heterogeneously over the landscape. Current information suggests that CWD occurrence and prevalence varies among geographic areas (states), among regions 

within states, and occurs in clusters of affected animals within 

these regions. As a result, surveillance to detect CWD without 

considering potential risk factors is likely to be inefficient and 

less effective, and failure to understand risk factors may bias 

disease metrics, such as population prevalence or detection 

probability, used in CWD surveillance and monitoring efforts. 

The motivation behind using risk factors in surveillance 

and monitoring programs is to increase efficiency given existing or reduced resources (Stark and others, 2006). In other 

words, by utilizing our knowledge of various risk factors and 

incorporating this information into surveillance and monitoring activities, we can capitalize on all available information concerning the disease; consequently, the result is more 

efficient use of resources. The idea of using risk factors to 

enhance surveillance and monitoring efforts is not novel and 

has been successfully employed in surveillance for other diseases, including other transmissible spongiform encephalopathies (TSEs). For example, surveillance programs for bovine 

spongiform encephalopathy (BSE) in the European Union 

have exploited risk factors by focused sampling of animals 

that were most likely to be infected (Doherr and others, 2001; 

Wilesmith and others, 2004). Thus, CWD surveillance and 

monitoring programs can also be enhanced by exploiting existing knowledge regarding risk factors.

We describe below two classes of risk factors: spatial 

and demographic risk factors. Within these classes, we present 

various risk factors associated with CWD and detail our current knowledge or lack thereof for these factors. Knowledge 

of CWD risk factors has increased substantially since 2003, 

but despite advances in understanding, knowledge remains 

limited. Continued research to enhance understanding of risk 

factors is needed to improve the efficiency of surveillance programs and understanding of CWD epidemiology. Our purpose 

herein is to provide the current information regarding risk factors, which may be exploited for surveillance and monitoring 

activities, as well as elucidate knowledge gaps. 

Spatial Risk Factors

Spatial risk factors are attributes associated with the 

location, arrangement and composition of landscape, environmental, and animal characteristics that increase the probability 

of disease (CWD) occurrence and prevalence. Establishing the 

presence (or absence) and level of spatial risk factors in a target region is fundamental for focusing attention and allocating 

resources in any surveillance or monitoring program. This is 

particularly important for CWD, because in most areas disease 

may only occur at a low prevalence and is heterogeneously 

distributed over the landscape. As a result, identifying areas 

or populations at higher risk based on knowledge of spatial 

attributes can help focus surveillance and monitoring activities, reduce costs, and increase the likelihood of early disease 

detection. CWD spatial risk factors can generally be divided 

into two groups: those related to introduction of disease into a 

new area or target population and those specific to the rate of 

disease spread through the area or population. At the current 

1

Colorado Division of Wildlife, currently affiliated with the U.S. Geological Survey, National Wildlife Health Center.

2

U.S. Geological Survey, Wisconsin Cooperative Fish and Wildlife Research Unit. 
2 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

time, our knowledge of these spatial risk factors is limited and 

a better understanding would improve the efficiency and rigor 

of surveillance and monitoring programs.

Current information suggests that CWD occurrence 

and prevalence can vary at multiple spatial scales: among 

geographic areas (states or provinces), among regions within 

states/provinces, or in clusters within these regions (Samuel 

and others, 2003). In many cases it appears that the length of 

time CWD has been present in a specific area may be associated with spatial distribution and prevalence (Joly and others, 

2006; Heisey and others, 2010). For example, in Colorado and 

Wyoming, CWD appears to be fairly widely distributed; however, prevalence varies considerably among deer management 

units within those states (Conner and Miller, 2004; Miller and 

Conner, 2005; Wyoming Game and Fish Department, 2010). 

In Illinois and Wisconsin, where CWD was likely introduced 

more recently, CWD follows distinct patterns of spatial spread 

from the likely areas of introduction (Joly and others, 2003; 

Joly and others, 2006; Osnas and others, 2009; Heisey and 

others, 2010; Shelton and McDonald, 2011). Spatial distribution of CWD is also likely influenced by seasonal movement 

patterns, dispersal, and home range use of cervids (Conner and 

Miller, 2004; Farnsworth and others, 2006). Recent studies 

using landscape genetics suggest that rates of deer dispersal, 

landscape features that affect migration behavior, and dispersal by male white-tailed deer (Odocoileus virginianus) may 

be a useful predictors of spatial risk (Blanchong and others, 

2008; Kelly and others, 2010; Cullingham, Nakada, and others, 2011), but the time scale required for CWD spread is not 

currently understood. At a local scale, transmission of disease 

among individual animals appears to be associated with local 

interaction among animals within the same or adjacent home 

range areas (Farnsworth and others, 2006; Osnas and others, 

2009; Grear and others, 2010; Kelly and others, 2010; Cullingham, Nakada, and others, 2011) and especially for females 

in the same social group (Grear and others, 2010). These local 

interactions can lead to increased prevalence within localized 

areas and fine scale variability in CWD spatial patterns across 

the landscape. As a result in free-ranging cervid populations, 

detection of a CWD-infected individual strongly suggests an 

increased risk of infection for animals using similar or adjacent areas, although the spatial extent of risk may depend on 

the time CWD has been present and the prevalence of disease. 

Thus, it is clear that detection of a positive individual in an 

area is an important spatial risk factor that should be exploited 

when conducting CWD surveillance and monitoring. 

In addition to locations of known CWD-positive individuals, other spatial risk factors related to CWD exposure 

should be considered. For example, the risk of free-ranging 

animals being exposed to CWD is likely greater in areas where 

captive cervid facilities have or had CWD-positive animals. 

Current evidence indicates that CWD infection rates are much 

higher in captive facilities than in wild populations (Keane 

and others, 2008), and perhaps this is driven by environmental 

contamination (Miller and others, 2006). This higher rate of 

infection in captive animals can increase the risk of disease 

exposure to surrounding wild populations. Furthermore, movement of infectious animals, carcasses, or other materials across 

the landscape, naturally or with human assistance, likely 

increases the risk to uninfected populations. The frequent 

movement of farmed elk (Cervus elaphus) and deer between 

production facilities, the concentration of infected animals on 

some facilities, and the possibility of their escape into the wild 

increases the risk of spreading CWD to uninfected populations of free-ranging animals. Because the infectious prions 

may persist in the environment for long periods, the introduction of either captive or free-ranging uninfected animals 

into a contaminated environment could increase their risk of 

infection. For example, locations from which sheep have been 

removed may remain contaminated with scrapie agent for 

more than 15 years (Georgsson and others, 2006). In a similar 

manner, translocation of cervids from areas that have not been 

documented to be CWD-free could pose a risk of disease 

introduction. In this situation, the risk of introduction is likely 

related to the probability of infected animals being moved 

and their ability to spread CWD to other susceptible animals 

or into the environment. Thus, surveillance on and around 

cervid farms or free-ranging populations that have received 

animals from known CWD areas and bordering jurisdictions 

with CWD-positive animals can increase the likelihood of 

disease spread. Additional risk factors, such as the presence of 

scrapie in sheep populations that are sympatric with deer and 

elk (Greenlee and others, 2011), feeding of animal protein to 

cervids (Johnson, McKenzie, and others, 2011), baiting and 

feeding programs (Thompson and others, 2008), or other environmental factors also may be considered, although their roles 

in CWD epidemiology has not been clearly established.

The soil composition of a region may also play an 

important role in the occurrence and maintenance of CWD 

and other TSEs (Smith and others, 2011). Recently, it has been 

shown that certain soil types can chemically bind and increase 

infectivity of prion protein (PrP), creating the potential for 

the protein to be maintained at the soil surface for uptake by 

foraging animals (Johnson and others, 2006; Johnson and 

others, 2007; Polano and others, 2008; Imrie, 2009); however, the fate of prions may be highly dependent on source of 

deposition into the soil (for example, fluid or tissue; Saunders 

and others 2009). In addition, organic soil components (humic 

acids) appear to enhance the adsorption of PrP to clay minerals 

and show a great affinity for the protein as well; however, it is 

unclear whether the effect of the organic matter increases or 

decreases infectivity (Polano and others, 2008). The importance of soil in CWD epidemiology was reaffirmed by Walter 

and others (2011) who demonstrated an 8.9-percent increase in 

an individual’s deer’s odds of CWD infection with each 1-percent increase in soil clay content within its approximate home 

range in north-central Colorado. These results suggest that 

some regions may have a greater probability of maintaining 

and spreading CWD based solely on their geologic and chemical attributes. Thus, the soil characteristics within an animal’s 

range represent a potentially important spatial risk factor for 

CWD occurrence and maintenance.
Spatial Risk Factors 3

The risk of disease amplification (increasing the number 

of infected animals) in a target population or location postexposure likely increases as cervid population densities increase 

and predation decreases. In north-central Colorado, the greater 

the area of a mule deer’s (Odocoileus hemionus) approximate 

home range that contained wintering concentration areas of 

deer (high deer densities) the greater the odds of individual 

CWD infection (Farnsworth and others, 2006; Walter and 

others, 2011). The absence of predators or harvest potentially allows infected animals a longer period when they can 

transmit CWD to other animals by direct contact or indirectly 

through environmental reservoirs (Wild and others, 2011). 

Removal of infectious animals by harvest or other means 

likely reduces the rate of disease transmission and prevalence 

in free-ranging cervids (Gross and Miller, 2001; Schauber and 

Woolf, 2003; Wasserberg and others, 2009; Habib and others, 

2011), deposition of infectious prions into the environment, 

and the rate of disease spread. However, if infection rates 

are high and sustained, even intense selective predation of 

infected individuals may not reduce the spread and persistence 

of CWD (Miller and others, 2008), possibly due to the effects 

of indirect transmission on the disease process (Almberg and 

others, 2011). Baiting or feeding, which artificially increases 

concentrations of animals, may increase the chance of disease 

spread through direct contact among animals or indirect contact with environmental contamination (Thompson and others, 

2008; Mathiason and others, 2009; Tamguney and others, 

2009; Haley and others, 2011). Thus, variation in density of 

deer or infected deer across the landscape is another important 

spatial risk factor to consider when conducting disease surveillance or monitoring (Joly and others, 2009). 

Habitat attributes and landscape features that influence animal distribution, movements, and behavior may be 

other factors affecting the risk of disease amplification. For 

example, laboratory experiments have demonstrated that 

some extracts from certain lichen species produce an enzyme 

(serine protease) that degrades PrPTSE, potentially allowing 

these organisms to reduce TSE infectivity in the environment (Johnson, Bennett, and others, 2011). Although the 

importance of these findings outside the laboratory has not 

been ascertained, the presence and amount of certain species 

of lichens in the environment may influence the amount of 

infectious prion material available for uptake by uninfected 

individuals. This could impact the rate of CWD infection. The 

use of landscape genetics has demonstrated that landscape 

features that inhibit or promote deer movements (Blanchong 

and others, 2008; Kelly and others, 2010; Cullingham, Merrill, and others, 2011) and distance from the outbreak origin 

(Joly and others, 2006; Osnas and others, 2009; Robinson, 

2011) can affect CWD spread. CWD prevalence in an area is 

correlated with the amount of deer habitat available (Joly and 

others, 2006). In addition, Habib and others (2011) conducted 

computer simulations suggesting that landscape composition, 

habitat characteristics, and resource selection by deer may 

affect contact rates and associated risk of infection. These 

simulation results are supported by findings of Silbernagel and 

others (2011) who investigated interactions of mule deer in the 

Saskatchewan CWD endemic zone. They discovered that habitat type played an important role in the proximity and potential 

contacts of deer collared with global positioning system (GPS) 

devices to one another, which suggests that habitat may be 

an important factor in CWD transmission. Despite a limited 

understanding of the effects of specific habitat attributes and 

ecological characteristics, it is clear they can play a significant 

role in disease dynamics and spread and should be considered 

as spatial risk factors whenever possible. 

Anthropogenic activities, management policies, and land 

use patterns within an area may also be important considerations when designing CWD surveillance or monitoring strategies. The amount of private land within a deer’s approximate 

home range has been shown to influence CWD infection rates 

in Colorado (Farnsworth and others, 2006; Walter and others, 

2011). Although no causal mechanism has been established, 

it is probable that private lands may act as refugia from 

harvest pressure (Vieira and others, 2003), and private lands 

may often occur on more moist and productive soils, providing better production and habitat that may be selected for by 

cervids. In addition, these soils may be composed of soil types 

that may increase the infectivity of the prions as previously 

described. These conditions can concentrate animals, increase 

density, and affect the age-structure of local populations, 

which consequently affects disease dynamics. Management 

policies at multiple scales can also affect infection risk. For 

example, limited or no harvest or predation of deer in urban 

areas, compared to undeveloped areas, may promote higher 

densities of deer and prevent the removal of infected individuals (Farnsworth and others, 2005), thus increasing disease 

prevalence (Wasserberg and others, 2009). Likewise, spatially 

and temporally varying harvest regulations and management 

strategies across a jurisdiction affects cervid densities and 

population structure ( age and sex ratios), and may create spatial variability in CWD infection risk (Gross and Miller, 2001; 

Wasserberg and others, 2009; Bergman and others, 2011; 

Sharp and Pastor, 2011). 

Identification and evaluation of CWD spatial risk factors 

helps to focus limited resources on locations or target populations with a greater likelihood of being infected. Presently, 

the ability to quantify the importance of spatial risk factors 

remains limited, and determination of their importance for 

any specific area must rely on available knowledge and the 

judgment and experience of wildlife and disease experts. 

Understanding the distribution, movement, social behavior, 

population characteristics, and dynamics of affected deer and 

elk populations, as well as the anthropogenic forces acting on 

these populations, is helpful, if not essential, to fully evaluate 

the spatial risk factors for CWD in free-ranging populations.
4 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

Demographic Risk Factors

Demographic risk factors are specific attributes of groups 

of individuals within a target population or the target population itself that are associated with prevalence of CWD. Several 

demographic risk factors have been linked to CWD prevalence including sex, age, density, social interactions, genetics, 

mortality factors, and clinical signs of CWD. Clinical signs 

include loss of fear of humans, ataxia, marked weakness, 

inability to stand, dehydration, rough dull hair coat, excessive 

salivation, drooping of the head and ears, and severe emaciation (Williams and Young, 1980; Spraker and others, 1997). 

As research, surveillance, and monitoring efforts have 

continued following the discovery of CWD in free-ranging 

cervids, evidence has mounted demonstrating the validity 

of these attributes as factors influencing disease prevalence. 

For example, prevalence rates across sex and age classes of 

hunter-killed animals, culled individuals, and research activities demonstrated heterogeneity in prevalence across these 

various classes (Miller and Conner, 2005; Farnsworth and others, 2006; Osnas and others, 2009; Heisey and others, 2010; 

Walsh and Miller, 2010). Because the probability of CWD 

infection increases with time of exposure (age), older deer 

have higher prevalence of disease than fawns or yearlings. 

In Colorado and Wisconsin, adult male deer have a twofold 

higher prevalence than females, and within each sex class 

prevalence was considerably higher in adult animals compared 

to fawns or yearlings (Miller and Conner, 2005; Grear and others, 2006; Osnas and others, 2009; Heisey and others, 2010).

Different prevalence rates have also been reported in 

individuals succumbing to various mortality factors. Krumm 

and others (2005) reported that vehicle-killed mule deer had 

higher prevalence (0.146) than hunter-harvested deer (0.075) 

collected from the same vicinity. In addition, they found 

CWD-infected mule deer in later stages of the disease (with 

spongiform encephalopathy) were more frequently killed by 

vehicles compared to hunter-killed samples. However, CWD 

infected white-tailed deer in Wisconsin were not more vulnerable to harvest than uninfected deer (Grear and others, 2006). 

Research has also demonstrated an increased prevalence in 

predator-killed mule deer (Miller and others, 2008; Krumm 

and others, 2010). Thus, CWD-infected individuals can be 

more susceptible to some mortality factors than uninfected 

animals.

It has also been recognized that targeted surveillance of 

individuals demonstrating clinical signs consistent with CWD 

infection results an increased probability of detecting CWD. In 

one study, 66 percent and 43 percent of submitted deer and elk 

exhibiting clinical signs of CWD, respectively, were diagnosed 

as CWD-positive compared to 5 percent and 0.5 percent of 

randomly sampled deer and elk submitted for testing (Miller 

and others, 2000). Therefore, exhibition of clinical signs of 

CWD is a clearly an attribute that increases an individual’s 

likelihood of having CWD and is a demographic risk factor that can be exploited to increase surveillance efficiency. 

Targeted surveillance and investigation of clinical animals also 

has the added benefit of detection of other important wildlife 

diseases with similar clinical signs (Miller and others, 2000; 

Samuel and others, 2003).

Other demographic risk factors are less well understood. 

For example, there is evidence that genetics plays a role in 

individual susceptibility and rate of disease progression. Similar to other TSEs, polymorphisms of the prion protein gene 

(PRNP) may moderate individual susceptibility to and progression of CWD infection of elk, mule, and white-tailed deer 

(O’Rourke and others, 2004; Spraker and others, 2004; Jewell 

and others, 2005; Fox and others, 2006; Hamir and others, 

2006; Goldmann, 2008; Keane and others, 2008; Perucchini 

and others, 2008). Therefore, it appears that certain individuals are innately at higher risk of CWD infection based solely 

on their PRNP genotype. For example, Wisconsin white-tailed 

deer with the PRNP genotype G96G have approximately four 

times higher rate of infection and 8 months shorter survival 

after infection compared to G96S deer (Robinson and others, 

2012). However, unlike other TSEs, there is no evidence that 

any of the PRNP genotypes in wild cervids are immune to 

CWD infection. 

Genetic relatedness, a measure of the proportion of genes 

shared between two individuals, has also been demonstrated to 

be important in CWD disease dynamics, undoubtedly owing 

to the fact that related individuals are likely to have more 

frequent contact or share similar risk factors (Blanchong and 

others, 2008; Ernest and others, 2010; Grear and others, 2010; 

Kelly and others, 2010; Cullingham, Nakada, and others, 

2011). Genetic relatedness also affects social structure and 

associated interactions of cervids, thereby affecting an individual’s disease risk (Altizer and others, 2003). Farnsworth 

and others (2006) determined that patterns of CWD prevalence 

were best explained at the scale of an individual’s home range, 

suggesting that local interactions within matrilineal groups 

influences CWD dynamics. Likewise, several studies have 

found that CWD-infected female deer had increased genetic 

relatedness compared to sympatric non-infected deer (Blanchong and others, 2008; Grear and others, 2010; Cullingham, 

Nakada, and others, 2011). Schauber and others (2007) demonstrated that contact rates between white-tailed deer within 

social groups were significantly higher than contact rates of 

individuals between social groups, providing insight into a 

possible mechanism for these findings. In a further refinement, 

Silbernagel and others (2011) found direct and indirect contact 

varied by season and habitat characteristics. These studies suggest female relatives of an infected female are at higher risk 

of either direct or indirect transmission owing to their philopatric behaviors and formation of matriarchal groups (Altizer 

and others, 2003; Conner and Miller, 2004; Grear and others, 

2006; Joly and others, 2006; Blanchong and others, 2008; 

Grear and others, 2010; Kelly and others, 2010; Cullingham, 

Nakada, and others, 2011; Cullingham, Merrill, and others, 

2011). Thus, genetic relatedness and associated social interactions are important risk factors. However, despite the clear 
Conclusion 5

importance of genetic relatedness in determining an individual’s CWD-infection risk, its contribution to prevalence of 

CWD in wild cervid populations is not yet clearly understood. 

Finally, high population density, which also can be 

considered a spatial risk factor, is generally believed to create 

increased risk of disease transmission through higher direct 

or indirect contact rates (Swinton and others, 2001; Ramsey 

and others, 2002). This is the basis for population reduction 

strategies used by many wildlife management agencies for 

CWD management in free-ranging cervids (Williams and 

others, 2002; Joly and others, 2003; Williams, 2005; Joly and 

others, 2006). The actual transmission route of CWD is not 

known, however, experimental evidence from captive cervids 

suggests that CWD infection occurs via horizontal transmission through both direct and indirect contact between susceptible and infected individuals (Miller and Williams, 2003; 

Williams and Miller, 2003), and both kinds of contact can be 

influenced by density. Experimental inoculation with blood, 

urine, feces, and saliva from CWD-infected individuals has 

been shown to provide viable routes of transmission, suggesting direct contact with any of these infectious materials could 

act as a route of infection (Miller and others, 2004; Mathiason 

and others, 2006; Miller and others, 2006; Trifilo and others, 

2007; Safar and others, 2008; Haley and others, 2009; Mathiason and others, 2009; Tamguney and others, 2009; Haley 

and others, 2011). Indirect contact may play an important role 

in transmission dynamics via environmental contamination, 

because the CWD agent can persist in contaminated systems 

for 2 or more years (Miller and others, 2004), and if CWD is 

similar to scrapie, it may persist for 15 or more years (Georgsson and others, 2006). For captive cervids, the most likely 

route of exposure is orally through foraging activities in the 

immediate vicinity of fresh and decomposed carcasses or 

ingestion of fresh and residual excreta from infected individuals (Miller and others, 2004; Trifilo and others, 2007; Safar 

and others, 2008; Mathiason and others, 2009). However, 

the relative importance of direct and indirect transmission of 

CWD in wild cervids has not been determined. As previously 

mentioned, certain soil types can also increase oral infectivity 

of TSEs, which may allow environmental contamination to be 

problematic even in the presence of relatively low doses of the 

infective agent (Johnson and others, 2007). Thus, it is likely 

density of infected cervids can contribute to increased indirect 

contact rate between susceptible and infected individuals or 

contact with an environmental reservoir. However, it is unclear 

to what the extent density influences these processes. 

Although the risk associated with several demographic 

factors is not completely understood, current knowledge is 

adequate to allow for incorporation of several factors into 

surveillance activities. By using information on differential 

risk factors in surveillance and monitoring activities is critical, 

because it can increase efficiency of disease detection and 

allow wildlife management agencies to evolve and improve 

surveillance and monitoring efforts. In addition, as our understanding of the effects of other demographic parameters on 

CWD prevalence increase, this information can be incorporated to further streamline disease-management programs.

Conclusion

We have presented a wide range of risk factors that may 

be considered when designing surveillance or monitoring 

programs. Although this is not an exhaustive list and undoubtedly other factors not yet described will come to light, the 

risk factors and associated references detailed herein provide 

a useful starting point. Many jurisdictions currently collect 

information on many of these factors, especially for harvested 

cervids. Other data related to spatial risk factors may be readily available within Geographic Information Systems (GIS). 

Thus, the use of the risk factors in surveillance and monitoring 

activities is a natural progression in continued improvement of 

these activities. 

For jurisdictions lacking quantitative information on 

various risk factors, we recommend collaborating with similar 

entities that have available information and utilizing their data 

to incorporate risk factors into disease-management activities. 

In addition, even if information is limited or unavailable for 

some of the risk factors described, use of the information that 

is available for the remaining factors may provide gains in 

efficiency. However, we caution that, if possible, risk factors 

related to CWD infection should also consider potential differences in species of interest, differences in harvest strategies among areas, if CWD is in an early or advanced stage of 

epizootic (low vs. high prevalence), and how different cervid 

ecology and habitat features might affect estimate risk factors. 

At the current time, little is known about how many of these 

features might affect CWD prevalence and risk of infection.

Evaluation of risk factors helps to focus resources on 

locations or target populations with a greater likelihood of 

being infected and increases the efficiency of surveillance 

and monitoring efforts. Agencies charged with managing this 

disease should continue to look for efficient ways to conduct 

their activities. Identifying, understanding the importance of, 

and incorporating risk factors into surveillance and monitoring activities are potential means of meeting this need for 

increased efficiency. 
6 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

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Chapter 2

Disease Surveillance: Incorporating Available Information 

to Enhance Disease-Detection Efforts

By Daniel P. Walsh, Ph.D.1, and David L. Otis, Ph.D.2

1

U.S. Geological Survey, National Wildlife Health Center.

2

U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit.

Introduction

Disease surveillance is “an active, on-going, formal, 

and systematic process aimed at early detection of a specific 

disease or agent in a population, or early prediction of elevated 

risk of a population acquiring an infectious disease, with a 

prespecified action that would follow the detection of disease” (Thurmond, 2003). Thus, the main goal of any diseasesurveillance program is the ability to detect the presence of the 

disease as early as possible in hopes of being able to respond 

and minimize adverse impacts of the disease. 

The heightened public awareness, governmental concern, 

and improved diagnosis of infectious animal and zoonotic diseases, as well as the increased movement of animals, including 

wildlife, nationally and internationally has created demand 

for effective disease-surveillance programs (Stark and others, 

2006). Despite this demand, the amount of resources available 

for supporting these surveillance programs is finite, often with 

decreasing commitment over time as public concern wanes 

and other disease issues emerge. Therefore, the need for efficient programs is paramount if governmental agencies hope to 

meet their mandates to conduct disease surveillance.

One method of improving efficiency of disease discovery is to incorporate auxiliary information about specific 

segments of the target population that can be used to stratify 

these groups spatially and/or demographically, based on varying prevalence rates or heterogeneous disease risk that has 

been elucidated in epidemiological investigations where the 

disease occurs. This auxiliary information can be any covariate that enhances the ability to detect the disease of interest by 

focusing surveillance efforts on areas and individuals with the 

highest probability of being infected or, in other words, those 

at the greatest disease risk (Thurmond, 2003). This approach 

has been called “proportional risk sampling,” “risk-based sampling,” “targeted sampling,” and herein “weighted sampling” 

(Doherr and others, 2001; Hadorn and others, 2002; Thurmond, 2003; Stark and others, 2006; Walsh and Miller, 2010). 

This technique has been most widely documented and successfully used in bovine spongiform encephalopathy (BSE) 

surveillance in Europe and has been used with other animal 

diseases as well (Doherr and others, 2001; Hadorn and others, 2002; Wilesmith and others, 2004; Bohning and Greiner, 

2006; Walsh and Miller, 2010).

In the context of surveillance for chronic wasting disease 

(CWD), this weighted surveillance system has potential to 

greatly increase the disease discovery efficiency of current 

efforts. As described in the previous chapter, several spatial and demographic risk factors can be used to inform this 

system and provide the necessary covariate information to 

focus sampling efforts on specific regions and groups, which 

enhances early detection of CWD. Thus, the samples entering the surveillance stream from these various groups are 

weighted according to their probability of being infected or at 

risk. By utilizing information by the weights, the number of 

samples required for testing is decreased without a resulting 

loss of statistical confidence in the findings of the surveillance 

system. The savings from this strategy can be significant and 

increase the overall effectiveness of CWD surveillance. 

In the following pages, we describe how to design a 

CWD weighted surveillance system, including the sampling aspects: spatial and demographic, the incorporation of 

various risk factors, the calculation of weights, the practical 

implementation of the system, and the statistical theory upon 

which the system is based. We hope to provide a framework 

that interested individuals can use to design the most effective and efficient CWD surveillance system possible for 

their jurisdiction, given their current state of knowledge. We 

assume the reader is familiar with Samuel and others (2003) 

and the general principles, philosophies, and practicalities 

of CWD surveillance described therein and do not attempt 

to reiterate that information here. Rather, we focus solely on 

describing weighted surveillance systems and their utility 

when attempting to detect CWD on the landscape in a costeffective manner. We also discuss questions of temporal scale 
12 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

of surveillance programs and determining when surveillance 

efforts have reached an adequate level and can be discontinued. It is important to emphasize that the techniques described 

in this chapter are specific to efforts to detect CWD where it is 

not yet known to occur on the landscape, and it is not intended 

to address disease investigations in regions where CWD is 

already known to be present (see Chapter 3).

Weighted Surveillance System

A disease-surveillance program should be designed with 

the goal to produce statistically reliable inferences about 

the expected probability of disease detection given specific 

assumptions about the relevant sampling and biological processes. Design-based survey sampling methodology provides 

a well-developed set of principles and protocols for choosing 

sampling units in such a way as to produce valid statistical 

inferences from the data. Practical and efficient sampling 

designs for detection of rare events (for example, disease) in 

free-ranging wildlife populations are notoriously difficult to 

implement (Thompson 2004), and statistical compromises 

are to be expected as a result. Our objective is, therefore, to 

suggest reasonable sampling protocols that can be expected 

to generate the same levels of CWD detection probability as 

traditional disease-sampling approaches with fewer samples 

and, therefore, fewer resources. 

In any surveillance program, multiple and interactive 

economic, social, and biological factors affect choice of the 

sampling design. We cannot attempt to enumerate all the possibilities, but our intention in this brief discussion and presentation of examples is to illustrate that there is a rich collection 

of techniques for construction of sampling designs and that 

knowledge of risk factors, biology, and disease epidemiology 

can be formally used to determine design choices.

For our purposes, it is useful to think about any proposed 

sampling design as consisting of two general nested stages. 

In the first stage, we are concerned with the choice of explicit 

geographical units within which a sample of individual deer 

will be chosen for CWD testing. We refer to these selected 

geographical units as sampling units (SUs). The second 

sampling stage is concerned with how the individuals are 

chosen within a SU. As we shall see, sampling protocols for 

both stages can have important consequences for the statistical 

performance of the surveillance program, depending on the 

spatial distribution of the disease in the population.

For convenience, we define the target population as the 

population of individual deer within a State, although we note 

that methods described herein could be applied to any animal population in any region without loss of generality. The 

conceptual sampling frame is hierarchically structured into 

SUs and population elements (individual deer) within SUs. 

Sampling units could be townships, counties, game management units, or some other bureaucratic system of geographical 

boundaries determined by political mandates, cost efficiency, 

or biological factors.

In probability-based sampling designs, specific welldefined rules are invoked to select SUs and their population elements for measurement from the sampling frame. A 

required condition is that all elements have a controlled and 

non-zero probability of being selected. The selection rules 

and sampling frame allow us to calculate the probability that 

a population element is included in the sample and to derive 

estimators appropriate for a specific sample design. Random 

choice of SUs and population elements provides the basis for 

valid statistical inference to the population of interest.

Admittedly, it is often impractical or cost-prohibitive to 

implement a completely probability-based surveillance design. 

In particular, wildlife cannot be selected with controlled probability within an SU. However, we believe it is often possible 

to invoke probability sampling techniques (for example, stratification, cluster sampling, sampling with probability proportional to a risk factor) for at least some component of the 

sampling process, particularly in selection of SUs, which may 

reduce the impact of bias generated by convenience sampling 

or other non-probability-based sampling protocols. 

Probabilistic Sampling Designs for Selection of 

Sampling Units—Clustering and Stratification

We will begin by examining sample designs for the first 

stage of sampling: selection of SUs. Survey sampling methodology provides two basic tools, clustering and stratification, 

for imposing additional control over the spatial distribution of 

the sample. When a clustering approach is taken, the motivation is usually cost efficiency. In our situation, reduced cost 

can be expected, because the clustering design will result in a 

more spatially concentrated selection of deer for testing. Use 

of multistage cluster sampling in a CWD surveillance program 

would involve imposing a hierarchical structure of groups of 

geographically adjacent SUs into clusters. For example, let 

us define a SU as a section (640 acres) of land. Rather than 

selecting a completely random sample of sections from the 

entire State, we first group SUs into townships (36 sections), 

and then randomly select a sample of townships (clusters). We 

next select a random subsample of sections (SUs) within each 

selected township and collect a sample of deer for measurement from these sections. This design is similar to a threestage cluster design, with townships as the first stage, sections 

as the second stage, and individual deer as the third stage; 

however we have no control over probability of deer selection.

A cluster sampling design can be used in conjunction 

with stratification, which is the second standard survey sampling tool for controlling spatial distribution of SUs. Motivation for stratification often derives from a desire to ensure 

more exhaustive spatial coverage of the target population than 

may occur by chance in completely random sampling of the 

entire population. In stratified sampling designs, the population is divided into mutually exclusive and exhaustive strata, 

and samples are selected from every stratum. For example, 

instead of selecting townships at random from the entire State, 

we might first define counties as strata and then proceed with 
Weighted Surveillance System 13

the previously described cluster sampling approach in each 

county. As another example, instead of using political boundaries, we might use biologically based criteria such as major 

ecoregions within a State to create strata boundaries. Stratification can also be effective in increasing precision of estimates if the response of interest (disease prevalence) tends to 

be more homogeneous within strata than in the population as a 

whole. However, for surveillance sampling of a rare event in a 

spatially heterogeneous landscape, development of a practical 

stratification scheme that could improve statistical efficiency 

is unlikely. We believe it is preferable to define strata with the 

objective of increased control of spatial coverage of SUs, and 

then use the statistical technique of unequal probability sampling within strata to improve statistical efficiency.

Unequal Probability Sampling and Risk Factors

Survey sampling techniques can be applied that lead to 

probability-based over-sampling (sampling at a higher rate) of 

more informative SUs in a multistage cluster design. This is 

a form of unequal probability sampling called sampling with 

“probability proportional to size” (PPS). Size is a generic measure of the importance of some characteristic of an SU with 

respect to information on disease prevalence, and represents a 

formal method of more intensive sampling of more interesting 

units. In addition, if the size measure is directly related to the 

probability that the disease is present in the sample unit, then 

PPS leads to higher precision of the estimates (Lohr, 1999). 

For CWD surveillance programs, we strongly recommend 

construction of unequal probability sampling schemes based 

on knowledge of disease risk factors. If available, some factors, for example, soil clay content, number of captive cervid 

facilities, deer density, could be used directly in calculation 

of selection probabilities. Other indirect factors, such as an 

assumption about the spatial distribution of the disease— random or highly clustered—could also influence the choice of 

scale for sampling units or boundary definitions for strata. 

As an illustration, suppose we want to randomly select 

a number (n) townships for sampling deer within a county 

comprised of N townships. If we select them completely at 

random or systematically, then each township has an equal 

probability of being chosen. However, suppose we want to 

give preference to townships with more woodland habitat, 

which we assume is highly correlated with deer density, 

which in turn may be correlated with increased probability 

of disease transmission. By using a geographic information 

system (GIS) coverage of the county, we can calculate the 

number of woodland acres in each township, and scale our 

probabilities of selection to be proportional to woodland acres. 

Now, although all townships still have a positive probability of 

being sampled, a township A with X hectares of woodland has 

double the chance of being sampled compared to a township B 

with only X/2 woodland hectares. 

Unequal probabilities of selection could also be applied 

at any stage of the design. Consider a few examples:

1. Townships within counties are selected with probability proportional to the number of known captive cervid 

facilities in the township, or to the estimated deer density, 

or some combination of both factors. The number of 

sampled sections is constant for all selected townships 

and is selected completely at random or systematically.

2. Townships are selected completely at random, but sections within townships are selected with probability 

proportional to the risk factors in the previous example.

Another technique for incorporating suspected risk factors into the selection of SUs is to use them to allocate relative 

sampling effort within the strata. Consider the following 

examples:

1. The risk factors in the previous examples could be measured at the strata scale instead of the township scale, and 

strata sample size allocation, that is, the number of townships selected completely at random, is proportional to the 

strata risk, with an equal number of sections sampled in 

each selected township. 

2. Strata sample size is proportional to the proximity of the 

strata to the nearest known CWD-positive case in an adjacent State. The number of sampled townships within each 

stratum is the same, but the number of sampled sections 

within a township is larger in strata in closer proximity to 

known CWD-positive deer.

Thus, there are a myriad of possibilities of sampling 

designs that can be used for selecting SUs. The examples 

detailed above provide a framework for developing a suitable 

sampling scheme; however, a unique and specific design needs 

to be developed for each study area. If possible, any sampling 

design created to select SUs should be based on a probabilistic 

sampling scheme to account for all relevant information and 

knowledge with regards to spatial risk factors in the study 

area, and the result will be good spatial distribution of the 

samples over the entire study area.

Simulation Studies—Spatial Distribution 

of Samples

In particular, the need for good spatial distribution of 

samples across the study area may be important, given the 

likely clumped or non-random distribution of CWD-infected 

individuals, if a surveillance program is to approach the 

theoretical probability of detection (Conner and Miller, 2004; 

Miller and Conner, 2005; Farnsworth and others, 2006; Joly 

and others, 2006; Osnas and others, 2009; Heisey and others, 

2010). Several simulation studies have illustrated the importance of good spatial distribution in the face of non-random 

disease distribution and various sampling procedures.

The first example is excerpted from Samuel and others 

(2003) . Computer simulation was used to illustrate how spatial aggregation in disease prevalence and sampling effort can 

reduce the probability of detecting CWD from the idealized 
0

5

10

15

20

25

30

0 5 10 15

Miles

Miles

20 25 30

1 2 3 4

EXPLANATION

Number of chronic wasting disease-positive deer 

 in each cell

1 2 3 4

14 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

situation where CWD is randomly distributed and is randomly 

sampled. Specifically, they examined how the probability of 

detecting at least one CWD-positive individual from hunterharvested animals is affected by sample size and spatial autocorrelation in sampling effort. Based on current understanding of CWD distribution in Wisconsin, they assumed CWD 

prevalence was greatest in a disease cluster at the center of the 

landscape, and declined with distance from the center of the 

cluster (fig. 1). 

Samuel and others (2003) used different sample sizes 

(50–500) and different spatial distributions of samples. They 

simulated the non-random nature of samples obtained from 

hunter-killed deer by using three levels of spatial autocorrelation: 1) “dispersed” sampling with a small degree of 

autocorrelation such that most of the landscape was sampled, 

2) “clustered” sampling with a moderate degree of spatial 

autocorrelation in sampling effort, and 3) “highly clustered” sampling with a high degree of spatial autocorrelation 

(fig. 2A).

The probability of detecting at least one CWD-positive 

deer was strongly correlated with both degree of aggregation in sampling locations and number of samples (fig. 2B). 

The estimated detection probability from simulations converged to that predicted by random sampling when sample 

sizes were large (, more than 300 deer) and when sampling 

was evenly distributed across the landscape. However, for 

spatially aggregated samples the probability of detecting at 

least one CWD-positive deer was considerably below that 

expected from a truly random sample. These results emphasize 

the importance of uniformly sampling from SUs to achieve 

the expected detection probability. Local variation in deer 

densities, age, and sex-specific variation in CWD prevalence, 

Figure 1. Landscape used in sampling simulations. 

Each 1-square mile cell was assumed to hold 20 deer, 

for a total of 18,000 deer. 
30

25201510

50

0 5 10 15 20 25 30 0 5 10 15 20 25 30 0 5 10 15 

3025201510

50

3025201510

50

Miles north

Miles east Miles east Miles eas

Dispersed sampling

A.

Clustered sampling Highly clustered s

Miles north

Miles north

0.60.80.41.00.20.0

0 100 200 300 400 500

Probablility of detecting at least one positive

Number of samples

Dispersed samplingClustered samplingHighly clustered sampling

B.

Figure 2. A, Examples of distributions of sampling locations 

simulations. For each set of simulations, samples were taken was dispersed (left panel), clustered (center panel), or highly These panels correspond with the probability curves shown by common symbol. B,The probability of detecting chronic wlandscape as a function of sample size and dispersion of sam
16 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

hunter behavior, and other factors also may affect the probability of detecting CWD. However, the general pattern illustrated 

will likely remain true: sampling effort must be well distributed throughout the population to minimize the possibility of 

missing disease clusters. In addition, appropriate stratification 

of sampling effort with respect to suspected risk factors will 

increase the probability of detection of CWD. 

A second simulation example is the study by Nusser and 

others (2008), who conducted a landscape-scale simulation 

study to investigate the properties of several non-probabilityand probability-based sampling designs. They used a GIS 

coverage of land cover in Fayette County in northeastern 

Iowa to distribute a population of deer based on presumed 

habitat associations and informed estimates of expected deer 

density and specified a 1.5-percent disease prevalence in the 

population. Diseased animals were spatially distributed either 

1) completely at random, or 2) highly clustered into a single 

area with a few additional single spatially random outliers. 

Two probability-based sampling designs were evaluated: 

simple random sampling and stratified two-stage cluster 

sampling, as were two non-random/convenience designs 

(sampling of deer killed in collisions with vehicles (DVC) and 

hunter harvest). In the DVC design, diseased deer within the 

boundaries of a narrow roadside buffer were twice as likely 

to be killed by vehicles as those outside of this buffer. In the 

hunter-harvest design, harvest pressure, deer density, and disease prevalence were assumed to be spatially correlated, and a 

hunter avoidance bias was incorporated so that diseased deer 

had a 20-percent relative decrease in the probability of being 

harvested. Samples size for all designs was n = 120.

Their results demonstrated that:

1. All sampling designs achieved the theoretical probability of detection = 0.83 (for n = 120) when diseased deer 

were randomly distributed. The average number of years 

until detection (waiting time) of at least one infected deer 

was 1.2.

2. The random sampling design and the stratified cluster 

design maintained the theoretical probability of detection 

under the highly clustered disease distribution.

3. The DVC design had only a 9-percent probability of 

detection and an average waiting time of 11.2 years when 

the road system did not intersect the disease cluster.

4. The hunter selection bias in the hunter-harvest design 

resulted in a 7-percent absolute decrease in probability of 

detection and a minor increase in waiting. 

Thus, these findings suggest the importance of selecting 

SUs from across the study area by using a probabilistic design, 

the danger of having a decreased probability of detecting 

CWD on the landscape, or an increased waiting time when 

all samples entering the surveillance stream are coming from 

convenient and spatially aggregated sources. Joly and others 

(2009) also provide another example of using a probabilistic 

design for selecting SUs in a CWD surveillance program.

Selection of Population Elements from within 

Sampling Units—No Risk Data

Once SUs have been selected with the desired spatial 

coverage and accounting for appropriate spatial risk factors, population elements (individual animals) from within 

these SUs must be collected and entered into the surveillance 

stream. If no information about demographic risk factors is 

available, ideally, these population elements would be collected according to some known probability sampling scheme 

(for example, random selection by helicopter gunning); however, in practice most samples are collected by convenience 

sampling (hunter harvesting). Therefore, when making inference about disease status, we are invariably forced to assume 

that the population from which samples are drawn is representative of the target population. The size and direction of the 

bias associated with this assumption is generally unknown and 

related to the level of dependency between the response variable (disease state) and the selection process. When disease 

detection is attempted, the result of having a biased selection 

process is only problematic if diseased animals are less likely 

to be included in the sample, which creates a lower detection 

probability than the theoretical detection probability (the probability of detecting a diseased animal at the specified prevalence is less than 1–α, where 1-α is the theoretical detection 

probability). For example, if hunters harvest infected animals 

at the same rate as healthy individuals, bias in the sample from 

this group is negligible because the selection of individuals is 

not dependent on disease status. However, because the dependency is unknown and, in general, bias will affect our probability of detecting a diseased individual, we should attempt to 

minimize it by applying probabilistic sampling schemes when 

possible. 

In most CWD surveillance systems, samples are collected 

from multiple sources, some of which involve active collection by agency personnel by culling, collection of deer killed 

in collisions with vehicles, and so forth, and others that enter 

through passive collection (for example, hunter submissions). 

For samples that are actively collected, we strongly urge collecting population elements by using a probabilistic scheme 

whenever possible to ensure the realized disease detection 

probability is as close as possible to the theoretical detection 

probability. In addition, partial implementation of probability 

designs such as espoused here, where SUs are selected in a 

probabilistic manner and population elements are selected by 

convenience sampling, will help to reduce the bias associated 

with using a non-probability sample of population units, but 

will not eliminate it. Practitioners must be aware of this shortcoming. Nusser and others (2008) provide some insight into 

the effect of selection bias on disease detection probability and 

waiting time.
Weighted Surveillance System 17

Selection of Population Elements from within 

Sampling Units–Risk Data Available

For diseases such as CWD that have been studied, information often is available regarding demographic risk factors 

(see previous chapter). This information can be exploited to 

increase disease detection probability. By employing unequal 

probability sampling, such as PPS, in selection of individual 

animals within the SUs, we can increase the sampling rate 

within segments of the population with the greatest risk or 

highest prevalence, and thereby increase our probability of 

detecting CWD. The underlying motivation for this approach 

is to optimally allocate limited resources by sampling population elements based on disease risk. This assumes a fixed 

budget for sampling and that high risk animals are no more 

costly to sample. For example, prevalence in suspect animals 

that exhibit clinical signs of CWD is considerably higher than 

prevalence in hunter-harvested individuals (Miller and others, 

2000). By “over-sampling” suspect animals, therefore, our 

probability of detecting CWD, if present, will be greater, and 

waiting time until first detection will be reduced compared to 

sampling exclusively from hunter-harvested animals, assuming the same total sample sizes in either case. This principle 

is the basis for the weighted surveillance system (Walsh and 

Miller, 2010).

Ideally, all the samples entering the surveillance stream 

should come from the segment of the population with the 

highest risk; however, in practice this is highly problematic, 

because there will be a limited number of CWD suspect animals in the population. Thus, acquiring a sufficient number of 

samples from this population segment may not be practical or 

feasible, and samples from other segments must be collected. 

Collection from the remaining segments should be done with 

respect to their risk (focusing on the highest risk groups first) 

to attain the underlying surveillance goal: achieving the theoretical disease detection probability with the fewest number of 

samples or least cost. 

Within a subpopulation, individuals should be sampled 

according to some probabilistic scheme. But, in reality the 

samples being collected come from various population 

segments and will be attained by convenience sampling. 

Although, as described above, convenience sampling creates 

biased detection probabilities, nevertheless Walsh and Miller 

(2010) demonstrated by simulation that disease detection 

probabilities were acceptable by using the weighted surveillance scheme, even in the face of moderately biased weights as 

would arise from individual heterogeneity (each individual has 

its own unique probability of entering the surveillance stream), 

or convenience sampling (Diefenbach and others, 2004). Thus, 

when adequate information regarding demographic risk factors is available, surveillance activities can exploit this data to 

increase overall efficiency and effectiveness.

As an aside, we strongly recommend that whether an 

agency employs a weighted surveillance scheme or not, 

all known clinical suspect animals should be collected by 

agency personnel and submitted to proper diagnostic labs 

for necropsy. These individuals can provide critical information regarding and, possibly, early detection of a wide-range 

of wildlife diseases in addition to CWD. Therefore, suspect 

animals are a valuable tool for monitoring overall population 

health and viability and should be exploited as such.

Sample Size

Once an agency decides to implement a weighted surveillance scheme, the question then becomes how many samples 

are required. Sample size decisions are driven by the survey 

design and the specific statistical estimation objective. Most 

commonly, a statistical objective is stated in terms of desired 

precision of the estimator of a population characteristic, such 

as the mean or total for the target population. In a detection 

surveillance program, the statistical objective is to make an 

inference that “if the prevalence of CWD in animals in the 

target population is at least X percent, then we are Y-percent 

confident1 that our surveillance plan will detect at least one 

infected animal.” 

The difficulty when employing the weighted surveillance 

scheme is determining the number of population elements 

within and across SUs that need to be collected to designate an 

area as disease-free. Generally, sampling from an area continues until an adequate number of samples is collected to ensure 

the theoretical probability of detecting the disease is at least 

equal to some statistical power when the disease in the target 

population occurs at some user-specified prevalence. These 

samples come from various subpopulations with varying 

disease risks. Therefore, to employ the weighted surveillance 

system and determine needed sample sizes, weights, which 

incorporate risk or prevalence information, have to be calculated for each subpopulation of interest. Historically, each 

individual entering the surveillance stream received the same 

weight (1); however, this is inefficient if only a limited number 

of samples can be examined.

The first step in employing a weighted surveillance 

system is to estimate the weights associated with various 

demographic groups of interest. In simplest terms, these 

weights are a measure of worth of each demographic group 

for detecting CWD compared to some baseline demographic 

group (the group of animals to which the design prevalence 

applies and whose weight is defined to be 1). These weights 

are commonly used metrics of relative risk or hazard ratios 

in survival analyses. Walsh and Miller (2010) describe a 

maximum likelihood methodology for calculating these 

weights based on Poisson approximation to the binomial and 

designating harvested adult males as the baseline population. 

We employed a recently developed methodology by using a 

Bayesian approach to estimating “real weights” that allows 

for incorporation of variability in the hazard ratios estimated 

for each population segment (Chris Jennelle, written commun, 

1

Note, in the statistical context this would be described as power of the 

study design or test, whereas confidence is associated with a measure of precision of an estimate generated from data that has already been gathered. 
18 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

2012). This accounting for variability is lacking in the Walsh 

and Miller (2010) approach due to difficulties in addressing 

the issue within the frequentist framework; however, the methods produce similar results when the appropriate population 

segment is chosen as the baseline and the weights are precisely 

estimated (Chris Jennelle, written commun, 2012). 

We used this Bayesian technique to estimate “real 

weights” (hereafter referred to just solely as weights) for two 

species: elk and mule deer. Data collected from data analysis 

units (DAUs) known to contain CWD in Colorado during 

2003–6 were used to estimate the elk and mule deer weights. 

We used 20,400 mule deer samples and 21,946 elk samples for 

this analysis. We stratified the submitted cases for both elk and 

mule deer into eight groups: 

1. and 2. clinical CWD “suspects,” female and male more 

than 1-year old; 3.–7. harvested animals: adult males, adult 

females, yearling males, yearling females, or fawns (young 

of the year of either sex); and 8. all other mortalities, such 

as road-kills, predation mortalities, and other deaths for both 

sexes and ages, except fawns.

For elk, harvested yearling males and calves were 

assigned a weight of zero, because no positive cases were 

found in these groups during the years of 2003–6. When using 

a weighted surveillance system, a baseline population segment to which the specified design prevalence applies must 

be determined. This population segment is the demographic 

group from which all other groups are scaled; that is, they will 

have a weight of 1 in our surveillance system. Although any 

segment can arbitrarily be selected as the baseline, we recommend that it be the population stratum for which the sample 

sizes are consistently the largest, and it should also be chosen 

with herd management goals in mind. Our baseline population 

segment (the segment of the population to which the specified design prevalence applies) was harvested adult males for 

mule deer and harvested adult females for elk. We chose these 

groups because they are consistently the most represented in 

our surveillance stream and are a focus of our herd management strategies for these species. 

By using the Bayesian software WinBUGS (Lunn and 

others, 2000), we calculated the mean, median and the 95-percent credible intervals of the hazard ratio for each population 

segment of each species (tables 1 and 2). From these hazard 

ratios, we calculated the weight for each demographic group 

(tables 3 and 4). As expected, clinical suspect mule deer had 

the highest weight, followed by the “other” population segment, adult-harvested animals, yearling-harvested animals, 

and young-of-the-year. Elk showed the same general rankings; however, the “other” group had a lower weight than 

the harvested adult population segments. Similar patterns 

and weightings have been shown in white-tailed deer as well 

(Chris Jennelle, written communication, 2012). This ranking 

is intuitive, and a higher weight is associated with individuals from population strata with higher estimated prevalence. 

The largest difference between the estimated hazard ratios 

and weights were associated with groups of animals with 

small sample sizes. Baseline prevalence estimates varied from 

0.027 to 0.037 in mule deer and from 0.0074 to 0.0092 in elk. 

We also calculated the number of samples needed from each 

population segment if all the samples entering the surveillance 

stream arose from that subpopulation (tables 3 and 4). 

Table 1. Estimated mean hazard ratios, associated standard deviations, Markov Chain Monte Carlo 

(MCMC) errors, and quantiles for various demographic groups of mule deer (Odocoileus hemionus) based 

on samples collected from chronic wasting disease-positive areas in Colorado during 2003–6.

Group Mean Standard 

deviation

Markov Chain 

Monte Carlo 

error

2.50 

percent Median 97.50 percent

Suspect female 14.13 2.390 0.0151 9.90 13.97 19.24

Suspect male 12.19 2.070 0.0135 8.50 12.06 16.64

Other 1.93 0.245 0.0018 1.49 1.92 2.44

Harvest—adult males 1.00 NA NA NA NA NA

Harvest—adult females 0.57 0.065 0.0005 0.46 0.57 0.71

Harvest—yearling females 0.44 0.150 0.0009 0.20 0.43 0.78

Harvest—yearling males 0.25 0.077 0.0004 0.12 0.24 0.43

Harvest—fawns 0.03 0.032 0.0002 0.001 0.02 0.12
Weighted Surveillance System 19

Table 2. Estimated mean hazard ratios, associated standard deviations, Markov Chain Monte Carlo 

(MCMC) errors, and quantiles for various demographic groups of elk (Cervus elaphus) based on samples 

collected from chronic wasting disease-positive areas in Colorado during 2003–6.

Group Mean Standard 

deviation

Markov Chain 

Monte Carlo 

error

2.50

percent Median 97.50 percent

Suspect female 29.25 11.920 0.045 11.23 27.44 57.26

Suspect male 21.28 12.830 0.048 4.24 18.72 53.08

Other 1.06 0.633 0.002 0.21 0.94 2.62

Harvest—adult males 1.28 0.264 0.001 0.83 1.25 1.86

Harvest—adult females 1.00 NA NA NA NA NA

Harvest—yearling females 0.46 0.244 0.001 0.12 0.42 1.06

Table 3. The “real weights” that account for 

uncertainty in the estimates of the hazard ratios and 

associated number of samples (N) needed to reach 

a 95-percent confidence that an area of interest is 

“disease-free” if the prevalence is less than or equal 

to 0.01 for mule deer (Odocoileus hemionus) from 

chronic wasting disease-positive areas in Colorado 

during 2003–6.

Group Weight

Number of 

samples

(N)

Suspect female 13.60 22

Suspect male 11.50 26

Other 1.90 158

Harvest—adult males 1.00 300

Harvest—adult females 0.56 536

Harvest—yearling females 0.33 909

Harvest—yearling males 0.19 1579

Harvest—fawns 0.001 300,000

Table 4. The “real weights” that account for 

uncertainty in the estimates of the hazard ratios and 

associated number of samples (N) needed to reach 

a 95-percent confidence that an area of interest is 

“disease-free” if the prevalence is less than or equal 

to 0.01 for elk (Cervus elaphus) from chronic wasting 

disease-positive areas in Colorado during 2003–6. 

Group Weight

Number of 

samples

(N)

Suspect female 18.75 16

Suspect male 8.57 35

Other 0.41 725

Harvest—adult males 1.16 259

Harvest—adult females 1.00 300

Harvest—yearling females 0.23 1,310
20 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

Once the weights have been calculated, we need to 

determine the total number of samples needed from each of 

the population segments. The problem is that rarely do we 

have control over when and from what risk groups the samples 

entering the CWD surveillance stream are collected, which 

makes having preset sample sizes for each group infeasible. 

One solution is a “points system.” With this system, regardless 

of how the population elements are selected, once an element 

enters the surveillance stream it is awarded a specified number 

of “points.” Point values assigned to each individual sample 

equal the estimated weights described above. Samples continue to enter the surveillance stream until total accumulation 

of points equals some target value. Thus, this points-based system provides an intuitive approach to disease surveillance that 

can be readily implemented by field personnel. This type of 

system was first espoused by Cannon (2002) and later developed for BSE surveillance (Wilesmith and others, 2004), with 

most recent application to CWD (Walsh and Miller, 2010). 

Under this points system, the target value (n), which is 

the number of points that need to be accumulated during surveillance activities, must be specified for each SU. It is based 

on the desired confidence (α) and the predetermined design 

prevalence (the minimum prevalence level in the baseline 

group at which the disease is to be detected; pdesign). It is calculated as follows:

n

pdesign

= − − ln ( ) 1 α

.

This equation is recognizable as the standard equation 

for calculating the number samples needed to demonstrate 

freedom from disease, assuming the number of positive cases 

follow a Poisson distribution (Dohoo and others, 2003). An 

alternative when population size is known and relatively small 

is to use the hypergeometric distribution (Samuel and others, 

2003); however, both formulations provide nearly identical 

results for population sizes of ungulates likely to be encountered in practice, and thus we will only discuss the simpler 

Poisson model. A Bayesian approach can also be taken to 

estimate the target value; however, the values are once again 

nearly identical to those derived from the Poisson model. 

We will now provide a simple example to illustrate the 

use and benefit of the weighted surveillance system within a 

SU. For purposes of this example, the total population of the 

SU is estimated as 10,000 mule deer. It was predetermined 

that we wished to detect the presence of CWD when the 

number positive deer in the population was greater than or 

equal to (≥) 100. Thus, the design prevalence was set at 0.01 

and α = 0.95, which resulted in a target value for the SU of 

300. This means we desire a 95-percent probability of detecting disease if prevalence is 1 percent or greater. Samples 

were collected until the target value was reached by using the 

weights for mule deer from table 3 to assign points. The following samples were collected: 17 harvested yearling females, 

12 harvested yearling males, 5 CWD-suspect males, 4 CWDsuspect females, 105 harvested adult males (the baseline population segment), 21 road-killed or predator-killed deer, and 

lastly 63 harvested adult females. A total of 227 deer were submitted into the surveillance stream to reach the target value. 

Thus, use of the weighted surveillance system reduced the 

required sample size by 73 animals, which represents a cost 

savings for this single SU of approximately $5,525, assuming the cost of testing is approximately $75 per animal. When 

multiple SUs are sampled, the cost savings increase significantly. In addition to showing the benefit of using a weighted 

surveillance system, this example also illustrates the importance of understanding the implication of setting the target 

value. For example, if we designate an entire state as an SU 

and the design prevalence is set at 0.01 with α = 0.95, then by 

using the above equation n = 300. Thus, once the total points 

from samples entering the surveillance stream equal 300, we 

will be 95-percent confident the disease prevalence is less than 

or equal to (≤) 0.01. However under this scenario, if the total 

population size is 1,000,000, this implies that there may be as 

many as 10,000 CWD-positive individuals in the SU, which is 

likely well above the threshold for what is acceptable to most 

agencies. Therefore, each agency should first determine the 

number of positive individuals they are willing to allow to go 

undetected in each SU, and then set the design prevalence and 

the resulting target values accordingly. 

Once the target value for each SU is specified and the 

weights are calculated, the final step is to determine the number of SUs to sample. It is clear that standard approaches to 

sample size determination, such as use of a binomial probability argument based on the assumption of completely random 

sampling of individual deer for measurement (Samuel and 

others 2003,) are inadequate for supporting informed decisions 

about SU selection within complex CWD surveillance programs. This difficulty is fairly common in complex estimation 

procedures used in wildlife population biology, and in such 

cases biometricians turn to computer simulation experiments 

to investigate and compare statistical performance under a 

variety scenarios specified by assumptions about the biological and sampling processes of interest. For CWD surveillance 

designs, evaluation of non-probabilistic or partially probability-based approaches for SU selection can also be evaluated. 

We encourage the use of simulation modeling to inform State 

agencies about the relative costs and effects associated with 

various sample sizes and distributions of SUs. Examples of 

such simulation modeling were previously detailed. Although 

we cannot provide specific guidance on the appropriate sample 

size of the SUs because each jurisdiction will vary in its 

requirements; nevertheless, as previously described, enough 

SUs must be selected to ensure adequate spatial distribution. 

For example, in Colorado animals are submitted for surveillance from all data analysis units (DAUs are SUs for Colorado) across the State with a target value of 300 points for 

each DAU.
When Is Enough...Enough? 21

Temporal Scale of Disease 

Surveillance

As resources have become more limiting, agencies have 

accordingly become more interested in conducting periodic 

as opposed to continuous (annual) disease surveillance. One 

advantage of periodic surveillance is the ability for funding 

sources to allocate additional resources in a cyclic manner 

to agencies during years of surveillance while allocating 

resources to other agencies in years when surveillance is not 

being conducted. The benefit of this type of allocation is more 

intensive surveillance efforts (increased sampling activities) 

can be conducted during surveillance years with the additional resources. This should allow for more sampling and/or 

population units to enter the surveillance stream, increasing 

confidence that a jurisdiction is below the specified design 

prevalence. However, social and political considerations may 

require an annual surveillance effort. In addition, operational 

considerations may make periodic surveillance problematic. 

For example, it may be difficult or more costly to mobilize and 

maintain the needed personnel and infrastructure for periodic 

surveillance than if surveillance is conducted annually. Also, if 

an agency is relying on passive sampling, such as hunter submissions, as a major component of surveillance, then sample 

submission may decrease when collection doesn’t occur on 

an annual basis. Lastly, with periodic surveillance, there is an 

increased probability that if CWD becomes established it may 

go undetected for a longer period, which has implications on 

control efforts. One possible periodic sampling scheme, which 

may alleviate some of the aforementioned operational, social, 

and political considerations yet still reduce costs, is to conduct 

surveillance annually, while rotating the geographic region at 

which is surveillance is conducted. Regardless of the design 

of the periodic surveillance system, if an agency intends to 

employ disease surveillance over varying time scales, it must 

carefully weigh the risks associated with this approach compared to an annual surveillance system. If a periodic surveillance system is instituted, the lag period between surveillance 

activities should be based on biological and epidemiological 

characteristics of the study species and CWD.

 Another temporal consideration is whether or not 

multiple-year inference can be used to make surveillance 

more efficient. One form of multiyear inference in surveillance programs is expected waiting time to first detection, or 

more formally, “for a specified surveillance design conducted 

annually, what is the expected number of years until the first 

detection, assuming a disease prevalence of X percent?” This 

question is of particular relevance under the philosophy that a 

detection surveillance program could be viewed in a cumulative, multiyear framework, in which data from annual surveys 

is formally accumulated into a statistical framework that 

allows multiyear inference about waiting time. A consequence 

of this approach might be that annual cost would be reduced 

when compared to a design framework in which inferences are 

made independently in each year, because reduction in sample 

size or incomplete sampling of the target population in a given 

year might still result in acceptable statistical confidence for 

multiyear inference. Readers interested in this approach to 

multiyear inference are referred to Nusser and others (2008). 

A second type of multiyear inference is described by 

Hadorn and others (2002). With this technique, the critical 

assumption is that the probability of a CWD-infected individual entering the system either from immigration of freeranging animals or by movement of captive animals is known 

or can be estimated. It is important when employing multiyear 

inference approaches, or any surveillance system, that the 

assumptions, advantages, and disadvantages of the various 

techniques be carefully evaluated and their implications for 

management and disease control be understood. Readers are 

referred to the aforementioned article for more information.

When Is Enough...Enough?

Another common question is “when have I conducted 

enough surveillance to confidently believe my jurisdiction is 

disease-free?” Bohning and Greiner (2006) provide a statistical framework for estimating the smallest number of samples 

required over multiple survey events, which need to be tested 

to ensure an area is free of disease for a given design prevalence and power. Their approach is based on a geometric distribution for waiting time (that is, the time until first detection 

of disease) and was developed for surveillance of BSE. An 

extension of this framework allows for heterogeneity in design 

prevalence, which would be particularly useful for CWD 

surveillance.

Several factors need to be considered when proposing to 

discontinue disease surveillance in addition to assumptions of 

the above model. First, what is the risk of an infected individual entering the study area through immigration of free-ranging animals from known or unknown positive jurisdictions or 

movement of captive cervids? If this risk is greater than zero, 

decisions on whether or not to continue to conduct surveillance should carefully weigh the level of this risk, because 

ultimately some chance exists of introduction of the disease. 

Secondly, agencies need to consider if they have adequate 

spatial coverage of sampling units across the jurisdiction to 

ensure all potential pockets of disease are sampled, and if they 

have effectively sampled individual animals within sampling 

units. Third, agencies need to consider the social and political repercussions of stopping surveillance activities. Lastly, 

availability of resources will play perhaps the most important 

role in determining whether surveillance should proceed or 

not. A cautionary tale against stopping surveillance too soon is 

provided by past CWD developments in Michigan. CWD surveillance in Michigan had continued since 1998 with 23,739 

white-tailed deer, 887 elk, and 46 moose tested as of October 

21, 2008 (Daniel O’Brien, Michigan Department of Natural 

Resources, oral commun). In addition, 8,452 captive cervids 

have been tested by the Michigan Department of Agriculture. 
22 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

Even with 10 years of surveillance effort, the first positive 

case was discovered in a captive animal in fall of 2008. Thus, 

even with a reasonable level of surveillance effort for a considerable period of time, a new case may be detected where a 

jurisdiction was previously believed to be CWD-free when the 

probability of introduction is greater than zero. 

Conclusion

In this chapter, we illustrated how a weighted surveillance system can be used to effectively and efficiently conduct 

CWD surveillance. This surveillance system is based on a 

number of assumptions, which need to be met if the system 

is to achieve the theoretical detection probability (Walsh and 

Miller 2010; Chris Jennelle, written commun, 2012). Understanding these assumptions and the impact of the failure to 

meet these assumptions is critical when implementing this 

and any surveillance strategy. We also recognize the potential 

for species differences, spatial and temporal variability in 

CWD disease dynamics may affect the estimated weights and 

strongly recommend each agency develop its own weights for 

their region and species of interest whenever possible. However, for regions where CWD has not yet been detected and 

data are not available to estimate the weights, we recommend 

using the weights provided in tables 3 and 4 for the appropriate species. In lieu of available data, we believe that this is a 

reasonable approach because the various risk factors (age, sex, 

and so forth) upon which the weights are derived have shown 

consistent patterns in the epidemiology of CWD throughout 

its range and across time (Joly and others, 2003; Conner and 

Miller, 2004; Miller and Conner, 2005; Farnsworth and others, 

2006; Grear and others, 2006; Joly and others, 2006; Osnas 

and others, 2009; Grear and others, 2010; Heisey and others, 

2010; Walsh and Miller, 2010), which would indicate that 

the observed differences in risk used to calculate the weights, 

arise largely from underlying disease processes rather than 

local processes that may vary considerably across locales. For 

example, accounting for the different baseline population segments used in analysis, the estimated weights for mule deer in 

Colorado are generally similar to those estimated for whitetailed deer in Wisconsin. These represent two different species 

from two regions with vastly different histories of CWD and 

varying environmental characteristics (Walsh and Miller 

2010; Chris Jennelle, written commun, 2012). This provides 

additional credence to the assertion that temporal and spatial 

differences in the weights may exist, but those differences 

are not expected to be substantial. In addition, the Bayesian technique used to estimate the weights presented herein 

explicitly accounts for the variability within data from which 

the weights are derived, which addresses concerns of withinsite temporal variability. Also, it is important to note that even 

if differences between the weights used in surveillance and 

the true underlying weights do exist, as long as these differences do not cause significant bias in the resulting weights 

(that is, ≥ 30 percent), the overall effect on probability of 

detecting CWD-positive individuals is likely minimal (Walsh 

and Miller, 2010). Therefore, because the ultimate goal of 

surveillance is early disease detection, we believe that using 

the weighted surveillance is preferred over the traditional 

surveillance approach even if the estimated weights employed 

in the surveillance efforts are somewhat different than the true 

underlying weights for a jurisdiction. 

We believe the two-stage surveillance scheme presented 

herein, which is based on sampling SUs and subsequently 

population elements within SUs, provides a powerful and costeffective tool for conducting disease surveillance. By accounting for both spatial and demographic risk factors in the design 

of the surveillance system, we are able to effectively utilize 

this information to create a more efficient surveillance system. 

In addition, using a points approach to create a weighted 

surveillance system provides an intuitive surveillance mechanism, which can be easily employed by field personnel. With 

limited resources available to agencies to conduct disease 

surveillance, we believe the surveillance system described 

herein represents the next step in the evolution of surveillance 

for CWD.

Acknowledgments

We would like to acknowledge Dr. Dennis M. Heisey for 

his thoughts, contributions and helpful suggestions during the 

development of this chapter.

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Chapter 3

Monitoring Surveillance: Disease Investigations in 

Chronic Wasting Disease-Positive Areas

By Julie A. Langenberg, VMD1, Daniel J. O’Brien, DVM, Ph.D.2, Daniel P. Walsh, Ph.D.3, 

Lynn H. Creekmore, DVM4

, Jenny G. Powers, DVM, Ph.D.5, and Michael W. Miller, DVM, Ph.D.6

1

Wisconsin Department of Natural Resources, currently affiliated with the International Crane Foundation.

2

Michigan Department of Natural Resources.

3

Colorado Division of Wildlife, currently affiliated with the U.S. Geological Survey, National Wildlife Health Center.

4

U.S. Department of Agriculture–APHIS.

5

National Park Service. 

6

Colorado Division of Wildlife.

Introduction

After chronic wasting disease (CWD) detection, surveillance goals may shift to disease monitoring. The first step in 

monitoring is usually to assess and establish a baseline for the 

intensity of disease occurrence and/or its spatial distribution 

on the landscape. Once this initial baseline assessment is completed, further monitoring surveillance then can be tailored as 

needed to address one or more of the following questions: 

• “Is there change in the prevalence of disease over 

time?” 

• “Is there change in the rate of disease transmission or 

force of infection over time?” 

• “Is there a change in the spread or contraction of CWD 

over the landscape over time?”

• “What are the most important means of transmission 

and spread in the population?”

• “What are the impacts of management actions on epidemic dynamics and/or geographic spread over time?” 

Determining the specific goals of monitoring efforts and 

formulating specific questions to be answered are critical to 

collecting useful surveillance data. Moreover, determining the 

magnitude or type of change to be detected in the pathogen, 

the host(s), or the environment is important to consider when 

planning monitoring actions to collect the data necessary to 

measure such changes. 

Although approaches for CWD detection surveillance are 

relatively well defined here and elsewhere (Samuel and others, 2003), surveillance intended primarily to monitor trends 

represents an arena of considerably less experience, guidance, and certainty of outcome. Consequently, investing some 

effort toward designing and conducting monitoring programs 

in a rigorous and scientifically valid fashion to collect the 

highest quality data possible will provide useful information to advance our understanding about the most effective 

monitoring approaches in addition to gathering the trend data 

themselves. 

Monitoring to detect changes in disease patterns should 

be particularly valuable when linked with research to understand the epidemiology of CWD. Striving to link monitoring 

programs with research objectives can create a synergistic 

effect that may provide more informative data than would be 

obtained if these activities occurred in isolation. Monitoring 

is also an integral component of agency programs designed 

to manage CWD. Monitoring changes in disease patterns 

and impacts of disease on susceptible populations provides 

the primary source of information for assessing the effect of 

management programs and is crucial for evaluating population 

responses to adaptive management approaches for CWD. 

In shifting program emphasis from surveillance for disease detection to monitoring for disease trends, the complexity 

of surveillance strategies, methods, and analyses generally 

increases. Some components of a surveillance program are 

common to all of the surveillance objectives; for example, if 

the main question concerns spread of CWD to new locations 

on the landscape, detection strategies necessarily will be part 

of the approach. 
26 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

In formulating a plan for CWD monitoring surveillance, 

it is important to consider some broad principles and how 

they apply to a specific jurisdiction and the disease patterns 

observed:

1. Choices about how to monitor CWD trends in freeranging populations should not only be defined by the 

questions to answer, but also with consideration of what 

ultimately will be done with monitoring results. Among 

other uses, trend data may be of value in (1) deciding if, 

to what degree, and how CWD control will be attempted; 

(2) helping to target control actions such as culling; 

(3) implementing risk management by local and neighboring jurisdictions, for example, carcass movement restrictions, feeding/baiting restrictions, deer rehabilitation bans.

2. For most wildlife management agencies, there will inevitably be a need to communicate to the public, administrators, and key stakeholders, such as legislators and hunters, 

about specific monitoring approaches and results. It is 

worth keeping in mind the need to be able to communicate accurately, but meaningfully, to a diverse audience 

when making choices about strategies, such as sampling 

frequency, location, or metrics used.

3. To achieve most goals for monitoring CWD trends, consistent application of a sampling plan over an extended 

timeframe will be needed. Variation in surveillance 

methods can make meaningful analysis of results difficult. Managers with funding and staffing responsibilities should be aware of and supportive of the need for 

long-term commitment of resources before a jurisdiction 

embarks on a CWD monitoring plan.

4. Although commonalities exist, differences between cervid 

host species and between geographic areas and habitats 

where CWD occurs likely have significant effects on spatial spread and epidemic dynamics. Choices about goals 

and strategies for monitoring CWD should be informed 

by available local information regarding the biology of 

affected species (for example, recruitment rates, mortality rates, population densities) and cervid landscape use 

(for example, dispersal, migratory movements, seasonal 

congregation).

5. As with any large-scale collaborative effort, it is valuable 

in the formulation of a CWD monitoring plan to be clear 

about the assumptions underlying design choices. For 

example, specific to monitoring choices, it is important to 

acknowledge that there may be limitations in how representative the sample population is to the general population and also limitations in how chosen spatial sampling 

units represent the heterogeneous landscape. 

6. In the course of developing the best monitoring strategies 

for a jurisdiction, the CWD status, surveillance programs, 

and management activities of surrounding jurisdictions 

should be considered. Efforts should be made to share 

surveillance goals and data among neighboring jurisdictions with a goal of working together to develop regional 

objectives and monitoring approaches when feasible. 

The following sections of this chapter will address concepts 

and strategies to consider when conducting monitoring surveillance for CWD.

Initial Assessment of a New Chronic 

Wasting Disease Focus: Evaluating 

Intensity and Spatial Extent 

Once CWD has been detected in an area where the 

disease was previously unrecognized, it is important to 

effectively and efficiently assess disease intensity and spatial 

distribution to establish a baseline to monitor trends over time. 

Such an assessment requires defining a biologically relevant 

susceptible population of one or more host species to survey, 

choosing a disease-intensity metric (for example, disease 

prevalence, incidence, or force of infection) with consideration of long-term monitoring goals and desired precision, 

and choosing a strategy for assessing geographic distribution. 

Because the territorial ranges of the cervid populations at risk 

often span jurisdictional boundaries, it may be important to 

access and incorporate existent knowledge about the CWD 

status of surrounding jurisdictions in making a decision about 

assessment approaches. In some circumstances, a regional 

approach to assessment surveillance may be warranted. 

Statistically valid sampling, using either antemortem or 

postmortem surveillance, is worth striving for to maximize 

accuracy of the initial disease-intensity estimate. This will 

facilitate making difficult choices as a jurisdiction embarks 

on CWD management in a newly identified affected area. It 

is important to remember that valid methods and sample sizes 

for disease detection often are not appropriate for establishing the extent and distribution of the disease in a given area or 

population (see the discussion on sampling sizes later in this 

chapter for additional information). For example, although 

“targeted” or “clinical suspect” surveillance is an excellent 

tool for detecting the disease, it is not an accurate method for 

determining the intensity of infection within a population, 

because infected animals are not likely to demonstrate clinical 

signs until late in the disease course. Likewise, using weighted 

sampling may be a good tool for assessing spatial distribution 

but it has limitations for estimating disease intensity. Sampling 

design, even for initial assessment of disease in a new area, 

will benefit greatly from input from professionals with experience in biometrics or biostatistics, disease and host ecology, 

and study design. 

Surveillance in the vicinity of a CWD “index case” 

(defined here as the first case identified in a new area) is 

an appropriate initial approach for both estimating disease 

intensity and also initial exploration of the geographic extent 
Monitoring Trends in Intensity of Disease 27

of disease. However, given that CWD is known to be heterogeneously distributed across landscapes (Joly and Ribic, 

2003; Miller and Conner, 2005; Farnsworth and others, 2006; 

Osnas and others, 2009; Heisey and others, 2010; Walter and 

others, 2011), representative sampling in all areas of concern 

will be necessary to achieve the goal of accurately assessing spatial extent and estimating disease intensity in different 

areas. Therefore, identifying areas of concern [for example, 

migration corridors (Conner and Miller, 2004), gene flow corridors (Blanchong and others, 2008; Kelly and others, 2010; 

Cullingham and others, 2011), areas with high cervid habitat 

abundance (Joly and others, 2006), and refugia (Farnsworth 

and others, 2005)] will provide a basis for deciding where to 

sample to make most efficient use of surveillance resources in 

a short period of time. 

Once the initial assessment surveillance is complete, it is 

probably valuable to revisit disease-management goals before 

designing an ongoing CWD monitoring program. Although 

it is desirable to start with a precise estimate of the baseline 

disease occurrence and distribution, management goals and 

resource limitations will be important considerations in the 

degree of certainty needed for these initial disease-assessment 

estimates. For example, if the goal of disease management is 

to attempt disease eradication, then a high degree of certainty 

about both the distribution and extent of infection will be 

needed to determine if this is a reasonable goal. In contrast, 

if the management goal is spatial containment of the disease, 

then having a good estimate of disease distribution may 

be of primary importance, with perhaps less emphasis on 

precisely estimating the intensity of the infection within the 

population. Alternatively, if the goal is limiting the number 

of new infections within an area where the disease is likely 

established, then estimating local disease prevalence may be 

more important than focusing on finding the leading edge of 

disease spread. Regardless of the management goals, the scope 

of the problem is defined during the assessment phase, and 

future monitoring and management decisions will be based 

upon what is discovered. Therefore, it is worthwhile to devote 

resources to fully understanding the problem early on for 

future efforts to be successful. 

Once assessment surveillance is completed, ongoing 

monitoring of an existing focus of CWD will generally include 

measuring three aspects of the disease: its spatial distribution, 

its intensity, and how these change over time. Monitoring of 

spatial distribution and intensity of disease will usually be 

done simultaneously. However, in specific circumstances, or 

with limitations in resource availability, the goals of surveillance or management questions may dictate performing either 

intensity or spatial distribution monitoring alone. These topics 

are treated separately below to facilitate discussion of each 

concept, but because they are generally implemented together, 

there is some necessary redundancy in our coverage of the 

respective issues. 

Monitoring Trends in Intensity 

of Disease

One form of disease monitoring is to examine changes or 

trends in the intensity of disease within a population or region 

of interest. Tracking fluctuations in disease intensity can be 

used to answer questions regarding rates of change in disease 

dynamics or parameters. Before embarking on a monitoring 

program for investigating disease intensity, it is essential to 

establish the purpose of the monitoring program or, in other 

words, what information an agency hopes to gain or what 

questions need to be answered through this program. If this 

critical step is foregone, the monitoring program likely will 

be ineffective and may result in the waste of resources. Thus, 

framing questions is the first and most important step of developing a monitoring program. Once some direction has been 

provided, then various metrics, strategies and protocols for 

monitoring of disease intensity can be more fully considered.

Relevant Questions

Strategies for evaluating the intensity of disease within 

populations depend largely on the question(s) monitoring is 

intended to explore. Such questions might include:

• How much disease is present in the target population?

• How is the disease intensity changing over time?

• How rapidly are CWD-negative animals becoming 

infected in the target population?

• Is disease intensity changing in response to CWD management actions?

Metrics for Monitoring Trends in Intensity

A variety of epidemiological metrics exist for measuring 

the occurrence of diseases in populations. Many of these can 

be used to express the intensity of CWD in a given population, 

and when recorded over time, they can be used to monitor 

trends in CWD occurrence. As noted in the introduction, the 

specific aims of the monitoring program will dictate to a great 

degree which of the metrics is chosen. If an agency has modest 

objectives and needs only to convey the most basic information on the state of the disease to the public, and is willing to 

accept the severe limitations (and perhaps consequences) of 

using such ambiguous measures, then the number of CWDpositive animals (in aggregate or per some unit of time) may 

be a sufficient metric. However, more ambitious objectives, 

such as disease control or addressing specific research questions integrated into the monitoring program, require more 

precise metrics. As with any scientific measurement, each 
28 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

metric noted below has its strengths and weaknesses. These 

metrics should be considered carefully before implementing a 

CWD monitoring program.

Number of Chronic Wasting Disease-Positive 

Animals

Arguably, the most basic measure of CWD occurrence 

in an area is the number of animals shown to be infected (that 

is, “number testing positive”). The principal strength of this 

metric is its comprehensibility by the media and the lay public. 

Often, the first piece of information the media wants to know 

is the number of test-positive animals, and that information is 

easy to convey. However, unless the number of CWD-positive 

animals is zero, this metric has little meaning as a stand-alone 

measure and can be easily misinterpreted and misconstrued. 

Because this section discusses surveillance in areas where 

CWD already has been found, the number of positives will, 

in most cases, be greater than zero, unless infection rates 

are low or minimal sampling is occurring. This metric has 

little or no utility for most purposes without reference to the 

size of the sampled population. For example, the finding of 

2 CWD positive animals in Area X has dramatically different 

interpretations if the sampled population was 5 animals rather 

than 5,000.

Prevalence

Prevalence, a proportion defined as the number of 

disease-positive animals divided by some reference population, is a measure of the existing reservoir of disease in that 

population at (or over) a specified period of time (Rothman 

and Greenland, 1998). The reference population is specified 

by the investigator. Often, it is specified as the number of 

animals tested. In general, prevalence is a useful metric, commonly and readily calculated from data generated by standard 

surveillance programs. It is relatively easily communicated 

to the media and the lay public. Consistently measured over 

time, prevalence can be used to track the size of the reservoir 

of CWD over time, information of intuitive value for management. Statistical tests for trends are available (Margolin, 

1988). However, prevalence measures both new and existing cases of disease at a point in time, and so depending on 

the disease in question, it may not accurately reflect disease 

dynamics. In general, changes in population-level transmission of disease are reflected more slowly in prevalence than 

in incidence (see below). Consequently, it may take several 

monitoring cycles before CWD occurrence is confirmed to 

be on the rise (or fall), and if the objective of monitoring is to 

be able to adaptively apply stronger control measures in the 

face of increasing infection rates, then this time lag can make 

control more difficult. As with any proportion, prevalence 

can be unstable and may be imprecise in situations where few 

animals are sampled. Attention also must be paid to ensuring 

that the reference populations that compose the denominator 

are reasonably consistent across sampling periods. Wide variations in sample gender or age composition should be avoided. 

Also, it is the responsibility of the resource manager or investigator to consider the extent to which the reference population 

is representative of the general population to which inference 

is being made. The possibility of bias in the animals selected 

for testing, as always, must be considered. For example, a 

sample of hunter-harvested animals may not be representative 

of the target population (Conner and others, 2000).

Yearling Prevalence

One principal limitation of prevalence as a metric is that 

often it is impossible to determine when an animal testing 

positive contracted the disease. One way around this constraint 

is to use prevalence in young animals, say yearlings, as a 

metric of disease occurrence. Because “yearlings” have only 

been alive for 12–24 months, animals found infected must 

have been infected during that time period; for samples based 

on harvest in most North American jurisdictions, “yearling” 

cervids are usually 15−18 months old. Consequently, yearling 

prevalence can be used (O’Brien and others, 2006) as a crude 

metric for rate of new infections (incidence), a potentially 

valuable quantity epidemiologically (Rothman and Greenland, 1998). The metric may be most useful in settings such 

as eastern white-tailed deer populations where large numbers 

of yearlings are readily available for testing by hunter harvest. 

However, this metric is subject to instability where sample 

sizes are low and may have little meaning or utility where 

yearlings are rarely infected with CWD (Miller and others, 

2000; Miller and Conner, 2005). 

Force of Infection

The common desire to use readily available age-specific 

prevalence data to estimate incidence underlies the metric 

known as force of infection (FOI). Although more explicit 

mathematical definitions are available (see for example, Caley 

and Ramsey (2001), Caley and Hone (2002), and Heisey and 

others (2006)). FOI can conceptualized as the probability over 

a short period of time that an uninfected animal will become 

infected. Because it is more sensitive to changes in transmission than aforementioned disease metrics, tracking trends in 

FOI over time can provide earlier warning of changes that may 

have bearing on disease-management decisions than trends in 

prevalence would provide. FOI has some significant mathematical relationships to other quantities that may be useful 

epidemiologically, such as probability of disease transmission, 

which is a critical parameter of most epidemiological models, 

and methods to derive FOI within an epidemiological framework are available elsewhere (Caley and Hone, 2002; Heisey 

and others, 2006). In addition, good working estimates of FOI 

can be derived by using only age-specific prevalence data and 

spreadsheet programs (G. Hickling, University of Tennessee, 

oral commun). Depending on the duration of disease and the 
Monitoring Trends in Intensity of Disease 29

amount of disease-associated mortality (Heisey and others, 

2006), trends of FOI over time may or may not closely correspond to trends in disease prevalence (D. O’Brien, Michigan 

Department of Natural Resources, unpub. data). In situations 

where disease-associated mortality is low, there may be no 

particular management advantage to using FOI. In general, 

it is a more difficult metric to explain to the media and the 

lay public. However, in situations where there is evidence of 

significant CWD-associated mortality (Miller and Conner, 

2005), or in situations where early detection of managementassociated changes in transmission is desired, use of FOI may 

have advantages over other simpler metrics.

Incidence

In monitoring human diseases, incidence is typically the 

epidemiological metric of choice. Incidence may be defined 

as the number of new cases of disease in a population at 

risk over a defined period of time (Rothman and Greenland, 

1998). Incidence conveys what is happening with respect 

to the occurrence of disease at any given point in time. It is 

the most sensitive and commonly used metric of change in 

disease transmission rate in a population, and it is a true rate, 

because some unit of time is included in the denominator. In 

addition, because incidence also accounts for the size of the 

population at risk, it allows for comparison across populations 

or within a population through time. For these reasons, it can 

provide early warning of changes in conditions that may be 

relevant to disease control when other disease metrics, such as 

prevalence, are not able to detect any perturbations. However, 

the use of incidence in animal populations is considerably 

less straightforward than in human populations. Surveillance 

systems that do not test the same individuals repeatedly over 

time commonly cannot distinguish between newly infected 

individuals and existing cases. Moreover, quantifying a population at risk is problematic, because it is often impossible to 

determine which animals are susceptible—as opposed to those 

already infected and that not susceptible—without testing 

every individual in the population. Incidence may be very useful as a metric in circumstances where a well-defined population is available for repeated testing over time. For example, 

the incidence metric may be useful in situations, where live 

animal testing is being utilized to monitor a discrete population. Because some public familiarity exists with the use of 

incidence in public health, it may be more easily communicated by the media to the general public. 

Strategies and Approaches for 

Intensity Monitoring

Approaches for monitoring are dependent on the pertinent questions of interest, the system and metric being 

examined, and available resources. We begin by describing 

general strategies that agencies can use to conduct monitoring 

efforts at varying intensity levels. The first and most intensive 

monitoring strategy we will detail is the monitoring of cohorts 

of marked individuals through time. Under this strategy, individual animals are randomly selected from the population or 

population segments of interest, marked, and followed through 

time with either periodic testing (for example, tonsil or rectal 

mucosa biopsy) or testing after the mortality of an individual. 

In addition, new marks are added each year to maintain 

adequate sample sizes in each population segment for estimating the metric of interest such as incidence or prevalence. This 

approach requires significant resources; however, it is likely to 

provide the most precise information on disease dynamics and 

the effectiveness of control strategies at a fine resolution. This 

strategy is most appropriate for small populations, populations 

of high value where precise disease information is required, or 

in conjunction with research efforts. An example of using this 

approach is illustrated by a study from Colorado that followed 

individually marked mule deer through time to investigate the 

impacts of CWD and associated selective predation on deer 

survival (Miller and others, 2008). By using this cohort study 

design, the authors were able to estimate the intensity metrics 

of both prevalence and incidence rates for their population of 

interest.

A second strategy that is also intensive is to actively 

sample individuals at some desired interval (for example, 

annually) from the population for disease testing. Selection 

is assumed to follow some probability sampling scheme (for 

example, random sampling) that will allow unbiased estimation of the desired intensity metrics. The advantage of active 

sampling techniques is that they can provide less biased estimates of the metrics of interest than convenience or passive 

sampling, because the sampling probabilities are known and 

are under the control of agency personnel. Several approaches 

have been developed for active sampling, but it is important 

to keep in mind that they still have some limitations in terms 

of achieving a completely non-biased approach if underlying 

assumptions are violated. For example, in areas where road 

networks provide good spatial coverage of a region, roadkilled individuals may actively be collected and entered into 

the surveillance stream to monitor CWD on the landscape. 

However, if road networks are not evenly located across the 

habitat of the species of interest to account for a heterogeneous 

distribution of CWD on the landscape, then estimates of intensity metrics may be biased. In addition, the intensity of CWD 

in road-killed animals may not be representative of CWD in 

the population of interest (Krumm and others, 2005). Therefore, a thorough understanding of the population of interest, 

the underlying assumptions, and the biological system are 

critical when instituting an active monitoring approach. One 

common approach to active CWD monitoring is random culling of individuals. Although culling is generally implemented 

as a disease-control mechanism, it can also be simultaneously 

used as a form of active sampling for disease monitoring if 

selection of individuals is not targeted. Several studies have 

used culled individuals to successfully estimate diseaseintensity metrics, most commonly prevalence (Joly and others, 

2003; Conner and Miller, 2004; Miller and Conner, 2005; 
30 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

Conner, Miller, and others, 2007; Shelton and McDonald, 

2011). However, culling, in addition to requiring significant 

resources, has the added difficulty of potential socio-political 

repercussions. 

A third strategy is to collect individual samples by using 

passive sampling. Passive sampling is the least field-intensive 

monitoring tactic. Under this approach, samples enter the 

surveillance stream without any direct collection action by 

agency personnel. The most common form of passive sampling is disease testing of hunter-harvested animals, and 

it is the most common form of CWD intensity monitoring 

employed by wildlife management agencies. In passive sampling, the probabilities of an individual animal being sampled 

are unknown, and therefore it is generally assumed that the 

samples are collected in a random fashion and are representative of the population of interest, although this assumption is 

undoubtedly violated (Conner and others, 2000; Samuel and 

others, 2003). In addition, many of the risk factors described 

in Chapter 1 would suggest this assumption is violated to 

some degree. Violation of this assumption will lead to biased 

population estimates for the metric of interest, with the amount 

of bias dependent on the relationship between the submission probability and the disease state. For example, hunters 

may be more likely to harvest CWD-infected animals and to 

submit animals that obviously appear unhealthy, or conversely 

they may avoid harvesting obviously unhealthy individuals. 

Many studies have used passive sampling of hunter-harvested 

animals to investigate CWD intensity for a variety of purposes 

in varying biological systems (Conner and Miller, 2004; Farnsworth and others, 2005; Farnsworth and others, 2006; Grear 

and others, 2006; Joly and others, 2006; Osnas and others, 

2009; Heisey and others, 2010; Walter and others, 2011). 

Other strategies also can be developed that combine those 

previously described. Both active and passive sampling can 

be employed for collecting samples for submission into the 

surveillance stream; for example, collection of road-killed 

individuals, culling of suspect animals, and hunter-harvest 

submissions can be combined and used to calculate metrics 

of interest. Combination strategies generally bring both the 

advantages and disadvantages of each of the strategies previously described and are those most commonly employed in 

CWD monitoring.

Within each of these overarching strategies, various 

approaches can be taken. For example, in systems consisting 

of multiple susceptible ungulate species, only one species may 

be selected for monitoring disease intensity. In this case, the 

selection of the species should be driven by the social, political, and economic importance of the species, the sensitivity of 

the species to CWD, the amount of disease variability within 

the species, and the ability to collect adequate sample sizes to 

detect changes in the metric of interest with reasonable statistical certainty. The advantage of selecting a single species is that 

limited resources can be applied to providing the most precise 

estimates of intensity metrics possible for the species most 

likely to demonstrate trends, rather than spreading resources 

over several species with decreases in precision for each 

species-specific estimate. The disadvantage is that it assumes 

the system is adequately understood to ensure that monitoring of a single species will adequately reflect the underlying 

disease dynamics and processes of interest for all species. For 

instance, in some areas in western North America, the ranges 

of all four susceptible host species overlap. However, in most 

(but not all) of these situations, deer (Odocoileus spp.) tend 

to show higher prevalence than sympatric elk and moose 

populations. Although such streamlining may seem logical 

and efficient, it may be difficult or impossible for biological or 

political reasons to select one single species to monitor.

Similarly, within a species, only certain population segments may be selected for monitoring. Once again, if only one 

segment is to be targeted for surveillance, the same considerations for disease sensitivity, variability, and sample size apply 

as described for selecting a single species. For example, given 

the significantly higher prevalence rate for CWD identified 

in 2-year and older male deer in several jurisdictions (Miller 

and Conner, 2005; Grear and others, 2006), a focus on older 

male deer for monitoring disease-intensity trends could be 

considered. This could be attractive where there is a premium 

on controlling surveillance costs and effort over the extended 

period needed to detect management-related changes. Available sample sizes, however, may be a major limiting factor for 

such a focused sampling strategy. Moreover, it is important to 

realize that when using subsets of the population, estimates 

of intensity metrics only apply to the segment from which 

samples were drawn, and inference cannot be made to the 

entire population unless the probability of an individual animal 

falling into the particular population segment is known or can 

be estimated and the variation in disease dynamics between 

population segments is clearly understood. Further analysis 

of existent CWD surveillance data sets and experimental 

implementation of a monitoring plan focused on a population 

segment like older-age bucks will likely be needed before its 

utility is better understood.

Monitoring may also be conducted with varying temporal 

frequency or resolution, for example, on an annual basis, biannual basis, and so forth. In deciding the appropriate lag period 

between monitoring events, the biology and epidemiology of 

the species and the disease of interest need to be considered. 

For example, because the average course of CWD infection 

in mule deer is about 2−3 years (Williams, 2005), sampling 

in multiyear intervals (for example, every 2 or 3 years) may 

be sufficient for demonstrating temporal trends in intensity 

of infection. One advantage of using periodic over continuous (annual) monitoring is that supporting resources can be 

distributed on a rotational basis. In the context of distributing 

the Federal funding that supported CWD surveillance in many 

jurisdictions, this approach could be used to provide more 

funds to agencies that are intensively surveying and reduce 

allocations to the remaining States in “off years.” Theoretically, this rotational system would allow an agency to have a 

more precise estimate of intensity metrics during monitoring 
Monitoring Trends in Intensity of Disease 31

years because more resources would be available rather than 

being diluted across years (Appendix 1, fig. 1–1). If adequate 

monitoring is conducted, this increased precision also may 

permit the detection of changes or trends slightly more frequently than what would be discernible by using lower-intensity annual surveys (Appendix 1, figs. 1–2 and 1–3). However, 

producing estimates of trend and its direction or change detection would require more years of monitoring using a periodic 

monitoring scheme rather than a continuous monitoring program because of the fewer estimates of intensity metrics generated through time (Appendix 1, figs. 1 –2 and 1–3). Another 

consequence of monitoring periodically is that annual intensity 

estimates will only be available during monitoring years. 

This may be problematic if political and constituent pressures 

dictate that information from monitoring is available annually, 

but it may be ideal for situations where antemortem sampling 

or culling are the main sources of monitoring data. Episodic 

monitoring could present logistical problems: for example, it 

may be difficult or more costly to mobilize and maintain the 

needed personnel and infrastructure for sampling harvested 

animals periodically, as compared to annually. In addition, if 

an agency is relying on passive sampling, hunter participation 

may decrease when an annual monitoring program is not in 

place, and submissions may not return to needed quotas without a concerted public information effort in survey years. 

The foregoing is not an exhaustive list of possible CWD 

monitoring strategies, and there are undoubtedly a myriad of 

other potentially viable approaches that could be employed. 

Our purpose here was to describe the advantages and disadvantages of a few of the most common approaches. Ultimately, 

each agency will develop its own monitoring plan based on 

its own specific needs. Regardless of the chosen monitoring 

strategy, several design aspects should be considered. First, the 

spatial scale of monitoring activities and the sampling scheme 

for acquiring samples from across the monitoring area should 

be carefully described. Given the specified spatial scale, many 

of the design-based sampling techniques described in Chapter 

2 for disease-detection surveillance provide a useful framework for obtaining the needed spatial samples in a rigorous 

manner. Secondly, within this spatial sampling framework, the 

methods for acquiring samples from individual animals should 

be carefully defined and should be based on a probabilistic 

sampling approach to generate statistically useful estimates 

of intensity metrics. In addition, at both stages of the sampling procedure, consideration of the population to which the 

intensity metrics apply is critical. We strongly recommend 

that responsible agency personnel meet with a statistician or 

biometrician prior to implementing any monitoring scheme to 

ensure data are gathered in a manner that assures statistically 

rigorous estimation of the selected intensity metrics, so that 

these data can be used to inform management programs and 

compare trends across jurisdictions.

Where and How Many to Sample for 

Intensity Monitoring

Common problems encountered when designing a 

disease-monitoring program involve determining where and 

how many individuals to monitor. Unfortunately, as in the 

foregoing discussion on strategies and approaches, there is 

no set answer to these questions. The answer once again is 

dictated by the questions being asked, the system and metrics 

being studied, and the availability of resources. For example, 

if the purpose is to detect changes in intensity metrics in the 

response to control programs, then it may be most appropriate to focus sampling efforts in the vicinity of CWD foci 

where prevalence rates are the highest and in which changes 

in intensity are more likely to be detected (see, for example, 

Conner, Miller, and others, 2007). Conversely, if the interest 

is in monitoring disease intensity on the “leading edge” of 

an epidemic, the sampling effort probably should be focused 

away from foci and instead concentrated in the areas of low 

disease intensity.

The question of where to sample likely will have a 

unique solution for each agency; nevertheless, the following 

four guidelines may be useful for deciding where to monitor. First, monitoring areas should be selected where changes 

can be detected or trends can be estimated with reasonable 

precision. Although with infinite sample sizes, theoretically 

this can be accomplished for any site, in practice, with finite 

sample sizes changes or trends in intensity, metrics will be 

most precisely estimated where prevalence is highest. Second, monitoring areas should be established where adequate 

samples of animals can be collected for disease testing. For 

example, if an area is largely private land with small deer 

populations where only a few samples can be collected within 

the desired timeframe, then this would not be a viable monitoring area, because intensity metrics would vary widely and 

thereby preclude reliable change or trend estimation. Third, 

monitoring areas should be selected based on the importance 

of changes/trends in disease intensity for that region. For 

example, if disease-control activities have been initiated in 

a locale and agencies are interested in determining if control 

efforts are having the desired effect, then monitoring changes/

trends in this area is a high priority, and it should be selected 

for monitoring. Spatial risk factors as previously described 

may be used to aid in determining the location of monitoring 

areas. For example, if CWD is first discovered in a captive 

cervid facility within a jurisdiction, then monitoring efforts 

may be focused initially in the vicinity of this facility. Fourth 

and finally, resources are an important factor for determining 

regions to monitor. For example, long-term monitoring of a 

core disease area may require the bulk of the available surveillance resources, thereby limiting monitoring of CWD in other 

regions.

Once monitoring areas have been established, the next 

step is to determine how many samples are required from each 

area. It is critical to note that sample size formulas used for 
32 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

detection surveillance are not applicable to disease monitoring! Sample size requirements for disease monitoring are 

determined by asking the question, “What is the desired size 

of change in the intensity metric to be detected at a given precision level?” For example, an agency’s managers might want 

to be able to detect with 90-percent confidence that a 25-percent change in CWD prevalence will, hopefully, result from 

culling over the next 5 years. Once the question is framed, a 

probability model may be assumed depending on the metric 

of interest; provided appropriate simplifying assumptions are 

made, this will allow sampling theory to be used to provide 

insight into the number of samples needed. For interested 

readers, Lohr (1999) provides an in-depth look at sampling 

theory and sample size calculation. In addition, for more 

complex problems that may be encountered in wildlife disease 

monitoring, computer simulation experiments as described in 

the previous section also can be used to determine adequate 

sample sizes to achieve changed detection at the desired precision level. Because of the complexity, we highly recommend 

at this stage that a statistician or biometrician be consulted to 

ensure data will be collected in a manner to ensure acceptable 

precision for intensity metrics. Modeling can be particularly 

useful in understanding trade-offs between duration, intensity, and replication of monitoring strategies (Conner, Miller, 

and others, 2007; Nusser and others, 2008), and the failure to 

invest in such assessments prior to acting may limit the utility of resulting data and ultimately squander opportunities to 

advance understanding of CWD dynamics and their responses 

to management.

Challenges with Intensity Monitoring

The dynamics of any disease, including CWD, are an 

interwoven chain of complex processes. The purpose of 

monitoring disease intensity is to detect important changes in 

these processes, either due to natural events in the progression 

of the disease or due to management interventions. Metrics 

of intensity are mathematical constructs aimed at summarizing available information into understandable forms that can 

signal such changes. However, as with any simplifying model 

or summarizing statistic, limitations exist. For example, when 

tracking prevalence over some sampling unit, prevalence is 

generally reported for that sampling unit. Thus, prevalence 

is essentially averaged over the unit and ignores the spatial 

heterogeneity of disease within the sampling unit (Farnsworth 

and others, 2006). Therefore, when applying intensity metrics, it is important to understand their limitations as well as 

the appropriate inference that can be drawn from them. It is 

also important to anticipate that monitoring to detect trends in 

infection rates or numbers of infected animals on the landscape, especially as part of an effort to control CWD, will be 

very challenging and will require many years of effort and 

the investment of significant resources into the surveillance 

program. 

Monitoring Trends in Spatial 

Distribution

Monitoring changes in the spatial distribution of CWD 

represents an important aspect of CWD surveillance in most 

jurisdictions where foci have been detected. Detecting natural 

changes (defined here as having arisen from movements of 

live, free-ranging animals) in the spatial distribution and 

extent of a CWD focus may be necessary for providing public 

information and assessing natural trends or the efficacy of disease management. To separate actual disease spread from sampling artifacts, understanding with some certainty the initial or 

contemporary geographic extent of CWD among susceptible 

host species in an area of interest is prerequisite to monitoring 

subsequent changes in distribution. For example, in assessing 

either natural trends or the effectiveness of management practices intended to minimize geographic spread, if CWD already 

occurs more widely than initially believed, then subsequent 

“discovery” of preexisting foci results from an expansion of 

sampling activities rather than CWD spread. These “discoveries” can lead managers to overestimate the true rate of natural 

spread, as illustrated during 2002−3 by the “new” discoveries 

of CWD foci ascribed by some to be evidence of its “rapid 

spread,” or to underestimate the efficacy of their practices. 

Alternatively, if surveillance to detect new foci is inadequate, 

then the failure to detect new foci could lead managers to 

underestimate the extent of geographic expansion of a focus 

or to overestimate the efficacy of their control actions. It follows that careful attention to this aspect of spatial monitoring 

should be part of any comprehensive CWD monitoring or 

management program.

Relevant Questions

As with other aspects of surveillance and monitoring 

discussed in this report, the approaches to and extent of monitoring efforts directed toward describing changes in the spatial 

distribution of CWD depend largely on the question(s) monitoring is intended to answer. Such questions might include:

• What is the current, annual, and/or cumulative geographic distribution of known CWD cases within a 

jurisdiction?

• How has the geographic distribution of CWD changed, 

either naturally, or in response to management over 

time?

• How rapidly is a CWD focus expanding or shrinking?
Monitoring Trends in Spatial Distribution 33

Considerations in Monitoring Spatial Changes

In designing monitoring programs intended to detect 

changes in the spatial distribution of CWD, a variety of biological, landscape, and operational aspects should be considered. Many of these are common to considerations underlying 

the design of surveillance programs for detecting new CWD 

foci as described in the previous chapters, and readers are 

referred to those chapters for a more detailed discussion.

The first consideration in detecting spatial distribution 

changes is developing an adequate understanding of the host 

species biology and ecology. Because natural changes in the 

spatial distribution of CWD are likely driven almost entirely 

by movements of infected hosts, understanding the probabilities, distances, demographics, and habitat or other influences 

on host movement patterns is essential to devising effective 

monitoring plans. For example, patterns for the spatial spread 

of CWD in migratory mule deer in montane habitats (Conner 

and Miller, 2004) may be less predictable than in non-migratory white-tailed deer in an area of relatively homogeneous 

mixed hardwood and cropland habitat (Joly and others, 2006; 

Osnas and others, 2009; Heisey and others, 2010). 

Another key component of measuring changes in the 

spatial distribution of CWD is landscape attributes. These factors may influence not only the movements of infected hosts, 

but also the ability to acquire diagnostic samples or impose 

management. For example, public land may be more readily 

and uniformly sampled than private land in some jurisdictions, 

but private land refugia could be important in establishing 

or sustaining new CWD foci (Farnsworth and others, 2006; 

Walter and others, 2011). 

Undoubtedly, the most important factor leading to expansion of CWD is proximity to a CWD source. The risk of exposure is highest for animals that have the potential to interact 

with infected individuals or environments. Thus, intuitively, 

areas adjacent to infected free-ranging or infected captive 

cervid facilities are the regions most likely to become newly 

infected and may be targeted for monitoring activities. 

In addition to spatial and demographic risk factors, 

another important design consideration for any monitoring 

program is determining which measure of change in spatial 

distribution is appropriate. The proper measure should be 

dictated largely by the question the monitoring is intended 

to answer. The simplest measure may be a minimum known 

distribution of CWD cases at various times or a minimum 

convex polygon (MCP) capturing all known locations. In other 

cases, more complex measures like total landscape affected 

(for example, MCP buffered by some distance representing 

natural movement patterns of infected individuals; Joly and 

others [2006]), host range(s) affected (Conner and Miller, 

2004), or intensity of infection across a landscape (Farnsworth 

and others, 2006; Joly and others, 2006; Osnas and others, 

2009; Heisey and others, 2010) may be needed to provide the 

information required. 

The resolution and intensity are also important factors 

to address during creation and implementation of monitoring 

programs. Both the desired spatial and temporal resolution 

of detectable change will influence the intensity of surveillance needed. As with other surveillance applications, smaller 

increments of change demand proportionally larger sampling 

effort to detect with confidence. Given likely limitations on 

available resources in most jurisdictions, developing realistic 

expectations for detecting changes in spatial patterns may 

be a significant challenge. Where available, temporal and 

spatial intervals derived from biological and epidemiological 

knowledge (for example, mean dispersal distances, dispersal 

probability, corridors, prevalence at source, and time from first 

introduction to reasonable likelihood of detecting a new focus 

based on projected epidemic dynamics) may be the best basis 

for making such determinations.

Lastly, other confounding risk factors may exist that play 

a role in CWD spatial dynamics. Sources of potential CWD 

exposure not related to natural movements of live, free-ranging animals could confound interpretation of spatial distribution and changes therein. Consequently, the occurrence and 

distribution of other potential risk factors (for example, game 

farms, taxidermy operations, wildlife rehabilitation activities, artificial baiting or feeding sites, depots for carcasses 

of vehicle-killed cervids) should be taken into account when 

designing monitoring plans.

Metrics for Monitoring Trends in 

Spatial Distribution

Depending on the specific aims of the monitoring 

program, it may be considered important to keep track of 

where CWD exists on the landscape and how that spatial 

distribution changes over time. The relatively recent ability to 

acquire spatially referenced data with ease has made several 

techniques available for monitoring the spatial distribution of 

CWD. An entire workshop has already been devoted to details 

of that topic (Conner, Gross, and others, 2007). That work 

notwithstanding, some general principals warrant restatement 

here. In general, diseases are not random processes and they 

do not distribute randomly on the landscape. Extensive data 

from multiple settings have shown that CWD is no exception 

(Joly and others, 2003; Miller and Conner, 2005; Farnsworth 

and others, 2006; Joly and others, 2006; Blanchong and others, 2008; Osnas and others, 2009; Heisey and others, 2010; 

Walter and others, 2011). Inferences drawn from the distribution of CWD cases must take into account the distribution 

of the underlying population on the landscape. Knowledge 

of that distribution is often a required assumption for valid 

application of statistical methods used to assess the existence 

and extent of disease clustering. Consequently, monitoring of 

spatial distribution of CWD needs to account for landscape 

features such as habitat, winter ranges, topography, and land 

use that affect distribution of the underlying cervid population 

(Farnsworth and others, 2006; Joly and others, 2006; Blanchong and others, 2008; Heisey and others, 2010; Kelly and 

others, 2010; Walter and others, 2011).
34 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

Consideration also should be given to how CWD may 

be distributed in particular demographic groups of cervids 

(for example, specific sex and age classes) that are of greatest interest to a particular monitoring program. Occurrence of 

CWD has been shown to vary significantly by species, age, 

and gender (Miller and others, 2000; Miller and Conner, 2005; 

Grear and others, 2006; Walsh and Miller, 2010). Movements 

of cervids are known to affect the spatial distribution of CWD 

on the landscape (Conner and Miller, 2004; Blanchong and 

others, 2008; Skuldt and others, 2008; Kelly and others, 2010; 

Cullingham and others, 2011). To the extent that age and sex 

differences influence such movements (for example, dispersal), monitoring programs may be tailored to gather information on particular groups of animals.

Finally, the sensitivity of spatial resolution of monitoring 

programs deserves some consideration. Species- and genderspecific behavioral factors influence the occurrence of many 

diseases, including CWD. Behavioral characteristics that limit 

the number of contacts between different groups of animals 

affect the probability of exposure to disease (Schauber and 

others, 2007; Blanchong and others, 2008; Grear and others, 

2010; Kelly and others, 2010; Cullingham and others, 2011). 

For example, once one member of a matriarchal group of 

white-tailed deer is infected, the number of repeated direct and 

indirect contacts among those group members likely increases 

the probability that they will become infected and subsequently increases the prevalence of CWD in the group. Thus, 

in the limited geographic area that group occupies, CWD is 

likely to be much more prevalent than disease-testing results 

aggregated over a larger geographic area would suggest. The 

practical difficulties of monitoring such small and constantly 

shifting groups adequately means that monitoring programs 

must implicitly recognize their limited ability to detect smallscale differences in CWD occurrence (Blanchong and others, 

2008; Cullingham, Nakada, and others, 2011).

Strategies and Approaches 

for Monitoring Surveillance

In many respects, detecting changes in the spatial distribution of CWD is a special case of detection designed to 

uncover new CWD foci, as discussed previously, where the 

greatest single risk factor is proximity to an established (and 

reasonably well defined) focus of CWD. Consequently, many 

of the principles, strategies, and approaches for monitoring 

programs intended to detect changes in spatial patterns are the 

same as those already described in the previous chapters. As 

with other detection-oriented CWD surveillance, some form 

of ongoing targeted or weighted sampling should be regarded 

as a minimum foundation for monitoring changes in spatial 

distribution of established foci. Biologically relevant coverage 

areas should include likely distances and corridors of dispersal and, where applicable, migration movements beyond the 

periphery of established foci. Selection or prioritization of 

target areas also could be influenced by intensity of infection 

in nearby areas, because the probability of an infected animal 

moving may be higher from those areas than from those farther away, and type of expected movement, because the probability of one or more infected animals moving would likely 

be greater with migration (a regular, group event) compared to 

dispersal (a less regular, individual event) (Miller and Conner, 

2005, Skuldt and others, 2008) . Random sampling may be 

of value in augmenting monitoring efforts to detect changes 

in spatial patterns, and some forms of management such as 

increased harvest or culling may serve an additional purpose 

in generating additional random samples. In cases where the 

entire periphery of an endemic focus cannot be intensively and 

simultaneously sampled, some form of sampling (for example, 

systematic sampling) of surrounding areas at biological- or 

risk-based time intervals may be adequate to detect spatial 

trends. For example, to achieve the level of sampling needed 

to detect relevant changes in spatial distribution of disease, 

given limited resources, Wisconsin has chosen to divide the 

periphery of its CWD-affected area into sampling regions 

surrounding its CWD core areas. Intense sampling efforts are 

rotated between these regions at intervals chosen based on 

spatial risk assessments using deer landscape genetics and 

habitat barrier analyses (fig. 3).

Challenges with Spatial Monitoring

As is the case with finding new CWD foci, detecting 

expansion of an established CWD focus soon after it occurs 

will be problematic, because relatively few infected animals 

are in such areas. Similarly, demonstrating true reduction in 

the size of an established CWD focus (naturally, or in response 

to some form of management) will be problematic, because 

small numbers of infected individuals could remain undetected 

on the landscape. Detection, assessment, and monitoring are 

perhaps best viewed as a continuum of surveillance activities. If or when occurrence of CWD within a target population 

becomes undetectable, ongoing disease monitoring effectively 

reverts to the disease-detection phase. Particularly with a disease such as CWD, where environmental contamination may 

play a significant role in disease transmission, demonstrating 

disease eradication from animal populations will require a 

combination of disease-detection efforts that are intensive (at 

least at intervals), sustained over time, or both. Attaining a 

high degree of confidence that CWD is absent from the landscape will likely be a much more difficult matter. However, 

to achieve management goals in particular areas, such as the 

desire to remove an area from CWD-related restrictions (for 

example, deer feeding ban, deer rehabilitation ban, carcass 

movement restrictions), managers may choose to accept some 

level of probability of disease eradication as being close 

enough, and accept some level of risk, especially the risk that 

CWD transmission is still possible from an environmental 

reservoir. 
Chronic Wasting Disease Core Areas (Sample yearly)

EXPLANATION

County boundary

Deer Management Unit boundary

Area 1—Sample 2006

Area 2—Sample 2007

Area 3—Sample yearly

W ISCONS IN

Monitoring Trends in Spatial Distribution 35

Figure 3. Sampling regions for detecting changes in spatial distribution of chronic wasting disease in Wisconsin during 2006–7.
36 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

Research and Chronic Wasting 

Disease Monitoring Surveillance

There are many advantages to considering combined 

development of a CWD monitoring and epidemiologic 

research program. There are significant opportunities to use 

data collected in CWD monitoring surveillance to address 

important CWD research questions: for example, defining 

patterns and rates of transmission in different species and in 

different places, evaluating how landscape features may affect 

geographic spread, or investigating what role different age or 

sex cohorts play in epidemiology. In addition, development 

and adaptation of a monitoring plan will be greatly enhanced 

by incorporating the results of epidemiologic research, 

especially focused analyses using regional surveillance data. 

Though agencies, of course, need to make choices about how 

limited funds and staff time will be used for CWD surveillance, do not underestimate the value of implementing from 

the beginning a synergistic CWD monitoring and management-oriented research plan.

One important consideration in planning research using 

monitoring surveillance data is the quality of the data and 

its applicability for the intended research questions. The use 

of opportunistically collected CWD data, for example data 

derived from hunter service testing programs, may be difficult and very possibly misleading in investigating many 

CWD research questions. Although the resources available 

will often be a limiting factor, in general, consider trying to 

collect as much demographic and spatial data associated with 

CWD samples as possible. The ideas below are suggestions 

for beginning research plans; however, they are not an exhaustive list, and others may be more appropriate for a particular 

situation.

Research That Could Improve Planning of 

Monitoring Surveillance 

• Use of real CWD datasets to identify which metrics, 

possibly those applicable for monitoring trends in 

intensity of disease, are most appropriate to detect disease trends, especially disease trends associated with 

control actions.

• Use of existent datasets to evaluate what might be 

appropriate intervals between sampling to achieve specific monitoring surveillance goals, for example, how 

do prevalence estimates based on data collected every 

2–3 years (or every 6 years?) compare to data collected on an annual basis to detect trends in intensity of 

disease?

• Investigation of how landscape or cervid biology 

attributes affect spatial spread of CWD in different 

habitats and with different species (for example, elk) to 

help refine where monitoring for spatial change may be 

most important.

• Experimental investigations of if CWD susceptibility 

changes with age or is different between sexes.

• Analysis of epidemiologic information from cervid 

facilities (for example, elk or deer farms) within an 

experimental framework to help estimate parameters 

such as transmission rates.

Research Ideas to Consider Incorporating With 

a Monitoring Plan

• Evaluating age and/or sex differences in disease prevalence, which could assist with focusing sampling for 

further monitoring or perhaps targeting disease-control 

efforts.

• Evaluating spatial patterns or spatial “hot spots” 

of CWD on the landscape, which could assist with 

identifying areas for focused monitoring for evaluating prevalence trends or identifying areas for enhanced 

control efforts.

• Collecting data to populate parameters, such as 

transmission rates and the patterns of transmission in 

epidemiological models.

• Identifying “matched” treatment and control areas for 

CWD management efforts, so that scientific evaluation 

of the efficacy of control efforts is feasible.

Conclusion

Throughout this chapter we have presented a range of 

ideas and concerns associated with conducting CWD surveillance after the disease has been detected on the landscape. 

We have highlighted some of the main issues that arise when 

managers are faced with the challenges of disease monitoring 

and hoped to illuminate potential alternatives based on collective experience, epidemiological theory, and our current 

understanding of CWD disease dynamics. Although the topics 

addressed herein are not exhaustive with regards to the implementation of a CWD monitoring program, we believe they 

provide a sound foundation upon which managers can build an 

effective CWD surveillance program.
Literature Cited 37

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B.V.; Langenberg, J.A.; and Filcek, K.B., 2008, Landscape 

genetics and the spatial distribution of chronic wasting 

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Caley, P., and Ramsey, D., 2001, Estimating disease transmission in wildlife with emphasis on leptospirosis and bovine 

tuerculosis in possums and effects of fertility control: Journal of Applied Ecology, v. 38, p. 1362–1370.

Caley, P., and Hone, J., 2002, Estimating the force of infection: Mycobacterium bovis infection in feral ferrets Mustela 

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of chronic wasting disease: Salt Lake City, Utah Division 

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Appendix 1. Periodic Sampling Simulation 39

Appendix 1. Periodic Sampling Simulation

To assess the impacts of periodic sampling on monitoring 

efforts, we conducted a simple simulation study. We created 

a homogeneous population of deer in which chronic wasting 

disease (CWD) was discovered in 5 animals after sampling 

250 deer during the first year of a surveillance program. We 

specified a linear increase in CWD over a 10-year sampling 

period with a rate of increase of 0.11 on the logit scale. This 

resulted in prevalence ranging from 0.02 in year 1 to 0.05 

in year 10. We then randomly sampled from the population, 

employing two different sampling regimes. In Scenario A, we 

randomly sampled 100 individuals from the population each 

year; whereas in Scenario B, we randomly sampled 300 individuals from the population every third year. The total number 

of individuals tested for disease was identical after 10 years 

between the two alternative sampling strategies. To determine 

if an individual selected for sampling was infected with CWD, 

we generated a random uniform number between zero and 

one. If this number was less than or equal to the true prevalence, the animal was determined to be CWD positive. This 

provided the binomial data to estimate prevalence and measure 

potential disease trends. We examined two simple models: 1) 

an intercept only model (constant prevalence model) and 2) an 

intercept model with an additive linear trend (the true model). 

We use Akaike’s Information Criterion (AIC) to compare 

the models and select the most parsimonious model given 

the data. We examined associated parameter estimates and 

95-percent confidence intervals generated by using PearsonKlopper exact methods. We repeated the simulation for 10,000 

repetitions. 

We evaluated each scenario to examine its ability to 

adequately describe the underlying disease process. First, 

we estimated the mean prevalence under each scenario and 

compared the mean precision of the estimates averaged across 

all repetitions. Secondly, we examined the proportion of the 

simulation repetitions where the true model was correctly 

selected, and if the true model was selected, we examined the 

mean estimates of the trend parameter and the associated mean 

standard 95-percent confidence intervals.

The results from this simulation demonstrated that unsurprisingly during “monitoring years,” prevalence estimates 

under Scenario B were more precise than those employing 

Scenario A (fig. 1–1). This is due to the larger sample size 

during monitoring years under Scenario B compared with 

Scenario A. The simulations also portrayed the difficulty 

in estimating trends through time due to the binomial variability of the data. Under Scenario A, the correct model was 

not selected in ≥ 50 percent of the simulations until year 9; 

whereas under Scenario B, the correct model was not selected 

in ≥ 50 percent of the simulations until the final year of monitoring (fig. 1–2). In years where comparisons were possible, 

the correct model was selected more frequently by employing 

Scenario B. If the correct model was selected, both Scenarios 

produced slightly biased trend estimates with both bias and 

variance of these estimates decreasing through time (fig. 1–3); 

however, trend parameters were not reasonably estimated (that 

is, standard errors < 1) until year 5 and year 7 for Scenarios A 

and B, respectively.

Based on these simple simulation results, it appears that 

both sampling strategies have strengths and weaknesses. The 

continuous monitoring strategy provides annual prevalence 

estimates, and when the correct model is selected, this strategy 

by virtue of its intensity provides trend estimates sooner than 

periodic monitoring. In contrast, annual prevalence estimates 

are available only during select years under the periodic 

sampling scheme; however, during those monitoring years the 

larger sample size provides more precise prevalence estimates, 

leads to selection of the correct model somewhat more often, 

and estimation of the trend parameter with slightly less bias 

and more precision. Thus, information about CWD disease 

dynamics may be gained by using periodic sampling or continuous monitoring strategies, and both alternative sampling 

strategies potentially have merit based on the needs of the 

practitioner. 
0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0

50

100

150

200

250

300

350

1 2 3 4 5 6 7 8 9 10

Prevalence

Number of samples

Year

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0

50

100

150

200

250

300

350

1 2 3 4 5 6 7 8 9 10

Sample size

Prevalence

Year

Sample size

Upper 95-percent confidence limit 

Lower 95-percent confidence limit 

Estimated prevalence

EXPLANATION

A

B

Figure 1-1

40 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

Figure 1–1. Comparison of mean chronic wasting disease 

prevalence estimates and their measures of precision between A, 

Scenario A with continuous disease monitoring and B, Scenario 

B with periodic sampling generated during 10,000 simulation 

repetitions. Ninety-five percent confidence intervals are based on 

the Pearson-Klopper method. 
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

2 3 4 5 6 7 8 9 10

Proportion

Year

Scenario A

Scenario B

e 1-2.

Appendix 1. Periodic Sampling Simulation 41

Figure 1–2. Comparison of proportion of 10,000 simulation 

repetitions where the correct underlying disease model was 

selected based on Akaike’s Information Criterion (AIC) between a 

continuous sampling regime (Scenario A) and a periodic sampling 

scheme (Scenario B). 
0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

5 6 7 8 9 10

Estimate (logit scale)

Year

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

5 6 7 8 9 10

Estimate (logit scale)

Year

A

B

Trend estimate

95-percent confidence bounds

True trend

EXPLANATION

42 Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids

Figure 1–3. Comparison of mean trend estimates and mean, standard 95-percent confidence bounds for models where the 

correct disease model was selected by using Akaike’s Information Criterion (AIC) between A, Scenario A with continuous 

disease monitoring and B, Scenario B with periodic sampling (generated during 10,000 simulation repetitions). 

Walsh—Enhanced Surveillance Strategies for Detecting and Monitoring Chronic Wasting Disease in Free-Ranging Cervids—Open-File Report 2012–1036

CWD