Printer Friendly

A location-specific spreadsheet for estimating Zika risk and timing for Zika vector surveillance, using US military facilities as an example.

In 2016, Zika virus and congenital infections became nationally notifiable conditions in the United States. (1) A total of 2,382 confirmed and probable cases of Zika virus (ZIKAV) with illness onset were reported to ArboNET, the US national arboviral surveillance system managed by the Centers for Disease Control and Prevention and state health departments, from January 1 to July 31, 2016. (2) In July 2016, the first locally acquired case of ZIKAV disease from mosquitoes was confirmed in the state of Florida. (3) Aedes mosquitoes transmit ZIKAV, chikungunya virus (CHIKV), dengue virus (DENV), and yellow fever virus, among others. Although Aedes albopictus Skuse is thought to be a competent vector of ZIKAV, (4) Ae. aegypti (L.) has been implicated as the primary transmitter of the virus in human populations in the ongoing outbreak in the Americas. (5,6) This is likely the result of Ae. aegypti preferring to feed more frequently on humans, (7,8) and being highly peridomestic compared to Ae. albopictus, which can inhabit more rural environments. (9,10) The role of other mosquito species in ZIKAV transmission is either unknown, refers to species not present in the United States, or is controversial, (11) therefore only Ae. aegypti and Ae. albopictus are considered here.

In this study, we concentrated on US Department of Defense (DoD) facilities, but the approach could be used for any area of interest. Some military facilities have longstanding mosquito surveillance programs, (12) and Zika virus surveillance is being enhanced in the US military as a result of the recent threat. For example, surveillance efforts are supported by funding from the Global Emerging Infections Surveillance and Response section of the Armed Forces Health Surveillance Branch in the Defense Health Agency's Public Health Division. (13) According to a March 2016 DoD memo, 190 DoD installations are located in areas where mosquitoes capable of carrying ZIKAV occur, and increased vector monitoring will be conducted in installations in 27 states, the District of Columbia, Guam, and Puerto Rico. (14) Four regional commands exist under the US Army Medical Command, all of which have entomological sciences divisions that conduct mosquito surveillance. Additionally, the US Air Force School of Aerospace Medicine, the US Navy and Marine Corps Public Health Center and regional Navy Environmental and Preventive Medicine Units, and the Navy Entomology Center of Excellence assist those undertaking vector surveillance or arbovirus testing.

For a military entomologist tasked with establishing and maintaining an Aedes spp./ZIKAV surveillance program in temperate areas that experience high mosquito seasonality, two important questions arise: (1) is ZIKAV transmission possible here?; (2) when should vector surveillance be conducted? In temperate zones like the continental United States, the answers to these questions may vary depending on the time of year. In this article, we describe an Excel-based tool that is designed to assist entomologists and other health professionals address these 2 questions throughout the year.

Habitat suitability models displaying potential distribution have been published for both Ae. aegypti and Ae. albopictus, (15,19) as well as for ZIKAV. (20-23) While these models often display average yearly suitability, they do not necessarily provide information that could be used for decisions about the timing of surveillance activities, and are global in extent rather than focused on particular areas where a surveillance program might be established. Questions about timing of mosquito monitoring and allocation of resources requires a consideration of what conditions limit adult mosquito activity and ZIKAV dissemination in the field.

Relative humidity, rainfall, drought, and wind velocity affect survival and behavior of mosquitoes, and therefore transmission. (24) However, temperature is the most important ecological determinant of the development rate of Ae. aegypti, (25) and one of the principal determinants of Aedes survival. (26) Temperature also directly affects the replication rate of arboviruses, thus affecting the extrinsic incubation period. (27) What then do we know about how temperature limits Aedes and arboviruses such as ZIKAV?

In their review of reports published in the early 20th century, Bonne-Wepster and Brug (28) presented data about the effects of temperature on the activity and survival of Ae. aegypti mosquitoes, summarized below:

* Female mosquitoes were observed to feed most readily between 26[degrees]C and 35[degrees]C, between 19[degrees]C and 25[degrees]C they are slow to blood feed, and below 15[degrees]C to 19[degrees]C they do not feed (Marchoux et al, (29) Howard et al, (30) Connor (31)).

* Female mosquitoes in Montevideo can continue to bite at 14[degrees]C to 15[degrees]C (Cossio (32)).

* Adults were observed to die when exposed to temperatures above 38[degrees]C, but Davis (33) reported some adults surviving exposure to 40[degrees]C during 7 hours, and amongst another group exposed to 45[degrees]C for 2 hours.

* At 7[degrees]C to 9[degrees]C, adult females may live up to 80 days, and the male up to 14 days (Guiteras; Otto; Newman (34)).

* The female dies within 24 hours, if exposed to a temperature of 6[degrees]C (Flu (35)).

* Dinger et al (36) reported survival of adult female mosquitoes kept for 6 days at 5[degrees]C.

More recently, in Saudi Arabia, Khormi et al (37) found that the minimum temperature range of 18[degrees]C to 25[degrees]C is suitable for Ae. aegypti survival, and the survival rate increases up to 38[degrees]C. Conner (38) and Wayne and Graham (39) found that Ae. aegypti is most active at temperatures between 15[degrees]C and 30[degrees]C, while other field and laboratory observations found survival rates from about 18[degrees]C to 38[degrees]C or less, based on daily or monthly minimum and maximum temperatures. (40-42) In a study of Ae. aegypti distribution using the program CLIMEX, Khormi and Kumar (18) set the limiting low temperature at 18[degrees]C, the lower optimal temperature at 25[degrees]C, the upper optimal temperature at 32[degrees]C, and the limiting high temperature at 38[degrees]C. Brady et al (16) limited their predictions of temperature suitability to areas with a maximum monthly temperature exceeding 13[degrees]C for Ae. albopictus and 14[degrees]C for Ae. aegypti. These threshold temperatures were based on previous studies of the observed temperatures below which biting and movement behaviors were impaired. (42-45)

Studies suggest that an increase between 14[degrees]C to 18[degrees]C and 35[degrees]C to 40[degrees]C can lead to higher transmission of dengue. (46) Xiao et al (47) found that oral infections of DENV2 did not produce antigens in the salivary glands of Ae. albopictus kept at 18[degrees]C for up to 25 days but did produce antigens at 21[degrees]C. It is not known if Ae. albopictus held longer at the lower temperature would have disseminated infections, but Dohm et al (48) found that Culex pipiens required 25 days at 18[degrees]C to disseminate infections of West Nile virus (WNV). For comparison, WNV is capable of replication from 14[degrees]C to 45[degrees]C. (49,50) Tilston et al (51) analyzed monthly average temperature of cities that experience chikungunya outbreaks and found that start and finish occurred when average monthly temperatures were 20[degrees]C or higher. At the upper temperature limit, Kostyuchenko et al (52) found that ZIKAV is more thermally stable than DENV, and is also structurally stable even when incubated at 40[degrees]C, mimicking the body temperature of extremely feverish patients after virus infection. However, a study by Goo et al (53) indicates that the thermal stability of some ZIKAV strains, including those involved in recent outbreaks, falls between those of DENV and WNV.

Remotely sensed temperature data is freely available from multiple sources as both near-real time recordings and forecast predictions. Combining remotely-sensed temperature data with predicted distributions of the vectors and virus could provide insight into when areas of interest are suitable for transmission and should be actively monitored. Our aim was to produce a knowledge product and surveillance decision tool that uses publicly available information about potential distribution and thermal requirements of the vectors and virus at US military facilities.


Areas of Interest

The location and boundary of US military facilities was obtained from the US Census Bureau's TIGER/Line 2015 shapefile product. (54) This shapefile lists facilities in the continental United States, Alaska, Hawaii, Puerto Rico, and Guam. As some facility names comprised multipart polygons, these were reduced from 804 to 733 to match the number of unique facility names, using the Dissolve tool in ArcMap 10.4 (ESRI, Redmond, CA). The centroid of each facility was selected to produce a shapefile of points using the Feature to Point tool (inside polygon option checked) of ArcMap. The georeference of each centroid was obtained by the Add XY Coordinates tool and joined to the points shapefile. Extraction of all facility centroid raster values was first obtained by the Extract Values to Points tool, then for polygons using the Zonal Statistics as Table tool, and the results merged. This approach was required because smaller polygons would not produce results using the Zonal Statistics as Table tool, which necessitated use of the raster data associated with the points for these facilities.

Temperature Data

To monitor temperature in near real-time, daily time averaged maps of air temperature at the surface (daytime/ ascending) were downloaded from the Giovanni 4.19 (Released Date: 2016-04-12. Data provided by the National Air and Space Administration (NASA) Goddard Earth Sciences Data and Information Services Center) data portal at 1[degrees] spatial resolution. (55) Daily gridded temperature analyses were also collected from the National Oceanic and Atmospheric Administration (NOAA) US National Weather Service Climate Prediction Center (CPC). (56) Forecast temperature data was also provided by the CPC and the NOAA National Digital Forecast Database at 5 km spatial resolution. (57) For predictions based on monthly averages, monthly gridded climate data with a spatial resolution of 1 km were downloaded from WorldClim. (58)

Habitat Suitability Models

We chose the models of Ae. aegypti and Ae. albopictus by Kraemer et al, (59) which were based on an extensively documented set of presence observations for each vector. We also used the habitat suitability model for ZIKAV transmission by Messina et al. (21) This model used Zika case reports and data on temperature, precipitation, humidity, enhanced vegetation index, and urban versus rural. Both vector and virus maps are at a 5 km x 5 km spatial resolution. The 0.5 model suitability score was arbitrarily used as a presence/absence threshold.


The temperature suitable for activity of Ae. aegypti and Ae. albopictus combined was estimated as 13[degrees]C to 38[degrees]C, and 18[degrees]C to 42[degrees]C for ZIKAV. We chose to be conservative, using temperatures at the extremes of the reported suitable temperature range, and maximum rather than mean air temperatures.

Human Population Data

In order to more fully understand the potential impact of ZIKAV risk to military and nonmilitary personnel and their families in and around each facility, we explored risk in terms of human population data, with the following considerations. The flight range of Ae. aegypti and Ae. albopictus is in the order of hundreds of meters only, (60,61) and each facility would differ in the average distance that human carriers of ZIKAV would routinely travel to and from each facility. Additionally, some facilities were remote, while others were adjacent to or enclosed within urban and suburban areas. To address these complications, we created a buffer of 5 km around all facility polygons to capture the human population density according to LandScan 2011. (62) This was accomplished using the LandScan raster and the Buffer and Sum output in the Zonal Statistics as Table tools in ArcMap. A buffer of 5 km is a conservative estimate and is meant to give a uniform measure for each facility of the potential host density affected in an outbreak or vector control situation.

Excel-based Zika Risk Tool

A goal of this project was to display disparate data sources visually and in a simple and intuitive way in order to more effectively communicate the level of risk at each military facility. The risk estimation and alert system had to be in a format that was readily understandable and easily accessible by military users, who often have information system security restrictions or bandwidth caps. We chose MS Excel (Microsoft Corp, Seattle, WA), as a universal platform for performing calculations and reporting results. This software had the added advantage that the scatterplot function can be used to map each military facility, (63) with icons displaying various categories of risk, and using a geocorrected map background (64) for each state. Other notable features that were used in the Excel risk estimation tool were the formula functions, conditional formatting to represent categories of numbers as different types of symbols, dependent dropdown lists and hyperlinks to allow users to navigate more quickly to the results of individual facilities, and textualized results that users can read as statements describing the situation and as guidance for vector surveillance.

Calculations Within the Excel Risk Estimation Tool

Given that the maximum temperature is available for a site (ie, "Temp"), the following lists an example sequence of tasks and their calculations, with explanations and the Excel formula (in square brackets), culminating in a risk rating:

1. Column A. "Was temperature suitable during period for the vector?", ie, if the maximum was 13[degrees]C to 38[degrees]C, it is 1, otherwise 0 [=IF((Temp>=13)-(Temp>38),1,0)],

2. Column B. "Was temperature suitable during period for virus replication in mosquito?", ie, if the maximum was 18[degrees]C to 42[degrees]C, it is 1, otherwise 0 [=IF((Temp>=18)-(Temp>42),1,0)]

3. Column C. "What is the sum of the thermal suitability values for vector (Column A) and virus (Column B)?", ie, possible choices are: 0, 1, or 2.

4. Column D. "If temperature for the vectors (Column A) was within the required range, what is the model suitability for vector?", ie, this was the maximum modeled suitability (0-1.00) for either Ae. aegypti or Ae. albopictus.

5. Column E. Score vector model suitability as 3 if [greater than or equal to] 0.5, otherwise 2 [=IF(Column D<0.5,2,IF(Column D>=0.5,3))]. The 0.5 model suitability score was arbitrarily used as the cutoff for presence/absence.

6. Column F. "If temperature for the virus (Column B) was within the required range, what is the model suitability for the virus?" (0-1.00)

7. Column G. Score virus model suitability as 7 if [greater than or equal to] 0.5, otherwise 5 [=IF(Column F<0.5,5,IF(Column F>=0.5,7))]. The 0.5 model suitability score was arbitrarily used as the cut-off for presence/absence.

8. Column H. "What is the 'Combined Score' for the interaction of temperature suitability of vector and virus, vector model suitability, and virus model suitability score (ie,=CxExG)?" The use of 0, 1, and prime numbers for the component scores produces a unique semi-prime number for the product, ie, 0, 10 (=1x2x5), 14 (=1x2x7), 15 (=1x3x5), 21 (=1x3x7), 20 (=2x2x5), 28 (=2x2x7), 30 (=2x3x5), or 42 (=2x3x7). A zero indicated that the temperature at the site was unsuitable for both vector and pathogen, so suitability scores were irrelevant, and combination scores were all scored zero.

9. The 9 possible Combined Scores were divided into 6 categories based on whether preconditions do not exist for transmission, are unsuitable for transmission, are somewhat suitable for transmission, or are suitable for transmission (Figure 1).

10. Column I. An Overall Zika Risk Code was established based on the Combined Score (Figure 1), and rates conditions as low (Blue: Code 1) to high risk (Red: Code 4). The 9 possible Combined Scores are initially divided according to whether temperature conditions are not suitable (Code 1) or suitable for the vectors and virus (Codes 2-4). Codes 2-4 are then characterized according to increasing habitat suitability, with Code 4 being where models predict suitable habitat for vectors and virus.

11. Action statements were constructed based on the temperature and habitat model suitability scores (Figure 2). For example, if conditions are too cold for the development of the vectors and models predict that the location is unsuitable for the vectors, the Action statement would be: "Too cold or hot for vectors-surveillance unnecessary. When Temp suitable, model suggests vectors unlikely or low numbers." Alternatively, if conditions are warm enough for the development of the vectors and models predict that the location is highly suitable for the vectors, the Action statement would be: "Temp suitable for vectors-surveillance may be needed. When Temp suitable, model suggests vectors likely-may need control, education, and policies minimizing exposure."


The Excel files comprise 12 monthly files based on average monthly maximum temperatures (suitable for longer term planning), and near real-time and forecast file, updated weekly. These files are freely available via the VectorMap website. (65) The tool provides risk maps of facilities as a continental overview (Figure 3), and on a state by state basis (Figure 4). Results for individual facilities are navigable via dropdown menus and hyperlinks (Figure 5). Statewide summary data of risk profile and humans potentially affected is shown in Figure 6. The temporal changes in average risk based on the 12 monthly files is given in Figure 7 in terms of the number of facilities affected (of 733) and the number of people within 5 km of the facilities. April to October was the period of greatest risk with suitable conditions for Zika transmission (ie, code 4) potentially affecting a maximum of 114 facilities in 12 states and territories, and 4,546,505 people within the vicinity of these facilities, of a total of 32,811,618 within the vicinity of all 733 facilities. The maximum number of facilities recording code 4 in any one month (eg, August) were: Florida (36), Hawaii (16), Louisiana (12), Texas (11), and Virginia (11). Of these, the number of people within 5 km of these facilities were: Texas (1,215,230), Florida (1,125,032), Louisiana (462,586), Virginia (409,066), and Hawaii (247,918).

These data may assist public health planning, and can be seen as an indicator of potential disease burden, or of people potentially benefiting from a well-informed vector surveillance and control program conducted on military facilities. Results are provided in a variety of symbologies and as textualized statements of how the factors examined may affect ZIKAV transmission, and recommended actions for entomologists conducting routine vector surveillance. The action statement textualizes the data and is designed to assist a preparedness posture, particularly around vector surveillance and control. Changes in the action statement over the year, for example as a result of rising temperature, can be used as a guide to affect changes in vector surveillance and control activities at particular facilities.


The Excel tool is designed to provide insights into ZIKAV transmission potential at US military facilities, but could be applied to other arboviruses and situations, such as cities, (66) tire dumps, or parks. The spreadsheet is flexible in that vector and virus suitability model scores, temperature limits, and the wording of action statements can be replaced depending on the context, and as new information is compiled.

For US military situations, this tool could be used in conjunction with the Electronic Surveillance System for the Early Notification of Community-based Epidemics (67) or Medical Situational Awareness in Theater, (68) which reports on febrile illnesses and rash in the military population. Coordination of result reporting through the Armed Forces Pest Management Board (69) and VectorMap may also be desirable. The Navy and Marine Corps Public Health Center (NMCPHC) guide, Aedes Surveillance and Control Plan for U. S. Navy and Marine Corps Installations, (70) states that "each installation's medical personnel should conduct ongoing Aedes surveillance during the mosquito season appropriate to their region and take preventive and responsive action to reduce disease risk to active duty, government employees, and family member populations." (70) (p4) In addition, it points out that "OPNAVINST 6250.4C ... requires all Navy and Marine Corps installations to have an Emergency Vector Control Plan (EVCP) for disease vector surveillance and control during disease outbreaks." (70) (p4) The spreadsheet described in this study should provide an additional resource for installations as they use the NMCPHC guide to ". complement installation pest management plans, including the EVCP, as a way to assess the risk of vector borne diseases, and implement strategies to reduce the risk to personnel assigned to installations." (70) (p4)

Knowing when conditions are suitable for vectors is crucial for monitoring the success or failure of any control program. Appendix C of the NMCPHC guide (70) is a chart to determine the risk of infection on an installation and when to apply vector control measures. This 4-level vector threat response plan relies on information about vector abundance and reports of disease transmission. We believe the Excel spreadsheet risk tool to be a valuable adjunct to the NMCPHC plan, as it would assist with defining the length of the mosquito season, and the judicious deployment and timing of entomological resources. Each military facility is unique and varies in size, function, human density, and suitable mosquito habitat, so not all of the 733 facilities addressed in this study will be at risk of mosquito-borne disease and suitable candidates for mosquito surveillance. However, in any case, all locations should be useful as points of reference for other nearby locations where mosquito surveillance is conducted.

Development of the Zika Risk Code in Figure 1 derived some inspiration from Figure 3 of Fischer et al, (71) who combined models of vector habitat suitability with temperature categories for CHIKV replication to produce a matrix of climate related risk classes.

It is important to note that each data source used in this analysis has the potential for errors which should be considered when determining risk. For example, habitat suitability models for each vector may not be accurate for all areas, and may only predict average yearly suitability. Traits like temperature vary continuously over the surface of the earth but are effected by elevation and use of multiple scale data, including weekly and monthly, and 1 km and 1[degrees] spatial resolution data used here will result in important spatial and temporal variations in accuracy. Temperature data refers to the maximum daytime air temperature near the surface (averaged over various spatial resolutions) from daily data for a recent date range, which NASA acknowledges has limitations. Vectors can also seek microclimates (eg, indoors, subterranean habitats) that may be warmer or cooler than the outside temperature that is estimated by remote sensing data.

Temperatures within the suitable range may not affect organisms uniformly. According to Westbrook et al, (72) adult female mosquitoes reared from immature stages at 18[degrees]C, were 6 times more likely to be infected with CHIKV than those reared at 32[degrees]C. Westbrook et al (72) noted that climate factors, such as temperature, experienced at the larval stage (which would not be detected by adult trapping programs) can influence the competence of adult female mosquitoes to vector arboviruses. We also do not account for temperature fluctuations; according to Lambrechts et al, (73) mosquitoes lived longer and were more likely to become infected with DENV under moderate temperature fluctuations rather than under large temperature fluctuations. Thangamani et al (74) and Ferreira-de-Brito et al (6) found that ZIKAV can be vertically transmitted in Ae. aegypti but not Ae. albopictus. This capability suggests mechanisms for the virus to survive in eggs that can survive for months in a dried dormant state during adverse conditions, for example, a harsh winter that would normally kill adult mosquitoes.

The approach to estimating risk levels in this project deliberately uses simplified assumptions about temperature and mosquito physiology and relies on published models to consider other drivers, which could include: precipitation; interspecific competition; and anthropogenic factors such as imported cases, built-up areas, vegetation indices, human behavior, and economic indices that can modify risk in complex and less understood ways. Other models could be used in place of the ones included in this study. For example, Samy et al (23) used proxies for poverty and accessibility that may further increase the biological reality of estimates of transmission risk. As a risk factor for ZIKAV infections is congenital brain abnormalities including microcephaly, (75) risk estimates would be greatly enhanced by quantifying the proximity of pregnancies to areas of interest (76,77) (ie, military facilities). Among US military women, those of reproductive age represent the majority, (78) and from 2001 to 2010, an average of 15,600 children were born to active component women each year. (79) Data from the Defense Medical Surveillance System do not include records of non-reimbursed care received at medical facilities outside of the military health system, adding to the difficulty in estimating numbers of potentially at-risk mothers, or those in the vicinity who could benefit from vector control programs conducted within military facilities.

The Zika virus can be imported and spread by nonvector transmission routes (eg, sexual transmission (80)), so it is recommended that a level of caution be taken when interpreting the data provided by this system. It is wise to monitor activity in surrounding facilities and any reputable information from other sources before acting on any recommendations given here. It is further recommended that the near-real time and forecast analysis should be viewed in conjunction with the monthly average Excel vector hazard files which uses average monthly maximum temperature, to gain further longer-term insights into where thermal conditions will support vector activity.

While this tool could be improved with higher resolution data and more nuanced models, our aim was to create an accessible and adaptable, yet useful platform for entomological decision-making that uses readily available data and models. New models and higher resolution climate data can be easily incorporated into this tool as they become available. Validation of the output from this tool using mosquito surveillance results, and obtaining user feedback, would be useful goals for future research.


We thank Jean-Paul Chrietien (Armed Forces Health Surveillance Branch (AFHSB)) for constructive feedback during the development of the Excel Risk Tool, as well as Jim Writer and Penny Masuoka for providing references and feedback useful in the development of the ZIKAV threshold information. We thank LTC Jeff Clark and MAJ Wes McCardle (former and current Walter Reed Biosystematics Unit Chiefs respectively) for their support during the development of this tool. We also thank LTC (Ret) Jason Richardson, MAJ Karl Korpal, CDR Fred Stell, and Dr Terry Carpenter of the Armed Forces Pest Management Board for their feedback. This study was made possible by FY2016 grant P2007_16_WR from the AFHSB and its Global Emerging Infections Surveillance and Response Section.

This research was performed under a memorandum of understanding between the Walter Reed Army Institute of Research and the Smithsonian Institution's National Museum of Natural History, with institutional support provided by both organizations. The opinions contained herein are those of the authors and do not reflect official views of the supporting agencies.


(1.) Council of State and Territorial Epidemiologists Zika virus disease and congenital Zika virus infection interim case definition and addition to the Nationally Notifiable Disease List. Atlanta, GA; 2016. Available at: rus_Disease_and_Congenital_Zika_Virus_Infec tion_Interim.pdf. Accessed February 27, 2017.

(2.) Walker WL, Lindsey NP, Lehman JA, et al. Zika virus disease cases--50 states and the District of Columbia, January 1-July 31, 2016. MMWR Morb Mortal Wkly Rep. 2016; 65(36); 983-986.

(3.) Likos A, Griffin I, Bingham AM, et al. Local mosquito-borne transmission of Zika virus--Miami-Dade and Broward counties, Florida, JuneAugust 2016. MMWR Morb Mortal Wkly Rep. 2016; 65(38):1032-1038.

(4.) Grard G, Caron M, Mombo IM, et al. Zika virus in Gabon (Central Africa)-2007: a new threat from Aedes albopictus?. PLoS Negl Trop Dis. 2014; 8(2):e2681.

(5.) Guerbois M, Fernandez-Salas I, Azar SR, et al. Outbreak of Zika virus infection, Chiapas State, Mexico, 2015, and first confirmed transmission by Aedes aegypti mosquitoes in the Americas. J Infect Dis. 2016; 214(9): 1349-1356. DOI: 10.1093/infdis/ jiw302.

(6.) Ferreira-de-Brito A, Ribeiro IP, Miranda RM, et al. First detection of natural infection of Aedes aegypti with Zika virus in Brazil and throughout South America. Mem Inst Oswaldo Cruz. 2016; 111(10):655-658. DOI: 10.1590/0074-02760160332.

(7.) Scott TW, Amerasinghe PH, Morrison AC, et al. Longitudinal studies of Aedes aegypti (Diptera: Culicidae) in Thailand and Puerto Rico: blood feeding frequency. J Med Entomol. 2000; 37(1):89-101.

(8.) Scott TW, Clark GG, Amerasinghe PH, Lorenz LH, Reiter P, Edman JD. Detection of multiple blood feeding patterns in Aedes aegypti (Diptera: Culicidae) during a single gonotrophic cycle using a histological technique. J Med Entomol. 1993; 30:94-99.

(9.) Braks MA, Honorio NA, Lourengo-De-Oliveira R, Juliano SA, Lounibos LP. Convergent habitat segregation of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in southeastern Brazil and Florida. J Med Entomol. 2003; 40(6):785-794.

(10.) Tsuda Y, Suwonkerd W, Chawprom S, Prajakwong S, Takagi M. Different spatial distribution of Aedes aegypti and Aedes albopictus along an urban-rural gradient and the relating environmental factors examined in three villages in northern Thailand. J Am Mosq Control Assoc. 2006; 22(2):222-228.

(11.) Leal WS. Zika mosquito vectors: the jury is still out [version 2; referees: 5 approved]. F1000Res. 2016; 5:2546. DOI: 10.12688/f1000research.9839.2.

(12.) Foley DH, Maloney FA Jr, Harrison FJ, Wilkerson RC, Rueda LM. Online spatial database of USAPHCR-W mosquito surveillance records for 1947-2009. US Army Med Dep J. July-September 2011:29-36.

(13.) Pellerin C. DoD adds funding to enhance Zika surveillance by military labs. DoD News, Defense Media Activity. May 16, 2016. Available at: http:// dod-adds-funding-to-enhance-Zika-surveillanceby-military-labs. Accessed February 27, 2017.

(14.) Kime P Zika threat hits home: which US military hubs are most

at risk? Military Times. April 10, 2016. Available at: story/military/2016/04/10/Zika-threat-hits-homewhich-us -military-hub s -mo st-risk/82664202/. Ac cessed February 27, 2017.

(15.) Attaway DF, Waters NM, Geraghty EM, Jacobsen kH. Zika virus: endemic and epidemic ranges of Aedes mosquito transmission. J Infect Public Health. 2016; 10(1):120-123. DOI: 10.1016/j. jiph.2016.09.008.

(16.) Brady OJ, Golding N, Pigott DM, et al. Global temperature constraints on Aedes aegypti and Ae. albopictus persistence and competence for dengue virus transmission. Parasit Vectors. 2014; 7:338. Available at: http://parasitesandvectors.biomed Accessed March 29, 2017.

(17.) Campbell LP, Luther C, Moo-Llanes D, Ramsey JM, Danis-Lozano R, Peterson AT. Climate change influences on global vector distributions for dengue and chikungunya viruses. Philos Trans R Soc Lond B Biol Sci. 2015; 370(1665). DOI: 10.1098/ rstb.2014.0135.

(18.) Khormi HM, Kumar L. Climate change and the potential global distribution of Aedes aegypti: spatial modelling using geographical information system and CLIMEX. Geospat Health. 2014; 8(2):405-415.

(19.) Medley KA. Niche shifts during the global invasion of the Asian tiger mosquito, Aedes albopictus Skuse (Culicidae), revealed by reciprocal distribution models. Glob Ecol Biogeogr. 2010; 19:122-133.

(20.) Carlson CJ, Dougherty ER, Getz W. An ecological assessment of the pandemic threat of Zika virus. PLoS Negl Trop Dis. 2016; 10(8):e0004968. DOI: 10.1371/journal.pntd.0004968.

(21.) Messina JP, Kraemer MU, Brady OJ, et al. Mapping global environmental suitability for Zika virus. Elife. 2016; 5. pii: e15272. DOI: 10.7554/eLife.15272.

(22.) Perkins TA, Siraj AS, Ruktanonchai CW, Kraemer MU, Tatem AJ. Model-based projections of Zika virus infections in childbearing women in the Americas. Nat Microbiol. 2016; 1(9):16126. DOI: 10.1038/nmicrobiol.2016.126.

(23.) Samy AM, Thomas SM, Wahed AA, Cohoon KP, Peterson AT. Mapping the global geographic potential of Zika virus spread. Mem Inst Oswaldo Cruz. 2016; 111(9):559-560. DOI: 10.1590/0074-02760160149.

(24.) Kramer LD, Ebel GD. Dynamics of flavivirus infection in mosquitoes. Adv Virus Res. 2003; 60:187-232.

(25.) Couret J, Benedict MQ. A meta-analysis of the factors influencing development rate variation in Aedes aegypti (Diptera: Culicidae). BMC Ecol. 2014; 14:3. DOI: 10.1186/1472-6785-14-3.

(26.) Brady OJ, Johansson MA, Guerra CA, et al. Modelling adult Aedes aegypti and Aedes albopictus survival at different temperatures in laboratory and field settings. Parasit Vectors. 2013; 6:351.

(27.) Gubler DJ, Kuno G, Markoff L. Flaviviruses. In: Knipe DM, Howley PM, Griffin DE, et al, eds. Fields Virology. Vol. 1, 5th Ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2007; 1153-1252.

(28.) Bonne-Wepster J, Brug SL. The Subgenus Stegomyia in Netherland India. Geneeskundig Tijdschrift voor Nederlandsch-Indie. 1932; 72:39-119.

(29.) Marchoux, Salimbeni, Simond. La Fievre Jaune. Rapport de la mission frangaise. Annals de l 'institut Pasteur. 1903; XVIL665-734. Cited by: BonneWepster J, Brug SL. The Subgenus Stegomyia in Netherland India. Geneeskundig Tijdschrift voor Nederlandsch-Indie. 1932; 72:39-119.

(30.) Howard LO, Dyar HG, Knab F. The Mosquitoes of North and Central America and the West-Indies. Vol I. Washington DC: The Carnegie Institution of Washington; 1912. Cited by: Bonne-Wepster J, Brug SL. The Subgenus Stegomyia in Netherland India. Geneeskundig Tijdschrift voor Nederlandsch-Indie. 1932; 72:39-119.

(31.) Connor ME. Renseignements sur l'Aedes calopus (Stegomyia). Mexico [abstract]. Bull Off Int Hyg Publ. 1922; XV:506. Cited by: Bonne-Wepster J, Brug SL. The Subgenus Stegomyia in Netherland India. Geneeskundig Tijdschrift voor Nederlandsch-Indie. 1932; 72:39-119.

(32.) Cossio V. Bol Cons Nac Hig Uruguay, January; [abstract] Bull Off Int Hyg Publ. 1931; XXIII:1664; and Bull Inst Past. 1931; XXIX:895. Cited by: BonneWepster J, Brug SL. The Subgenus Stegomyia in Netherland India. Geneeskundig Tijdschrift voor Nederlandsch-Indie. 1932; 72:39-119.

(33.) Davis NC. Brazil Medico. Trop Dis Bull. 1931; XLV:77; [abstract] 1931; XXVIII:726.Cited by: Bonne-Wepster J, Brug SL. The Subgenus Stegomyia in Netherland India. Geneeskundig Tijdschrift voor Nederlandsch-Indie. 1932; 72:39-119.

(34.) Guiteras, Otto, Neumann [detailed citation information not available] In: Howard LO, Dyar HG, Knab F. The Mosquitoes of North and Central America and the West-Indies. Vol I. Washington DC: The Carnegie Institution of Washington; 1912. Cited by: Bonne-Wepster J, Brug SL. The Subgenus Stegomyia in Netherland India. Geneeskundig Tijdschrift voor Nederlandsch-Indie. 1932; 72:39-119.

(35.) Flu PC. Tests re the period in which Stegomia fasciata can live at low temperatures. Mededeel. v. d. burgerl. geneesk. dienst. In: Nederl- Indie. Batavia, Deel vii:95-105. Cited by: Bonne-Wepster J, Brug SL. The Subgenus Stegomyia in Netherland India. Geneeskundig Tijdschrift voor Nederlandsch-Indie. 1932; 72:39-119.

(36.) Dinger JE, Schuffner WAP, Snijders EP, Swellengrebel NH. NedTijdschr v Gen. 1929; LXXV(I):2964 Cited by: Bonne-Wepster J, Brug SL. The Subgenus Stegomyia in Netherland India. Geneeskundig Tijdschrift voor Nederlandsch-Indie. 1932; 72:39-119.

(37.) Khormi HM, Kumar L, Elzahrany R. Describing and analyzing the association between meteorological variables and adult Aedes aegypti mosquitoes. J Food Agr Environ. 2011; 9:954-959.

(38.) Connor ME. Suggestions for developing a campaign to control yellow fever. Am J Trop Med. 1924; 4:277-307.

(39.) Wayne AR, Graham CL. The effect of temperature and relative humidity on the flight performance of female Aedes aegypti. J Insect Physiol. 1968; 14:1251-1257.

(40.) Macfie JWS. Heat and Stegomyia fasciata, short exposures to raised temperatures. Ann Trop Med Parasitol. 1920; 14:73-82.

(41.) Bliss AR, Gill JM. The effects of freezing on the larvae of Aedes aegypti. Am J Trop Med Hyg. 1933; 13:583-588.

(42.) Christophers SR. Aedes aegypti (L.) the Yellow Fever Mosquito. Its Life History, Bionomics and Structure. Cambridge, UK: Cambridge University Press; 1960.

(43.) Estrada-Franco JG, Craig GB. Biology, Disease Relationships, and Control of Aedes albopictus. Washington DC: Pan American Health Organization; 1995.

(44.) Carrington LB, Armijos MV, Lambrechts L, Barker CM, Scott TW. Effects of fluctuating daily temperatures at critical thermal extremes on Aedes aegypti life-history traits. PLoS One. 2013; 8(3):e58824.

(45.) Carrington LB, Seifert SN, Willits NH, Lambrechts L, Scott TW. Large diurnal temperature fluctuations negatively influence Aedes aegypti (Diptera: Culicidae) life-history traits. J Med Entomol. 2013; 50(1):43-51.

(46.) Wallis RC. A GIS Model for Predicting Potential "High Risk" Areas of West Nile Virus by Identifying Ideal Mosquito Breeding Habitats [master's thesis]. Starkville, MS: Mississippi State University; 2005.

(47.) Xiao FZ, Zhang Y, Deng YQ, He S, Xie HG. The effect of temperature on the extrinsic incubation period and infection rate of dengue virus serotype 2 infection in Aedes albopictus. Arch Virol. 2014; 159:3053-3057. DOI: 10.1007/ s00705-014-2051-1.

(48.) Dohm DJ, O'Guinn ML, Turell MJ. Effect of environmental temperature on the ability of Culex pipiens (Diptera: Culicidae) to transmit West Nile virus. J Med Entomol. 2002; 39:221-225.

(49.) Cornel AJ, Jupp PG, Blackburn NK. Environmental temperature on the vector competence of Culex univittatus (Diptera: Culicidae) for West Nile Virus. J Med Entomol. 1993; 30:449-456.

(50.) Kinney RM, Huang CY, Whiteman MC, et al. Avian virulence and thermostable replication of the North American strain of West Nile Virus. J Gen Virol. 2006; 87:3611-3622.

(51.) Tilston N, Skelly C, Weinstein P. Pan-European chikungunya surveillance: designing risk stratified surveillance zones. Int J Health Geogr 2009; 8:61. DOI: 10.1186/1476 -072X-8-61.

(52.) Kostyuchenko VA, Lim Elisa XY, Zhang S, et al. Structure of the thermally stable Zika virus. Nature. 2016; 533(7603):425-428. DOI: 10.1038/ nature17994.

(53.) Goo L, Dowd KA, Smith AR, Pelc RS, DeMaso CR, Pierson TC. Zika virus is not uniquely stable at physiological temperatures compared to other flaviviruses. MBio. 2016; 7(5). Available at: https:// Accessed March 30, 2017.

(54.) US Census Bureau, TIGER/Line 2015. Available at: ger.html. Accessed February 28, 2017.

(55.) NASA/GSFC (Air temperature at surface Daytime/ Ascending AiRx3StD v006). Available at: http:// Accessed February 28, 2017.

(56.) Climate Prediction Center, NOAA, US National Weather Service. Available at: http://www.cpc. Ac cessed February 28, 2017.

(57.) National Digital Forecast Database. Available at: Ac cessed February 28, 2017.

(58.) WorldClim-Global Climate Data center. Available at: Accessed March 30, 2017.

(59.) Kraemer MU, Sinka ME, Duda KA, Mylne AQ, Shearer FM, Barker CM, et al. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. ELife. 2015; 4:e08347. DOI: 10.7554/ eLife.08347.

(60.) Honorio N, Alves S, Wellington da Costa L, et al. Dispersal of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in an urban endemic dengue area in the State of Rio de Janeiro, Brazil. Mem Inst Oswaldo Cruz. 2003; 98(2):191-198.

(61.) Harrington LC, Scott TW, Lerdthusnee K, et al. Dispersal of the dengue vector Aedes aegypti within and between rural communities. Am J Trop Med Hyg. 2005; 72:209-220.

(62.) Oak Ridge National Laboratory. LandScan [internet]. 2011. Available at: landscan/landscan_data_avail. shtml. Accessed February 28, 2017.

(63.) Foley DH. A spreadsheet mapping approach for error checking and sharing collection point data. Biodiversity Informatics. 2011; 7(3):137-142.

(64.) ArcGIS. World Terrain Base. Redlands, CA: Esri; Available at: ml?id=c61ad8ab017d49e1a82f580ee1298931. Accessed Feb 28, 2017.

(65.) VectorMap; 2016. Available at: http://vectormap. Accessed February 28, 2017.

(66.) Monaghan AJ, Morin CW, Steinhoff DF, et al. On the seasonal occurrence and abundance of the Zika virus vector mosquito Aedes aegypti in the contiguous United States. PLoS Curr [serial online]. March 16, 2016. Available at: http://currents.plos. org/outbreaks/article/on-the-seasonal-occurrenceand-abundance-of-the-Zika-virus-vector-mosqui to-aedes-aegypti-in-the-contiguous-united-states/. Accessed March 30, 2017.

(67.) Defense Health Agency. Electronic Surveillance System for the Early Notification of Community-based Epidemics [internet]. 2017. Available at: Technology/Clinical-Support/Centralized-Credentials-Quality-Assurance-System/Decision-Sup port/Electronic-Surveillance-System-for-the-Early-Notification-of-Community-based-Epidemics. Accessed Feb 28, 2017.

(68.) US Army. Medical Situational Awareness in Theater [internet]; 2017. Available at: http://www.mc4. Accessed February 28, 2017.

(69.) Armed Forces Pest Management Board. Available at: Accessed June 13, 2016.

(70.) Navy and Marine Corps Public Health Center. Ae des Surveillance and Control Plan for U. S. Navy and Marine Corps Installations. February 2016. Available at: nmcphc/Documents/program-and-policy-support/ NMCPHC-Aedes-Surveillance-Contro-for-NMCInstallations.pdf. Accessed February 27, 2017.

(71.) Fischer D, Thomas SM, Suk JE, et al. Climate change effects on chikungunya transmission in Europe: geospatial analysis of vector's climatic suitability and virus' temperature requirements. Int J Health Geogr. 2013; 12:51. DOI: 10.1186/1476-072X-12-51.

(72.) Westbrook CJ, Reiskind MH, Pesko KN, Greene KE, Lounibos PL. Larval environmental temperature and the susceptibility of Aedes albopictus Skuse (Diptera: Culicidae) to Chikungunya virus. Vector Borne Zoonotic Dis. 2010; 10(3):241-247.

(73.) Lambrechts L, Paaijmans KP, Fansiri T, et al. Impact of daily temperature fluctuations on dengue virus transmission by Aedes aegypti. Proc Natl AcadSci USA. 2011; 108(18):7460-7465.

(74.) Thangamani S, Huang J, Hart CE, Guzman H, Tesh RB. Vertical transmission of Zika virus in Aedes aegypti mosquitoes. Am J Trop Med Hyg. 2016; 95(5): 1168-1173.

(75.) Krauer F, Riesen M, Reveiz L, et al. Zika virus infection as a cause of congenital brain abnormalities and Guillain-Barre syndrome: Systematic Review. PLoSMed. 2017; 14(1):e1002203. DOI: 10.1371/journal.pmed. 1002203.

(76.) Tatem AJ, Campbell J, Guerra-Arias M, de Bernis L, Moran A, Matthews Z. Mapping for maternal and newborn health: the distributions of women of childbearing age, pregnancies and births. Int J Health Geogr. 2014; 13:2. DOI: 10.1186/1476-072X-13-2.

(77.) Alex Perkins T, Siraj AS, Ruktanonchai CW, Kraemer MU, Tatem AJ. Model-based projections of Zika virus infections in childbearing women in the Americas. Nat Microbiol. 2016; 1(9):16126. DOI: 10.1038/nmicrobiol.2016.126.

(78.) Braun L, Kennedy H, Womack J, Wilson C. Integrative literature review: US military women's genitourinary and reproductive health. Mil Med. 2016; 181(1):35-49.

(79.) Armed Forces Health Surveillance Center. Births, active component, US Armed Forces, 2001-2010. MSMR. 2011; 18(12):16-17.

(80.) Foy BD, Kobylinski KC, Chilson Foy JL, et al. Probable non-vector-borne transmission of Zika virus, Colorado, USA. Emerg Infect Dis. 2011; 17:880-882.

Desmond H. Foley, PhD

David B. Pecor, BS


Dr Foley is a Research Entomologist at the Walter Reed Biosystematics Unit, Entomology Branch, Walter Reed Army Institute of Research, and a Research Associate of the Entomology Department within the National Museum of Natural History, located at the Smithsonian Institution, Museum Support Center, Suitland, Maryland.

Mr Pecor is the VectorMap technical assistant at the Walter Reed Biosystematic Unit, Entomology Branch, Walter Reed Army Institute of Research, located at the Smithsonian Institution, Museum Support Center, Suitland, Maryland.

Caption: Figure 3. Average maximum temperature conditions for January for vectors and ZIKAV at military facilities within the contiguous 48 states.

Caption: Figure 4. Thermal conditions for January and August for vectors and ZIKAV at military facilities in California. Note: unsuitable conditions in August in the south are due to temperatures too high for the vectors.

Caption: Figure 7. Summary risk data for 733 US military facilities over 12 months.

Caption: Aedes aegypti (courtesy of the CDC)

Caption: Aedes albopictus (courtesy of the CDC)
Figure 1. An overall Zika Risk Code based on the combined score,
which rates conditions from low risk (Blue: Code 1) to high risk
(Red: Code 4).

Zika   Derailed explanation     Preconditions for        Combination
risk                            transmission                score

1      Temperature not OK for   Preconditions do not          0
       Vector and Virus         exist for Transmission

1      Temperature OK Tor       Preconditions do not
       Vector or Virus but      exist for Transmission     10, 14
       not both. Model says
       habitat not very
       suitable for Vector

1      Temperature OK for
       Vector or Veus but not   Preconditions do not       15, 21
       both. Model says         exist for Transmission
       habitat suitable for

2      Temperature OK for       Preconditions
       Vector and Virus Model   unsuitable for               20
       says habitat not very    Transmission
       suitable tor Vector
       and Virus

3      Temperature OK for       Preconditions somewhat
       Vector and Virus Modal   suitable for                23,30
       says habitat suitable    Transmission
       for Vector or Virus
       nut not both.

4      Temperature OK for       Preconditans suitable
       Vector and Virus Modal   for Transmission             42
       says habitat suitable
       for vector and Virus.

Figure 2. Action statements constructed on the basis of the
temperature and habitat model suitability scores.

Variable                       Outcome            Action Statement

Temperature threshold   Temperature below       Too cold for
for Zika vectors        threshold (<            vectors--surveillance
                        13[degrees]C)           unnecessary.

                        Temperature ablove      Warm enough for
                        threshold (=>           vectors--surveillance
                        13[degrees]C)           may be needed.

Vector Suitability      Low vector              When warm enough,
                        suitability (< 50%)     model suggests
                                                vectors unlikely or
                                                low numbers.

                        High vector             When warm enough,
                        suitability (=> 50%)    model suggests
                                                vectors liely--may
                                                need control,
                                                education, and
                                                policies minimizing

Temperature threshold   Temperature below       Too cold for Zika--
for Zika virus          threshold (<            surveillance
                        18[degrees]C)           unnecessary.

                        Temperature above       Warm enough for Zika-
                        threshold (=>           -surveillance may be
                        18[degrees]C)           needed.

Zika Suitability        Low Zika suitability    When warm enough,
                        (< 50%)                 model suggests Zika

                        High Zika suitability   When warm enough,
                        (=> 50%)                model suggests Zika
                                                likely--may need
                                                control, education,
                                                and policies
                                                minimizing exposure.

                        No data                 No Model result for
                                                vector suitability.

Figure 5. An overall Zika virus risk code for near real-time and
forecast periods (A) is assigned based on a combination of:
temperature suitability for adult activity of the vectors (Ae.
aegypti and Ae. albopictus) (B), modeled habitat suitability of the
vectors (C), temperature suitability for Zika virus replication
within the vectors (D), and modeled habitat suitability of Zika virus
transmission (E). If suitable conditions exist (Zika Risk Code 4),
the number of people within 5 km is shown (F) as one indication of
the number of potential hosts in the vicinity, or the number of
people potentially benefiting from facility-wide vector surveillance
and control programs.

Overall Zika Risk       Is it usually warm      If above threshold
Code "1" (Blue)= low   enough during Jan for   temperature, what is
risk "4" (red)= low       Aedes aegypti/       annual % suitability
risk                    albopictus? (Red =     for Aedes aegypti or
                         Yes, Green = No)       albopictus? (Red =
                                                 Yes; Green= No)


Overall Zika Risk       Is it usually warm      If above threshold
Code "1" (Blue)= low   enough during January   temperature, what is
risk "4" (red)= low    for Zika replication    annual % suitability
risk                    in mosquito? (Red =    for Zika? (More red =
                         Yes; Green = No)         more suitable)


Overall Zika Risk        Number of people
Code "1" (Blue)= low   within 5 km with Code
risk "4" (red)= low        4 Zika level

4                              3944
4                              11288
4                              74609
3                                0
3                                0
3                                0
3                                0
4                              68609
1                                0

Figure 6. Summary risk data for each US state (top of list shown) to
assist with public health and resource allocation planning.

Summary of Vector hazard during January for U.S. Military facilities
by State for Zika virus

State           # Military     Zika Risk    # facilities where
              facilities (1)   Code (2-5)     preconditions
                               (average)       suitable for
                                            transmission (Zika
                                             Risk Code 4) (5)

Alaska              20             1                0
Alabama             14             1                0
Arkansas            4              1                0
Arizona             11            2.19              0
California         100            1.71              0
Colorado            9              1                0
Connecticut         10             1                0
District of         8              1                0
Delaware            2              1                0
Florida             48            2.44              17
Georgia             16             1                0
Hawaii              36            3.2               16
Iowa                2              1                0

State         # facilities with   # facilities with   Total population
               above threshold     above threshold     within 5 km of
               temperature for     temperature for     facilities (8)
               the vectors (6)    the pathogen (7)

Alaska                0                   0                   357,044
Alabama              10                   0                   553,648
Arkansas              0                   0                   187,622
Arizona              10                   0                   556,120
California           79                   0                 6,655,724
Colorado              0                   0                   386,446
Connecticut           0                   0                   212,038
District of           0                   0                   842,772
Delaware              0                   0                    52,946
Florida              48                   1                 1,373,859
Georgia               9                   0                 1,021,113
Hawaii               36                   1                   980,370
Iowa                  0                   0                   266,567

State         Total population
              within 5 km with
               conditions for

Alaska                       0
Alabama                     0
Arkansas                    0
Arizona                     0
California                  0
Colorado                    0
Connecticut                 0
District of                 0
Delaware                    0
Florida               653,556
Georgia                     0
Hawaii                 247,918
Iowa                        0
COPYRIGHT 2017 U.S. Army Medical Department Center & School
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2017 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Foley, Desmond H.; Pecor, David B.
Publication:U.S. Army Medical Department Journal
Article Type:Report
Geographic Code:1USA
Date:Jan 1, 2017
Previous Article:Georgia's collaborative approach to expanding mosquito surveillance in response to Zika virus: a case study.
Next Article:Biosurveillance and morphological variations of larvae and pupae of common malaria vectors, Anopheles (Anopheles) Hyrcanus group species in the...

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters