The importance of vector abundance and seasonality : results from an expert consultation.
Date: 2018-12-10
Institutions: European Centre for Disease Prevention and Control, European Food Safety Authority
Subjects: animal disease, data collection, disease prevention, disease vector, health risk, insect, sampling

TECHNICAL REPORT

The importance of 
vector abundance and 
seasonality

www.ecdc.europa.eu

ECDC TECHNICAL REPORT 

The importance of vector abundance and 
seasonality 

Results from an expert consultation 

 
 
 
 
 
 
 
 
 
This report by the European Centre for Disease Prevention and Control (ECDC) and the European Food Safety 
Authority (EFSA) was coordinated by Céline Gossner, and written and produced by the following contributors: 

G.R.W Wint (ERGO, Oxford), B. Alten (Hacettepe University, Beytepe-Ankara, Turkey), T. Balenghien (CIRAD, 
Montpellier, France), E. Berriauta (University of Murcia, Spain), M. Braks (RIVM, Netherlands), J Medlock (Public 
Health England, Salisbury, UK), D. Petric (University of Novi Sad, Novi Sad, Serbia), F. Schaffner (Francis Schaffner 
Consultancy, Riehen, Switzerland), S. Dhollander (EFSA, Parma, Italy), C.M. Gossner (ECDC, Stockholm, Sweden), 
O.J.T. Briët (ECDC, Stockholm, Sweden), E. Ducheyne (Avia-GIS, Zoersel, Belgium) 

This report was a deliverable in a contract that was awarded by ECDC to VectorNet; contract title: Specific Contract 
No. 4 – ECD. 7499 in the framework of VectorNet project 2014–2018; contract number: OC/EFSA/AHAW/2013/02-
FWC1; report on the relevance of seasonality and abundance of vectors. 

Acknowledgements 
The authors are grateful to the entire VectorNet community for working hard during four years to collect the data 
and for sharing the knowledge upon which this document is based. We are also grateful for the support of the 
team at Avia-GIS for providing the organisation and perspective that underpinned the whole VectorNet project. The 
authors drew on the expertise of many colleagues in the VectorNet project, including Agustin Estrada Pena, Zati 
Vatansever, Thomas Jaenson, Zdenek Hubalek, Kayleigh Hansford, Alexander Vaux, Petr Volf, Vladimir Ivovic, Vit 
Dvorak, René Bødker, Simon Carpenter, Isabel Pereira da Fonseca, Marta Verdun Castello, Vincent Robert and 
Helge Kampen. Thanks are also due to Céline M. Gossner at ECDC and Sofie Dhollander at EFSA for their guidance 
and encouragement. 

Suggested citation: European Centre for Disease Prevention and Control and European Food Safety Authority. The 
importance of vector abundance and seasonality – Results from an expert consultation. Stockholm and Parma: 
ECDC and EFSA; 2018. 

Stockholm and Parma, November 2018 

ISBN 978-92-9498-271-1 
DOI 10.2900/37171 
Catalogue number TQ-06-18-175-EN-N 

Cover photo: USDA, Scott Bauer, Creative Commons attribution non-commercial (CC BY 2.0) license via Flickr 

© European Centre for Disease Prevention and Control, 2018 

Reproduction is authorised, provided the source is acknowledged 

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TECHNICAL REPORT 

The importance of vector abundance and seasonality 

Contents 

Abbreviations ............................................................................................................................................... iv 
Glossary ....................................................................................................................................................... iv 
Executive summary ........................................................................................................................................ 1 
Background ................................................................................................................................................... 2 
Types of measures of abundance and seasonality ............................................................................................. 2 
Using abundance and seasonality data ................................................................................................... 3 
Methods ........................................................................................................................................................ 3 
Vector-related parameters ......................................................................................................................... 4 
Epidemiological concepts ........................................................................................................................... 4 
Results and discussion .................................................................................................................................... 5 
Assessment of the importance of abundance and seasonality ........................................................................ 5 
Availability of abundance and seasonality data in the VectorNet database ...................................................... 9 
Requirements for abundance and seasonality assessments ......................................................................... 12 
Field sampling ........................................................................................................................................ 13 
Overview of sampling strategies .................................................................................................................... 13 
Ticks...................................................................................................................................................... 13 
Culicoides .............................................................................................................................................. 13 
Mosquitoes ............................................................................................................................................. 14 
Sandflies ................................................................................................................................................ 14 
Spatial modelling of vector abundance and seasonality .................................................................................... 15 
Mechanistic models ................................................................................................................................. 15 
Stochastic models ................................................................................................................................... 16 
Conclusions and potential implications ...................................................................................................... 18 
References .................................................................................................................................................. 20 
Appendix 1. Vector groups ............................................................................................................................ 26 
Appendix 2. Overview of sampling methods ................................................................................................... 42 

Figures 

Figure 1. Locations with abundance values derived from standardised sampling of active vectors, VectorNet 
database, as of March 2018 .......................................................................................................................... 10 
Figure 2. Comparison of values extracted from spatially predicted surfaces of maximum abundance with ranked 
normalised abundance category for Culicoides imicola (top) and probability of presence of C. dewulfi (bottom) .... 17 

Tables 

Table 1A. Summary of expert opinion of vector-related drivers of epidemiological concepts .................................. 6 
Table 1B. Summary of expert opinion of vector-related drivers of epidemiological concepts .................................. 7 
Table 2. Sum of normalised scores by epidemiological concept .......................................................................... 9 
Table 3. Sum of normalised scores by epidemiological concept and vector group ................................................. 9 
Table A1. Expert opinion of vector-related drivers of epidemiological concepts: ticks .......................................... 26 
Table A1, continued ..................................................................................................................................... 28 
Table A2. Expert opinion of vector-related drivers of epidemiological concepts: midges ...................................... 29 
Table A2, continued ..................................................................................................................................... 32 
Table A3. Expert opinion of vector-related drivers of epidemiological concepts: mosquitoes ................................ 35 
Table A3, continued ..................................................................................................................................... 36 
Table A4. Expert opinion of vector-related drivers of epidemiological concepts: sandflies .................................... 37 
Table A4, continued ..................................................................................................................................... 39 
Table A5. Expert scores of importance of drivers of epidemiological concepts .................................................... 41 

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TECHNICAL REPORT 

Abbreviations 

AHSV 
BTV 
CCHFV 
CO2 
ECDC 
EEA 
EFSA 
EID2 
ESA 
E–W 
GBIF 
R0 
S–N 
TBD 
TBEV 
WNV 
WNF 

African horse sickness virus 
Bluetongue virus 
Crimean-Congo haemorrhagic fever virus 
Carbon dioxide 
European Centre for Disease Prevention and Control 
European Economic Area 
European Food Safety Authority 
Enhanced Emerging Infectious Disease database 
European Space Agency 
East to west 
Global Biodiversity Information Facility 
Basic reproduction number 
South to north 
Tick-borne disease 
Tick-borne encephalitis virus 
West Nile virus 
West Nile fever 

Glossary 

Abundance 

Basic reproduction number 
Human activity  

Human biting rate 
Intrinsic incubation period 

Longevity 

Pathogen transmission by vectors 

Seasonality  

Vector-free period  
Vector season  

Quantity (i.e. a number of specimens of a species in a site at a given 
time), which can be expressed in absolute terms, relative terms, as 
an index and as a rank or category. 
The number of secondary cases per case in a naïve population [1]. 
Anthropogenic factors, which affect potential contact rate with 
vectors, as well as vector spread, and which may include farming 
practices, human behaviour and moving of animals. 
The number of bites received per day [2]. 
The time taken by an organism to complete its development in the 
definitive host. 
A statistical measure of the average time an organism is expected to 
live [3]. 
The process of a pathogen being passed from vector to host and vice 
versa. 
The change in abundance of a species over the course of a calendar 
year. It is defined as the fluctuation of the active population over the 
period that it is present. 
Period of year when active (adult) vectors are not present. 
The period between the date from which active individuals are first 
recorded and the date after which it is no longer recorded (which is 
the end date of the season). It is also referred to as season length. 

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TECHNICAL REPORT 

The importance of vector abundance and seasonality 

Executive summary 

Knowing where vector species occur is crucial for the assessment of vector-borne disease risk. This was recognised 
by EFSA and ECDC, who, over the period 2014–2018, funded VectorNet, a European network for gathering and 
sharing data on the geographic distribution of arthropod vectors of disease agents affecting humans and livestock. 

The VectorNet database contains distribution data on four vector groups: mosquitoes, sandflies, Culicoides midges 
and ticks. The majority of these data are on vector presence or absence, but since 2015, count data from a 
number of targeted field surveys, which used standardised sampling methods and that were funded through the 
network, have been added. 

The objective of this document is to describe an assessment, for each vector group, of whether vector count data 
(abundance) and the way these change throughout the year (seasonality) can provide useful information about 
epidemiological processes of interest, and therefore, whether resources should be devoted to collecting such data. 
The document also summarises what measures of abundance and seasonality can be collected for each group, 
what gaps remain in the sampling coverage and what can be done to fill these gaps. Furthermore, it provides 
guidance for prioritising the acquisition of information. 

For each vector group, expert opinion was canvassed to provide a semi-quantitative assessment of whether and 
how vector abundance and seasonality, each individually or in combination, are related to pathogen establishment, 
persistence, transmission and spread, are predictor variables useful for early warning, and are useful to spatially 
and temporally target vector or pathogen control. 

Abundance and seasonality may each be closely related to pathogen establishment, persistence, transmission and 
spread, and thus each serve as epidemiologically relevant indicators. However, their significance may only become 
apparent when combined with each other or with other variables that are related to the vector’s ability to transmit 
infection — for example, the proportion of vectors that are infected with the pathogen (the vector infection rate). 
The importance of abundance and seasonality was, therefore, considered for each in isolation and for both 
combined as well as both combined with other variables that may determine vector occurrence such as vector 
behaviour, vector infection rate and habitat. 

The information needed to make the assessments was collated from dedicated VectorNet group discussions, field 
sampling studies, and both peer-reviewed and grey literature. Each vector group leader scored the importance of 
abundance and seasonality (alongside other vector-related characteristics) for the assessment of whether early 
warning of pathogen introduction must be issued, for the assessment of pathogen establishment and spread and 
to guide vector control strategies. 

In the group discussions, experts assessed that abundance, by itself, plays a role in the epidemiological processes 
of most vector-borne infections, although the importance of the role varied among the vector groups. Seasonality 
was assessed to have relatively little influence on vector abundance but to be more important in determining the 
phenology of overwintering vector populations. 

In all vector groups, abundance was considered to have an effect on pathogen establishment and transmission. 
This applied less to early warning systems and assessments of pathogen persistence. Abundance measures are 
routinely used for targeted control of mosquitoes, but this is not the case for the other vector groups. 

For ticks, a special case exists: tick presence was considered to be a better indicator of risk of pathogen 
transmission than tick abundance if abundance is considered alone. Indeed, the number of ticks observed by itself 
cannot be considered an indicator of risk of infection. This changes when abundance is combined with vector 
infection rates. If information on abundance and seasonality is combined with other vector-related characteristics 
(such as vector infection rate), then abundance and seasonality are judged to be important. 

This argues for acquiring both abundance and seasonality data on the disease vectors of public and animal health 
concern, alongside collecting and recording additional variables such as vector infection rates and biting rates. 

While the VectorNet database already contains a substantial amount of vector abundance and seasonality data 
collected during targeted field surveys, important gaps at the continental scale remain. Additional data are, 
therefore, needed to fill these gaps, for example through additional field work and literature searches. Additionally, 
mechanistic, environmental and spatial distribution modelling may aid to maximise the use of the data currently 
available as well as help minimise the amount of additional information that would be needed to generate large-
scale transmission risk maps. This report outlines the sampling strategies and prioritisation steps needed to acquire 
such information most efficiently. 

Finally, in order to understand and ultimately reduce the potential risk of infection by (new) vector-borne 
pathogens, it is useful to compile comprehensive abundance and seasonality databases. Such a reduction of the 
potential risk of infection will, however, only be possible if there is the political will to support long-term 
transboundary programmes and if the necessary professional skills base is maintained. 

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The importance of vector abundance and seasonality 

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Background 

Knowing where a vector species occurs is crucial information for vector-borne disease risk assessment. This was 
recognised by EFSA and ECDC who funded VectorNet, the European Network for gathering and sharing data on the 
geographic distribution of arthropod vectors transmitting both human and animal disease agents (2014–2018). The 
maps generated under VectorNet are very widely used by the professional community and include published vector 
data, field data collected under targeted VectorNet field surveys, and information provided on a voluntary basis by 
the extensive expert network. 

The VectorNet data database currently includes mainly presence/absence data on four vector groups: mosquitoes, 
sandflies, Culicoides midges, and ticks. Presence/absence data may be used as a first indication of potential risk for 
vector-borne pathogen transmission. The presence of a vector alone is, however, unlikely to be a reliable indicator 
of pathogen transmission, and it is reasonable to assume that measures of the number of vectors, also called 
abundance, would provide better indication of transmission risk. 

In temperate regions, such as Europe, the importance of abundance and seasonality as factors in arthropod-borne 
(arbo) viral outbreaks vary widely from region to region [4]. In general, active vectors are less numerous or even 
absent during the winter time, whereas they are more numerous during the rest of the year and will show one or 
two peaks in abundance, usually in summer time. The timing and pattern of these abundance cycles, called 
seasonality, affects the impact of vectors on infection dynamics. In turn, the variation in vector numbers and in 
vector activity are related to environmental drivers such as temperature and humidity. Neither vector abundance 
nor seasonality can be measured by taking a single sample at a given time and place, and sequential samples 
during longitudinal studies are required to obtain information on seasonality. 

A number of epidemiological indicators are relevant to the preparation of outbreak response and mitigation 
strategies and might be used for early warning of pathogen introduction, and as indicators of pathogen 
establishment, persistence, transmission and spread. These indicators are determined by a range of factors related 
to the vector, its hosts, the geographical location, and the characteristics of the pathogens themselves. Vector 
abundance and its associated seasonality are, therefore, only two of the epidemiological drivers, and the degree to 
which they are relevant varies between the vector groups and between the pathogens these vectors carry. 

The objective of this document is to assess the degree to which estimates of vector abundance and seasonality, for 
each vector group, can be used to inform assessments of epidemiological concepts. It does not aim to be a 
comprehensive overview of the subject but rather to inform decision makers whether it is worth investing the 
resources needed to acquire abundance and seasonality information. The document also summarises what 
measures of abundance or seasonality can be collected for each group, and the extent to which these are available 
within the VectorNet database. The document also outlines what gaps remain and what can be done to fill them. 

Types of measures of abundance and 
seasonality 

Abundance. According to Eldridge and Edman [5], abundance is a general term that addresses the question: 
‘How many?’. In this report, the term abundance refers to a quantity (i.e. a number of specimens of a species in a 
site at a given time), and can be expressed in absolute terms, relative terms, as an index and as a rank or 
category. Density, expressed as the number of individuals per unit area or volume at a given time point, is one 
measure of abundance. 

Absolute abundance. The number of specimens per unit area or volume is called the absolute population [6]. 
When it is the number of a species per unit of the habitat, e.g. per volume of water or per host, it is called 
‘population intensity’ [6]. 

Relative abundance. The number of specimens collected in a specific trap or sampling method, which is a 
sample of the population. When vectors are sampled by traps containing an attractant (CO2, light, odour, sound, 
animal or human bait) or by dragging or sticky papers, their relative abundance is expressed as numbers per 
standardised sample. Only when collection methods are standardised, it is possible to compare relative abundance 
estimates between different places and/or different times. 

Index. Individual specimens are not necessarily counted, but their occurrence in a breeding site or habitat is 
recorded. This measure is expressed as the number of sampled sites with vector presence divided by the total 
number of sampled sites. The resulting estimate is the abundance index. Examples are the container index and the 
house index for yellow fever mosquito (Aedes aegypti) surveillance [7,8].  

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TECHNICAL REPORT 

The importance of vector abundance and seasonality 

Rank/category. Sometimes, relative densities across a large study area (e.g. Europe) are collected and classified 
into categories (e.g. high, medium, low), when the collection methods are not standardised. This provides an 
indication of the rank of the abundance on such a large scale. This can only be achieved when the category limits 
are defined by taking the entire area into consideration. Categorical density estimates can be useful as input to risk 
assessment models such as the Rift Valley fever risk assessment for EFSA [8]. 

Seasonality. The change in abundance of a species over the course of a calendar year is called the seasonality, 
which is defined as the fluctuation of the active population over the period during which it is present. The season 
and seasonality can vary from year to year and is influenced by a variety of landscape-level drivers including 
climate, vegetation, and host availability. Especially for long-lived vectors such as most tick species, fluctuations in 
the number of active individuals may differ from fluctuations in the population as a whole.  

The more practical term ‘vector season’ or ‘season length’ is defined as the period between the date from which 
active individuals are first recorded (which is the start date of the season) and the date after which they are no 
longer recorded (which is the end date of the season). The ‘vector-free’ period is then defined as a period when 
active (adult) vectors are not present. 

Using abundance and seasonality data 

Vector abundance and seasonality data may be used to inform a number of epidemiological concepts that are of 
particular public and veterinary health interest. These include the early warning of pathogen introduction, the 
potential for pathogen transmission, its establishment and persistence, the consequent spread of the pathogen to 
new localities and the control of pathogen spread (these are defined in the Methods section). Many factors may 
influence these concepts, including factors related to vector ecology and behaviour, and they interact in a non-
linear way, as recognised by Ross in 1916 when he published the first mathematical vector-borne disease model for 
malaria infection [9]. 

The interaction of these factors (parameters) is also illustrated by the estimation of, for example, the basic 
reproduction number (R0), which can be considered to be a measure of the likelihood of success of the pathogen’s 
establishment [1]. For example, the R0 of malaria can be calculated in a formula using as parameters the number 
of bites per human per day (human biting rate or aggressive density, ma), the number of bites per day by each 
female vector (feeding rate, a), the longevity of vectors, the length of the intrinsic incubation period and of the 
duration of pathogen presence in blood [10]. Under the model assumption of homogeneous mixing (each human 
has the same probability to be bitten by each vector), the aggressive density can be calculated as ma = [(number 
of vectors/number of humans) x biting rate], and vector numbers are therefore an integral part of this formula and 
linearly related to R0. 

R0 can serve as an estimate of the level of (potential) pathogen circulation between vector and host. 

In vector-borne disease systems in which the pathogen reservoir species is also the host of interest to public or 
veterinary health, R0 and the risk of infection (in hosts of interest) are similar, correlating linearly with vector 
abundance. The linear relationship may not hold if, for example, the hosts in which infection is of interest are 
‘dead-end hosts’ – i.e. hosts from which the pathogen cannot be transmitted onwards by the vector. An example is 
West Nile fever. A high transmission rate of West Nile virus (WNV) among birds by mosquitoes does not directly 
imply that horses or humans will become infected and sick. Horses and humans are dead-end hosts that do not 
contribute to the virus transmission cycle, and therefore are not part of ‘classical’ R0 calculations. Another example 
is Lyme borreliosis. The causative agent of this disease (Borrelia burgdorferi) cycles between ticks and wildlife 
hosts such as small mammals and birds, while humans are dead-end hosts. Therefore, high transmission rates 
between ticks and wildlife do not necessarily imply a high risk of transmission between ticks and humans. 

Methods 

Abundance and seasonality cannot be considered in isolation from other vector characteristics and factors that 
influence the ability of vectors to carry and transmit pathogens. At the start of VectorNet, four experts were 
appointed as vector group leaders, each responsible for the management of data for one of the following vector 
groups: mosquitoes (Francis Schaffner), ticks (Jolyon Medlock), sandflies (Bulent Alten) and Culicoides biting 
midges (Thomas Balenghien). In order to assess the importance of abundance and seasonality in a wide context, 
these vector group leaders were asked to assess the importance of a range of vector characteristics to a series of 
epidemiological concepts. The group leaders were also asked to assess whether quantitative estimates of each 
vector characteristic are related to specific epidemiological parameters. 

The approaches used to make the assessments were developed by consensus during a face-to-face meeting of the 
vector group leaders and members of the VectorNet consortium. This meeting was followed by a number of 
electronic exchanges in which parameters and epidemiological concepts were defined as outlined below.  

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The importance of vector abundance and seasonality 

TECHNICAL REPORT 

Vector-related parameters 

 

 

 

 

 

 
 

 

Vector presence/absence. Whether a population of a species is present or not at any time of year. Vector 
presence data can be collected by ad hoc as well as systematic sampling efforts. Confirmation of absence 
requires a more intensive and standardised sampling regime. 
Vector abundance. Any measure of the number of vectors per sample, collected in a known and 
standardised way [11], at some point during a year. The vector may be sampled as epidemiologically active 
stages or inactive stages (e.g. mosquito eggs and larvae). 
Vector seasonality. Timing and duration of the period of vector activity or presence during the year, which 
embraces the start and end dates of the vector season and, by inference, the period when vectors are not 
present (the ‘vector-free period’). 
Vector infection rate. Proportion of vectors in which the pathogen has been detected (out of the total 
tested). 
Vector behavioural traits. Behavioural traits that may be related to a vector’s host seeking and blood meal 
feeding efficiency. These include endo/exophagy, circadian activity, flight capacity, biting rate, longevity, 
etc. Many of these factors are incorporated into epidemiological models that quantify the risk of 
establishment and transmission of the pathogen. 
Reservoir host numbers. Any standardised indicator of numbers of one of more host species. 
Human activity. Anthropogenic factors, which affect potential contact rate with vectors, as well as vector 
spread, and which may include farming practices, human behaviour and moving of animals. 
Habitat change. Environmental changes affecting vector abundance or distributions. 

Epidemiological concepts 

 

 

 

 

 

 

Early warning. Systems designed to provide advance warning of pathogen introduction or to detect 
pathogen circulation before the onset of disease in hosts of interest (on time to allow for implementation of 
prevention methods). 
Pathogen transmission by vectors. The process of a pathogen being passed from vector to host and vice 
versa. 
Pathogen establishment. A pathogen is considered to be established if there is at least one confirmed 
autochthonous case of transmission. 
Pathogen persistence. A pathogen is persistent if transmission in a given place continues from one 
transmission season to the next (by, for example virus overwintering). 
Pathogen spread. The movement of the pathogen to a previously pathogen free area. This includes, but is 
not limited to, vector-mediated spread. 
Pathogen/disease control. Any measures implemented to reduce levels of pathogen transmission, or disease 
prevalence. This includes control of the vector and of the disease in hosts. 

As this document aims to provide an overview of the importance of abundance and seasonality of several groups of 
vectors of multiple diseases in relation to these epidemiological concepts, a scoring system, based on the VectorNet 
expert opinions and complemented with literature information, was applied. This scoring system, despite its 
limitations, was selected as the most appropriate method to achieve the objectives over more formal expert 
elicitation approaches such as Delphi elicitation. In addition to the expert opinion and data collected from peer-
reviewed and grey literature, VectorNet experts also used material derived from a series of dedicated discussions 
among vector group network members at VectorNet annual general meetings and selected evidence from 
VectorNet field sampling studies. 

The ‘importance scores’ assigned to each vector characteristic ranged from 0 to 4 (0: do not know, 1: no 
importance, 2: importance unlikely, 3: probably important, 4: certainly/almost certainly important). Each score was 
accompanied by a supporting comment which represented expert opinion unless formal references were supplied. 
A table was completed by each vector group leader (see Annexes). 

To enable comparison between the vector groups and across the epidemiological concepts, the original scores from 
Tables in the Annexes were normalised so that the sum of scores for each vector group is 100. To provide some 
indication of the interaction between the various factors considered, these normalised scores for each vector 
characteristic were also summed for each epidemiological concept and each vector group. 

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The importance of vector abundance and seasonality 

Results and discussion 

Assessment of the importance of abundance and seasonality 

The group leaders’ expert opinions and scoring are presented in Tables A1–A4, and the scoring is summarised in 
Table A5 in the Appendix. These tables have been condensed into two overview tables that summarise the results 
and highlight the similarities and contrasts between the different groups. Tables 1A and 1B provides a textual 
overview of the comments from each vector group, while Tables 2 and 3 give the normalised scores to help 
compare the assigned importance levels by epidemiological concept and vector group. 

This series of tables suggests quite clearly that both abundance and seasonality (alone or in combination with 
other factors) are important for the majority of the epidemiological concepts for all vector groups and therefore 
should be included in any quantitative assessments of infection risk. This is not unexpected, particularly for 
abundance, given its central place in epidemiological models such as the one employed to calculate the 
reproductive number (R0), outlined above. 

There is thus a good case for acquiring quantitative data on these two parameters, and the data gathered can be 
used to provide useful quantitative estimates for a range of epidemiological concepts. The results also show, 
however, that the interpretation of abundance and seasonality data is not straightforward, as their contribution to 
infection dynamics is complex and often contradictory. 

For ticks, vector numbers are unlikely to inform early warning. More tick vectors may, however, mean more chance 
of pathogen establishment in reservoir hosts and an increased likelihood of pathogen persistence. Tick abundance 
is weakly related to pathogen transmission as vector infection rates can be very variable and may compensate for 
low numbers. These interactions are illustrated by the example of rural and urban ticks and Lyme borreliosis 
provided in Box 1A. Pathogen spread is mediated largely by the movement of hosts and is more dependent on 
synchrony between vector and host than on vector abundance. Control tends to be focused on the host and is not 
dependent on vector numbers. 

For mosquitoes, high abundance is likely to be linked to increased likelihood of pathogen establishment, and an 
increase in abundance may sometimes provide an early warning of potential pathogen establishment and 
transmission. However, this relationship is not necessarily linear, and high vector abundance does not necessarily 
mean that there is a high risk of pathogen transmission (see Box 1B), or that either vector or pathogen will spread. 
Control may be best targeted to areas of high mosquito abundance where the aim is to reduce vector numbers. 

Because sandfly-borne disease and vector distributions usually match, a rise in abundance might provide some 
early warning of pathogen introduction, though there is too little direct evidence to consistently link sandfly 
numbers to increased pathogen establishment or infection risk (Box 1C). Vector abundance tends to be linked more 
closely to vector infection levels in outbreak situations than in situations where sandfly-borne diseases are 
considered to be endemic. The spread of sandfly-borne pathogens may be faster when populations are abundant, 
like with all vector-borne pathogens, but spread may be constrained by the heterogeneity of the local microhabitats 
(there might be barriers of unsuitable habitats that the vector cannot cross). Sandfly control is normally targeted to 
areas, and at times, where confirmed disease case numbers are high; it is usually not guided by vectors 
abundance. 

For midges, populations rising above an abundance threshold may provide an early warning, and such events have 
been theoretically implicated in increased establishment of midge-borne pathogens like bluetongue (Box 1D). As is 
the case for other groups, abundance is a component of the basic reproductive number and vectorial capacity, 
which include transmission rates. Local spread of pathogens to surrounding areas may be high from farms with 
high midge abundance, and, as for ticks, control is most often focused on the pathogen within the host rather than 
the vector.  

For all vector groups, seasonally unfavourable conditions in the environment may prevent activity or survival of 
vector life stages that are capable of transmission throughout the year. This affects pathogen persistence unless 
the pathogen survives the ‘vector-free period’ in the mammalian host or is transmitted vertically across vector life 
stages. Climatic seasonal changes are also closely linked to vector activity and development rate which will have 
some impact on vector numbers and pathogen transmission. 

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Table 1A. Summary of expert opinion of vector-related drivers of epidemiological concepts 

Concept* 

Early warning of pathogen introduction 

Pathogen establishment 

Vector presence  Vector presence may not mean the pathogen is 
also present. For ticks, presence may not imply 
risk of infection, as ticks may not be 
established. Mosquitoes and midges may be 
present and established but not infected. 
Sandfly and disease distributions usually 
match. 

Vector 
abundance 

Vector 
seasonality 

Vector infection 
rate 

Vector 
behavioural 
traits 

Reservoir host 
numbers 

Human activity 

For less abundant vectors like ticks, vector 
infection rates are likely to be a better indicator 
of the risk of pathogen introduction than 
numbers of ticks. For more abundant species 
like midges and mosquitoes, populations rising 
above a threshold density may provide warning 
of potential transmission. 
The start of seasonal increase of midge, 
sandfly and mosquito vector populations or 
feeding activity in ticks could provide some 
short-term warning of pathogen transmission. 

High vector infection rates are likely to provide 
early warning of pathogen introduction into 
uninfected host populations for all vector 
groups. For midges, vector infection rates are, 
however, generally too low for this to be a 
reliable early warning indicator. 
If accompanied by high vector abundance and 
vector infection rates, high biting rates provide 
warning of disease. Biting rates are unlikely to 
be a practical indicator for midges, as infection 
rates are often very low. 
Tick- and mosquito-, but not midge-borne 
pathogens are more likely to become 
introduced in areas with abundant hosts. 
Sudden increases in densities of hosts of 
sandflies may be followed by epidemics. 
If human activity (e.g. trade, livestock farming 
and movement, tourism) determines contact 
rates with infected vectors, the occurrence of 
such activity might be used to identify where 
new infections are more likely. 

For tick-borne pathogens, especially zoonotic ones, vectors 
are usually needed for transmission to hosts. Many midge-
borne pathogens have multiple vectors, so the absence of one 
vector species does not prevent establishment of pathogens. 
For mosquitoes, presence is a prerequisite of establishment 
but this is highly influenced by other factors such as climate or 
host numbers. Sandfly-borne pathogens are found where there 
are vectors. 
More vectors may mean more chance of establishment of tick-, 
mosquito- and midge-borne pathogens. There is little field 
evidence to link high sandfly numbers with increased pathogen 
establishment. 

Tick development may take several years, and longer seasons 
with suitable climate may encourage pathogen development. 
Pathogens of other vectors are more likely to establish 
themselves if there is no vector free period. 

High vector infection rates in all vector groups will increase 
likelihood of pathogen establishment, especially if there are 
overwintering infected vectors. 

For ticks, only co-feeding by several stages is likely to increase 
establishment. For the other groups, biting rate and longevity 
combined with abundance may help identify where pathogen 
establishment is more likely. 

Tick- and mosquito-borne pathogens may be more likely to 
become established in areas supporting high (amplification) 
host densities. Rises in host densities may be followed by 
epidemics (sandflies). 

Pathogen persistence 
For all groups, pathogen persistence requires 
the presence of the vector. If a pathogen has 
multiple vectors, the link between persistence 
and a single vector may be less close. If hosts 
help to substantially amplify the pathogen (as 
for some tick-borne pathogens), persistence 
may be determined by host rather than vector. 

For many tick-borne pathogens, both vector 
and host abundance need to be high to lead to 
persistence. For all vector groups, pathogen 
persistence is likely to be linked to the vector’s 
capability of surviving throughout the year as 
infected nymphs, larvae or adults, rather than 
survival of the pathogen in the host. 
The longer the season, the more likely that 
vectors will become infected, be able to 
transmit pathogens to humans or reservoir 
hosts and will be able to overwinter to ensure 
pathogen persistence. 
Vector infection rates are a good general 
indicator of the likelihood of pathogen 
persistence, where vectors are abundant, 
though less so for midges because of their low 
infection rates. 

For ticks, host rather than vector density or 
activity is the more important factor. For 
mosquitoes and sandflies, high biting rates 
combined with high abundance will lead to 
increased persistence. 
Host density is linked to pathogen persistence 
in all vector groups, though only if it exceeds a 
threshold and particularly if the pathogen 
overwinters in the host. 

Human activity may aim to prevent establishment and may 
create conditions such as concentrations of hosts, or vector 
overwintering sites where establishment is more likely. 

Human activity may aim to prevent persistence, 
but it may also create conditions such as 
concentrations of hosts, or vector overwintering 
sites where persistence is more likely. 

Habitat change  Habitat or environmental change can create 

areas that are suitable for vectors and are 
therefore known before the vectors become 
established. The reverse is also true. This is 
less important for mosquitoes and midges. 

Habitat or environmental change can create new areas 
suitable for vectors and allow them to become established. 
The reverse is also true. This is least important for midges as 
they are less associated with specific natural habitats than 
other vectors. 

Habitat or environmental change can create 
new areas that are suitable for vectors and 
allow them to persist. The reverse is also true. 
This is least important for midges. 

*: for definitions, see Methods section. 

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TECHNICAL REPORT 

The importance of vector abundance and seasonality 

Table 1B. Summary of expert opinion of vector-related drivers of epidemiological concepts 

Pathogen transmission to hosts 

Pathogen and vector spread 

Vector presence  Vector presence is not directly linked to pathogen 

transmission, especially if vectors are widespread. Vector 
role in transmission is reduced if there are other 
transmission routes (such as nosocomial) or via milk (e.g. 
tick-borne encephalitis). 

Vector 
abundance 

Vector 
seasonality 

Vector infection 
rate 

Vector 
behavioural 
traits 

Reservoir host 
numbers 

Human activity 

For midges, low abundance restricts pathogen 
transmission, and vector abundance is a component of 
the basic reproductive number and vectorial capacity 
calculated for midges and mosquitoes. Tick abundance is 
less closely related to pathogen transmission and vector 
infection rates can be very variable and compensate for 
low numbers. For all vectors, however, pathogen 
transmission and disease occur when vectors are 
abundant and when they are not. 
The longer the season, the more likely that vectors will 
become infected, be able to transmit pathogens to 
humans or reservoir hosts and will be able to survive over 
the winter to ensure pathogen persistence. 

High infection rate is likely to increase pathogen 
transmission and disease risk. However, for very 
abundant groups like midges this relationship is not linear, 
so the impact of low infection rates can be offset by high 
abundance. 
For ticks, transmission is highest when human and vector 
activity are highest. High biting rate and high abundance 
of the other groups will increase transmission. 

The vectors rather than hosts drive the transmission of 
tick borne disease. However, high host density is 
associated with increased transmission rates of tick, 
midge and sandfly-borne pathogens. 
If human activity (trade, livestock farming and movement, 
tourism) determines contact rate with infected vectors, 
then it may promote transmission. Other human activities 
like vaccination, vector control or host treatment are 
meant to break the transmission cycle. 

Habitat change  Habitat or environmental change can increase or 

decrease the suitability of an area for vector development 
and activity, affecting pathogen transmission. This is least 
important for midges 

Vector presence is not directly linked to 
pathogen spread, especially if the vectors 
are widespread. Vector presence in 
periendemic regions may not imply spread 
for sandfly-borne pathogens. As the vectors 
are limited by microhabitat conditions with 
very patchy suitability, this prevents spread 
from patch to another. 
Vector abundance may enhance local 
spread, for example from farms with high 
midge abundance. Long-distance spread 
may be more affected by other factors like 
bird migration routes (ticks), wind and 
climate (midges), trade or animal 
movements (mosquitoes, midges). Sandfly 
spread is constrained by the patchiness of 
the local microhabitats. 
The longer the season, the more chance for 
spread, provided the neighbouring habitat 
is suitable (for sandflies) and host and 
vector activity coincide (for ticks). 

High vector infection rates will promote 
pathogen spread in all vector groups, but 
this may be counteracted by limited host 
numbers for ticks, and habitat unsuitability 
in sandflies. 
Ticks will spread fastest when high host 
density and vector activity coincide. For 
other vectors, high biting rate, longevity and 
abundance will promote spread. 
Movement of infected hosts spreads 
pathogens. High host densities mean faster 
spread rates (midges). 

Human activity can lead to the movement 
of infected vectors, such as ticks on farmed 
animals and mosquitoes in trade goods, as 
well as the movement of infected hosts 
such as companion animals or livestock. 
Habitat or environmental changes, 
including urbanisation, can affect the 
suitability for hosts and vectors, creating 
areas into which they can spread, and 
where they can get established and persist. 
It is not relevant for midges. 

Pathogen transmission control 
 (vector control and other) 

Control is by definition only needed where the vectors 
are present, but may not be needed everywhere they 
are present, especially if the vectors are not restricted to 
particular habitats or very abundant like midges and 
mosquitoes. 

Control may be best targeted on areas of high vector 
abundance especially of mosquitoes where the aim is to 
reduce vector numbers. For ticks and midges, the 
control is often focused on the hosts and may not 
primarily be intended to reduce vector numbers in the 
environment as a whole, and for sandfly-borne diseases 
the control is targeted on disease case numbers not 
vector abundance as most such diseases are 
asymptomatic. 
Vector-free periods determine when restrictions to 
animal movement are relaxed and vaccination 
campaigns can be run. Long seasons mean control 
programmes need to run for longer (sandflies and 
mosquitoes). Abundance cycles affect timing of public 
health measures against tick-borne diseases. 
Vector infection rate is a useful indicator for target 
control, providing sufficient sampling is practical. 

Tick control is more effective if vectors are active. Biting 
rate of other vectors may help prioritise areas for control 
and be used as an indicator to assess impact. 

Control of tick vectors on the hosts may reduce 
pathogen, but not if infection persists in the host. For the 
other vector groups control targeting is unlikely to be 
determined by host numbers. 
 Not applicable.  

Habitat change can be used to control vectors, for 
example modification of vegetation to control ticks or 
removal of larval breeding sites to control mosquitoes. It 
is not relevant for midges. 

7 

 
 
 
The importance of vector abundance and seasonality 

TECHNICAL REPORT 

Box 1. Examples of the importance of abundance and seasonality for 
each vector group 

A. Tick abundance and Lyme disease 
The complexity of the interplay between tick density, 
tick infection rate and human exposure is illustrated 
by the rural and urban risks of Lyme disease, 
transmitted by the sheep tick Ixodes ricinus. Tick 
densities tend to be higher in rural areas, where 
there are also more large mammalian hosts (deer, 
livestock). Infection is not always transmitted from 
these larger animals to the ticks, so this can cause a 
dilution effect creating lower tick infection rates. With 
the relatively low human populations densities in rural 
areas, contact rates between humans and ticks may 
also be low, so that even where infected tick density 
may be high, few humans will get infected and the 
risk of infection at a population level is low [12].  

In urban areas, the habitats suitable for ticks may 
have relatively few large animal hosts, but they do 
support small mammalian and bird hosts which can 
transmit infection to ticks. As a consequence, even if 
tick densities are low; the infection rates may be 
higher because infection rates are not diluted by 
large mammal hosts. Furthermore, in towns, the high 
human population densities may mean that more 
people come into contact with ticks with higher 
infection rates. Therefore, the main drivers of disease 
are neither tick density nor high infection rate alone, 
but the density of infected ticks coupled with levels 
human exposure to the vectors. 

C. Sandfly abundance and leishmaniasis 
In both outbreak (Madrid) and endemic (Murcia, 
Catalonia) situations in Spain, high vector abundance 
has been associated with high infection rates of 
L. infantum in the sandfly vectors, in humans or in 
dogs [16–18]. The outbreak in Madrid clearly showed 
that high sandfly abundance could be a major factor 
triggering a human leishmaniosis epidemic. However, 
this outbreak was epidemiologically unusual as it was 
associated with an explosion in the population of 
lagomorph hosts in the green amenity areas that 
were integrated into new housing developments built 
on agricultural land. Sandfly infection rates in 
endemic areas, however, seem to vary greatly, 
ranging from 4% or less in Portugal [19–23] to 39% 
in northwest Spain. None of these studies correlated 
sandfly abundance with L. infantum infection rate in 
vectors. Indeed there is evidence that high 
prevalence of human leishmaniasis was associated 
with a relatively low density of infected sandflies [24]. 
In this group, the relationship between vector 
abundance and infection rates is also inconsistent and 
contradictory. 

B. Mosquito abundance and West Nile fever 
The severity of West Nile fever (WNF) outbreaks is 
determined by a complex interplay of avian host density, 
vector infection rate and human or animal exposure. For 
WNF, the numbers of human or horse cases are related 
to the numbers of bites by infected mosquitoes, and 
these numbers are themselves related to the intensity of 
pathogen transmission between birds and mosquitoes.  

There is some limited field evidence to quantify the role 
of vector abundance in this system. During the 2012–
2015 WNF outbreaks in Serbia, there was a close match 
between the distribution of clusters of infected 
mosquitoes, birds, horses and humans. There was also a 
strong positive correlation between both annual 
maximum and average mosquito abundance, and 
infection rate with annual incidence of WNF cases [13]. 
In the 2010 WNF outbreak in Maricopa County, USA, 
densities of the mosquito vector Cx. quinquefasciatus 
were also higher in outbreak areas than regions with no 
cases [14,15]. 

Despite these correlations, the relationship between 
vector abundance and infection rates is not consistent. 
The virus may be absent from areas of high mosquito 
density and WNF outbreaks may occur when vector 
abundance is low and the vector infection rate is high.  

D. Midge abundance and BTV infection 
The case of bluetongue infection in Europe illustrates 
the relationships between midge vector abundance, 
seasonality and pathogen establishment, transmission 
and spread. Over two decades, several serotypes of 
bluetongue virus (BTV) have emerged in the 
Mediterranean Basin and (elsewhere) in Europe. Rise in 
temperature, due to climate change, is likely to have 
increased the abundance of the midge vector C. imicola 
populations in Spain, southern France and Italy during 
the 1990s, facilitating BTV emergence.  

Rising temperatures have also led to changes in the 
biting rate and development rates of other midge 
species in north-western Europe making them more 
effective vectors [25]. After its introduction in 2006, 
BTV-8 was spread throughout northern Europe largely 
by wind-blown midge vectors [26]. The following year 
saw the disease spread from Belgium to the UK, 
Germany to Denmark and then to Sweden [27]. The 
speed of the BTV-8 spread in France was influenced by 
parameters linked to vector abundance, by land cover 
and by host density [28,29]. 

The interplay between abundance, seasonality and the other vector related factors provide the most insight into 
the drivers of epidemiological processes described by the concepts considered here. Table 2 shows the normalised 
scores summed for each vector related parameter by epidemiological concept and Table 3 shows the scores by 
vector group. The scoring in this table implies a fairly consistent pattern, as indicated by the distribution of the 
highest scores in red. 

8 

TECHNICAL REPORT 

The importance of vector abundance and seasonality 

Table 2. Sum of normalised scores by epidemiological concept 

Concept 

Early warning 
Pathogen 
establishment 
Pathogen 
persistence 
Pathogen 
transmission 
Pathogen 
spread 
Pathogen 
control 
Total 

Vector 
presence 
8.0 

Vector 
abundance 
8.6 

7.7 

5.6 

7.4 

6.7 

6.0 

41.3 

10.1 

8.6 

10.8 

9.1 

7.5 

54.7 

Infection rate 

6.8 

10.3 

8.4 

10.9 

9.6 

10.0 

55.9 

Behavioural 
traits 
7.4 

10.1 

7.6 

10.9 

7.6 

8.9 

52.5 

Seasonality 

8.3 

7.6 

7.6 

8.3 

7.6 

7.9 

47.2 

Reservoir 
host numbers 
7.4 

Human 
activity 
7.4 

Habitat 
change 
6.7 

8.9 

9.9 

9.2 

10.1 

7.6 

53.1 

7.0 

6.4 

8.9 

9.5 

7.6 

8.0 

8.0 

8.9 

8.9 

8.0 

46.8 

48.5 

Table 3. Sum of normalised scores by epidemiological concept and vector group 

Vector group 

Ticks 

Mosquitoes 

Midges 

Sandflies 

Total 

Vector 
presence 
14.3 

Vector 
abundance 
12.3 

9.8 

6.6 

10.6 

41.3 

13.8 

17.9 

10.6 

54.7 

Infection rate 

12.3 

11.5 

15.1 

17.0 

55.9 

Behavioural 
traits 
11.7 

13.2 

14.2 

13.5 

52.5 

Seasonality 

10.4 

13.2 

12.3 

11.3 

47.2 

Reservoir 
host numbers 
14.9 

Human 
activity 
9.1 

12.6 

14.2 

11.3 

53.1 

12.6 

12.3 

12.8 

46.8 

Habitat 
change 
14.9 

13.2 

7.5 

12.8 

48.5 

Scores 9.0 or above in process and 14.0 or above in the vector group are shown in bold. 

Sum 

60.6 

69.7 

62.1 

75.3 

69.1 

63.5 

400.0 

Sum 

100 

100 

100 

100 

400 

Abundance, vector infection rate, vector activity and behaviour, and host numbers are seen as the most important 
drivers of vector-borne disease epidemiology. By contrast, vector presence alone is seen as a relatively poor 
indicator (except for ticks), while the importance of seasonality and the effects of human activity and habitat 
change are considered to be of intermediate importance. 

Abundance is to some degree important for all the epidemiological concepts, but most obviously in relation to 
establishment and transmission. Vector abundance matters most for midge- and mosquito-borne pathogens and 
somewhat less for those borne by ticks and sandflies. Seasonality alone is not generally seen as a frequent primary 
driver of any of the concepts. However, it is clear from Tables 2 and 3 that seasonality is considered a more 
important epidemiological driver for mosquitoes than the other groups and that it is an important determinant 
when combined with other drivers such as vector abundance. Vector season length is also not seen as a major 
driver of pathogen dynamics, except that it determines the vector-free period (when pathogen transmission by 
vectors is not possible). 

These overarching patterns obviously conceal some characteristics of individual groups. Vector presence is most 
useful as an indicator of tick-borne disease, for which host abundance and the effect of the habitat on the vector 
are also key factors. This set of drivers highlights the difference between ticks and the other groups for which 
abundance, infection rate and vector behaviour are more widely identified as drivers of disease. The impact of 
anthropogenic influences on the vectors is considered to be comparatively small, especially for ticks. 

Availability of abundance and seasonality data in the 
VectorNet database 

It should be noted that much of the VectorNet database was compiled in the legacy of projects (TIGERMAPS, V-
BORNE and VBORNET) and consists largely of presence/absence data for polygons, based on entomological 
observations, or on point data extracted from the literature or from national data surveillance programmes. While 
some of these data may have had numbers per trap or per sample, the earlier entries were converted to presence 
(or absence) per polygon or point location and recorded as such. 

Collecting abundance data (numbers per sample) has only been a priority since 2016. An overview of the main 
methods used to sample abundance (and thus the potential for quantifying seasonality) of the four vector groups is 
provided in the Appendix. A summary of the data that have been assembled to date is provided below. While the 
field data in VectorNet were collected using standardised trapping techniques, and thus provide indicators of 
abundance per sample, most sites (with the exception of the Culicoides and the mosquito Culex pipiens) were 
sampled on a single date and were therefore not part of longitudinal studies. Therefore, they can only be used to 
provide indices of relative abundance within a group of sites sampled at the same time. 

9 

The importance of vector abundance and seasonality 

TECHNICAL REPORT 

VectorNet funded substantial amounts of field sampling activities from 2014 until 2018, which were largely 
executed using standardised sampling or trapping methods and which yielded data on vector numbers with known 
denominators (per trap, per drag). In addition, many of the results from the wide-ranging national surveillance 
programmes for Culicoides species for much of western Europe have been incorporated into the database. These 
longitudinal surveys conducted in response to the outbreaks of bluetongue and Schmallenberg virus in the first 
decade of the new millennium. 

All four vector groups have been sampled in several hundred locations as illustrated in Figure 1. Ticks and 
mosquitoes have been sampled throughout the continent, with a focus on northern and eastern Europe for ticks, 
and central and eastern countries for mosquitoes. Sandfly sampling in peri-Mediterranean countries, the Balkans 
and the Caucasus was informed by spatial distribution models produced in preceding projects. The Culicoides data 
obtained are concentrated in western Europe, supplemented by significant numbers of field samples in the Balkans 
and a transect crossing eastern Europe from north to south. Extensive Culicoides data have also been collected for 
other countries [30], focusing on species complexes rather than individual species. In order to integrate new data 
from these sources, VectorNet is continually contacting national surveillance authorities. 

Figure 1. Locations with abundance values derived from standardised sampling of active vectors, 
VectorNet database, as of March 2018 

a) Midges 

10 

 
 
 
TECHNICAL REPORT 

The importance of vector abundance and seasonality 

b) Mosquitoes 

c) Sandflies 

11 

 
 
The importance of vector abundance and seasonality 

TECHNICAL REPORT 

d) Ticks 

Blue dots for ticks represent passive surveillance records. 

For Culicoides midges, VectorNet holds rather extensive abundance data for much of Europe, and parts of northern 
Africa, though eastern European countries are less extensively sampled. These data therefore provide a reliable 
indicator of midge abundance in western Europe. For the other groups, few of the samples were longitudinal so 
there is little information on seasonality. There are, however, significant amounts of abundance data available for 
the four main tick species of interest, for some sandfly species, and for a few mosquito species (e.g. Aedes 
albopictus, Culex pipiens). For these groups, and for midges in the Balkans and some other areas, the 
standardisation of sampling provides at least some indication of abundance for the majority of sampled locations, 
even if it is only for a single date and a limited set of neighbouring locations. This resource may offer an 
opportunity to estimate abundance indices for limited regions or time periods, or perhaps provide opportunities for 
assessing the relationships between numbers and environmental drivers that could be used to generate models for 
larger areas. Additional information can be found in the section on spatial modelling of vector abundance and 
seasonality. 

Requirements for abundance and seasonality assessments 

The previous section demonstrates that the amount and coverage of vector abundance and seasonality data 
currently available for Europe and northern Africa are more complete for some groups than for others. With the 
exception of the Culicoides midges, longitudinal sampling efforts are available from relatively few locations at a 
continental scale, and both seasonality and harmonised abundance measures are comparatively rare. There 
remain, therefore, gaps in both the abundance and seasonality data for all groups. These gaps can be filled either 
by additional field sampling, or by model predictions that use environmental and existing distribution data to 
calculate consistent and complete coverage. 

12 

 
TECHNICAL REPORT 

The importance of vector abundance and seasonality 

Field sampling 

Field sampling, especially the longitudinal sampling needed for abundance and seasonality estimates, is expensive. 
Desk-based sampling methods are therefore likely to be the first port of call to acquire additional data. These 
methods include extraction from online databases such as EID2 [31] and systematic literature reviews. Though 
databases and literature reviews may provide useful information on abundance or seasonality, they are likely to 
focus on priority or high-profile species, and data for lower priority species tend to be excluded. Furthermore, 
neither method is guaranteed to provide information about particular areas of interest, and some form of further 
sampling or gap filling is likely to be necessary if continental level distribution maps are required. 

Ways to prioritise which species and which locations should be sampled are set out in the Conclusions section 
below. 

Overview of sampling strategies 

A number of lessons have been learned during the VectorNet project. Sampling should be carried out using 
standardised or harmonised methods (which will be specific for each group) and run for several years to make sure 
that annual variation does not bias the outcomes. Transect sampling should be performed by standardised trapping 
starting at the beginning of season (potentially defined according to degree-days for each location) and continuing 
at regular two weekly intervals for the whole vector period that vectors are active. There should be sampling for 
consecutive weeks at 3–5 sites within each location. 

Using large scale transects in Europe, S–N and E–W directed transects would reduce the amount of detailed 
sampling needed. If different trap types are used in different locations, conversion factors should be calculated 
from Latin square field comparisons or from evidence collated from the literature. 

VectorNet experts from all vector groups were consistent in stating that it is not realistic to produce abundance (or 
seasonality) maps without substantially more resources than are needed for presence/absence maps with the same 
level of detail. Assuming, therefore, that the major aim of further field sampling is to fill the gaps in continental 
scale distributions (rather than, for example, to monitor the expansion of invasive species), sample or transect 
locations should be chosen to ‘fill the gaps’ and establish species distribution limits. If distribution models are 
available (see section on spatial modelling of vector abundance and seasonality), these can be used to determine 
where both the ‘edges’ and the major unsampled distribution foci are likely to be. Sample locations should also be 
well dispersed, rather than be close to regions for which data are available. 

If, in addition to ‘filling the gaps’ in observed data, the objective of further sampling can be to feed spatial 
distribution models (see section on spatial modelling), then the target number of locations should be set 
accordingly. The main issues that limit spatial distribution modelling is the number of sample points available and 
whether they are sufficiently dispersed to be representative of the entire area for which the modelled map is 
required. There are no hard and fast rules to define the sample number needed to produce a reliable map, as it 
depends on the desired level of uncertainty, the method, the values themselves, and the strength of the statistical 
relationships between the target variable and the covariates used, which depends on group, species, and life stage 
as well as on habitat type, structure and other factors. As a guideline, at least one sample point every 100 km (i.e. 
10 000 km2) is required [32] for continental-scale areas, implying a minimum of 450 points for the EU.  

There are a number of vector-group-specific issues that are summarised below. 

Ticks 

Earlier sections have suggested that ticks should be monitored by dragging of vegetation or estimating vector 
numbers on hosts. As for the other vector groups, sufficient longitudinal data will allow seasonal patterns in 
relative activity and relative abundance to be detected and can be compared with weather data on temperature 
and rainfall that can be robustly modelled (see section on spatial modelling below). A network of field stations 
where tick activity is monitored on a regular (preferably weekly) basis across the continent might be developed to 
generate data for a continent-wide temporal activity model. Data on tick seasonality and activity are likely to be 
more useful metrics to collect than vector abundance data, though these also require longitudinal sampling efforts.  

Culicoides 

Many extensive national schemes provide (or used to provide) seasonality and abundance estimates for Culicoides 
for much of western Europe. Annual maxima can be extracted from these data to compare abundance over time 
and locality, though additional information may be needed to establish seasonality as sampling dates may not have 
covered the start and end of the season. While many of these datasets have been incorporated into the VectorNet 
database, for several large countries, data on some species or species complexes exist but are not yet available to 

13 

The importance of vector abundance and seasonality 

TECHNICAL REPORT 

the project. Future data acquisition efforts should be focussed on obtaining these existing datasets generated by 
existing surveillance programmes. 

Though these data could most probably be used to generate reliable models for the whole VectorNet region 
(EU/EEA and neighbouring countries), there is a case for field sampling along the eastern and southern margins of 
this region from which Culicoides data are not readily available. Sampling should follow the protocols used for the 
national programmes such as those in France, namely longitudinal samples from selected farms every two weeks 
throughout the season, with the peak annual number per trap used as the abundance metric.  

For Culicoides, in addition to climate, livestock density and land cover have been demonstrated to be determinants 
of species occurrence as well as where and how many vectors can be found [33]. An efficient strategy to limit the 
number of collection sites for assessment of abundance would be to use latitudinal and altitudinal transects, while 
sampling different classes of livestock and land cover. The number of collection time points needed per year would 
be dependent on the length of the activity period. The number of collections necessary to estimate maximum 
abundance could be reduced in areas where the Culicoides dynamics has been previously characterised by targeted 
trapping.  

Searle also states ‘Culicoides surveillance methods should be adapted to focus on concentrated assessments of 
species-specific abundance during the start and end of seasonal activity in temperate regions to facilitate 
refinement of ruminant movement restrictions thereby reducing the impact of Culicoides-borne arboviruses’ [33]. 
Searle also asserts that multi-species measures of diversity or richness are too variable to provide usable 
information [33]. 

Mosquitoes 

Field work undertaken during VectorNet suggests a seasonal maximum of relative abundance might be the most 
suitable metric to describe continent-wide differences in basic ‘risk’ (Petric, personal communication) for pathogen 
transmission. Annual time series of relative abundance of eggs (e.g. Aedes albopictus) or adults (Culex pipiens 
complex) could be used to describe seasonality. Start and end date of vector activity (or vector-free period) can 
then be used to map the differences in seasonality across Europe. 

A threshold level above which a vector’s abundance is considered to pose a risk or be a nuisance is likely to vary 
regionally, as well as with pathogen and vector species. The threshold abundance to indicate WNV transmission 
onset in Italy was set to 300 specimens of Cx. pipiens/trap/night [34], while the citizen science programmes within 
the same area declared 150 specimens of Cx. pipiens/trap/night as a nuisance threshold. 

At least for C. pipiens, VectorNet field sampling showed that one measurement at one location can be 
representative of a wider area and that a limited number of measurements per location at the beginning of the 
season gives a good indication of both seasonality and peak abundance later in the year. This has not been 
substantiated for other vector groups. 

Sandflies 

In rural areas, where sandflies are most common, they tend to concentrate where animals congregate, in farms 
and stables [35], but their abundance varies substantially with farm type [16]. Several sites of different type should 
therefore be sampled to get representative results. As with other species, standardised samples throughout the 
year can provide information on seasonality. As for mosquitoes, and indeed midges, annual maxima in numbers are 
likely to be a useful metric. 

Light traps should be used and placed indoors in farms, close to walls and the floor, and within five meters of 
animal pens. Female sandflies are most abundant one to two meters from the animal group, and male sandflies 
may be more abundant four to five meters from the animal group [35]. Traps should be left for at least 24 hours. 
In order to estimate seasonality, sampling should be repeated at least every two weeks. Shorter sampling intervals 
will increase precision of the estimated seasonality and of the relationship between abundance and changing 
weather conditions. 

14 

 
 
TECHNICAL REPORT 

The importance of vector abundance and seasonality 

Spatial modelling of vector abundance and 
seasonality 

Mechanistic models 

By using environmental determinants as proxies for vector presence or abundance, there are a number of ways in 
which vector distributions can be inferred. The simplest are the use of climatic thresholds to define areas where 
some aspect of development is limited or impossible. These thresholds can be applied as a determinant of absence 
(where for example species cannot overwinter and thus become fully established) or as a condition for 
development, which limits population growth to a specific season. This is well illustrated for Culicoides [30] and for 
the Aedes mosquitoes [36]. 

A number of more complex relationships between vector abundance or seasonality and environmental and other 
factors can be used to drive deterministic models. These models can be based on experimental or empirical 
determination of the relationships between the target variables and environmental (usually climatic) determinants. 
In particular, it may be possible to calibrate abundance models by matching contemporaneous climatic conditions 
with the standardised sample data already in the VectorNet database and make predictions for wider areas. Some 
examples of such relationships, investigated during the VectorNet project, are outlined below. 

Temperature, and more generally climate, is an important determinant of the length of the activity period for most 
vectors [37]. For example, at the beginning of the year, activity starts when a threshold temperature is reached. 
Regional differences in the temperature thresholds that determine the start of the season for C. pipiens are 
recognised [38,39]. Risueño [16] reports a similar phenomenon for sandflies (e.g. Phlebotomus perniciosus; 
Central Spain: first occurrence early May (13.8 °C); northern Italy: first occurrence early June (14.3 °C). These 
thresholds are certain to be species specific. 

Such relationships may be scale-dependent. Risueño et al. [16] report that sandfly (P. perniciosus) numbers are 
also related to micro-habitat, but that relative humidity, temperature and wind speed can be used to model 
numbers at a regional scale. 

Apart from using simple temperature thresholds, some aspects of vector seasonality can be predicted using 
cumulative temperature or the number of day-degrees above a threshold. Examples include the questing activity of 
the tick Ixodes ricinus [40]. 

Relationships between environmental drivers and both seasonality and abundance may be affected by conditions 
which occurred several months earlier. An example is I. ricinus in Germany, where temperature and relative 
humidity determined vector densities 3–6 months later [41]. 

Other proxies for abundance may include vector hosts as well as environmental determinants. The number of tick 
larvae in Spain is related to the number of horses, as well as the temperature and the number of frost days, while 
nymphs are more dependent on temperature, rainfall and land cover type [42]. Deer numbers have also been 
linked to the number of ticks [43] and, therefore, tick numbers are likely to be low where there are no deer or 
where the livestock are treated with acaricides. 

The types of models that can be constructed from such relationships are illustrated by an example from the 
VectorNet sandfly group. A recent article [44] describes the development of a discrete-time stochastic difference 
equations model to predict abundance and seasonality of the Leishmania vectors P. neglectus, P. tobbi and 
P. papatasi in eastern Mediterranean countries. The model includes four compartments, one for each life stage: 
egg, larvae, pupa and adult and models the flow between them dependent on environmental variables, including 
temperature and humidity. Basic survival, development and fecundity requirements are parameterised using 
experimental data obtained under controlled conditions [45]. Moreover, surveillance datasets from Turkey, Greece 
and Cyprus are used to adjust model parameters and validate the model. 

These studies show that precise information on these parameters allows accurate prediction of field observations 
for sandfly populations in Turkey. The model and methodology used are extendable for predicting sandfly 
abundance and seasonality elsewhere, provided basic information on life history parameters are used for different 
vector species. 

Clearly, such models are species specific and rely on a substantial amount of laboratory or field-based calibration, 
but they do provide an opportunity to produce distribution maps using readily available climatic indicators, such as 
those available from remotely sensed satellite imagery. As these become more widely available with, for example, 
the advent of the ESA Copernicus programme, such models may prove ever more cost efficient for a range of 
species. 

15 

The importance of vector abundance and seasonality 

TECHNICAL REPORT 

Stochastic models 

For those species for which information is not available to drive mechanistic models such as described above, the 
gaps in knowledge about distributions can be filled using long-established statistical spatial distribution modelling 
methods. 

As for presence/absence modelling, there are a plethora of available methods suited for abundance modelling [46], 
three of the currently popular ones being boosted regression trees, random forest, and generalised linear mixed 
modelling. All the methods use the same principal: a statistical relationship is established between the known 
values from the sampled locations and the values of a series of predictor covariates at the sample locations. These 
equations are then applied to generate predictive maps using data layers of the identified covariates that cover the 
whole region of interest, usually at a fixed resolution of 0.25 to 10 km. It is also important to provide estimates of 
the uncertainty of the modelled predictions, either though inherent features of the modelling techniques (such as 
Bayesian) or by running the models a number of times. 

While modelling presence and absence does not rely on repeated samples throughout the year, it does need 
confirmed absence data. However, confirmed absence data (zero values) are not a prerequisite for abundance 
models, which use the whole range of values available and can predict absence (missing zero values) based on the 
assumption about the marginal statistical distribution (e.g. the negative binomial distribution). This simplifies the 
data gathering process significantly and means that fewer sample locations may be needed to fit an abundance or 
seasonality spatial model than a presence/absence one. 

These techniques can be used on any datasets with numerical values, such as the standardised measures of 
abundance described above (the maximum number per trap and number per square meter are equally suitable). 
Given the difficulty of obtaining abundance data, there is a question as to whether proxy variables can be used to 
generate epidemiologically relevant spatial predictions. 

Preliminary evaluations using the VectorNet Culicoides data suggest that, for most of the species, spatial model 
predictions of maximum abundance correlated well with model predictions of an outcome variable based on 
normalised abundance classes (which combine a number of different abundance value ranges; Figure 2, left). This 
implies that it might be possible, for some species, to calibrate some relative or ranked abundance measures in 
locations with reliable abundance values and use the resulting conversion factors to incorporate all relative or 
ranked abundance data into a single harmonised dataset. In addition, applying an appropriate threshold, model 
predictions of probability of presence, based on presence/absence data, could reliably be used to differentiate 
between low or zero abundance and medium or high abundance (Figure 2, right). For ticks, however, this was not 
found to be the case. 

The ‘standard’ spatial modelling techniques and sample size requirement calculations used for abundance data can 
also be applied to indicators of seasonality, such as the length of the vector free period. These methods are also 
suited to model the start and end day of activity. For the date of peak activity, they may be less appropriate, as 
peak days may be difficult to define: there may be one or more peaks during the year, depending on climate or 
location. 

16 

TECHNICAL REPORT 

The importance of vector abundance and seasonality 

Figure 2. Comparison of values extracted from spatially predicted surfaces of maximum abundance 
with ranked normalised abundance category for Culicoides imicola (top) and probability of presence 
of C. dewulfi (bottom) 

6

5

4

3

2

1

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g
e
t
a
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e

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e
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o
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t
i
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a
b
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e

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o
M

C. imicola

France

Spain

Portugal

Italy

0

1

2

3

4

5

6

7

8

Modelled abundance value

C. dewulfi

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

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Modelled Ln(maximum abundance)

17 

 
 
 
 
 
 
 
 
 
The importance of vector abundance and seasonality 

TECHNICAL REPORT 

The top panel of Figure 2 shows that the two models are very similar for all countries despite the fact that the 
abundance values are not measured the same way across countries. This means that converting abundance data 
into categorical data may be a way to standardise data from variable data sources. The bottom panel of Figure 2 
shows that the modelled probability of presence, which uses presence/absence data as input, increases with the 
log maximum abundance value but saturates above a logged abundance value of two. 

Conclusions and potential implications 

Abundance and seasonality of vector activity play a role in the epidemiology of vector-borne diseases, even if that 
role is sometimes minor. Therefore, there is a case for collecting both abundance and seasonality data of vectors 
that carry pathogens of concern to public and animal health. 

Collecting abundance and seasonality data through field sampling is, however, more expensive and time consuming 
than collecting most other vector-related data. With a few exceptions (such as for C. pipiens), multiple samples 
have to be taken throughout the year to ensure season start and end dates are adequately assessed and that 
seasonal peaks are detected. Furthermore, as vector distributions and abundance may change from one year to 
the next, sampling needs to be done over several years. As disease vectors typically occur in several countries, the 
production of maps requires continental scale approaches, which require standardisation among countries. 

The importance of abundance and seasonality data varies considerably according to the vector group and the 
epidemiological concept of interest. Epidemiological concepts of tick-borne disease are less informed by abundance 
data than are those of the other vector groups, and presence/absence data of ticks may be sufficient if considered 
in conjunction with other factors. Abundance data are generally considered to be of relevance to pathogen 
establishment and transmission but less useful to inform early warning systems, estimates of the likelihood of 
pathogen persistence and – for sandflies, midges and ticks – to inform targeting of control measures. 

Box 2. Prioritisation of abundance and seasonality sampling 

 

 

Step 1. Desk study to define whether detailed abundance data are necessary and/or useful 
Define ecological concept of interest (early warning, pathogen transmission, spread, etc.). 
 
Define target variables for the study (abundance, start/end season, peak, etc.). 
 
Confirm that abundance is a useful epidemiological indicator (e.g. of transmission). 
 
Confirm whether or not seasonality metrics are useful. 
 
Assess whether the required outputs need precise abundance data or whether categorical are data 
 
sufficient. 
Check what data are available in existing databases (presence/absence data, standardised sample 
data, indicator dates). 
Assess whether a combination of other available data variables can be used to infer high risk (e.g. 
presence of host, habitat or climate). 
Determine whether there are species for which abundance is a more reliable indicator for risk. 
Assess whether proxies such as climate can be used to calculate the required metric. 
Define the time frame (snapshot, seasonal or change detection). 

 
 
 
Step 2. Regionalisation (select from where these data are needed) 
 
 
 
 
 
 
 
Step 3. Assess the collection effort needed if precise abundance or seasonality data are required  
 
 

The whole species range  
Pan-European 
Priority areas, e.g. outbreak areas or areas with a high risk 
Core areas 
Edges 
Specific habitats 
Locations optimised to calibrate models or maximise interpolation 

Define outcome measure and degree of precision required. 
Tailor sampling methods and outcome measures (e.g. number per trap or per square meter, maximum 
number per year) to selected species and region. 
Determine sample size, location and frequency required (using models if available). 
Use modelling of pre-existing (comparable) data to estimate the degree of extrapolation/interpolation 
possible. 

 
 

While not generally amenable to large-scale regional or continental analyses, the majority of the abundance data in 
VectorNet may be suitable for localised analyses using proxy measures such as climate as predictors. Mechanistic, 
environmental and spatial distribution modelling can, through advanced interpolation and extrapolation techniques, 
make predictions for areas where data are sparse, and thus minimise the amount of field-collected data needed to 
fill gaps. Some models can generate maps that display the degree of uncertainty in the predictions, and where 
additional data are most needed. Despite this, there is a pressing need to reduce costs by prioritising among 

18 

TECHNICAL REPORT 

The importance of vector abundance and seasonality 

abundance sampling strategies (Box 2). Essentially this involves 1) a desk study and extensive literature survey to 
define the programme, identify the target species and assess whether full-scale abundance surveys are really 
necessary; 2) if abundance surveys are required, selection of the areas where they are required; 3) an assessment 
of the sampling and data collection effort needed by defining sample strategy and required metrics. 

Comprehensive abundance and seasonality surveys are clearly possible, as shown by a number of longitudinal 
sampling studies carried out as part of VectorNet and by the very extensive Culicoides surveillance programmes 
currently in place across much of Europe. Such large programmes are dependent on political will, which is often 
motivated by outbreaks, such as the unexpected emergence of bluetongue and Schmallenberg virus in Europe. 

Other threats, such as the potential spread of vector-borne disease due to climate change and the potential 
emergence of new diseases, have yet to lead to similarly comprehensive monitoring efforts; in the latter category, 
the surveillance of invasive mosquitoes is the most extensive. 

Finally, it must be emphasised that any form of extensive vector sampling depends not only on the provision of 
resources to fund the sampling programmes, but also on the availability of entomologists that have the skills to 
implement surveillance and monitoring programmes. The latter can only be guaranteed by continuous and 
sufficient strategic capacity building combined with career perspectives to attract recruits. 

19 

 
 
The importance of vector abundance and seasonality 

TECHNICAL REPORT 

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25 

 
 
The importance of vector abundance and seasonality 

TECHNICAL REPORT 

Appendix 1. Vector groups 

Vector group tables 

Table A1. Expert opinion of vector-related drivers of epidemiological concepts: ticks 

Vector 
presence 

Vector 
abundance 

Early warning of pathogen 
transmission 
4 

A key pre-requisite, assuming 
vector establishment; e.g. 
Hyalomma ticks are frequently 
found in northern Europe, but they 
are not established [47], so their 
importation is not a sign of early 
warning of transmission [48,49].  

Pathogen establishment 

Pathogen persistence 

3 

3 

Depends if the pathogen is 
zoonotic and whether the pathogen 
becomes established in hosts and 
amplifies within these hosts. 
Vectors are likely needed for 
transmission. 

Persistence of a zoonotic pathogen 
may be reliant upon host presence 
and host density, but vectors are 
needed for transmission. It is 
possible that secondary vectors 
(e.g. Ixodes hexagonus or Ixodes 
uriae [50,51] in Lyme borreliosis) 
play a role in establishing the 
pathogen (which may occur if no 
primary vector is present). 

2 

Less important, as outbreaks can 
start at low abundance. In cities, 
tick abundance may be low, but if 
there are high prevalence rates of 
Borrelia and high human exposure 
[12], pathogens may be 
transmitted. 

3 
More vectors mean possibly more 
opportunities for transmission to 
take place (depending upon 
prevalence rate), which increases 
the chance of pathogen 
establishment, particularly if this 
involves an animal reservoir of 
infection. 

2 

Vector abundance will not 
necessarily determine transmission, 
as it is host abundance that will 
determine persistence, unless 
vector is a key reservoir. 

Infection rate 

2 

3 

3 

Density of infected ticks is more 
important than prevalence rates, 
however no infection in ticks 
means no risk. 

High density of ticks: higher 
infection rates increases chances of 
pathogen establishment. 

Assuming a high density of ticks, 
higher infection rates increases 
chances of pathogen 
establishment. 

Vector 
behavioural 
traits 

4 

More activity/biting means more 
pathogen transmission. Very low 
activity can hinder pathogen 
transmission [52]. 

3 
If pathogen is established in hosts 
and vectors, only activity 
determines transmission (e.g. for 
Lyme). However, co-incident 
feeding of tick stages can impact 
TBE transmission or local 
establishment. 

2 

Persistence of zoonotic tick-borne 
disease (TBD) is more linked to 
host presence/density than tick 
activity. 

Seasonality 

3 

2 

The season start does not 
determine establishment. The 
longer the season, the longer the 
chance of pathogen establishment; 
however, abundance and exposure 
are more important. 

2 
Only important if the length of the 
season determines the degree of 
disease transmission and hence the 
likelihood of the pathogen to 
persist in amplification hosts. 
Season start not likely to be 
important. 

4 

4 

In zoonotic TBD, reservoirs are 
important for pathogen 
establishment, particularly for 
Borrelia, Babesia. 

In zoonotic TBD, reservoirs are 
important for pathogen persistence. 
Borrelia persists in small mammals 
and birds. For tick-borne 
encephalitis virus (TBEV), ticks play 
a greater role as reservoirs. 

Assuming vectors are the only 
route of transmission, pathogen 
transmission will not start until 
vectors are active, however, 
transmission may be linked to 
vector abundance, so vector 
season start may be less 
important. Pathogen transmission 
will cease once vectors stop biting, 
so season length can be important. 
However, vector abundance will be 
more important. 

4 
If hosts are significant in amplifying 
the transmission, this is important. 
However, if vectors are required, 
this could be a constraining factor 
required for amplification. 

Reservoir host 
numbers 

26 

 
TECHNICAL REPORT 

The importance of vector abundance and seasonality 

Human 
activity 

Habitat 
change 

Early warning of pathogen 
transmission 
4 

There may be aspects of human 
activity which determine 
transmission. If humans, for 
example, spend more time in the 
countryside (holidays, travel, 
events) and there is an ongoing 
outbreak in this area, transmission 
may be affected [53]. 

Pathogen establishment 

Pathogen persistence 

1 

1 

Humans are usually dead-end 
hosts, except in nosocomial 
transmission of Crimean–Congo 
haemorrhagic fever virus. 

Humans are usually dead-end 
hosts. 

3 

4 

If a habitat is changed from an 
unsuitable habitat (farmland) to a 
suitable habitat (woodland), 
amplification hosts and vectors 
may benefit. 

Changing a habitat may not 
immediately lead to an early 
outbreak warning. However, 
changes in vegetation (as part of 
the natural vegetation cycle) can 
be linked to a change in tick 
activity. This is often driven by 
climatic changes which change the 
habitat and also change tick 
activity. 

4 
Habitat change can be relevant if a 
habitat is managed in a way that 
allows vectors or amplification 
hosts to get established and 
flourish. Conversely, grubbing out a 
habitat and turning it into arable 
farmland negatively impacts 
persistence of the pathogen. 

27 

 
 
 
The importance of vector abundance and seasonality 

TECHNICAL REPORT 

Table A1, continued 

Vector 
presence 

Pathogen transmission 

Pathogen/vector spread 

4 

4 

Important if mode of 
transmission is only vector-
borne. Becomes less important 
for nosocomial transmission, 
e.g. Crimean–Congo 
haemorrhagic fever virus 
(CCHFV) [54] or transmission 
through milk, e.g. TBEV [55].  

Presence can be due to 
importation. Rhipicephalus ticks 
were imported into northern 
Europe and are now infesting 
houses [56]. 

Vector 
abundance 

4 
More vectors may correlate with 
more transmission; this is, 
however, dependent on human 
exposure and infection rates. 

4 
More vectors can mean a higher 
likeliness for spread; however, 
spread is dependent on routes of 
spread, e.g. migratory birds, 
travelling pets [58,59]. 

Infection rate 

3 

4 

High infection rates are 
important if high exposure is 
combined with high tick 
density.  

If more infected vectors are 
spreading, and more infected 
hosts are spreading [61]. 

3 

3 

If ticks are active when hosts 
(e.g. migratory birds) are active, 
they will spread further [62]. 

Pathogen transmission control 
(vector control and other aspects) 
4 
Knowledge of vector distribution in the 
risk area is important. Without data on 
vector distribution, it is difficult to take 
response measures. For example, 
defining the Lyme risk in a city is 
dependent upon mapping the presence 
of Ixodes ricinus [12]. Similarly, the 
presence of Hyalomma marginatum is 
important for CCHFV transmission. Being 
able to control ticks and transmission 
requires knowledge of vector distribution 
(assuming that nosocomial transmission 
is not relevant) [57]. 
4 

The more ticks, the harder they are to 
control. Data on areas of high 
abundance may inform target control, 
but the key areas for control are those 
where exposure is also high [60]. 

4 
R0 will be higher if infection rate, density 
of ticks, and exposure are higher. This 
also has implications for vector control 
measures. 

4 
Control of vectors is more efficient when 
they are biting/active (e.g. ticks on pets 
using acaracides, or Hyalomma on 
cattle). For Ixodes, management of 
vegetation may be important when ticks 
are not active (before or between 
periods of activity) [63]. 

Pathogen transmission 
generally occurs during times 
when humans are more active 
outdoors (e.g. in summer), 
which coincides with the time 
when (infected) ticks are also at 
their most active. 
3 
Season start determines when 
first transmission takes place 
(assuming it is only vector-
borne); exposure, however, is 
just as important. Season 
length determines when 
pathogen transmission occurs 
and when it ends, but not the 
amount of transmission. 

3 
Hosts can amplify a pathogen, 
but for TBD it is the tick that 
drives transmission. 

4 
Humans need to be exposed for 
disease transmission, so human 
activity is crucial for 
transmission to humans 
[similarly for livestock]. 

2 

4 

Again, if ticks start their activity 
after their hosts have migrated, 
season start is important; if not, it 
is negligible. Season length not 
very important for spread, unless 
ticks are active at a time of year 
when dissemination hosts are 
active. 

Alerting the public/veterinarians at the 
start of the vector season to take 
precautions is standard procedure, so 
determining the season start is 
important [64]. The length of the vector 
season is also crucial in alerting the 
public/veterinarians to risk periods and 
can inform awareness campaigns 
[65,66]. 

4 

4 

Any attempt to control the vector will 
not necessarily control the persistence of 
the pathogen if the hosts remain 
abundant and infected. 

2 
Unless vector/pathogen control depends 
on control measures, human activity is 
not relevant. 

Dissemination of infected or 
infested vectors is important in 
spread of the pathogen or vector, 
e.g. migratory birds, dispersal of 
deer, movement of livestock [61]. 
2 
Unless humans can infect vectors 
or have a significant role in 
pathogen transfer, then human 
activity is not relevant. However, 
for Crimean–Congo haemorrhagic 
fever virus, there is nosocomial 
transmission, which is does not 
need a vector [54]. 

4 

4 

4 

If habitat changes lead to 
increased usage of that habitat 
by a range of animals, it will 
impact on pathogen 
transmission. 

If habitats are changed so as to 
become connected, this provides 
corridors for vectors and their 
hosts to move into new areas and 
thus disperse the vector and 
pathogen along these corridors 
and exploit/infest/infect new 
habitats. 

Managing vegetation is a key way of 
controlling human exposure to the 
vector by minimising public/animal 
exposure and/or making the habitat 
inimical for ticks. 

Vector 
behavioural 
traits 

Seasonality 

Reservoir host 
numbers 

Human 
activity 

Habitat 
change 

28 

 
 
 
TECHNICAL REPORT 

The importance of vector abundance and seasonality 

Table A2. Expert opinion of vector-related drivers of epidemiological concepts: midges 

Vector 
presence 

Early warning of pathogen 
transmission 
1 

As Culicoides under interest are 
autochthonous species in some 
European and Mediterranean areas 
(no invasive species), detection of 
presence of species will not lead to 
an early warning. Moreover, 
presence does not necessary mean 
risk of transmission. 

Pathogen establishment 

Pathogen persistence 

1 
As Culicoides are present everywhere 
(except Iceland for Avaritia or 
Culicoides species), vector presence 
could not be used to infer the 
suitability of specific areas for 
pathogen persistence. There could be 
other places where Culicoides are 
present, but the climate makes 
persistence of the pathogen unlikely. 

2 
Probable bluetongue virus (BTV) 
vectors are present everywhere 
(at least one species) in European 
and Mediterranean areas, with 
the only exception of Iceland 
[67]. Absence of these Culicoides 
species in Iceland means 
probable absence of possible 
pathogen establishment. Up to 
now, C. imicola is considered the 
main vector in European and 
Mediterranean areas (as for BTV 
before BTV-8 emergence). Thus, 
C. imicola presence means that 
the area is at risk of African horse 
sickness virus (AHSV) 
establishment if introduced. 
However, as other species may be 
involved in AHSV transmission 
(with the previous example of 
BTV), absence of C. imicola does 
not mean necessary absence of 
risk of pathogen establishment. 

Vector 
abundance 

3 
Several publications compare vector 
abundance and BTV transmission in 
the United States, but not in Europe 
[68,69]. ‘Aggressive density’ 
assessed by CO2-baited traps was 
correlated with the risk of BTV 
transmission (assessed by 
seroconversion follow-up) with a 
time-lag of four weeks [68]. 
Authors highlighted that 
seroconversion occurred only when 
‘aggressive density’ was above a 
threshold of 60 C. sonorensis per 
host per night. These findings are 
compatible with those observed by 
Mayo et al. [69]. We can conclude 
that a certain threshold of 
abundance (as proxy for ‘aggressive 
density’) is necessary for the start 
of virus transmission and thus for 
disease onset. However, this 
abundance threshold is only 
meaningful in areas where virus is 
present and has overwintered. 
Practical use of abundance for an 
early disease warning system has 
never been implemented as a 
routine method. 

4 

3 

Culicoides abundance (assessed 
by light traps) is related to the 
‘aggressive density’ [70,71] and 
related to R0. Some authors have 
developed an adapted R0 formula 
to take into account the 
transmission of BTV among cattle 
and sheep populations [72]. It 
could thus be considered a key 
parameter to determine the 
possible risk of pathogen 
establishment after introduction. 
R0 was used to assess the risk of 
BTV in the United Kingdom 
[73,74]. However, there is no 
experimental proof that 
abundance is directly correlated 
to the risk of pathogen 
establishment. 

Mechanisms of overwintering and 
long-term persistence of Culicoides-
borne viruses are not fully 
understood. Theoretically, virus could 
persist in vectors (through 
transovarial transmission and adult 
survival), in hosts (through direct 
transmission or long viraemia 
periods), and through continued 
transmission between vectors and 
hosts. Transovarial transmission has 
not been demonstrated in vectors and 
long viraemia periods in hosts are 
unlikely. Infected Culicoides were 
collected in winter [75], suggesting 
possible survival of infected females 
throughout winter. Long-term 
persistence is likely to occur through 
undetectable cycles of transmission 
and is more likely in areas of high 
Culicoides abundance. 

29 

 
The importance of vector abundance and seasonality 

TECHNICAL REPORT 

Pathogen establishment 

Pathogen persistence 

3 
Infection rates cannot provide an 
indicator of the potential for a 
pathogen to establish in an area; 
detection of pathogens in vectors 
(especially at an early stage of 
the transmission season) may 
reflect the success of pathogen 
establishment. 

3 

Detection of infected midges during 
the winter would support the 
possibility of virus overwintering 
through infected females [69] or 
continuous transmission. However, 
due to the low infection rates, a 
systematic widespread assessment of 
infection rates cannot be used to 
routinely determine the suitability of 
areas for pathogen persistence. 

4 
Both biting rate and longevity are 
important parameters of R0. 
Assessing them may allow for 
determining the areas suitable for 
pathogen establishment, or more 
precisely, the period during which 
these areas are susceptible for 
pathogen establishment. Indeed, 
values for biting rate or longevity 
could be related to climatic 
variables, mainly temperature, 
and the relation between both 
parameters may be used to 
compute R0 maps at a continental 
scale [25]. 

2 
The longer the potential period of 
pathogen establishment, the 
higher is the probability of the 
actual establishment of the 
pathogen. Even in regions with a 
very short season, such as 
southern Scandinavia, the 
pathogen can become established 
and spread (e.g. BTV-8 in 2008) if 
the pathogen is introduced during 
the favourable period.  
Season start may only be used as 
a dichotomy: before the start of 
the season, establishment is 
unlikely; after it, it is possible. 

2 

The capacity of infected vectors to 
survive several months during winter 
is a key parameter to assess the 
probability of a pathogen to survive 
through the winter months. However, 
these mechanisms are poorly 
understood and rarely investigated. 

2 

In theory, the overwintering of 
pathogens is more likely to happen in 
areas with a long season because this 
implies a short winter, which makes it 
more likely that an infected vector 
survives. Moreover, in some 
Mediterranean areas, depending on 
the year, activity could be continuous 
through the year: overwintering 
seems more likely to occur under 
these conditions. However, from 2007 
to 2008, BTV-8 has overwintered in 
more northern latitudes (e.g. 
Belgium, the Netherlands or 
Germany).  
Season start is not useful to indicate 
persistence. 

Infection rate 

Vector 
behavioural 
traits 

Early warning of pathogen 
transmission 
3 
BTV has been detected in Culicoides 
sonorensis 2 to 6 weeks before the 
detection of infection in cattle [69]. 
Thus, following up on the infection 
rate in vector populations may allow 
for the detection of virus presence 
before the transmission to 
vertebrate hosts. However, the use 
of infection rates in an early 
warning system has never been 
implemented at a large scale due to 
low sensitivity, especially in light 
traps [76], costs, and the absence 
of practical prevention methods. 

1 

No information on which other 
behavioural traits may be used for 
an early warning system. 

Seasonality 

2 

Season length will define the time 
period during which transmission is 
possible. However, using season 
length as a measure will 
overestimate the time of the 
possible transmission, which will 
occur only in areas where the virus 
is present at the beginning of the 
season. Thus, season length is 
unlikely to be used as an indicator 
for an early warning system. In 
Europe, the concept of a ‘vector-
free’ period, i.e. the period during 
which the abundance estimated by 
UV light traps remained under a 
threshold, has been used for more 
than a decade to manage animal 
movements under EU regulations 
and has demonstrated its utility. 
Thus, the season start could be 
considered for a rough estimate for 
an warning system, meaning that 
transmission may occur from the 
start of the season to the end of the 
year. However, this would not take 
into account the presence of the 
virus, which is essential for disease 
appearance, or the suitability of the 
area for pathogen transmission. 

30 

 
TECHNICAL REPORT 

The importance of vector abundance and seasonality 

Reservoir 
host numbers 

Early warning of pathogen 
transmission 
1 

Changes in host numbers are not 
sufficient to infer an imminent 
increase in pathogen transmission. 

Human 
activity 

1 
No information on which changes in 
human activity may be used for an 
early warning system. 

Habitat 
change 

1 
No information on which changes in 
a habitat may be used for an early 
warning system. 

Pathogen establishment 

Pathogen persistence 

2 

3 

After the introduction of an 
infected vertebrate or an infected 
vector, pathogen establishment is 
possible if R0>1. Host density is 
not really a limiting factor if 
density is above a threshold that 
allows for the initiation of 
pathogen transmission. This 
threshold should be low, and thus 
host density should not be a 
limiting factor in most European 
and Mediterranean areas. There 
is, however, no experimental 
evidence for this. 
2 
Although human activities such as 
vaccination, animal restriction, 
slaughtering, etc. may be efficient 
control measures to reduce 
pathogen establishment, it is 
difficult to identify human 
activities that result in areas that 
are suitable or unsuitable for 
pathogen establishment. 

1 

Although habitat changes may 
modify the suitability of an area 
for pathogen establishment, there 
is no obvious habitat change that 
could transform a suitable area 
into a non-suitable area (the 
opposite is also true). 

Overwintering of the pathogen is 
related to the number of infected 
hosts and/or infected vectors at the 
beginning of winter. Thus, this 
number should be higher in locations 
with high host density than in those 
with low host density. Pathogen 
persistence over several years is likely 
linked to undetectable transmission 
cycles. These cycles are more likely to 
happen in areas of high vector 
density. There is, however, no 
experimental evidence for this. 

2 

Though farming and animal 
movement may encourage 
establishment, only vaccination 
directly affects the probability of a 
Culicoides-borne pathogen to persist. 

1 
Although habitat changes may modify 
the suitability of an area for pathogen 
persistence, there is no obvious 
habitat change that could transform a 
suitable area into a non-suitable area 
(the opposite is also true). 

31 

 
 
 
The importance of vector abundance and seasonality 

TECHNICAL REPORT 

Table A2, continued 

Vector 
presence 

Vector 
abundance 

Infection rate 

Pathogen transmission 

Pathogen/vector spread 

1 

Areas suitable for pathogen 
transmission are by definition 
suitable for pathogen 
establishment. ‘Vector presence’ 
does therefore not add any 
additional information to pathogen 
transmission. 

1 
Pathogen spread can only occur 
from areas suitable for pathogen 
transmission, i.e. areas where 
vectors are present by definition. 
‘Vector presence’ does therefore 
not add any additional 
information to the spread of the 
pathogen. 

Pathogen transmission control 
(vector control and other) 
1 

No specific control measures are 
implemented after the detection of 
Culicoides because Culicoides are 
virtually everywhere; presence does 
not necessary imply a risk of 
transmission. 

4 

As vector abundance is a 
component of R0, it should also be a 
key parameter of pathogen 
transmission. R0 was used to 
determine the BTV transmission in 
European and Mediterranean areas 
in the past decades [25] in order to 
predict BTV transmission under 
different climate change scenarios. 
Seroconversions occurred only in 
the two farms (of the four followed 
up) where Culicoides abundance 
was high. No seroconversion was 
detected in two farms: one farm 
where abundance was low (even 
though BTV could be isolated from 
Culicoides in the farm) and one 
farm where abundance was very 
low [69]. This shows that 
‘aggressive density’ is linked to 
pathogen transmission.  
In Europe, abundance estimated by 
measuring with UV light traps have 
been used for more than a decade 
to manage animal movements 
under EU regulations, and this 
system has demonstrated its utility. 
Threshold values for low abundance 
have been routinely used to 
guarantee the (very likely) absence 
of disease during times of the year 
when low abundance could be 
verified. 

3 
Intensity of pathogen transmission 
is related to the infection rate of 
vector populations. However, the 
dynamics of infection rates do not 
necessary follow the dynamics of 
host infections [68,69], which is 
more related to ‘aggressive density’ 
or to the number of infected female 
C. sonorensis midges (mean 
number of female C. sonorensis 
midges captured per trap night × 
mean proportion of BTV-infected 
female C. sonorensis midges per 
trap night). 

2 
Some control measures (application of 
pour-on insecticides) are mandatory 
for animal exportation when Culicoides 
populations are active, i.e. when 
Culicoides abundance is above a 
certain threshold. This is not required 
when Culicoides populations are 
‘inactive’, i.e. when abundance 
remains under a certain threshold. 
Otherwise, control measures are not 
linked to the assessment of vector 
abundance. Moreover, control 
measures are usually not aimed at 
Culicoides abundance. 

3 
Pathogen spread may be due to 
1) active Culicoides dispersal, 
2) Culicoides transported by wind, 
and 3) animal movements. 
Several journal articles have 
investigated the pathogen spread 
by modelling approaches 
[26,27,72,77,78]not exhaustive]. 
These papers highlight the 
importance of Culicoides dispersal 
in the spread of BTV, with 
Culicoides using both downwind 
and upwind movements, mainly 
at short and medium distances 
(below 31 km). This was also 
highlighted for Schmallenberg 
virus transmission [78]. Modelling 
results also suggested that 
pathogen spread is more likely to 
originate from farms favourable 
to vector abundance [78]. The 
importance of midges transported 
by wind was shown to explain the 
pathogen spread over large 
distances and over bodies of 
water [27]. These events are 
more likely to originate from 
areas of high Culicoides 
abundance. 

3 
Pathogen spread from A to B by 
vector movement depends on the 
probability of a vector to move 
from A to B, the number of 
vectors at A, and the infection 
rate of vector population at A. 
Thus, pathogen spread is related 
to infection rate, however, very 
few studies have explored virus 
screening in vectors at a large 
scale, and if so, mostly with a 
retrospective perspective [79]. 

3 
In theory, this is the most interesting 
indicator (detection of pathogens in 
vectors) to anticipate the imminence 
of pathogen transmission. However, it 
is quite impossible to carry out 
extensive and real-time assessments 
of infection rates. Thus, there are no 
examples where the detection of virus 
in vectors was used to implement 
control measures. 

32 

 
TECHNICAL REPORT 

The importance of vector abundance and seasonality 

Vector 
behavioural 
traits 

Pathogen transmission 

Pathogen/vector spread 

4 

2 

The longer an infected vector 
lives, the farther it can spread the 
pathogen. However, there is no 
experimental evidence that 
variability of longevity (within the 
range of possible lifespan) has an 
impact on vector spread 
capacities. 

Both biting rate and longevity are 
important parameters of R0. 
Assessing them may allow for the 
detection of areas suitable for 
pathogen transmission, or more 
precisely, the period during which 
these areas are susceptible for 
pathogen transmission. Indeed, 
values for biting rate or longevity 
could be related to climatic 
variables, mainly temperature, and 
this could be used to compute 
monthly R0 maps at a continental 
scale [25]). 

Pathogen transmission control 
(vector control and other) 
2 

Assessments of biting rates or 
longevity are used to evaluate the 
efficiency of some control measures 
(mainly application of insecticides on 
animals). 

2 
The longer the season, the higher 
the probability that a spread 
event occurs during this period. 
However, vector dispersal occurs 
throughout the season and seems 
sufficient to spread the pathogen. 
Before the start of the season, 
the spread is unlikely; after the 
start, it is possible. 

3 

Measures for disease management 
may be based on season length, 
restriction of animal movements, or 
authorisation of animal movements 
with insecticide applications, etc. This 
depends on the active/non-active 
status of Culicoides populations. The 
same applies to the use of attenuated 
vaccines, which should be limited to 
the period of vector inactivity. 

Seasonality 

2 

BTV-8 has been successfully 
transmitted in areas where the 
season length is quite short, e.g. in 
southern Scandinavia. However, it is 
unlikely to find an area in Europe 
for which the temperature 
conditions are not favourable for at 
least three months per year. In 
Europe, the concept of a ‘vector-
free’ period, i.e. the period during 
which the abundance estimated by 
UV light traps remains under a 
certain threshold, has been used for 
more than a decade to manage 
animal movements under EU 
regulations, and this system has 
demonstrated its utility. Thus, the 
date of the season start could be 
considered a rough index for 
transmission suitability, meaning 
that transmission may occur from 
the start of the season to the end of 
the year. 

Reservoir 
host numbers 

Human 
activity 

3 

4 

2 

Even in areas where R0>1, 
stochastic events may stop 
pathogen introduction. These 
events are more likely to happen in 
low density areas, where most of 
the hosts may become infected and 
then immune, leading to the 
absence of naïve hosts. 
Transmission may become endemic 
in areas where the host turnover or 
the pathogen circulation between 
different areas are sufficient to 
maintain a favourable number of 
naïve hosts. These conditions are 
more likely to happen in areas with 
high density, but there is no 
experimental evidence of that. 

2 
Although human activities such as 
vaccination, animal restriction, 
slaughtering, etc. may be efficient 
control measures to reduce 
pathogen establishment, it is 
difficult to identify human activities 
that result in areas that are suitable 
or unsuitable for pathogen 
establishment. 

Control measures do not depend on 
host density, but management 
decisions can, to a degree, be based 
on host density: if vaccine availability 
is limited, health authorities could 
decide to vaccinate those areas first 
that have the highest host density. 

Several studies comparing BTV 
spread and a set of 
environmental variables identified 
cattle and sheep density as 
important factors of BTV spread 
[28,29,78,80]. Host density may 
have positive or negative effects 
on BTV spread velocity, 
depending on species and type of 
production, with high density of 
beef cattle being the most 
favourable condition for rapid 
spread [28,29]. 

4 

International trade and animal 
transhumance are known factors 
enhancing pathogen spread. BTV-
8 or Schmallenberg viruses 
emerged in European hotspots for 
international trade (the route of 
introduction is still unknown). 
Exportation of animals are known 
to have been responsible for 
introduction of BTV-1 from 
southern to northern Spain, or of 
BTV-8 from France to Spain and 
Italy. 

2 
This is very difficult to assess because 
by definition, disease control is a 
human activity. If we ask whether 
some disease control measures are 
decided on the base of specific 
human activities (e.g. vaccination in a 
given production method, insecticide 
in another), the answer is no. The 
exception are specific control 
measures (regulations on animal 
movements) in areas where animals 
are exported. 

33 

 
The importance of vector abundance and seasonality 

TECHNICAL REPORT 

Habitat 
change 

Pathogen transmission 

Pathogen/vector spread 

2 

2 

Although habitat changes may 
modify the suitability of an area for 
pathogen transmission, there is no 
obvious habitat change that 
strongly modifies the suitability of 
an area. 

Land cover influenced the BTV 
spread of BTV-8 and BTV-1 in 
France [28,29]. Changes in the 
characteristics of the land cover 
may lead to changes in the 
pathogen spread conditions. 
However, these changes are 
unlikely to occur, and 
consequences are not easily 
predicted. 

Pathogen transmission control 
(vector control and other) 
1 

There are no examples of control 
decisions linked to habitat change. 

34 

 
 
 
TECHNICAL REPORT 

The importance of vector abundance and seasonality 

Table A3. Expert opinion of vector-related drivers of epidemiological concepts: mosquitoes 

Vector 
presence 

Vector 
abundance 

Infection rate 

Vector 
behavioural 
traits 

Seasonality 

Reservoir 
host numbers 

Human 
activity 

Habitat 
change 

Early warning of pathogen 
transmission 
4 
Basic requirement (risk or no risk), 
but to be completed for risk 
assessment by abundance of vector 
and pathogen presence/circulation 
or risk of introduction. Provided 
information is risk or no risk (not 
about level of risk), this is what 
public health asks when outbreak 
occurs in a neighbouring area. 

4 
Higher risk when vector is abundant 
[13,81]; standardisation of sampling 
procedures (VectorNet guidelines) 
and conversion ratios for different 
adult traps are necessary (not done 
yet). 

3 
Early pathogen screening in vectors 
is relevant when pathogen can 
circulate in healthy hosts, e.g. West 
Nile yes, Dengue no [13,81]. 

3 
Related to vector abundance, with 
focus on pathogen-targeted host; 
more precise risk assessment but 
still need for data on pathogen 
circulation. 

4 
Determines periods without or with 
risk; Also dependent to pathogen 
requirements. Earlier start of the 
season, earlier transmission should 
be logical output if pathogen is 
already present; seems that local 
vector population has great capacity 
for quick increase after adverse 
weather conditions (important but 
not yet evidenced). 
3 
Might intensify pathogen circulation 
and chances for early detection. 

3 
If evidence about infected travellers 
coming to areas where vector is 
present and abundant are available. 

3 
Temporary changes in habitat, for 
example flooding or evaporation of 
water bodies will affect larval and 
oviposition breeding sites. Rainfall 
has been used to predict some 
mosquito borne diseases such as 
Rift Valley Fever. 

Pathogen establishment  

Pathogen persistence 

3 
Basic requirement (risk or no risk 
of establishment of pathogen), 
but to be completed for risk 
assessment by vector abundance, 
environmental factors (including 
host presence/number) and 
human activity (e.g. travel, 
habitat change). 

1 
Persistence requires establishment first 
and therefore vector presence is 
already demonstrated; further risk 
assessment would focus on vector 
abundance, environmental factors 
(including host presence/number) and 
human activity (e.g. habitat change). 

4 

4 

Higher risk when vector is 
abundant [13,81]; standardisation 
of sampling procedures 
(VectorNet guidelines) and 
conversion ratios for different 
adult traps are necessary (not 
done yet). 

Higher risk when vector is abundant 
[13,81]; standardisation of sampling 
procedures (VectorNet guidelines) and 
conversion ratios for different adult 
traps are necessary (not done yet). 

3 

3 

When risk of pathogen 
maintenance/overwintering in 
vectors. 

When risk of pathogen 
maintenance/overwintering in vectors. 

4 

4 

Related to vector abundance, 
with focus on pathogen-targeted 
host; Positively correlated; 
necessity to follow ethical rules. 

Related to vector abundance, with 
focus on pathogen-targeted host; 
Positively correlated; necessity to 
follow ethical rules. 

4 
Longer season increases chances 
of transmission when exotic 
pathogen is introduced. Earlier 
start of the season increases 
chances of transmission when a 
pathogen is introduced; also 
increases chances for early start 
of circulation. 

4 

Positively correlated; travel and 
trade at risk for introduction. 

4 

Travel favours introduction; 
habitat change can favour 
establishment (of both vector and 
pathogen). 

4 

Positively correlated; also dependent 
on pathogen requirements. More 
dependent on the timing of the 
pathogen introduction. 

4 
Positively correlated. 

3 
Changes can also decrease changes of 
persistence (both vector and 
pathogen). 

4 

4 

Habitat change can 
increase/decrease vector 
abundance (breeding site 
availability and flooding) and 
favour/reject pathogen 
introduction. 

Increasing suitability for vector and 
host presence and abundance. 

35 

 
 
 
The importance of vector abundance and seasonality 

TECHNICAL REPORT 

Table A3, continued 

Pathogen transmission 

Pathogen/vector spread 

Vector 
presence 

Vector 
abundance 

3 
Basic requirement (risk or no risk), 
but to be completed for risk 
assessment by vector abundance, 
environmental factors (including 
host presence/number) and human 
activity (e.g. habitat change, 
habits). 

4 
High mosquito densities have been 
associated with arboviral disease 
outbreaks [82,83]; thus, risk 
assessments involve estimating 
mosquito abundance [84–86]. 
However, high mosquito abundance 
may occur in the absence of virus 
or detectable virus amplification, 
and WNV outbreaks often occur 
when abundance is low but the 
mosquito population is older and 
the infection rate is high. 

3 

Basic requirement (risk or no 
risk), but to be completed for risk 
assessment by vector abundance, 
environmental factors (including 
host presence/number) and 
human activity (e.g. habitat 
change, habits). 

Pathogen transmission control 
(vector control and other) 
3 

Basic requirement (control to be 
implemented where vector is present), 
but to be completed by abundance for 
evaluation of needs and efficacy of 
control measures. 

4 

4 

Higher risk when vector is 
abundant; Standardisation of 
sampling procedures (VectorNet 
guidelines) and conversion ratios 
for different adult traps are 
necessary (not done yet). 

Important in particular when vector 
control aims at reducing abundance 
[87]; Standardisation of sampling 
procedures (VectorNet guidelines) and 
conversion ratios for different adult 
traps are necessary (not done yet); 
There is a need to determine 
abundance thresholds for pathogen 
transmission; Some standard 
procedures for evaluation of control 
efficacy are available [88], tailoring and 
harmonisation are needed. 

Infection rate 

4 

4 

3 

Outbreaks often occur when 
abundance is low but the mosquito 
population is older and the infection 
rate is high. The infection rate in a 
vector population is an estimate of 
the prevalence of infected 
mosquitoes in the population and is 
a good indicator of human risk 
Requires pathogen surveillance in 
vectors [13,81].  

4 
Related to vector abundance, with 
focus on pathogen-targeted host; 
positively correlated; Necessity to 
follow ethical rules. 

Vector 
behavioural 
traits 

Seasonality 

4 

Positively correlated; Also 
dependent to pathogen 
requirements. Positively correlated; 
travel and trade a risk for 
introduction. 

More vectors infected, more 
spreading of pathogen by vector 
movement. More vectors infected, 
more spreading of pathogen by 
vector movement. 

4 
High biting rate increases the risk 
of pathogen spread by travel and 
trade, e.g. infected Aedes 
albopictus entering vehicles by 
following hosts; Necessity to 
follow ethical rules. 

4 
Positively correlated with the risk 
of dissemination to other regions; 
Also dependent to pathogen 
requirements for the region at 
risk for spread. Increased 
probability of establishment would 
stimulate the spread. 

By identifying thresholds are below 
levels associated with disease 
outbreaks, integrated vector 
management programs can institute 
proactive measures to maintain 
mosquito populations at levels below 
which amplification can occur. Requires 
pathogen surveillance in vectors 
[13,81]. 

4 

Could be used as good quantifier of 
success of the control ( but collectors 
should be protected from mosquito 
bites) [87]. 

3 
Longer season requires more resource-
intensive control activities [87]. 
Intensive control pressure in that period 
might influence overall seasonal 
abundance of the vector (important but 
not yet evidenced) [87]. 

4 

4 

3 

Positively correlated; travel and 
trade at risk for introduction. 

More hosts infected, more 
spreading of pathogen by host 
movement. 

Control measure could focus where 
reservoirs or susceptible hosts are 
present [87]. 

4 
Human daily activities can increase 
vector-host contact; habitat change 
can increase/decrease vector 
abundance (breeding site 
availability); some countries like 
Italy, France and Spain are more 
frequently visited by travellers from 
pathogen-endemic countries; 
hence, outbreaks are most frequent 
there compared with other 
countries with same vectors. 

4 
Habitat change can 
increase/decrease vector 
abundance (breeding site 
availability and flooding). 

4 

4 

Travel and trade combined with 
environmental factors are the 
major drivers. 

Private areas hardly accessible, major 
ordinance needed for successful control. 

4 

4 

Habitat change can 
increase/decrease vector and host 
movements. 

Readjustment of vector control 
measures according to habitat changes. 

Reservoir 
host numbers 

Human 
activity 

Habitat 
change 

36 

 
TECHNICAL REPORT 

The importance of vector abundance and seasonality 

Table A4. Expert opinion of vector-related drivers of epidemiological concepts: sandflies 

Vector 
presence 

Vector 
abundance 

Infection rate 

Vector 
behavioural 
traits 

Seasonality 

Reservoir 
host numbers 

Early warning of pathogen 
transmission 
3 (periendemic areas only) 

Leishmania is the main pathogen 
transmitted by sandflies. Sandfly 
and Leishmania infection 
distributions generally match, so we 
can assume that where there are 
vectors and hosts there is also 
Leishmania transmission [89], so 
the issue of ‘early warning’ is not 
relevant in endemic areas. It may 
be relevant in transition 
(periendemic) areas such as central 
France, southern Germany, etc. 
[90] where sandfly density is 
presently too low to maintain the 
parasite, but mere presence 
indicates potential for pathogen 
transmission and establishment. 

3 (periendemic areas only) 

As mentioned above, sandfly 
presence alone probably implies 
that Leishmania is present and 
being transmitted, so this is not 
relevant, except in periendemic 
areas where a rise in abundance 
would be a good early indicator of 
Leishmania transmission and 
establishment. 

4 
High sandfly infection rates indicate 
efficient transmission, so finding 
infection in sandflies in transition 
areas clearly is a good early 
indicator of pathogen transmission. 

4 

High biting rates are probably 
accompanied by high sandfly 
abundance. A sudden rise in sandfly 
biting rates would be a good early 
warning of Leishmania transmission 
in periendemic and endemic areas. 
3 

Increased length of active season 
usually means more cycles and 
sandfly generations. In periendemic 
areas, abundance may rise and 
reach the critical sandfly mass to 
allow Leishmania transmission, so it 
would be a good early indicator. 
Good early climatic conditions do 
not necessarily mean good 
conditions mid-way through the 
season and therefore high 
abundance. Having said that many 
areas with high abundance have 
early and long seasons. 

3 

Sudden rises in host (wild 
lagomorphs) density associated 
with an epidemic of urban 
Leishmaniosis in southern Madrid, 
an otherwise endemic 
Leishmaniosis area [91]. Generally, 
sandflies in endemic areas thrive in 
animal farms and dog kennels, the 
latter also having a high risk of 
clinical cases. 

Pathogen establishment  

Pathogen persistence 

3 
Sandfly and Leishmania infection 
distributions generally match. 

3 

Sandfly and Leishmania infection 
distributions generally match. 

3 
Sandfly and Leishmania infection 
distributions generally match. 

3 

Sandfly and Leishmania infection 
distributions generally match. 

4 or 2 (periendemic areas) 
Infection found in sandflies from 
endemic areas is a great indicator 
of Leishmania establishment. This 
may not necessarily be the case 
in transition areas (sporadic 
introductions). 

4 
High biting rates probably mean 
there is high abundance and 
greater chances of Leishmania 
endemicity. 

4 or 2 (periendemic areas) 
Infection found in sandflies from 
endemic areas is a great indicator of 
Leishmania persistence. This may not 
necessarily be the case in transition 
areas. 

4 

High biting rates probably mean high 
abundance and greater chances of 
Leishmania persistence. 

3 

3 

Areas with long sandfly seasons 
are sandfly and Leishmania 
endemic. 

Areas with long sandfly seasons are 
sandfly and Leishmania endemic. 

3 
Host density in endemic areas is 
positively correlated to sandfly 
abundance and establishment of 
Leishmania [91] and other 
vectors. 

3 

Host density in endemic areas is 
positively correlated to sandfly 
abundance and Leishmania 
persistence [91]. 

37 

 
 
The importance of vector abundance and seasonality 

TECHNICAL REPORT 

Pathogen establishment  

Pathogen persistence 

3 

Leishmania establishment 
depends on a critical mass of 
sandflies which is not often the 
case in non-endemic areas. 

3 
Leishmania transmission depends on a 
critical mass of sandflies which is not 
often the case in non-endemic areas. 

Early warning of pathogen 
transmission 
3 
If non-immune people (e.g. urban 
people) move to new housing 
developments in rural, endemic 
areas in Europe then the risk of 
transmission and disease can be 
high. [91]. Dogs (and people to a 
lesser extent because they are not 
as sensitive as dogs) to non-
endemic areas travelling from 
endemic areas may introduce 
Leishmaniosis into non-endemic 
areas and develop clinical 
signs [92]. 

3 

3 

Habitat changes such as 
urbanisation of agricultural 
Leishmania endemic areas that 
incorporated green leisure areas led 
to a population explosion of hares 
and sandflies that fed on them and 
to an epidemic of Leishmaniosis in 
people [91]. Other land use 
changes may lead to a reduction in 
sandfly habitat and Leishmania risk. 
Climate change has expanded 
sandfly distribution in Europe 
northwards [93]. 

Habitat changes such as 
urbanisation of agricultural 
Leishmania endemic areas that 
incorporated green leisure areas 
led to a population explosion of 
hares and sandflies that fed on 
them and to an epidemic of 
Leishmaniosis in people [91]. 
Other land use changes may lead 
to a reduction in sandfly habitat 
and Leishmania risk. Climate 
change has expanded sandfly 
distribution in Europe northwards. 

3 
Sandfly and Leishmania establishment, 
persistence and transmission in 
periendemic areas is related to climate, 
allowing a critical mass of sandflies. 
Infection will be introduced by wildlife 
and infected dogs. 

Human 
activity 

Habitat 
change 

38 

 
 
 
 
TECHNICAL REPORT 

The importance of vector abundance and seasonality 

Table A4, continued 

Pathogen transmission 

Pathogen/vector spread 

3 

2 

Pathogen transmission control 
(vector control and other) 
1 

Risk related to host immunity and 
transmission intensity, the latter 
depending on sandfly and host 
density and infection rates. [93]and 
expert opinion]. 

Low mark (2) because sandfly 
presence strongly depends on 
environmental/micro-environmental 
conditions, and neighbouring areas 
may not be adequate for Leishmania 
to spread into. This would be clearly 
the case in periendemic areas. 

Control decisions are based on 
disease, i.e. people/dogs sick with 
Leishmania, not on vector abundance 
or even on infection prevalence 
because prevalence of asymptomatic 
Leishmaniosis is very high (60–80%) in 
endemic areas. 

3 

2 

Sandfly presence depends on 
environmental conditions, and 
neighbouring areas may not be 
adequate for Leishmania to spread 
into. This would be clearly the case in 
periendemic areas. 

1 
Control decisions are based on disease 
(people sick with Leishmania) 
incidence, not on vector abundance or 
infection prevalence because 
prevalence of asymptomatic 
Leishmaniosis is very high (60–80%) in 
endemic areas. 

Vector 
presence 

Vector 
abundance 

Infection rate 

Vector 
behavioural 
traits 

Sandfly and Leishmania infection 
distributions generally match. There is 
evidence that disease risk is positively 
correlated to sandfly abundance 
[16,19,92]. However, top mark (4) 
not given because sandfly abundance 
may be large in areas where non-
susceptible hosts are the main source 
of blood for the sandflies. Also, there 
is evidence of high disease risk in 
areas with relatively low sandfly 
abundance and infection rates [24]. 

4 or 2 (periendemic areas) 
Infection found in sandflies from 
endemic areas is a great indicator of 
Leishmania persistence. This may not 
necessarily be the case in transition 
areas. There is evidence that disease 
risk is positively correlated to sandfly 
infection rates [17–19]. However, 
disease depends very much on host 
factors. 

4 
High biting rates probably mean high 
abundance and greater chances of 
Leishmania transmission. 

2-4 

4 

Neighbouring areas may not 
necessarily be adequate for 
Leishmania to spread into. However, 
if they are, high infection rates would 
help pathogen spread 

Important to carry out vector control 
interventions if sandfly infection rates 
are high because disease risk is likely 
to be high. 

2-3 
High biting rates probably mean high 
abundance and greater chances of 
spread, but only if neighbouring areas 
are environmentally suitable. 

4 

High biting rates may be sufficient to 
implement vector control to stop 
nuisance as well as disease risk. 

Seasonality 

3 

2-3 

Areas with long sandfly seasons are 
sandfly and Leishmania endemic with 
ongoing Leishmania transmission 
takes. Host factors also important. 

Neighbouring areas may not 
necessarily be adequate for 
Leishmania to spread into. 

Reservoir 
host numbers 

3 

2 

Host density in endemic areas is 
positively correlated to sandfly 
abundance and Leishmania 
transmission [16,91]. Most people 
and animals are resistant to 
developing clinical leishmaniosis, but 
high numbers mean more susceptible 
individuals. 

Depends on whether neighbouring 
areas have suitable conditions and 
enough hosts. 

Human 
activity 

3 
Leishmania establishment depends on 
a critical mass of sandflies, which is 
not often the case in non-endemic 
areas. 

3 
Leishmania establishment depends on 
a critical mass of sandflies which is 
not often the case in non-endemic 
areas. 

1 
Control decisions are based on disease 
incidence and/or biting rates. Presence 
of sandflies at unexpected times (e.g. 
late fall) may extend the vector control 
period. In many Mediterranean 
countries where sandfly and 
Leishmaniosis are abundant, control 
starts before active season as a 
standard procedure. 
2 
Depends on disease incidence and/or 
biting rates. 

3 

Control measures should be 
implemented when susceptible dogs 
(and people) are introduced to 
endemic areas, mainly using 
insecticides on the animal and 
domestically. 

39 

 
The importance of vector abundance and seasonality 

TECHNICAL REPORT 

Habitat 
change 

Pathogen transmission 

Pathogen/vector spread 

3 

3 

Sandfly and Leishmania 
establishment, persistence and 
transmission in periendemic areas are 
related to climate, allowing a critical 
mass of sandflies. Infection is 
introduced by wildlife and infected 
dogs. 

Spread to neighbouring areas 
depends on climatic conditions. 

Pathogen transmission control 
(vector control and other) 
3 

The epidemic in southern Madrid 
associated to a change in land use 
(urbanisation) led to the biggest 
epidemic of clinical human 
leishmaniosis in Europe. 

40 

 
 
 
TECHNICAL REPORT 

The importance of vector abundance and seasonality 

Table A5. Expert scores of importance of drivers of epidemiological concepts 

Early warning 

Pathogen establishment 

Pathogen persistence 

Tick 

Mosq 

Midge 

Sand 

Tick 

Mosq 

Midge 

Sand 

Tick 

Mosq 

Midge 

Sand 

Vector presence 

Vector abundance 

Vector infection rate 

Behavioural traits 

Vector seasonality 

Host numbers 

Human activity 

Habitat change 

4 

2 

2 

4 

3 

4 

4 

3 

4 

4 

3 

3 

4 

3 

3 

3 

1 

3 

1 

1 

2 

1 

1 

1 

3 

3 

4 

3 

3 

3 

3 

3 

3 

3 

3 

3 

2 

4 

1 

4 

3 

4 

3 

4 

4 

4 

4 

4 

2 

4 

4 

4 

2 

2 

2 

1 

3 

3 

4 

3 

3 

3 

3 

3 

3 

2 

3 

2 

2 

4 

1 

4 

1 

4 

3 

4 

4 

4 

3 

4 

1 

3 

2 

2 

2 

3 

2 

1 

3 

3 

4 

3 

3 

3 

3 

3 

Pathogen transmission 

Disease spread 

Pathogen control 

Tick 

Mosq 

Midge 

Sand 

Tick 

Mosq 

Midge 

Sand 

Tick 

Mosq 

Midge 

Sand 

3 

4 

4 

4 

4 

4 

4 

4 

1 

4 

4 

4 

2 

3 

2 

2 

3 

3 

4 

3 

3 

3 

3 

3 

4 

4 

4 

2 

2 

4 

2 

4 

3 

4 

4 

4 

4 

4 

4 

4 

1 

3 

2 

2 

2 

4 

4 

2 

2 

2 

4 

3 

3 

2 

3 

3 

4 

4 

4 

4 

4 

4 

2 

4 

3 

4 

3 

4 

3 

3 

4 

4 

1 

2 

3 

2 

3 

2 

2 

1 

1 

1 

4 

3 

1 

2 

3 

3 

Vector presence 

Vector abundance 

Vector infection rate  

Behavioural traits 

Vector seasonality 

host numbers 

Human activity 

Habitat change 

4 

4 

3 

3 

3 

3 

4 

4 

Tick = ticks 
Mosq= mosquitoes 
Midge = midges 
Sand= sandflies. 

41 

 
 
 
 
The importance of vector abundance and seasonality 

TECHNICAL REPORT 

Appendix 2. Overview of sampling methods 

This section contains a brief overview of field sampling protocols, all of which are described in detail in the ECDC 
technical report ‘Field sampling methods for mosquitoes, sandflies, biting midges and ticks’ [11]. 

Ticks 

Tick sampling aims to collect larvae, nymphs and adults, but not eggs. Abundance of ticks can be estimated by 
surveying the vegetation or potential hosts over specific periods of time [95,96]. Though Ixodes and Dermacentor 
species can be sampled from wildlife hosts, vegetation sampling is better able to provide accurate location and 
habitat data. There are a number of methods being used, but the most widely used in VectorNet has been 
vegetation flagging or dragging, which provides an indication of vector abundance in terms of number per square 
metre. All instars can be sampled in this way, but the number caught can be heavily influenced by vector activity 
patterns and vegetation structure [96]. 

Rhipicephalus species can also be sampled from the vegetation using flagging or dragging, but they are also 
commonly found on dogs, in their kennels, or the walls of homes where dogs are kept. Deploying a hair dryer over 
suspected resting sites will encourage the ticks to leave their resting sites so that they can be collected. 

Hyalomma species are not amenable to flagging methods as they typically do not quest in the vegetation, but 
actively hunt for a host. They are best sampled directly from domestic and wild ungulate hosts. Collections from 
the ground are difficult as there is no reproducible way to collect Hyalomma marginatum with a standard flagging 
method. 

Tick activity varies between European tick species, with some European species (e.g. Dermacentor) most active in 
the winter, others mainly in spring (e.g. I. Haemaphysalis) or late spring and summer (e.g. Hyalomma, 
Rhipicephalus). Even within a species, different life stages exhibit differing seasonality and each stage can vary in 
their potential to transmit disease. Ensuring that the correct stage of tick is monitored is important; larval I. ricinus, 
for example, present minimal disease risk while most Lyme cases result from of a bite by an infected nymph. 

Culicoides midges 

Most adult collections focus on flying adults, using passive aspiration traps such as Rothamsted suction traps or 
active traps such as truck traps [97]. However, UV light trap collections remain the most time-efficient and 
widespread method to assess Culicoides presence and abundance at a wide scale. UV light traps collect a fraction 
of the Culicoides populations, mostly host-seeking females. The assessed abundance depends to a large degree on 
the trap location, including distance to animals and height above ground level [99,100]. 

Mosquitoes 

Sampling methods for mosquitoes are the most elaborate of the four vector groups, as all developmental stages 
(eggs, larvae, flying and resting adults) can be assessed. Which stage is sampled depends on the target species, 
the environmental conditions at the selected sampling sites, on the availability of resources and the objective of the 
sampling. The main methods used are: baited traps with or without light, mainly outdoors for flying adults; larval 
sampling for those species less attracted to light traps or baits; ovitraps, especially if resources are restricted, or in 
remote areas as they need only to be inspected relatively rarely. Resting adults (including males) are sampled 
manually and can yield seasonality data. 

Sandflies 

Sandfly sampling for abundance is limited to flying or resting adults as the immature stages are too small and too 
well hidden in the substrate to be assessed. Trapping host-seeking adults is based on light traps, sticky traps or 
CO2 traps. Diurnal collections from sandfly resting sites in the vicinity of nocturnal trapping sites can reveal where 
these insects rest during the day. By design, the collection of resting sandflies does not require bait, rarely relies on 
traps, and supplies epidemiological rather than population-related information. 

Light traps are ideal to investigate abundance of species attracted to light (which includes all important vector 
species) because the area of influence of the trap is known: 2–6 m for CDC-light traps [100] and abundance is 
expressed as number per trap-day. Sticky traps are cheap, collect an unbiased sample of species, and abundance is 
expressed per trap surface (m2). 

42 

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