A location-specific spreadsheet for estimating Zika risk and timing for Zika vector surveillance, using US military facilities as an example.
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.
MATERIALS AND METHODS
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.
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.
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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 code 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 Vector 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 exposure. 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 unlikely. 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) 4 4 4 3 3 3 3 4 1 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) 4 4 4 3 3 3 3 4 1 Overall Zika Risk Number of people Code "1" (Blue)= low within 5 km with Code risk "4" (red)= low 4 Zika level risk 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 Columbia 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 Columbia 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 suitable conditions for transmission Alaska 0 Alabama 0 Arkansas 0 Arizona 0 California 0 Colorado 0 Connecticut 0 District of 0 Columbia Delaware 0 Florida 653,556 Georgia 0 Hawaii 247,918 Iowa 0
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|Author:||Foley, Desmond H.; Pecor, David B.|
|Publication:||U.S. Army Medical Department Journal|
|Date:||Jan 1, 2017|
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