CSIS Discussion Paper No. 112

Prospective Space-Time Analysis of the 2007 Cryptosporidiosis
Outbreak, Salt Lake County, Utah

Marissa Taddie a and Ikuho Yamada b

June 2012a) Ph.D. Student, Department of Geography, University of Utah, USAb) Associate Professor, Interfaculty Initiative in Information Studies, and Center for
Spatial Information Science, University of TokyoCSIS Discussion Paper No. 112
Taddie and Yamada 2012

Abstract
Cryptosporidiosis is a gastrointestinal illness caused by a protozoan parasite that is highly
contagious and resistant to multiple disinfectants. Utah experienced a large,
communitywide outbreak of cryptosporidiosis between June and December of 2007.
Nearly forty percent of laboratory-confirmed cases occurred in Salt Lake County (SLCounty), Utah. Using this case data, our study investigated if prospectively applied
space-time surveillance could have detected any significant clusters as cases werereported to Salt Lake Valley Health Department (SLVHD). This study utilized a spacetime scan statistic to test for the occurrence and location(s) of cryptosporidiosis clusters
using time-periodic prospective surveillance and a Poisson probability model. Report
dates were used in the prospective space-time analysis to replicate the realistic
surveillance processes that occur in health departments and to mimic a near real-time
surveillance system. The first cluster signaled approximately 20 days after the firstreported case in SL County. This cluster occurred two days before a statewide press
release was issued and 21 days prior to the implementation of major intervention
measures. From August through mid-September many significant clusters were detected
throughout the county. The results of this study suggest that there were distinct spatiotemporal patterns throughout the outbreak period. Therefore, space-time analysis would
have been a valuable and complementary tool to temporal surveillance since it could havedetected spatial clusters and high-risk areas of disease as they were reported, or emerged.
In addition, it may have been useful for targeting intervention strategies and prioritizing
investigations during this large communitywide outbreak.

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Introduction
Over the last thirty years, there has been a substantial rise in waterborne disease
outbreaks (WBDOs) associated with both natural and treated recreational water acrossthe United States (Yoder et al., 2008). Of the 48 gastroenteritis WBDOs that occurred
between 2005 and 2006, Cryptosporidium spp. was implicated in 31 of the outbreaks(Yoder et al., 2008). These “crypto” outbreaks caused thousands of illnesses, and the
majority of these were associated with treated (chlorinated) recreational water (Yoder etal., 2008). Since Cryptosporidium and its oocysts are immediately infective and resistant
to common treatments used in recreational waters, early detection of Cryptosporidium
outbreaks is needed to quickly identify at-risk areas and implement intervention measures.
During the summer and fall months of 2007, Utah experienced its first large,
communitywide outbreak of cryptosporidiosis (UDOH, 2008; SLVHD, 2008). In past
years, the state average was approximately 14.8 cases per year and SL County, which islocated at the center of the state and accounted for about 38% of the state population in2007 (U.S. Census Bureau, 2007), averaged fewer than five cases annually (UDOH,
2008; SLVHD, 2008). From June through December of 2007, the Utah Department of
Health (UDOH) received reports of 1,902 laboratory-confirmed cryptosporidiosis cases
across the state (UDOH, 2008). Approximately 38% of the laboratory-confirmed cases(718 cases) occurred in SL County and the incidence rate for the entire outbreak in the
county was 125.9 per 100,000 person-years (UDOH, 2008). In SL County, the bulk ofcases were reported between July 19 and December 5, 2007 (UDOH, 2008; SLVHD,
2008). Swimming in recreational water was considered a significant risk factor during

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the summer months and ongoing person-to-person transmission may have protracted theoutbreak into the fall (UDOH, 2008; SLVHD, 2008).
Health departments rely on a number of active, passive, and syndromic
surveillance methods to detect outbreaks in the community. Many times, reported casesof disease are the first clue of an emerging outbreak and trigger subsequent caseinvestigations. Ongoing temporal surveillance is used to establish (or examine) trendlines, risk factors, and the changing dynamics of an outbreak as more information isgathered. The spatial component of an outbreak is generally not an explicit part of thetime series analysis, however being able to identify geographic clusters as cases arereported to the health department may help elucidate areas of elevated risk, prioritize caseinvestigations, and customize or target interventions strategies.
Using SL County’s 2007 cryptosporidiosis case data, this study investigated if
prospectively applied space-time surveillance could have detected any significant,emerging clusters as cases were reported to SLVHD. To achieve this objective, thisstudy utilized a space-time scan statistic to analyze case-specific data. While true disease
outbreaks should be characterized by the onset date of each case, health departments’surveillance systems often rely upon the date the case is reported since this is the firstpiece of case-specific information available to the departments and onset dates are notcollected until after an investigation is started. Therefore, this study utilized report dates
in the prospective space-time analysis to replicate the realistic surveillance processes that
occur in health departments and to mimic a near real-time surveillance system.

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Cryptosporidium occurs worldwide and is a protozoan parasite that causes
gastrointestinal infections in both human and animals (Fayer & Xiao, 2008; APHA,
2004). Currently, there are at least 16 species as well as a number of different genotypesof Cryptosporidium; C. hominis and C. parvum are most commonly associated withhuman cryptosporidiosis (Fayer & Xiao, 2008; APHA, 2004). Cryptosporidiosis canoccur in both immunocompetent and immunocompromised persons (Chen, Keithly, Paya,& LaRusso, 2002; Huang & White, 2006; APHA, 2004). Once a host is infected, theparasite generally becomes established in the small intestine, reproduces, and its infective
oocysts are then shed in the stool (Fayer & Xiao, 2008; Chen et al., 2002; Huang &White, 2006; APHA, 2004).
Oocysts are immediately infective and are characterized by a thick outer wall
(Fayer & Xiao, 2008; APHA, 2004). This wall serves as protection and allows thepathogen to survive outside of the body for long periods of time in moist conditions(Fayer & Xiao, 2008; APHA, 2004). Between 108-109 oocysts can be shed in a single
bowel movement (Yoder & Beach, 2007). Cryptosporidium oocysts are resilient andhave proven to be resistant to a number of aldehyde-, ammonia-, alcohol-, alkaline-, andchlorine-based cleaning products (Fayer & Xiao, 2008; Chen et al., 2002; Huang &White, 2006). Normal chlorination levels in recreational swimming pools do notinactivate the infective parasite ((Fayer & Xiao, 2008; Huang & White, 2006), andstudies have demonstrated that even after exposure to household bleach for two hours, theoocyst is still infectious (Huang & White, 2006). Research suggests that oocysts are
sensitive to hydrogen peroxide, ozone, and ultraviolet (UV) radiation (Huang & White,2006).

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Cryptosporidium transmission follows a fecal-oral route and infection occurs
when a susceptible host ingests the infective oocysts (Fayer & Xiao, 2008; APHA, 2004).
Transmission can be person-to-person, animal-to-person, or from any soil, food, water, or
surface that has become contaminated with fecal matter (Fayer & Xiao, 2008; APHA,
2004). The infectious dose is low and between 10-30 oocysts can trigger an infection(Yoder & Beach, 2007). Symptoms generally appear one to two weeks after exposure,with seven being the average (APHA, 2004), and twenty-one days is generally considered
the maximum incubation period (Fayer & Xiao, 2008). Infected persons may be
asymptomatic or symptomatic. When symptoms are present they are dominated bywatery diarrhea, stomach cramps, nausea, weight loss, low-grade fever, and vomiting(APHA, 2004). Symptoms can persist from two days to one month and fecal shedding ofoocysts can continue up to 50 days after the last bout of diarrhea (Yoder & Beach, 2007).

Methods
Study area and data collection
The study area included Salt Lake County, Utah, which consisted of 567 census
block groups (U.S. Census Bureau, 2000a). Data on 677 laboratory-confirmed
cryptosporidiosis cases were acquired from the SLVHD and included all
cryptosporidiosis cases reported between June 1, 2007 and November 1, 2007 in SL
County. Case data contained information on age, gender, place of residence, zip code,
date of disease onset, date reported to SLVHD, and laboratory date. Population data was
obtained from the U.S. Census Bureau, using the July 1, 2006 population estimates (U.S.
Census Bureau, 2006). The study area encompassed approximately 737 square miles

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(1908.82 square kilometers (km)) and included a population of 978,701 individuals,
which was approximately 38 percent of the state’s population (U.S. Census Bureau,2006). This project was approved by the University of Utah Institutional Review Board.
Data preprocessing
To prepare the data for analysis, case data was geocoded at the block group level.
Of the 677 laboratory-confirmed cases, 670 cases (99%) were successfully geocoded.
Secondary cases within households were then excluded (52 cases) to avoid unfairly
weighting the statistic, which left 618 primary cases for the subsequent analysis. The
block group population was calculated using July 1, 2006 county-level estimates by
distributing it proportionally to the census 2000 population (U.S. Census Bureau, 2000b)
at the block group level. The result of the data preprocessing was two sets of data at thesame resolution: case counts and population counts for each block group in SL County.
Statistical methodology
A space-time scan statistic implemented in SaTScan software (Kulldorff &Information Management Services, 2007) was used to test for the occurrence andlocation(s) of cryptosporidiosis clusters using time-periodic prospective surveillance anda Poisson probability model (Kulldorff, 1997; Kulldorff & Information Management
Services, 2007; Kulldorff, 2001; Kulldorff, Athas, Feurer, Miller, & Key, 1998;
Kleinman, Abrams, Kulldorff, & Platt, 2005). The space-time scan statistic is based on
both a spatial and temporal window (Kulldorff, 1997; Kulldorff, 2001). These windows
vary from a minimum spatial distance and temporal length up to user-defined maximum
distance and time. In searching for clusters, or high rates, the space-time scan statistic
generates a large number of variably sized space-time cylinders that are located across

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the study region (Kulldorff, 1997; Kulldorff, 2001; Kulldorff et al., 1998; Kleinman et al.,2005). Each space-time cylinder yields a likelihood ratio based on whether the observed
number of cases within a cylinder is greater than would be expected if the cases were
distributed randomly in space and time with respect to the underlying population(Kulldorff, 1997; Kulldorff, 2001; Kulldorff et al., 1998). The cylinder with the
maximum likelihood ratio is reported as the most likely cluster (Kulldorff, 1997;
Kulldorff, 2001; Kulldorff et al., 1998). Significance is assessed using Monte Carlohypothesis testing (Dwass, 1957).
Case data provided by the SLVHD included onset date, laboratory date, andreport date. Given that the goal of this analysis was to imitate a near real-time
surveillance system and the report date was the first piece of case-specific information
available to epidemiologists during the outbreak, a decision was made to use the report
date in the space-time analysis. Report dates represent when SLVHD became aware oflaboratory-confirmed cryptosporidiosis cases in their jurisdiction. Hence, clusters
detected based on reports date would correspond to what SLVHD could have detected byincorporating the space-time prospective surveillance into their routine surveillancesystem during the summer and fall months of 2007.
The maximum spatial cluster size was a circle with a 2.5-mile (4.02 km) radius
and the maximum temporal cluster size was seven days. The study period, or time step,for the analysis was 28 days, which was determined by using the maximum incubation
period, generally considered to be 21-days, and then adding seven days to capture theperson reported on day 21. The first reported cryptosporidiosis case occurred on July 19
and the last reported case included in this study was on October 22, so that daily analysis

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in this study began 28 days prior to the first reported case and finished 28 days after thelast reported case in October. Therefore, 151 separate daily analyses were conductedfrom June 22 to November 18. The 28-day study period stepped forward through time as
one day was added and one day was removed during the outbreak. In order to imitate aprospective space-time analysis, cases that were outside of the time step were ignored.
Prospective surveillance necessitates a modification in the space-time scanstatistic due to the multiple testing problem inherent in repeated time-periodic
surveillance (Kulldorff, 2001). Therefore, an adjustment was made for earlier analysis,
which resulted in a p-value that was corrected for the repeated surveillance (Kulldorff,
2001; Kulldorff, 2007; Kleinman et al. 2005). SaTScan provides both a p-value and arecurrence, or null occurrence, interval for prospective analysis (Kulldorff, 2007). The
recurrence interval is how often one would expect to see the cluster just by chance if thenull hypothesis is true (Kulldorff, 2001, 2007). In this study, 999 Monte Carlo
replications were used to assess significance and thus the smallest p-value obtainable was
0.001. This significance level corresponded with a recurrence interval of once every 76.6
years when adjusted for earlier analyses within the 28-day study period. A recurrence
interval of approximately 1 year, corresponding with a p-value of approximately 0.074,
was used as the cut off to summarize the results.

Results
From June 22 to November 18, forty-seven clusters were detected. Table 1
contains only the space-time clusters that were representative of the patterns that emerged
during the early, middle, and late stages of the outbreak. In this table a total of 14

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Taddie and Yamada 2012clusters are listed and grouped into six 28-day time steps based on the first day of thesignal. A complete table summarizing all signals is available by request from the authors.
Figure 1 also summarizes all significant space-time clusters graphically and shows thegeneral geographic region where the clusters emerged. It is important to note that one
cluster can correspond to multiple signals. For example, the first cluster was detected on
August 8 and continued to signal until August 12. The six 28-day time steps listed in thetable are discussed below to illustrate the general pattern of cluster emergence thatdeveloped across SL County in the summer and fall months of 2007.
The first laboratory-confirmed cryptosporidiosis case was reported to SLVHD on
July 19, 2007 (Morbidity and Mortality Weekly Report (MMWR) Week 29), and the firstsignificant space-time signal (Figure 2a) was detected 21 days later on August 8, 2007
(MMWR Week 32). This cluster occurred two days before a statewide press release wasissued by the UDOH warning people to avoid swimming while ill and 21 days prior to
the implementation of major intervention measures. During this 28-day time step, there
were 20 reported cases and 12 of them were reported during the last week of the time step
(MMWR Week 32). The space-time signal (p = 0.001; recurrence interval = 76.6 yrs)
included one block group and was triggered by three cases observed over two days whenthe expected number of cases was 0.01, which produced a relative risk of approximately
581.7 (Signal ID 1 in Table 1). This initial cluster remained persistent through time as
subsequent daily analyses also showed this cluster as significant until August 12. As
expected, the significance of the cluster decreased on each subsequent day of analysis
since the space-time scan statistic adjusted for multiple testing.

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Figure 2b shows a larger cluster (p = 0.019; recurrence interval = ~ 4 yrs) that wasobserved on the opposite side of the county on August 17. This cluster extended across48 block groups and included 51 cases in the time step, of which 29 of them werereported during the week ending on August 18 (MMWR Week 33). The space-timecluster was triggered by eight cases observed over four days when the expected numberof cases was 0.65, which produced a relative risk of approximately 14.4 (Signal ID 6 inTable 1).
Figure 2c shows two large space-time clusters were detected on opposite sides of
the county on August 21. During this time step there were a total of 76 cases. The most
likely cluster (p = 0.006; recurrence interval = 12.7 yrs) occurred on the west side of SL
county, extended across 14 block groups, and had six cases observed over two days,
which was approximately 32.8 times greater than expected (Signal ID 8 in Table 1). The
secondary cluster (p = 0.024; recurrence interval = 3.1 yrs) was found on the east side of
the county, included 53 block groups, and included 12 cases observed over seven days,
which was approximately 7.7 times more cases than expected (Signal ID 8a in Table 1).
Figure 2d shows three significant space-time clusters across the county. During
this time step there were a total of 127 cases. The most likely cluster (p = 0.001;
recurrence interval = 76.6 yrs) on the west side of the county still contained the same
block groups as seen on August 21, but the number of cases has increased from six to 11
observed over five days, which was approximately 14.4 times more cases than expected(Signal ID 11 in Table 1). The secondary cluster (p = 0.048; recurrence interval = 1.5
yrs) was also similar to the cluster observed on the east side of the county on August 21,
but is spatially more compact, extending across 32 block groups. This cluster included

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six cases observed over one day, which was 22.0 times greater than expected (Signal ID
11a in Table 1). The tertiary cluster (p = 0.053; recurrence interval = 1.4 yrs) was thenorthern most signal during this time step and included eight cases observed over twodays across 54 block groups, which was 12.3 times more cases than expected (Signal ID
11b in Table 1).
On September 7, four significant clusters were detected across the county (Figure2e). During this time step there were a total of 342 cases. The most likely cluster
occurred on the west side of the county, extended across 23 block groups, and included
25 cases observed over four days, which was 9.5 times greater than expected (Signal ID
21 in Table 1). The secondary cluster occurred near the southern edge of the county,
extended across 16 block groups, and included 19 cases observed over four days, which
was 10.1 times more cases than expected (Signal ID 21a in Table 1). The tertiary cluster
was the northern most signal, extended across 25 block groups, and included 15 cases
observed over four days, which was 9.8 times greater than expected (Signal ID 21b in
Table 1). The p-value for all three of these clusters was 0.001, which corresponded to arecurrence interval of 76.6 years. The fourth signal (p = 0.011; recurrence interval = 6.9
yrs) included 16 block groups and occurred between the most likely and secondary
clusters, which could indicate spatial diffusion between the two areas. This cluster
included 13 cases observed over four days, which was 7.2 times greater than expected(Signal ID 21c in Table 1).
The last signals were detected on September 11, when the epidemic had begun
declining (Figure 2f). During this time step there were a total of 401 cases. The most
likely cluster (p = 0.001; recurrence interval = 76.6 yrs) on the west side of the county

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still contained the same block groups as seen on September 7, but the number of cases
has increased from 19 to 25 observed over seven days, which was approximately 4.5
times more cases than expected (Signal ID 25 in Table 1). The secondary cluster (p =0.003; recurrence interval = 25.5 yrs) was again near the southern edge of the county,
extended across six bock groups, and included 15 cases observed over seven days, which
was 7.1 times more cases than expected (Signal ID 25a in Table 1). The final cluster (p =0.021; recurrence interval = 3.6 yrs) was again located between the most likely andsecondary clusters, extended across 12 block groups, and included 15 cases observed
over seven days, which was 5.9 greater than expected (Signal ID 25b in Table 1).

Discussion and Conclusions
This study demonstrated that by utilizing a time-periodic prospective surveillancesystem, it may have been possible to detect high-risk areas of the outbreak that were
emerging from the cases being reported on a daily basis. While report dates clearly lag
behind onset dates, this is a limitation faced by health departments within the frameworkof the National Notifiable Disease Surveillance System (NNDSS) (Buehler et al., 2004).
Within the constraints of this system, health departments only become aware of specificcases of reportable diseases when they are notified by health care providers and/or
laboratories. While a variety of syndromic surveillance systems (e.g., chief complaintlogs, quantity of over-the-counter pharmaceuticals, school absenteeism, etc) can signal
the initiation of an outbreak in its early stages, determining the risk or causal factors
associated with the outbreak depends largely on timely and thorough case investigationsof reported disease cases.

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Report dates represent when SLVHD became aware of specific instances ofcryptosporidiosis cases within their jurisdiction. This outbreak quickly overwhelmed theresources of the SLVHD and as noted by SLVHD, “…[the] outbreak was incredibly
resource intensive and demanded that priorities be shifted, all [the while] maintaining
regular disease surveillance and investigation” (SLVHD, 2008, pg. 5). While it was clearthat cases were being reported from throughout the county, temporal surveillancemethods did not enable the investigators to discern if areas of excess risk were emerging
from the large number of daily reports. For instance, five of the six time steps presented
above clearly illustrate that the signal located on the west side of the county perpetuatedthroughout the entire period clusters were detected and only grew to include more cases
throughout August and September. Knowing this information might have helped local
officials prioritize the investigations and target interventions during this communitywide
outbreak.
Underreporting and time lags certainly affected the detection of the outbreak in its
early stages. However, previous research suggested that even with time lags of two
weeks, prompt and efficient action can limit the transmission potential (Yoder et al.,2008). Quickly identifying areas of elevated risk in conjunction with case investigations
may help elucidate common sets of risk factors or generate hypotheses as to the source(s)of the outbreak. As noted earlier, swimming in recreational water was considered asignificant risk factor in becoming ill with cryptosporidiosis. Therefore, by visually
mapping the locations of significant space-time clusters in conjunction with risk factors
obtained through investigational notes, it may have been possible to determine if various
recreational water venues, or other risk factors, were being implicated by the significant

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Taddie and Yamada 2012clusters. This type of visualization effort might have allowed more targeted intervening
efforts at the beginning of the outbreak.
In a smaller outbreak with fewer daily reports, it would be easier to investigate
and map potential exposure sources of all reported cases. However, with the large
number of cases that were reported on a daily basis during this outbreak, mapping
individual exposure sources in a timely manner would have not only become difficult, but
it would have been hard to visualize and tease apart any potential commonalities or
unique spatial patterns. By utilizing a time-periodic prospective surveillance system, it
was possible to detect space-time clusters that were emerging from the daily case reports.
Through the integration of space-time analysis, temporal surveillance, and risk factor
mapping it may have also been possible to evaluate if there were commonalities inpotential exposure sources and determine if the exposures were changing based on thedetection of different or expanding clusters.
The results are subject to various limitations due to the manner in which the data
was reported, geocoded, and analyzed. First, addresses were self-reported by cases andthere may have been bias in the reported address as well as the geocoding of thoseaddresses. Second, report date heaping by the various labs may have resulted in lesstemporal variability, which can impact the results of the analysis. Third, underreporting
and lag times between onset and report dates certainly affected the detection of theoutbreak in its early stages. Furthermore, it is possible that people with the earliest onset
may not have been the first to be reported. Fourth, there may be uncertainties associatedwith the cluster boundaries due to two specific limitations. First, given that case data was
geocoded at the block group level, significant space-time clusters represent combinations

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with a 2.5-mile (4.02 km) radius, may have restricted the size of the maximum cluster
detected. Future sensitivity analysis could help assess the potential impact of thisparameter setting.
Additional information provided by prospectively applied space-time surveillance
could be useful to local officials needing to quickly identify at-risk areas, implement
intervention or quarantine measures, and control or limit public exposure. This seems
especially relevant to large outbreaks that are associated with multiple exposure sources.
The results of this study suggest that there were distinct spatio-temporal patterns
throughout the outbreak period. Therefore, space-time analysis would have been avaluable and complementary tool to temporal surveillance since it could have detected
spatial clusters and high-risk areas of disease as they were reported, or emerged. In
addition, it may have been useful for targeting intervention strategies and prioritizing
investigations during this large communitywide outbreak.

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References
American Public Health Association. (2004). Cryptosporidiosis. In D. L. Heymann (Ed.),
Control of Communicable Diseases Manual pp. 138-141). Washington, DC:
American Public Health Association.
Buehler, J. W., Hopkins, R. S., Overhage, J. M., Sosin, D. M., & Tong, V. (2004).
Framework for evaluating public health surveillance systems for early detectionof outbreaks. MMWR Surveillance Summaries, 53(RR05), 1-11.
Chen, X.-M., Keithly, J. S., Paya, C. V., & LaRusso, N. F. (2002). Cryptosporidiosis.
New England Journal of Medicine, 346(22), 1723-1731.
Dwass, M. (1957). Modified randomization tests for non-parametric hypothesis. Annalsof Mathematical Statistics, 28, 181-187.
Fayer, R., & Xiao, L. (Eds.) (2008). Cryptosporidium and Cryptosporidiosis. Boca Raton,
FL: Taylor and Francis Group, LLC.
Huang, D. B., & White, A. C. (2006). An updated review on Cryptosporidium andGiardia. Gastroenterology Clinics of North America, 35, 291-314.
Kleinman, K. P., Abrams, A. M., Kulldorff, M., & Platt, R. (2005). A model-adjusted
space-time statistic with an application to syndromic surveillance. Epidemiology
and Infections, 133, 409-419.
Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics: Theory andMethods, 26(6), 1481-1496.
Kulldorff, M. (2001). Prospective time periodic geographical disease surveillance using ascan statistic. Journal of the Royal Statistical Society: Series A, 164, 61-72.
Kulldorff, M. (2007). SaTScan user guide for version 7.0.3. Retrieved fromhttp://www.satscan.org/techdoc.html.
Kulldorff, M., Athas, W. F., Feurer, E. J., Miller, B. A., & Key, C. A. (1998). Evaluatingcluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New
Mexico. American Journal of Public Health, 88, 1377-1380.
Kulldorff, M., & Information Management Services Inc. (2007). SaTScan: Software forthe spatial and space-time scan statistics. Bethesda: National Cancer Institute.
Salt Lake Valley Health Department - Bureau of Epidemiology (2008). 2007
cryptosporidiosis outbreak report. Salt Lake Valley Health Department.

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U.S. Census Bureau (2000a). Census 2000 block group cartographic boundary files –Utah. U.S. Department of Commerce Economics and Statistics Administration.
Retrieved from http://www.census.gov/geo/www/cob/bg2000.html.
U.S. Census Bureau (2000b). Summary File 1, P1 Total Population. U.S. Department ofCommerce Economics and Statistics Administration. Retrieved fromhttp://factfinder.census.gov/.
U.S. Census Bureau (2006). 2006 Population Estimates. U.S. Department of Commerce
Economics and Statistics Administration. Retrieved fromhttp://factfinder.census.gov/.
U.S. Census Bureau (2007). 2007 Population Estimates. U.S. Department of Commerce
Economics and Statistics Administration. Retrieved fromhttp://factfinder.census.gov/.
Utah Department of Health - Bureau of Epidemiology (2008). Report of investigation:
Cryptosporidium outbreak linked to multiple recreational water venues - Utah,2007. Utah Department of Health.
Yoder, J. S., & Beach, M. J. (2007). Cryptosporidiosis surveillance - United States, 20032005. MMWR Surveillance Summaries, 56(SS-7), 1-10.
Yoder, J. S., Hlavsa, M. C., Craun, G. F., Hill, V., Roberts, V., Yu, P. A., et al. (2008).
Surveillance for waterborne disease and outbreaks associated with recreational
water use and other aquatic facility-associated health events - United States, 20052006. MMWR Surveillance Summaries, 57(SS-9), 1-32.

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Table 1. Space-time clusters from August 8 to September 11, 2007 (MMWR Weeks
32 - 37)
Numberof Block
Groupsin
Cluster

Total
NumberofCases
in Time
Step

Observed
Cases inthe
Cluster

Expected
Cases

Relative
Riskp

Recurrence
Interval

Clusters

Signal
ID

SignalDate

Numberof Daysin
SignalMost Likely
Cluster1

8/8/07

2

1

20

3

0.01

581.767

0.001

Every 76.6
years

Most Likely
Cluster6

8/17/07

4

48

51

8

0.65

14.4390.019

Every 3.99
years

Most Likely
Cluster8

8/21/07

2

14

76

6

0.20

32.821

0.006

Every 12.7
years

Secondary
Cluster

8a

8/21/07

7

53

76

12

1.81

7.706

0.024

Every 3.1
years

Most Likely
Cluster

11

8/24/07

5

14

127

11

0.83

14.4670.001

Every 76.6
years

Secondary
Cluster

11a

8/24/07

1

32

127

6

0.29

22.0050.048

Every 1.5
years

Tertiary
Cluster

11b

8/24/07

2

54

127

8

0.69

12.3570.053

Every 1.4
years

Most Likely
Cluster21

9/7/07

4

23

342

25

2.81

9.507

0.001

Every 76.6
years

Secondary
Cluster

21a

9/7/07

4

16

342

19

1.97

10.1640.001

Every 76.6
years

Tertiary
Cluster

21b

9/7/07

4

25

342

15

1.58

9.875

0.001

Every 76.6
years

Quaternary
Cluster

21c

9/7/07

4

16

342

13

1.85

7.282

0.011

Every 6.9
years

Most Likely
Cluster

25

9/11/07

7

23

401

25

5.77

4.552

0.001

Every 76.6
years

Secondary
Cluster

25a

9/11/07

7

6

401

15

2.17

7.141

0.003

Every 25.5
years

Tertiary
Cluster

25b

9/11/07

7

12

401

15

2.58

5.996

0.021

Every 3.6
years

19

CSIS Discussion Paper No. 112
Taddie and Yamada 2012

List of Figures

Figure 1. Temporal length and general geographic regions of all significant space-timeclusters detected from August 8 to September 11, 2007 (MMWR Weeks 32 - 37). Only
signals with a recurrence interval of approximately 1 year are summarized in the graph.

Figure 2. Significant space-time clusters detected during six 28-day time steps from
August 8 to September 11, 2007 (MMWR Weeks 32 - 37). Detailed information on thenumber of days in the signal, observed number of cases, expected number of cases,
relative risk, p-value, and recurrence intervals for all clusters illustrated in these maps arepresented in Table 1. The scale and orientation of all maps are the same as 2a.

20

CSIS Discussion Paper No. 112
Taddie and Yamada 2012

Figure 1.

21

CSIS Discussion Paper No. 112
Taddie and Yamada 2012

Figure 222