Environmental Surveillance

PAGE 6 of 12

View Table of Contents

Vector-based Surveillance

Vector surveillance is an integral component of an Integrated Vector Management (IVM) program and is the primary tool for quantifying virus transmission and human risk. The principal functions of a mosquito-based surveillance program are to:

  • Collect data on mosquito population abundance and virus infection rates in those populations
  • Provide indicators of the threat of human infection and identify geographic areas of high risk
  • Support decisions regarding the need for and timing of intervention activities (e.g., enhanced vector surveillance and control, use of new technologies and public education programs)
  • Monitor the effectiveness of vector control methods, including susceptibility of target mosquitoes to control methods used

Mosquito-based arboviral monitoring complements disease surveillance programs by contributing timely results and data for action. Programs maintaining in-house laboratories may be able to process mosquito samples daily, giving results within a few days. Data on vector species community composition, relative abundance, and infection rates allow programs to rapidly compute infection indices, assess risk, and respond. Maintaining mosquito surveillance over the long-term provides a baseline of historical data to evaluate risk and guide mosquito control operations. However, the utility of mosquito-based surveillance depends both on the type and quality of data collected (e.g., number and type of traps, timing and frequency of sampling, testing procedures) and consistent effort across transmission seasons to link surveillance indices with human risk.

There are three main categories of vector surveillance: larval, adult, and transmission activity. Together, this information is used to determine where and when control efforts should be implemented. Larval surveillance involves sampling a wide range of aquatic habitats to identify the sources of vector mosquitoes and evaluating larval control measures applied. For adult mosquitoes, regular (e.g., monthly, weekly) sampling is done at fixed sites throughout the community that are representative of the habitat types present in the area. Adult mosquitoes are collected using a variety of trapping techniques, including traps for host-seeking, resting, or gravid (carrying eggs) mosquitoes seeking a place to lay eggs (oviposition site). Adult surveillance can also be used to evaluate control activities pre- and post-treatment. Transmission activity surveillance provides information on the level of infected mosquitoes in an area.

Specimen Collection and Traps

Mosquito species involved in enzootic or epidemic transmission are readily captured in CDC light traps (with or without CO2) and New Jersey light traps. For best results, the traps need to be placed in well-protected sites with very limited wind movement. Resting boxes may be used to increase the chances of capturing infected mosquitoes, and the CDC battery powered resting box traps can increase the number of mosquitoes captured, as well as improve consistency and ease of sampling (Panella et al. 2011). The resting populations can also be collected using backpack aspirators (e.g., modified CDC backpack aspirator https://www.johnwhock.com/products/aspirators/modified-cdc-backpack-aspirator/, or the lightweight battery-powered aspirator [Nasci 1981]) to remove mosquitoes from natural harborages or artificial resting structures (e.g., wooden resting boxes, red boxes, fiber pots, and other similar containers (Holderman et al. 2018)).

Specimen Handling and Processing

Because mosquito-based surveillance relies on identifying virus in the collected mosquitoes through detection of viral proteins, viral RNA, or live virus (see Laboratory Diagnosis and Testing section), specimens should be handled in a way that minimizes exposure to conditions (e.g., heat, successive freeze-thaw cycles) that would degrade the virus. Optimally, a cold chain should be maintained from the time mosquitoes are removed from the traps to the time they are delivered to the processing laboratory. Mosquitoes can be transported from the field in a cooler with cold packs or on dry ice, and then placed on a chill-table, if available, during sorting, identification, and pooling. Usually only female mosquitoes are tested in routine arboviral surveillance programs. If virus screening is not done immediately after mosquito identification and pooling, the pooled samples should be stored frozen (e.g., -70°C) or at temperatures below freezing for short-term storage. Although the lack of a cold chain might impact the ability to culture the virus, it does not appear to reduce the ability to detect viral RNA by reverse transcription-polymerase chain reaction (RT-PCR) (Turell et al. 2002).

Vector-based Surveillance Indicators

Data derived from mosquito surveillance include estimates of mosquito species abundance and infection rate in those mosquito populations. The indices derived from those data vary in information content, ability to be compared over time and space, and association with transmission levels and levels of human risk. Five indicators that have commonly been used include: vector abundance, number of positive pools, percent of pools positive, infection rate, and vector index (Table 1).

Vector abundance provides a measure of the relative number of mosquitoes in an area during a particular sampling period. It is the total number of mosquitoes of a particular species collected, divided by the number of trapping nights during a specified sampling period, and is expressed as the number/trap night. Risk assessments often consider mosquito abundance because high mosquito densities can be associated with arboviral disease outbreaks (Olson et al. 1979; Eldridge 2004). For example, during a WNV outbreak in Maricopa County, AZ in 2010, Culex quinquefasciatus densities were higher in outbreak compared to non-outbreak areas (Godsey et al. 2012; Colborn et al. 2013). High Culiseta melanura and Coquillettidia perturbans abundance has also been associated with elevated eastern equine encephalitis (EEE) virus activity. However, high mosquito abundance can occur in the absence of virus, and outbreaks can occur when abundance is low, but the vector infection rate is high. Vector abundance measures are used for planning IVM and monitoring the outcomes of mosquito control. Number of traps, their distribution, and the timing of sample collection should be sufficient to obtain spatially and temporally representative data.

Number of positive pools is the total of the number of arbovirus positive mosquito pools detected in a given surveillance location and period. These may be a tally of the total positive pools separated by species or for all species tested. This indicator provides evidence of arboviral activity, particularly during field investigations and outbreak response, but is not recommended as a stand-alone indicator. Instead, data can be used to produce more informative indices (i.e., infection rate and vector index).

Percent of pools positive is calculated by the number of positive pools divided by the total number of pools tested, as a percentage. It provides data that can be used to compare activity over time and place. However, the comparative value is limited unless the number of pools tested is large and the number of mosquitoes per pool remains constant. As with the number of positive pools index, these data can be used for calculation of the, often more informative, infection rate and vector index.

The infection rate in a sampled vector population estimates the true infection prevalence of infected mosquitoes in the population and is a good indicator of human risk. It provides a useful, quantitative basis for comparison, allowing evaluation of changes in population infection prevalence over time and space. Infection rate indices have been used successfully to link infection rates with human risk (Bell et al. 2005). When computing infection rate indices, variable pool numbers and pool sizes can be used, while retaining comparability, but larger sample sizes improve precision. Two methods are commonly used to calculate infection rate:

  • Minimum infection rate (MIR) for a given mosquito species is the number of positive pools divided by the total number of mosquitoes tested. Use of the MIR assumes that infection rates are low and that only one mosquito is positive in a positive pool.
  • Maximum likelihood estimate (MLE) corrected for bias is the preferred method, particularly during outbreaks. MLE-associated estimates are based on binomial probability models for pooled data and do not assume only one positive mosquito per positive pool.  Bias-corrected MLEs provide more accurate estimates than the standard MLE (Biggerstaff 2008; Hepworth and Biggerstaff, 2017, 2021) and are more accurate than the MIR (Gu et al. 2008; Biggerstaff 2008).  MLE-based estimates are computed from straightforward formulas when there is only one pool size, but computer iterative methods are needed when pool sizes differ.  Both an R package and a Microsoft Excel Add-in are available to compute infection rate estimates from pooled data (https://www.cdc.gov/westnile/resourcepages/mosqSurvSoft.html).

While the MLE-based estimates and the MIR are similar when infection rates are low, the assumption underlying the use of the MIR is untenable as the true infection rate increases, the MIR is less accurate than bias-corrected MLEs, and in any case confidence intervals based on the MIR have been shown to be poor (e.g., Biggerstaff 2008).

The Vector Index (VI) estimates the relative abundance of infected mosquitoes in an area and incorporates into a single index information on presence, relative abundance, and infection rates of individual species (Gujaral et al. 2007; Bolling et al. 2009; Jones et al. 2011). The VI is calculated by multiplying the average number of mosquitoes collected per trap night by the infection rate. VI is expressed as the average number of infected mosquitoes collected per trap night in the area during the sampling period. In areas with multiple vector species, a VI is calculated for each species, then individual VIs are summed to give a combined estimate of infected vector relative abundance.

Increases in VI reflect increased risk of human disease and serves as a more reliable prediction measure than vector abundance or infection rate alone (Bolling et al. 2009; Jones et al. 2011; Kwan et al. 2012; Colborn et al. 2013). As with other surveillance indicators, the accuracy of the VI depends on the number of trap nights used to estimate abundance and the number of specimens tested to estimate infection rate.

Use of Vector-based Surveillance Indicators. Mosquito-based surveillance indicators have two important roles in arboviral surveillance and response programs. First, they can provide quantifiable thresholds for proactive vector control efforts and public health messaging. By identifying thresholds for vector abundance and infection rates that are below levels associated with disease outbreaks, IVM programs can institute proactive measures to maintain mosquito populations at levels below which virus transmission would be likely. Second, if thresholds related to outbreak levels of transmission can be identified, surveillance can help determine when proactive measures were insufficient to dampen virus amplification and more aggressive measures are needed, such as expanded mosquito control measures and public messaging.

Table 1. Summary of Mosquito-Based Surveillance Indicators

Summary of Mosquito-Based Surveillance Indicators
Index Description Equation
Vector Abundance Number of mosquitoes of a particular vector species captured per trap per night Number of a particular mosquito species captured in a night/Number of traps set up that night
Number of Positive Mosquito Pools Number of positive mosquito pools detected in a given period of time Simple count of positive mosquito pools
Percentage of Positive Mosquito Pools Proportion of positive mosquito pools Number of positive mosquito pools/Total number of pools tested X 100
Infection Rate An estimate of the number of mosquitoes infected per 1000 tested Maximum likelihood estimate (MLE) with bias correction, use links in the footnote.

Minimum Infection Rate (MIR) = Number of positive pools/Total number of mosquitoes tested

Vector Index An estimate of the abundance of infected mosquitoes in an area
N with an overline

= Number of mosquitoes per trap night for a given species

P with a hat symbol over it.

= Estimated Infection Rate

Vector Index formula

For MLE-based computations use the mosquito surveillance software at https://www.cdc.gov/westnile/resourcepages/mosqSurvSoft.html

References

Bell JA, Mickelson NJ, Vaughan JA. 2005. West Nile virus in host-seeking mosquitoes within a residential neighborhood in Grand Forks. North Dakota Vector-Borne Zoonotic Dis. 5:373

Bolling BG, Barker CM, Moore CG, Pape WJ, Eisen L. 2009. Modeling/GIS, risk assessment, economic impact: Seasonal patterns for entomological measures of risk for exposure to Culex vectors and West Nile virus in relation to human disease cases in Northeastern Colorado. J Med Entomol. 46:1519-1531.

Colborn, J.M., K.A. Smith, J. Townsend, D. Damian, R.S. Nasci, J.P. Mutebi. 2013. West Nile Virus Outbreak in Phoenix, Arizona—2010: Entomological Observations and Epidemiological Correlations. J Am Mosq Control Assoc. 29(2):123-32.

Eldridge BF. 2004. Surveillance for arthropodborne diseases. In Eldridge and Edman eds. Medical Entomology, Kluwer Academic Press, Dordrecht, The Netherlands, pp 645

Godsey MS Jr., Burkhalter K, Young G, Delorey M, Smith K, Townsend J, Levy C, Mutebi JP. 2012. Entomologic investigations during an outbreak of West Nile virus disease in Maricopa County, Arizona, 2010. Am J Trop Med Hyg. 87(6):1125-1131.

Gu W, Unnasch TR, Katholi CR, Lampman R, Novak RJ. 2008. Fundamental issues in mosquito surveillance for arboviral transmission. Trans R Soc Trop Med Hyg. 102: 817-822.

Gujral IB, Zielinski-Gutierrez EC, LeBailly A, Nasci R. 2007. Behavioral risks for West Nile virus disease, northern Colorado, 2003. Emerg Infect Dis. 13(3):419-25.

Holderman CJ, et al. Mosquitoes (Diptera: Culicidae) collected from residential yards and dog kennels in Florida using two aspirators, a sweep net, or a CDC trap. J Med Ent 2018;55(1):230-6.

Jones RC, Weaver KN, Smith S, Blanco C, Flores C, Gibbs K, Markowski D, Mutebi JP. 2011. Use of the vector index and geographic information system to prospectively inform West Nile virus interventions. J Am Mosq Control Assoc. 27:315-319.

Kwan JL, Park BK, Carpenter TE, Ngo V, Civen R, Reisen WK. 2012. Comparison of enzootic risk measures for predicting West Nile disease, Los Angeles, California, USA, 2004-2010. Emerg Infect Dis. 18(8):1298-306.

Nasci RS. 1981. A lightweight battery-powered aspirator for collecting resting mosquitoes in the field. Mosq News. 41: 808-811.

Olson JG, Reeves WC, Emmons RW, Milby MM. 1979. Correlation of Culex tarsalis population indices with the incidence of St. Louis encephalitis and western equine encephalomyelitis in California. Am J Trop Med Hyg. 28: 335-343.

Panella NA, Crockett RJ, Biggerstaff BJ, Komar N. 2011. The Centers for Disease Control and Prevention resting trap: a novel device for collecting resting mosquitoes. J Am Mosq Control Assoc. 27(3):323-325.

Turell MJ, Spring AR, Miller MK, Cannon CE. 2002. Effect of holding conditions on the detection of West Nile viral RNA by reverse transcriptase-polymerase chain reaction from mosquito (Diptera: Culicidae) pools. J Med Entomol. 39(1):1-3.

Animal-based Surveillance

Bird-based Surveillance

Wild birds are the primary vertebrate hosts of eastern equine encephalitis (EEE) virus and serve as the principal amplification hosts for mosquito infection. EEE epizootics precede human epidemics and in the well-established enzootic EEE virus foci, EEE antibody prevalence among wild birds ranged from 5 to 85% (Elias et al. 2017; Dalrymple et al. 1972; Stamm 1968). However, during epizootics outside the well-established enzootic EEE virus foci, similar antibody prevalence rates in local wild bird populations were observed (Hayes et al. 1962; Emord and Morris 1984; Stamm 1958; McLean et al. 1985). Some “primary” bird species, typically passerine species, show higher EEE virus reactive antibodies than other bird species and are good sentinels for routine EEE surveillance. Antibody prevalence for primary species during EEE epizootics can range from 40 to 70% (Crans et al. 1994), suggesting intense EEE virus transmission. EEE antibody prevalence in wild bird populations can decline to less than 10% after 3 consecutive non-epizootic years (Hayes et al. 1962; Emord and Morris 1984). Virus activity and antibody seroprevalence for EEE virus in local bird populations usually correlate well with the risk of human infection. Accurate monitoring of virus and antibody prevalence in wild birds should provide early warning of increased transmission that may constitute a risk to equine and human populations. Wild birds are monitored by repeated sampling of local populations to test for antibody or virus. Free-ranging adult and immature birds are captured in ground-level mist nets set at locations appropriate for the desired species. The Australian crow trap also provides an effective method for collecting birds (Tsachalidis et al. 2006). Captured birds are bled, banded, and released for possible later recapture to check for seroconversion. Recapture data also gives useful insights on movement, survival, and other population characteristics of the birds. Successful use of this technique requires a labor-intensive sampling effort because of low recapture rates. Because antibodies may persist for 2 or more years, the results from carefully identified juvenile birds may provide the most useful index of current virus activity (Smith et al. 1983). This technique requires substantial resources. In addition, it requires highly-trained personnel as well as state and federal collecting permits.

Mortality from EEE virus infection occurs in ring-necked pheasants, emus, and other exotic game bird species (Morris 1988; Saxton-Shaw et al. 2015). Some surveillance programs monitor the morbidity and mortality in captive ring-necked pheasants as sentinels and as an indicator of EEE virus activity.

References

Crans WJ, et al. Eastern equine encephalomyelitis virus in relation to the avian community of a coastal cedar swamp. J Med Entomol 1994;31:711–728.

Dalrymple JM, et al. Ecology of arboviruses in a Maryland freshwater swamp. III. Vertebrate hosts. Am J Epidemiol 1972;96:129–140.

Elias SP, et al. Seasonal patterns in eastern equine encephalitis virus antibody in songbirds in southern Maine. Vector Borne Zoonotic Dis 2017;17:325–330.

Emord DE, Morris CD. Epizootiology of eastern equine encephalomyelitis virus in upstate New York, USA. VI. Antibody prevalence in wild birds during an interepizootic period. J Med Ent 1984;21:395–404.

Hayes RO, et al. Entomological aspects of the 1959 outbreak of eastern encephalitis in New Jersey. Am J Trop Med Hyg 1962;11:115–121.

McLean RG, et al. Investigations of the vertebrate hosts of eastern equine encephalitis during an epizootic in Michigan, 1980. Am J Trop Med Hyg 1985;34:1190–1202.

Morris CD. Eastern equine encephalomyelitis. Monath TP, ed. The Arboviruses: Epidemiology and Ecology. Boca Raton (FL): CRC Press; 1988. p. 1–20.

Saxton-Shaw KD, et al. The first outbreak of eastern equine encephalitis in Vermont: Outbreak description and phylogenetic relationships of the virus isolate. PLoS One 2015;10:e0128712.

Smith GC, et al. Correlation between human cases and antibody prevalence in house sparrows during a focal outbreak of St. Louis encephalitis in Mississippi, 1979. Mosq News 1983;43:322–325.

Stamm DD. Studies on the ecology of equine encephalomyelitis. Am J Pub Hlth 1958;48:328–335.

Stamm DD. Arbovirus studies in birds in south Alabama, 1959-1960. Am J Epidemiol 1968;87:127137.

Tsachalidis E, et al. The Australian crow trap and the Larsen trap: Their capture success in Greece. Proceedings of the 2006 Naxos International Conference on Sustainable Management and Development of Mountainous and Island Areas. Vol. II. 2006; pp. 325–329.

Live Bird Serology

Chicken flocks are widely used for Western equine encephalitis and St. Louis encephalitis virus surveillance and in some states for EEE virus surveillance. Surveillance for SLE and EEE viruses can take place simultaneously to reduce costs. Like most birds, chickens are susceptible to and can tolerate SLE and EEE virus infections. Chickens, especially older chickens, develop low titer viremia and, therefore, are not likely to contribute to local virus amplification. Chicken flocks can be inexpensively maintained on farms or in urban-suburban locations by residents or health officials. However, it is important to base the choice of locations for the sentinel chickens on historical records of virus activity. Spreading small groups of sentinel chickens throughout the area at risk yields more representative estimates of virus activity. Each spring, 6- to 8-week-old chickens are placed at the selected sentinel sites. Each sentinel site is stocked with 6 to 30 pretested, non-immune, individually banded chickens kept in standard sentinel sheds. Sentinel chickens are bled from the wing vein, the jugular vein, or from the heart weekly, biweekly, or monthly throughout the transmission season. Similar to wild bird surveillance, sentinel chickens were thought to be inappropriate as an early warning system for epidemic activity because the turnaround time from the field to the laboratory results was too long (Morris 1988). Currently, molecular biology-based methods such as RT-PCR and advanced serological methods such as EEE IgM antibody-capture enzyme-linked immunosorbent assay (MAC-ELISA) greatly shorten the turnaround time and in some locations sentinel chicken flocks may be used as early warning systems (Goodman et al. 2015). However, some studies reported failure in some locations (Crans 1986), therefore, use of sentinel chicken flocks needs to be evaluated for each area.

References

Crans WJ. Failure of chickens to act as sentinels during an epizootic of eastern equine encephalitis in southern New Jersey, USA. J Med Ent 1986;23(6):626–9.

Goodman CH, et al. Production of a Sindbis/eastern equine encephalitis chimeric virus inactivated cell culture antigen. J Virol Methods 2015;223:19–24.

Morris CD. Eastern equine encephalomyelitis. Monath TP, ed. The Arboviruses: Epidemiology and Ecology. Boca Raton (FL): CRC Press; 1988. p. 1–20.

Horses and Other Vertebrates

In areas with susceptible horse populations, surveillance for equine cases can provide a sensitive early warning system for EEE outbreaks. Horses are subject to high vector attack rates due to their field exposure. Reports by local veterinarians of equine encephalomyelitis give warning of increased arbovirus activity in an area. This can alert public health officials to investigate the situation. Equine surveillance can be active or passive. Active surveillance requires regularly contacting large-animal veterinarians, encouraging them to report clinically suspect equine cases and to submit blood and autopsy samples for laboratory confirmation. Record sheets, containing a case history and vaccination history, must accompany samples for laboratory testing if the results are to be useful. Some limitations in using equines include EEE virus immunity from prior vaccination, movement into and out of the surveillance area, and lack of prompt reporting of morbidity or mortality by attending veterinarians.

Several studies report EEE virus antibody-positive sera among populations of free-ranging white-tailed deer, Odocoileus virginianus, suggesting white-tailed deer are frequently exposed to EEE virus infections (Hoff et al. 1973; Bigler et al. 1975; Tate et al. 2005; Schmitt et al. 2007). Deer serosurveys have been utilized to study distribution ranges of EEE virus activity especially in northeastern United States (Berl et al. 2013; Mutebi et al. 2011; Mutebi et al. 2015). Odocoileus virginianus inhabit a geographically localized home range, often not exceeding a 1.6 km (1 mile) radius, where they both become infected and are harvested (DeNicola et al. 2000; Marchinton and Hirth 1984). Collecting O. virginianus blood samples is less labor intensive because of the seasonal deer harvests; samples are collected from the carcasses when hunters bring the harvested deer to the registration station. EEE virus antibody surveillance in harvested O. virginianus is a potential tool for EEE surveillance and distribution mapping. Deer serosurveys may be useful for monitoring EEE virus activity but have no predictive value for human infection because deer harvesting occurs in the early fall after the EEE virus transmission season.

Similar studies have been conducted using moose and game birds in the northeastern United States (Mutebi et al. 2012; Lubelczyc et al. 2014; Elias et al. 2017). However, these studies only provide information on distribution ranges of EEE virus activity and cannot be used as early warning systems.

References

Berl E, et al. Serological evidence for eastern equine encephalitis virus activity in white-tailed deer, Odocoileus virginianus, in Vermont, 2010. Am J Trop Med Hyg 2013;88:103–107.

Bigler WJ, et al. Arbovirus surveillance in Florida: Wild vertebrate studies 1965-1974. J Wildl Dis 1975;11:348–356.

DeNicola AJ, et al. Managing white-tailed deer in suburban environments: A technical guide. A publication of Cornell Cooperative Extension, the Wildlife Society—Wildlife Management Working Group, and Northeastern Wildlife Damage Research and Outreach Cooperative. New York (NY). 2000.

Elias SP, et al. Seasonal patterns in eastern equine encephalitis virus antibody in songbirds in southern Maine. Vector Borne Zoonotic Dis 2017;17:325–330.

Hoff GL, et al. Arbovirus serology in North Dakota Mule and White-Tailed Deer. J Wildl Dis 1973; 9:291–295.

Lubelczyk C, et al. Detection of eastern equine encephalitis virus antibodies in moose (Alces americana), Maine, 2010. Vector Borne Zoonotic Dis 2014; 14:77–81.

Marchinton RL, Hirth DH. Behavior in Halls LK. White-tailed deer: Ecology and management. Wildlife Management Institute: Stackpole Books; 1984. p. 129–168.

Mutebi JP, et al. Using wild white-tailed deer to detect eastern equine encephalitis virus activity in Maine. Vector Borne Zoonotic Dis 2011;11:1403–1409.

Mutebi JP, et al. Eastern equine encephalitis in moose (Alces americanus) in northeastern Vermont. J Wildl Dis 2012;48:1109–1112.

Mutebi JP, et al. Prevalence of eastern equine encephalitis virus antibodies among white-tailed deer populations in Maine. Vector Borne Zoonotic Dis 2015;15:210–214.

Schmitt SM, et al. An outbreak of eastern equine encephalitis virus in free-ranging white-tailed deer in Michigan. J Wildl Dis 2007;43:635–644.

Tate CM, et al. Eastern equine encephalitis in a free-ranging white-tailed deer (Odocoileus virginianus). J Wildl Dis 2005;41:241–245.