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Gizaw Z, Salubi E, Pietroniro A, Schuster-Wallace CJ. Impacts of climate change on water-related mosquito-borne diseases in temperate regions: A systematic review of literature and meta-analysis. Acta Trop 2024; 258:107324. [PMID: 39009235 DOI: 10.1016/j.actatropica.2024.107324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/04/2024] [Accepted: 07/12/2024] [Indexed: 07/17/2024]
Abstract
Mosquito-borne diseases are a known tropical phenomenon. This review was conducted to assess the mecha-nisms through which climate change impacts mosquito-borne diseases in temperate regions. Articles were searched from PubMed, Scopus, Web of Science, and Embase databases. Identification criteria were scope (climate change and mosquito-borne diseases), region (temperate), article type (peer-reviewed), publication language (English), and publication years (since 2015). The WWH (who, what, how) framework was applied to develop the research question and thematic analyses identified the mechanisms through which climate change affects mosquito-borne diseases. While temperature ranges for disease transmission vary per mosquito species, all are viable for temperate regions, particularly given projected temperature increases. Zika, chikungunya, and dengue transmission occurs between 18-34 °C (peak at 26-29 °C). West Nile virus establishment occurs at monthly average temperatures between 14-34.3 °C (peak at 23.7-25 °C). Malaria establishment occurs when the consecutive average daily temperatures are above 16 °C until the sum is above 210 °C. The identified mechanisms through which climate change affects the transmission of mosquito-borne diseases in temperate regions include: changes in the development of vectors and pathogens; changes in mosquito habitats; extended transmission seasons; changes in geographic spread; changes in abundance and behaviors of hosts; reduced abundance of mosquito predators; interruptions to control operations; and influence on other non-climate factors. Process and stochastic approaches as well as dynamic and spatial models exist to predict mosquito population dynamics, disease transmission, and climate favorability. Future projections based on the observed relations between climate factors and mosquito-borne diseases suggest that mosquito-borne disease expansion is likely to occur in temperate regions due to climate change. While West Nile virus is already established in some temperate regions, Zika, dengue, chikungunya, and malaria are also likely to become established over time. Moving forward, more research is required to model future risks by incorporating climate, environmental, sociodemographic, and mosquito-related factors under changing climates.
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Affiliation(s)
- Zemichael Gizaw
- Department of Geography and Planning, University of Saskatchewan, 117 Science Place, Saskatoon, Saskatchewan, S7N 5C8, Canada; Department of Environmental and Occupational Health and Safety, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia; Global Institute for Water Security, University of Saskatchewan, Saskatoon, Canada
| | - Eunice Salubi
- Department of Geography and Planning, University of Saskatchewan, 117 Science Place, Saskatoon, Saskatchewan, S7N 5C8, Canada
| | - Alain Pietroniro
- Schulich School of Engineering, University of Calgary, Calgary, 622 Collegiate Pl NW, Calgary, Alberta, T2N 4V8, Canada; Global Institute for Water Security, University of Saskatchewan, Saskatoon, Canada
| | - Corinne J Schuster-Wallace
- Department of Geography and Planning, University of Saskatchewan, 117 Science Place, Saskatoon, Saskatchewan, S7N 5C8, Canada; Global Institute for Water Security, University of Saskatchewan, Saskatoon, Canada.
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Holcomb KM, Staples JE, Nett RJ, Beard CB, Petersen LR, Benjamin SG, Green BW, Jones H, Johansson MA. Multi-Model Prediction of West Nile Virus Neuroinvasive Disease With Machine Learning for Identification of Important Regional Climatic Drivers. GEOHEALTH 2023; 7:e2023GH000906. [PMID: 38023388 PMCID: PMC10654557 DOI: 10.1029/2023gh000906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/15/2023] [Accepted: 10/21/2023] [Indexed: 12/01/2023]
Abstract
West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental United States (CONUS). Spatial heterogeneity in historical incidence, environmental factors, and complex ecology make prediction of spatiotemporal variation in WNV transmission challenging. Machine learning provides promising tools for identification of important variables in such situations. To predict annual WNV neuroinvasive disease (WNND) cases in CONUS (2015-2021), we fitted 10 probabilistic models with variation in complexity from naïve to machine learning algorithm and an ensemble. We made predictions in each of nine climate regions on a hexagonal grid and evaluated each model's predictive accuracy. Using the machine learning models (random forest and neural network), we identified the relative importance and variation in ranking of predictors (historical WNND cases, climate anomalies, human demographics, and land use) across regions. We found that historical WNND cases and population density were among the most important factors while anomalies in temperature and precipitation often had relatively low importance. While the relative performance of each model varied across climatic regions, the magnitude of difference between models was small. All models except the naïve model had non-significant differences in performance relative to the baseline model (negative binomial model fit per hexagon). No model, including the ensemble or more complex machine learning models, outperformed models based on historical case counts on the hexagon or region level; these models are good forecasting benchmarks. Further work is needed to assess if predictive capacity can be improved beyond that of these historical baselines.
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Affiliation(s)
- Karen M. Holcomb
- Global Systems LaboratoryNational Oceanic and Atmospheric AdministrationBoulderCOUSA
- Now at Division of Vector‐Borne DiseasesCenters for Disease Control and PreventionFort CollinsCOUSA
| | - J. Erin Staples
- Division of Vector‐Borne DiseasesCenters for Disease Control and PreventionFort CollinsCOUSA
| | - Randall J. Nett
- Division of Vector‐Borne DiseasesCenters for Disease Control and PreventionFort CollinsCOUSA
| | - Charles B. Beard
- Division of Vector‐Borne DiseasesCenters for Disease Control and PreventionFort CollinsCOUSA
| | - Lyle R. Petersen
- Division of Vector‐Borne DiseasesCenters for Disease Control and PreventionFort CollinsCOUSA
| | - Stanley G. Benjamin
- Global Systems LaboratoryNational Oceanic and Atmospheric AdministrationBoulderCOUSA
- Cooperative Institute for Research in Environmental SciencesUniversity of Colorado BoulderBoulderCOUSA
| | - Benjamin W. Green
- Global Systems LaboratoryNational Oceanic and Atmospheric AdministrationBoulderCOUSA
- Cooperative Institute for Research in Environmental SciencesUniversity of Colorado BoulderBoulderCOUSA
| | - Hunter Jones
- Climate Prediction OfficeNational Oceanic and Atmospheric AdministrationSilver SpringMDUSA
| | - Michael A. Johansson
- Division of Vector‐Borne DiseasesCenters for Disease Control and PreventionSan JuanPRUSA
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Schwarz ER, Long MT. Comparison of West Nile Virus Disease in Humans and Horses: Exploiting Similarities for Enhancing Syndromic Surveillance. Viruses 2023; 15:1230. [PMID: 37376530 DOI: 10.3390/v15061230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
West Nile virus (WNV) neuroinvasive disease threatens the health and well-being of horses and humans worldwide. Disease in horses and humans is remarkably similar. The occurrence of WNV disease in these mammalian hosts has geographic overlap with shared macroscale and microscale drivers of risk. Importantly, intrahost virus dynamics, the evolution of the antibody response, and clinicopathology are similar. The goal of this review is to provide a comparison of WNV infection in humans and horses and to identify similarities that can be exploited to enhance surveillance methods for the early detection of WNV neuroinvasive disease.
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Affiliation(s)
- Erika R Schwarz
- Montana Veterinary Diagnostic Laboratory, MT Department of Livestock, Bozeman, MT 59718, USA
| | - Maureen T Long
- Department of Comparative, Diagnostic, & Population Medicine, College of Veterinary Medicine, University of Florida, Gainesville, FL 32610, USA
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Gorris ME, Randerson JT, Coffield SR, Treseder KK, Zender CS, Xu C, Manore CA. Assessing the Influence of Climate on the Spatial Pattern of West Nile Virus Incidence in the United States. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:47016. [PMID: 37104243 PMCID: PMC10137712 DOI: 10.1289/ehp10986] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
BACKGROUND West Nile virus (WNV) is the leading cause of mosquito-borne disease in humans in the United States. Since the introduction of the disease in 1999, incidence levels have stabilized in many regions, allowing for analysis of climate conditions that shape the spatial structure of disease incidence. OBJECTIVES Our goal was to identify the seasonal climate variables that influence the spatial extent and magnitude of WNV incidence in humans. METHODS We developed a predictive model of contemporary mean annual WNV incidence using U.S. county-level case reports from 2005 to 2019 and seasonally averaged climate variables. We used a random forest model that had an out-of-sample model performance of R2=0.61. RESULTS Our model accurately captured the V-shaped area of higher WNV incidence that extends from states on the Canadian border south through the middle of the Great Plains. It also captured a region of moderate WNV incidence in the southern Mississippi Valley. The highest levels of WNV incidence were in regions with dry and cold winters and wet and mild summers. The random forest model classified counties with average winter precipitation levels <23.3mm/month as having incidence levels over 11 times greater than those of counties that are wetter. Among the climate predictors, winter precipitation, fall precipitation, and winter temperature were the three most important predictive variables. DISCUSSION We consider which aspects of the WNV transmission cycle climate conditions may benefit the most and argued that dry and cold winters are climate conditions optimal for the mosquito species key to amplifying WNV transmission. Our statistical model may be useful in projecting shifts in WNV risk in response to climate change. https://doi.org/10.1289/EHP10986.
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Affiliation(s)
- Morgan E. Gorris
- Information Systems and Modeling, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
| | - James T. Randerson
- Department of Earth System Science, University of California, Irvine, Irvine, California, USA
| | - Shane R. Coffield
- Department of Earth System Science, University of California, Irvine, Irvine, California, USA
| | - Kathleen K. Treseder
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, California, USA
| | - Charles S. Zender
- Department of Earth System Science, University of California, Irvine, Irvine, California, USA
| | - Chonggang Xu
- Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
| | - Carrie A. Manore
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
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Wimberly MC, Davis JK, Hildreth MB, Clayton JL. Integrated Forecasts Based on Public Health Surveillance and Meteorological Data Predict West Nile Virus in a High-Risk Region of North America. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:87006. [PMID: 35972761 PMCID: PMC9380861 DOI: 10.1289/ehp10287] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 07/29/2022] [Accepted: 08/01/2022] [Indexed: 06/09/2023]
Abstract
BACKGROUND West Nile virus (WNV), a global arbovirus, is the most prevalent mosquito-transmitted infection in the United States. Forecasts of WNV risk during the upcoming transmission season could provide the basis for targeted mosquito control and disease prevention efforts. We developed the Arbovirus Mapping and Prediction (ArboMAP) WNV forecasting system and used it in South Dakota from 2016 to 2019. This study reports a post hoc forecast validation and model comparison. OBJECTIVES Our objective was to validate historical predictions of WNV cases with independent data that were not used for model calibration. We tested the hypothesis that predictive models based on mosquito surveillance data combined with meteorological variables were more accurate than models based on mosquito or meteorological data alone. METHODS The ArboMAP system incorporated models that predicted the weekly probability of observing one or more human WNV cases in each county. We compared alternative models with different predictors including a) a baseline model based only on historical WNV cases, b) mosquito models based on seasonal patterns of infection rates, c) environmental models based on lagged meteorological variables, including temperature and vapor pressure deficit, d) combined models with mosquito infection rates and lagged meteorological variables, and e) ensembles of two or more combined models. During the WNV season, models were calibrated using data from previous years and weekly predictions were made using data from the current year. Forecasts were compared with observed cases to calculate the area under the receiver operating characteristic curve (AUC) and other metrics of spatial and temporal prediction error. RESULTS Mosquito and environmental models outperformed the baseline model that included county-level averages and seasonal trends of WNV cases. Combined models were more accurate than models based only on meteorological or mosquito infection variables. The most accurate model was a simple ensemble mean of the two best combined models. Forecast accuracy increased rapidly from early June through early July and was stable thereafter, with a maximum AUC of 0.85. The model predictions captured the seasonal pattern of WNV as well as year-to-year variation in case numbers and the geographic pattern of cases. DISCUSSION The predictions reached maximum accuracy early enough in the WNV season to allow public health responses before the peak of human cases in August. This early warning is necessary because other indicators of WNV risk, including early reports of human cases and mosquito abundance, are poor predictors of case numbers later in the season. https://doi.org/10.1289/EHP10287.
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Affiliation(s)
- Michael C. Wimberly
- Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, Oklahoma, USA
| | - Justin K. Davis
- Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, Oklahoma, USA
| | - Michael B. Hildreth
- Department of Biology and Microbiology, South Dakota State University, Brookings, South Dakota, USA
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Sofia M, Giannakopoulos A, Giantsis IA, Touloudi A, Birtsas P, Papageorgiou K, Athanasakopoulou Z, Chatzopoulos DC, Vrioni G, Galamatis D, Diamantopoulos V, Mpellou S, Petridou E, Kritas SK, Palli M, Georgakopoulos G, Spyrou V, Tsakris A, Chaskopoulou A, Billinis C. West Nile Virus Occurrence and Ecological Niche Modeling in Wild Bird Species and Mosquito Vectors: An Active Surveillance Program in the Peloponnese Region of Greece. Microorganisms 2022; 10:1328. [PMID: 35889046 PMCID: PMC9320058 DOI: 10.3390/microorganisms10071328] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/25/2022] [Accepted: 06/28/2022] [Indexed: 11/17/2022] Open
Abstract
West Nile Virus (WNV) is maintained in nature in a bird-mosquito cycle and human infections follow a seasonal pattern, favored by climatic conditions. Peloponnese Region, located in Southern Greece, initiated an active WNV surveillance program to protect public health during 2019-2020. The project included monitoring of avian hosts and mosquito vectors, while sampling locations were prioritized after consideration of WNV circulation in birds, mosquitos and humans during previous seasons. Biological materials were collected from 493 wild birds of 25 species and 678 mosquito pools, which were molecularly screened for WNV presence. In this case, 14 environmental variables were associated with WNV detection in wild birds and mosquitos by using two separate MaxEnt models. Viral RNA was not detected in the target species during 2019, although in 2020, it was reported on 46 wild birds of ten species and 22 mosquito pools (Culex pipiens and Aedes albopictus). Altitude and land uses were significant predictors for both models and in fact, suitable conditions for virus occurrence were identified in low altitude zones. Bird- and mosquito-based surveillance systems yielded similar results and allowed for targeted vector control applications in cases of increased virus activity. Human cases were not reported on Peloponnese in 2020.
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Affiliation(s)
- Marina Sofia
- Faculty of Veterinary Science, University of Thessaly, 43100 Karditsa, Greece; (M.S.); (A.G.); (A.T.); (Z.A.)
| | - Alexios Giannakopoulos
- Faculty of Veterinary Science, University of Thessaly, 43100 Karditsa, Greece; (M.S.); (A.G.); (A.T.); (Z.A.)
| | - Ioannis A. Giantsis
- European Biological Control Laboratory, USDA-ARS—U.S. Department of Agriculture Agricultural Research Service, 57001 Thessaloniki, Greece; (I.A.G.); (A.C.)
| | - Antonia Touloudi
- Faculty of Veterinary Science, University of Thessaly, 43100 Karditsa, Greece; (M.S.); (A.G.); (A.T.); (Z.A.)
| | - Periklis Birtsas
- Faculty of Forestry, Wood Science and Design, 43100 Karditsa, Greece;
| | - Kontantinos Papageorgiou
- Faculty of Public and One Health, University of Thessaly, 43100 Karditsa, Greece; (K.P.); (D.C.C.)
- Faculty of Veterinary Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (E.P.); (S.K.K.)
| | - Zoi Athanasakopoulou
- Faculty of Veterinary Science, University of Thessaly, 43100 Karditsa, Greece; (M.S.); (A.G.); (A.T.); (Z.A.)
| | - Dimitris C. Chatzopoulos
- Faculty of Public and One Health, University of Thessaly, 43100 Karditsa, Greece; (K.P.); (D.C.C.)
| | - Georgia Vrioni
- Department of Microbiology, Medical School, National and Kapodistrian University of Athens, 15772 Athens, Greece; (G.V.); (A.T.)
| | - Dimitrios Galamatis
- Hellenic Agricultural Organization DIMITRA (ELGO DIMITRA), 54248 Thessaloniki, Greece;
| | | | - Spyridoula Mpellou
- Bioefarmoges Eleftheriou LP-Integrated Mosquito Control, 19007 Marathon, Greece;
| | - Evanthia Petridou
- Faculty of Veterinary Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (E.P.); (S.K.K.)
| | - Spyridon K. Kritas
- Faculty of Veterinary Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (E.P.); (S.K.K.)
| | - Matina Palli
- Wildlife Protection & Rehabilitation Center, 24400 Gargalianoi, Greece; (M.P.); (G.G.)
| | | | - Vassiliki Spyrou
- Faculty of Animal Science, University of Thessaly, 41110 Larissa, Greece;
| | - Athanassios Tsakris
- Department of Microbiology, Medical School, National and Kapodistrian University of Athens, 15772 Athens, Greece; (G.V.); (A.T.)
| | - Alexandra Chaskopoulou
- European Biological Control Laboratory, USDA-ARS—U.S. Department of Agriculture Agricultural Research Service, 57001 Thessaloniki, Greece; (I.A.G.); (A.C.)
| | - Charalambos Billinis
- Faculty of Veterinary Science, University of Thessaly, 43100 Karditsa, Greece; (M.S.); (A.G.); (A.T.); (Z.A.)
- Faculty of Public and One Health, University of Thessaly, 43100 Karditsa, Greece; (K.P.); (D.C.C.)
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Rodgers MDSM, Fonseca E, Nieto PDM, Malone JB, Luvall JC, McCarroll JC, Avery RH, Bavia ME, Guimaraes R, Wen X, Silva MMN, Carneiro DDMT, Cardim LL. Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector <em>Lutzomyia longipalpis</em> in Sao Paulo and Bahia states, Brazil. GEOSPATIAL HEALTH 2022; 17. [PMID: 35686991 DOI: 10.4081/gh.2022.1095] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 05/20/2022] [Indexed: 06/15/2023]
Abstract
Visceral leishmaniasis (VL) is a neglected tropical disease transmitted by Lutzomyia longipalpis, a sand fly widely distributed in Brazil. Despite efforts to strengthen national control programs reduction in incidence and geographical distribution of VL in Brazil has not yet been successful; VL is in fact expanding its range in newly urbanized areas. Ecological niche models (ENM) for use in surveillance and response systems may enable more effective operational VL control by mapping risk areas and elucidation of eco-epidemiologic risk factors. ENMs for VL and Lu. longipalpis were generated using monthly WorldClim 2.0 data (30-year climate normal, 1-km spatial resolution) and monthly soil moisture active passive (SMAP) satellite L4 soil moisture data. SMAP L4 Global 3-hourly 9-km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V004 were obtained for the first image of day 1 and day 15 (0:00-3:00 hour) of each month. ENM were developed using MaxEnt software to generate risk maps based on an algorithm for maximum entropy. The jack-knife procedure was used to identify the contribution of each variable to model performance. The three most meaningful components were used to generate ENM distribution maps by ArcGIS 10.6. Similar patterns of VL and vector distribution were observed using SMAP as compared to WorldClim 2.0 models based on temperature and precipitation data or water budget. Results indicate that direct Earth-observing satellite measurement of soil moisture by SMAP can be used in lieu of models calculated from classical temperature and precipitation climate station data to assess VL risk.
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Affiliation(s)
| | | | - Prixia Del Mar Nieto
- Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA.
| | - John B Malone
- Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA.
| | | | - Jennifer C McCarroll
- Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA.
| | - Ryan Harry Avery
- Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA.
| | | | | | - Xue Wen
- Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA.
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Cloud-based applications for accessing satellite Earth observations to support malaria early warning. Sci Data 2022; 9:208. [PMID: 35577816 PMCID: PMC9110363 DOI: 10.1038/s41597-022-01337-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 04/11/2022] [Indexed: 11/25/2022] Open
Abstract
Malaria epidemics can be triggered by fluctuations in temperature and precipitation that influence vector mosquitoes and the malaria parasite. Identifying and monitoring environmental risk factors can thus provide early warning of future outbreaks. Satellite Earth observations provide relevant measurements, but obtaining these data requires substantial expertise, computational resources, and internet bandwidth. To support malaria forecasting in Ethiopia, we developed software for Retrieving Environmental Analytics for Climate and Health (REACH). REACH is a cloud-based application for accessing data on land surface temperature, spectral indices, and precipitation using the Google Earth Engine (GEE) platform. REACH can be implemented using the GEE code editor and JavaScript API, as a standalone web app, or as package with the Python API. Users provide a date range and data for 852 districts in Ethiopia are automatically summarized and downloaded as tables. REACH was successfully used in Ethiopia to support a pilot malaria early warning project in the Amhara region. The software can be extended to new locations and modified to access other environmental datasets through GEE.
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Shartova N, Mironova V, Zelikhina S, Korennoy F, Grishchenko M. Spatial patterns of West Nile virus distribution in the Volgograd region of Russia, a territory with long-existing foci. PLoS Negl Trop Dis 2022; 16:e0010145. [PMID: 35100289 PMCID: PMC8803152 DOI: 10.1371/journal.pntd.0010145] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 01/05/2022] [Indexed: 11/25/2022] Open
Abstract
Southern Russia remains affected by West Nile virus (WNV). In the current study, we identified the spatial determinants of WNV distribution in an area with endemic virus transmission, with special reference to the urban settings, by mapping probable points of human infection acquisition and points of virus detection in mosquitoes, ticks, birds, and mammals during 1999-2016. The suitability of thermal conditions for extrinsic virus replication was assessed based on the approach of degree-day summation and their changes were estimated by linear trend analysis. A generalized linear model was used to analyze the year-to-year variation of human cases versus thermal conditions. Environmental suitability was determined by ecological niche modelling using MaxEnt software. Human population density was used as an offset to correct for possible bias. Spatial analysis of virus detection in the environment showed significant contributions from surface temperature, altitude, and distance from water bodies. When indicators of location and mobility of the human population were included, the relative impact of factors changed, with roads becoming most important. When the points of probable human case infection were added, the percentage of leading factors changed only slightly. The urban environment significantly increased the epidemic potential of the territory and created quite favorable conditions for virus circulation. The private building sector with low-storey houses and garden plots located in the suburbs provided a connection between urban and rural transmission cycles.
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Affiliation(s)
- Natalia Shartova
- Faculty of Geography, Lomonosov Moscow State University, Moscow, Russia
| | - Varvara Mironova
- Faculty of Geography, Lomonosov Moscow State University, Moscow, Russia
| | | | - Fedor Korennoy
- FGBI Federal Center for Animal Health (FGBI ARRIAH), Vladimir, Russia
| | - Mikhail Grishchenko
- Faculty of Geography, Lomonosov Moscow State University, Moscow, Russia
- Faculty of Geography and Geoinformatics, HSE University, Moscow, Russia
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Erraguntla M, Dave D, Zapletal J, Myles K, Adelman ZN, Pohlenz TD, Lawley M. Predictive model for microclimatic temperature and its use in mosquito population modeling. Sci Rep 2021; 11:18909. [PMID: 34556747 PMCID: PMC8460783 DOI: 10.1038/s41598-021-98316-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 09/06/2021] [Indexed: 02/08/2023] Open
Abstract
Mosquitoes transmit several infectious diseases that pose significant threat to human health. Temperature along with other environmental factors at breeding and resting locations play a role in the organismal development and abundance of mosquitoes. Accurate analysis of mosquito population dynamics requires information on microclimatic conditions at breeding and resting locations. In this study, we develop a regression model to characterize microclimatic temperature based on ambient environmental conditions. Data were collected by placing sensor loggers at resting and breeding locations such as storm drains across Houston, TX. Corresponding weather data was obtained from National Oceanic and Atmospheric Administration website. Features extracted from these data sources along with contextual information on location were used to develop a Generalized Linear Model for predicting microclimate temperatures. We also analyzed mosquito population dynamics for Aedes albopictus under ambient and microclimatic conditions using system dynamic (SD) modelling to demonstrate the need for accurate microclimatic temperatures in population models. The microclimate prediction model had an R2 value of ~ 95% and average prediction error of ~ 1.5 °C indicating that microclimate temperatures can be reliably estimated from the ambient environmental conditions. SD model analysis indicates that some microclimates in Texas could result in larger populations of juvenile and adult Aedes albopictus mosquitoes surviving the winter without requiring dormancy.
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Affiliation(s)
- Madhav Erraguntla
- grid.264756.40000 0004 4687 2082Department of Industrial Engineering, Texas A&M University, College Station, USA
| | - Darpit Dave
- grid.264756.40000 0004 4687 2082Department of Industrial Engineering, Texas A&M University, College Station, USA
| | - Josef Zapletal
- grid.264756.40000 0004 4687 2082Department of Industrial Engineering, Texas A&M University, College Station, USA
| | - Kevin Myles
- grid.264756.40000 0004 4687 2082Department of Entomology, Texas A&M University, College Station, USA
| | - Zach N. Adelman
- grid.264756.40000 0004 4687 2082Department of Entomology, Texas A&M University, College Station, USA
| | - Tyler D. Pohlenz
- grid.264756.40000 0004 4687 2082Department of Entomology, Texas A&M University, College Station, USA
| | - Mark Lawley
- grid.264756.40000 0004 4687 2082Department of Industrial Engineering, Texas A&M University, College Station, USA
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11
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Keyel AC, Gorris ME, Rochlin I, Uelmen JA, Chaves LF, Hamer GL, Moise IK, Shocket M, Kilpatrick AM, DeFelice NB, Davis JK, Little E, Irwin P, Tyre AJ, Helm Smith K, Fredregill CL, Elison Timm O, Holcomb KM, Wimberly MC, Ward MJ, Barker CM, Rhodes CG, Smith RL. A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making. PLoS Negl Trop Dis 2021; 15:e0009653. [PMID: 34499656 PMCID: PMC8428767 DOI: 10.1371/journal.pntd.0009653] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
West Nile virus (WNV) is a globally distributed mosquito-borne virus of great public health concern. The number of WNV human cases and mosquito infection patterns vary in space and time. Many statistical models have been developed to understand and predict WNV geographic and temporal dynamics. However, these modeling efforts have been disjointed with little model comparison and inconsistent validation. In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. A total of 13 models were selected and described. The spatial and temporal scales of each model were compared to guide the timing and the locations for mosquito and virus surveillance, to support mosquito vector control decisions, and to assist in conducting public health outreach campaigns at multiple scales of decision-making. Our overarching goal is to bridge the existing gap between model development, which is usually conducted as an academic exercise, and practical model applications, which occur at state, tribal, local, or territorial public health and mosquito control agency levels. The proposed model assessment and comparison framework helps clarify the value of individual models for decision-making and identifies the appropriate temporal and spatial scope of each model. This qualitative evaluation clearly identifies gaps in linking models to applied decisions and sets the stage for a quantitative comparison of models. Specifically, whereas many coarse-grained models (county resolution or greater) have been developed, the greatest need is for fine-grained, short-term planning models (m-km, days-weeks) that remain scarce. We further recommend quantifying the value of information for each decision to identify decisions that would benefit most from model input.
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Affiliation(s)
- Alexander C. Keyel
- Division of Infectious Diseases, Wadsworth Center, New York State Department of Health, Albany, New York, United States of America
- Department of Atmospheric and Environmental Sciences, University at Albany, Albany, New York, United States of America
| | - Morgan E. Gorris
- Information Systems and Modeling & Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Ilia Rochlin
- Center for Vector Biology, Rutgers University, New Brunswick, New Jersey, United States of America
| | - Johnny A. Uelmen
- Department of Pathobiology, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Luis F. Chaves
- Instituto Costarricense de Investigación y Enseñanza en Nutrición y Salud (INCIENSA), Tres Rios, Cartago, Costa Rica
| | - Gabriel L. Hamer
- Department of Entomology, Texas A&M University, College Station, Texas, United States of America
| | - Imelda K. Moise
- Department of Geography & Regional Studies, University of Miami, Coral Gables, Florida, United States of America
| | - Marta Shocket
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, United States of America
| | - A. Marm Kilpatrick
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, California, United States of America
| | - Nicholas B. DeFelice
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Justin K. Davis
- Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, Oklahoma, United States of America
| | - Eliza Little
- Connecticut Agricultural Experimental Station, New Haven, Connecticut, United States of America
| | - Patrick Irwin
- Northwest Mosquito Abatement District, Wheeling, Illinois, United States of America
- Department of Entomology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Andrew J. Tyre
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America
| | - Kelly Helm Smith
- National Drought Mitigation Center, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America
| | - Chris L. Fredregill
- Mosquito and Vector Control Division, Harris County Public Health, Houston, Texas, United States of America
| | - Oliver Elison Timm
- Department of Atmospheric and Environmental Sciences, University at Albany, Albany, New York, United States of America
| | - Karen M. Holcomb
- Department of Pathology, Microbiology, and Immunology, University of California Davis, California, United States of America
| | - Michael C. Wimberly
- Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, Oklahoma, United States of America
| | - Matthew J. Ward
- Environmental Analytics Group, Universities Space Research Association, NASA Ames Research Center, Moffett Field, California, United States of America
- Department of Tropical Medicine, Tulane University School of Public Health & Tropical Medicine, New Orleans, Louisiana, United States of America
| | - Christopher M. Barker
- Department of Pathology, Microbiology, and Immunology, University of California Davis, California, United States of America
| | - Charlotte G. Rhodes
- Department of Entomology, Texas A&M University, College Station, Texas, United States of America
| | - Rebecca L. Smith
- Department of Pathobiology, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
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McMahon A, Mihretie A, Ahmed AA, Lake M, Awoke W, Wimberly MC. Remote sensing of environmental risk factors for malaria in different geographic contexts. Int J Health Geogr 2021; 20:28. [PMID: 34120599 PMCID: PMC8201719 DOI: 10.1186/s12942-021-00282-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 06/03/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Despite global intervention efforts, malaria remains a major public health concern in many parts of the world. Understanding geographic variation in malaria patterns and their environmental determinants can support targeting of malaria control and development of elimination strategies. METHODS We used remotely sensed environmental data to analyze the influences of environmental risk factors on malaria cases caused by Plasmodium falciparum and Plasmodium vivax from 2014 to 2017 in two geographic settings in Ethiopia. Geospatial datasets were derived from multiple sources and characterized climate, vegetation, land use, topography, and surface water. All data were summarized annually at the sub-district (kebele) level for each of the two study areas. We analyzed the associations between environmental data and malaria cases with Boosted Regression Tree (BRT) models. RESULTS We found considerable spatial variation in malaria occurrence. Spectral indices related to land cover greenness (NDVI) and moisture (NDWI) showed negative associations with malaria, as the highest malaria rates were found in landscapes with low vegetation cover and moisture during the months that follow the rainy season. Climatic factors, including precipitation and land surface temperature, had positive associations with malaria. Settlement structure also played an important role, with different effects in the two study areas. Variables related to surface water, such as irrigated agriculture, wetlands, seasonally flooded waterbodies, and height above nearest drainage did not have strong influences on malaria. CONCLUSION We found different relationships between malaria and environmental conditions in two geographically distinctive areas. These results emphasize that studies of malaria-environmental relationships and predictive models of malaria occurrence should be context specific to account for such differences.
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Affiliation(s)
- Andrea McMahon
- Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK USA
| | - Abere Mihretie
- Health, Development, and Anti-Malaria Association, Addis Ababa, Ethiopia
| | - Adem Agmas Ahmed
- Malaria Control and Elimination Partnership in Africa, Bahir Dar, Ethiopia
| | | | - Worku Awoke
- School of Public Health, Bahir Dar University, Bahir Dar, Ethiopia
| | - Michael Charles Wimberly
- Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK USA
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13
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Geffroy M, Pagès N, Chavernac D, Dereeper A, Aubert L, Herrmann-Storck C, Vega-Rúa A, Lecollinet S, Pradel J. Shifting From Sectoral to Integrated Surveillance by Changing Collaborative Practices: Application to West Nile Virus Surveillance in a Small Island State of the Caribbean. Front Public Health 2021; 9:649190. [PMID: 34178915 PMCID: PMC8222804 DOI: 10.3389/fpubh.2021.649190] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 05/11/2021] [Indexed: 11/13/2022] Open
Abstract
After spreading in the Americas, West Nile virus was detected in Guadeloupe (French West Indies) for the first time in 2002. Ever since, several organizations have conducted research, serological surveys, and surveillance activities to detect the virus in horses, birds, mosquitoes, and humans. Organizations often carried them out independently, leading to knowledge gaps within the current virus' situation. Nearly 20 years after the first evidence of West Nile virus in the archipelago, it has not yet been isolated, its impact on human and animal populations is unknown, and its local epidemiological cycle is still poorly understood. Within the framework of a pilot project started in Guadeloupe in 2019, West Nile virus was chosen as a federative model to apply the "One Health" approach for zoonotic epidemiological surveillance and shift from a sectorial to an integrated surveillance system. Human, animal, and environmental health actors involved in both research and surveillance were considered. Semi-directed interviews and a Social Network Analysis were carried out to learn about the surveillance network structure and actors, analyze information flows, and identify communication challenges. An information system was developed to fill major gaps: users' needs and main functionalities were defined through a participatory process where actors also tested and validated the tool. Additionally, all actors shared their data, which were digitized, cataloged, and centralized, to be analyzed later. An R Shiny server was integrated into the information system, allowing an accessible and dynamic display of data showcasing all of the partners' information. Finally, a series of virtual workshops were organized among actors to discuss preliminary results and plan the next steps to improve West Nile Virus and vector-borne or emerging zoonosis surveillance. The actors are willing to build a more resilient and cooperative network in Guadeloupe with improved relevance, efficiency, and effectiveness of their work.
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Affiliation(s)
- Mariana Geffroy
- CIRAD, UMR, ASTRE, Petit-Bourg, France.,ASTRE, CIRAD, INRAE. Univ Montpellier, Montpellier, France
| | - Nonito Pagès
- ASTRE, CIRAD, INRAE. Univ Montpellier, Montpellier, France
| | | | - Alexis Dereeper
- CIRAD, UMR, ASTRE, Petit-Bourg, France.,ASTRE, CIRAD, INRAE. Univ Montpellier, Montpellier, France
| | - Lydéric Aubert
- CIRE Antilles, Santé Publique France, Pointe-à-Pitre, France
| | - Cecile Herrmann-Storck
- Centre Hospitalier Universitaire, Department of Bacteriology, Virology and Parasitology, Pointe-à-Pitre, France
| | - Anubis Vega-Rúa
- Institut Pasteur de Guadeloupe, Laboratory of Vector Control Research, Unit Transmission, Reservoirs and Pathogen Diversity, Les Abymes, France
| | - Sylvie Lecollinet
- Anses, Laboratory for Animal Health, UMR1161 Virology, INRAE, Anses, ENVA, Maisons-Alfort, France
| | - Jennifer Pradel
- CIRAD, UMR, ASTRE, Petit-Bourg, France.,ASTRE, CIRAD, INRAE. Univ Montpellier, Montpellier, France
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14
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Liu AY, Trtanj JM, Lipp EK, Balbus JM. Toward an Integrated System of Climate Change and Human Health Indicators: A Conceptual Framework. CLIMATIC CHANGE 2021; 166:49. [PMID: 34912130 PMCID: PMC8670724 DOI: 10.1007/s10584-021-03125-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 05/17/2021] [Indexed: 05/28/2023]
Abstract
Environmental health indicators are helpful for tracking and communicating complex health trends, informing science and policy decisions, and evaluating public health actions. When provided on a national scale, they can help inform the general public, policy makers, and public health professionals about important trends in exposures and how well public health systems are preventing those exposures from causing adverse health outcomes. There is a growing need to understand national trends in exposures and health outcomes associated with climate change and the effectiveness of climate adaptation strategies for health. To date, most indicators for health implications of climate change have been designed as independent, individual metrics. This approach fails to take into account how exposure-outcome pathways for climate-attributable health outcomes involve multiple, interconnected components. We propose reframing climate change and health indicators as a linked system of indicators, which can be described as follows: upstream climate drivers affect environmental states, which then determine human exposures, which ultimately lead to health outcomes; these climate-related risks are modified by population vulnerabilities and adaptation strategies. We apply this new conceptual framework to three illustrative climate-sensitive health outcomes and associated exposure-outcome pathways: pollen allergies and asthma, West Nile Virus infection, and vibriosis.
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Affiliation(s)
- Ann Y. Liu
- National Institute for Environmental Health Sciences, Office of the Director, Bethesda, MD, USA
| | - Juli M. Trtanj
- National Oceanic and Atmospheric Administration, Office of Oceanic and Atmospheric Research, Silver Spring, MD, USA
| | - Erin K. Lipp
- University of Georgia College of Public Health, Dean’s Office, Office of Academic Affairs, Environmental Health Science, Athens, GA, USA
| | - John M. Balbus
- National Institute for Environmental Health Sciences, Office of the Director, Bethesda, MD, USA
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15
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Devarakonda P, Sadasivuni R, Nobrega RAA, Wu J. Application of spatial multicriteria decision analysis in healthcare: Identifying drivers and triggers of infectious disease outbreaks using ensemble learning. JOURNAL OF MULTI-CRITERIA DECISION ANALYSIS 2021. [DOI: 10.1002/mcda.1732] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Phani Devarakonda
- Department of Predictive Analytics and Artificial Intelligence KRIS Analytics Solutions Visakhapatnam India
| | - Ravi Sadasivuni
- Department of Predictive Analytics and Artificial Intelligence KRIS Analytics Solutions Visakhapatnam India
| | - Rodrigo A. A. Nobrega
- Department of Cartography Institute of Geosciences, Federal University of Minas Gerais Belo Horizonte Brazil
| | - Jianhong Wu
- Department of Mathematics and Statistics York University Toronto Ontario Canada
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16
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dos Anjos RS, Nóbrega RS, Ferreira HDS, de Lacerda AP, de Sousa-Neves N. Exploring local and global regression models to estimate the spatial variability of Zika and Chikungunya cases in Recife, Brazil. Rev Soc Bras Med Trop 2020; 53:e20200027. [PMID: 32997047 PMCID: PMC7523520 DOI: 10.1590/0037-8682-0027-2020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 08/11/2020] [Indexed: 11/23/2022] Open
Abstract
INTRODUCTION In this study, we aim to compare spatial statistic models to estimate the spatial distribution of Zika and Chikungunya infections in the city of Recife, Brazil. We also aim to establish the relationship between the diseases and the analyzed geographical conditions. METHODS The models were defined by combining three categories: type of spatial unit, calculation of the dependent variable format, and estimation methods (Geographical Weighted Regression [GWR] and Ordinary Least Square [OLS]). We identified the most accurate model to estimate the spatial distribution of the diseases. After selecting the model that provided best results, the relationship between the geographical conditions and the incidence of the diseases was analyzed. RESULTS It was observed that the matrix of 100 meters (as the spatial unit) showed the highest efficiency to estimate the diseases. The best results were observed in the models that utilized the kernel density estimation (as the calculation of the dependent variable). In all models, the GWR method showed the best results. By considering the OLS coefficient values, it was observed that all geographical conditions are related to the incidence of Zika and Chikungunya, while the GWR coefficient values showed where this relationship was more noticeable. CONCLUSIONS The model that utilized the combination of the matrix of 100 meters, kernel density estimation (as the calculation of the dependent variable) and GWR method showed the highest efficiency in estimating the spatial distribution of the diseases. The coefficient values showed that all analyzed geographical conditions are related to the illnesses' incidence.
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Affiliation(s)
- Rafael Silva dos Anjos
- Universidade Federal de Pernambuco, Departamento de Ciências Geográficas, Recife, PE, Brasil
| | - Ranyére Silva Nóbrega
- Universidade Federal de Pernambuco, Departamento de Ciências Geográficas, Recife, PE, Brasil
| | | | | | - Nuno de Sousa-Neves
- University of Évora, Department of Landscape, Environment and Planning, Évora, Portugal
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17
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Wimberly MC, Davis JK, Evans MV, Hess A, Newberry PM, Solano-Asamoah N, Murdock CC. Land cover affects microclimate and temperature suitability for arbovirus transmission in an urban landscape. PLoS Negl Trop Dis 2020; 14:e0008614. [PMID: 32956355 PMCID: PMC7529312 DOI: 10.1371/journal.pntd.0008614] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 10/01/2020] [Accepted: 07/20/2020] [Indexed: 01/02/2023] Open
Abstract
The emergence of mosquito-transmitted viruses poses a global threat to human health. Combining mechanistic epidemiological models based on temperature-trait relationships with climatological data is a powerful technique for environmental risk assessment. However, a limitation of this approach is that the local microclimates experienced by mosquitoes can differ substantially from macroclimate measurements, particularly in heterogeneous urban environments. To address this scaling mismatch, we modeled spatial variation in microclimate temperatures and the thermal potential for dengue transmission by Aedes albopictus across an urban-to-rural gradient in Athens-Clarke County GA. Microclimate data were collected across gradients of tree cover and impervious surface cover. We developed statistical models to predict daily minimum and maximum microclimate temperatures using coarse-resolution gridded macroclimate data (4000 m) and high-resolution land cover data (30 m). The resulting high-resolution microclimate maps were integrated with temperature-dependent mosquito abundance and vectorial capacity models to generate monthly predictions for the summer and early fall of 2018. The highest vectorial capacities were predicted for patches of trees in urban areas with high cover of impervious surfaces. Vectorial capacity was most sensitive to tree cover during the summer and became more sensitive to impervious surfaces in the early fall. Predictions from the same models using temperature data from a local meteorological station consistently over-predicted vectorial capacity compared to the microclimate-based estimates. This work demonstrates that it is feasible to model variation in mosquito microenvironments across an urban-to-rural gradient using satellite Earth observations. Epidemiological models applied to the microclimate maps revealed localized patterns of temperature suitability for disease transmission that would not be detectable using macroclimate data. Incorporating microclimate data into disease transmission models has the potential to yield more spatially precise and ecologically interpretable metrics of mosquito-borne disease transmission risk in urban landscapes. Predicting the effects of temperature on mosquito abundance and arbovirus transmission cycles is essential for mapping hot spots of disease risk and projecting responses to climate change. In urban landscapes, the built environment and natural features create distinctive environments. Buildings and roads generate warmer conditions through the urban heat island effect, while vegetation can have a cooling effect because of shading and evaporative heat loss. We used land cover data to map microclimate temperature in Athens-Clarke County, GA and applied a temperature-dependent vectorial capacity model to predict the effects of microclimate on dengue transmission by Aedes albopictus. The highest vectorial capacity was predicted in patches of trees located in the urbanized portion of the study area. These locations had relatively warm nighttime and cool daytime temperature, which kept temperatures close to the optimum for disease transmission. This work demonstrates the feasibility of predicting variation in mosquito microenvironments in urban landscapes using satellite Earth observations. Incorporating microclimate data into disease transmission models has the potential to yield more spatially precise and ecologically interpretable metrics of mosquito-borne disease transmission risk.
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Affiliation(s)
- Michael C. Wimberly
- Department of Geography and Environmental Suitability, University of Oklahoma, Norman Oklahoma, United States of America
- * E-mail: (MCW); (CCM)
| | - Justin K. Davis
- Department of Geography and Environmental Suitability, University of Oklahoma, Norman Oklahoma, United States of America
| | - Michelle V. Evans
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Andrea Hess
- Department of Geography and Environmental Suitability, University of Oklahoma, Norman Oklahoma, United States of America
| | - Philip M. Newberry
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
| | - Nicole Solano-Asamoah
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Courtney C. Murdock
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- Department of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
- Center for Tropical Global and Emerging Diseases, University of Georgia, Athens, Georgia, United States of America
- Center for Vaccines and Immunology, University of Georgia, Athens, Georgia, United States of America
- River Basin Center, University of Georgia, Athens, Georgia, United States of America
- Department of Entomology, College of Agriculture and Life Sciences, Cornell University, Ithaca, New York, United States of America
- * E-mail: (MCW); (CCM)
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18
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Kaiser JA, Barrett ADT. Twenty Years of Progress Toward West Nile Virus Vaccine Development. Viruses 2019; 11:E823. [PMID: 31491885 PMCID: PMC6784102 DOI: 10.3390/v11090823] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 08/26/2019] [Accepted: 08/27/2019] [Indexed: 12/14/2022] Open
Abstract
Although West Nile virus (WNV) has been a prominent mosquito-transmitted infection in North America for twenty years, no human vaccine has been licensed. With a cumulative number of 24,714 neurological disease cases and 2314 deaths in the U.S. since 1999, plus a large outbreak in Europe in 2018 involving over 2000 human cases in 15 countries, a vaccine is essential to prevent continued morbidity, mortality, and economic burden. Currently, four veterinary vaccines are licensed, and six vaccines have progressed into clinical trials in humans. All four veterinary vaccines require multiple primary doses and annual boosters, but for a human vaccine to be protective and cost effective in the most vulnerable older age population, it is ideal that the vaccine be strongly immunogenic with only a single dose and without subsequent annual boosters. Of six human vaccine candidates, the two live, attenuated vaccines were the only ones that elicited strong immunity after a single dose. As none of these candidates have yet progressed beyond phase II clinical trials, development of new candidate vaccines and improvement of vaccination strategies remains an important area of research.
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Affiliation(s)
- Jaclyn A Kaiser
- Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Alan D T Barrett
- Department of Pathology, University of Texas Medical Branch, Galveston, TX 77555, USA.
- Sealy Institute for Vaccine Sciences, University of Texas Medical Branch, Galveston, TX 77555, USA.
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