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Wang HR, Liu T, Gao X, Wang HB, Xiao JH. Impact of climate change on the global circulation of West Nile virus and adaptation responses: a scoping review. Infect Dis Poverty 2024; 13:38. [PMID: 38790027 PMCID: PMC11127377 DOI: 10.1186/s40249-024-01207-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/17/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND West Nile virus (WNV), the most widely distributed flavivirus causing encephalitis globally, is a vector-borne pathogen of global importance. The changing climate is poised to reshape the landscape of various infectious diseases, particularly vector-borne ones like WNV. Understanding the anticipated geographical and range shifts in disease transmission due to climate change, alongside effective adaptation strategies, is critical for mitigating future public health impacts. This scoping review aims to consolidate evidence on the impact of climate change on WNV and to identify a spectrum of applicable adaptation strategies. MAIN BODY We systematically analyzed research articles from PubMed, Web of Science, Scopus, and EBSCOhost. Our criteria included English-language research articles published between 2007 and 2023, focusing on the impacts of climate change on WNV and related adaptation strategies. We extracted data concerning study objectives, populations, geographical focus, and specific findings. Literature was categorized into two primary themes: 1) climate-WNV associations, and 2) climate change impacts on WNV transmission, providing a clear understanding. Out of 2168 articles reviewed, 120 met our criteria. Most evidence originated from North America (59.2%) and Europe (28.3%), with a primary focus on human cases (31.7%). Studies on climate-WNV correlations (n = 83) highlighted temperature (67.5%) as a pivotal climate factor. In the analysis of climate change impacts on WNV (n = 37), most evidence suggested that climate change may affect the transmission and distribution of WNV, with the extent of the impact depending on local and regional conditions. Although few studies directly addressed the implementation of adaptation strategies for climate-induced disease transmission, the proposed strategies (n = 49) fell into six categories: 1) surveillance and monitoring (38.8%), 2) predictive modeling (18.4%), 3) cross-disciplinary collaboration (16.3%), 4) environmental management (12.2%), 5) public education (8.2%), and 6) health system readiness (6.1%). Additionally, we developed an accessible online platform to summarize the evidence on climate change impacts on WNV transmission ( https://2xzl2o-neaop.shinyapps.io/WNVScopingReview/ ). CONCLUSIONS This review reveals that climate change may affect the transmission and distribution of WNV, but the literature reflects only a small share of the global WNV dynamics. There is an urgent need for adaptive responses to anticipate and respond to the climate-driven spread of WNV. Nevertheless, studies focusing on these adaptation responses are sparse compared to those examining the impacts of climate change. Further research on the impacts of climate change and adaptation strategies for vector-borne diseases, along with more comprehensive evidence synthesis, is needed to inform effective policy responses tailored to local contexts.
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Affiliation(s)
- Hao-Ran Wang
- Department of Veterinary Surgery, Northeast Agricultural University, Harbin, 150030, Heilongjiang, People's Republic of China
- Heilongjiang Key Laboratory for Laboratory Animals and Comparative Medicine, College of Veterinary Medicine, Northeast Agricultural University, Harbin, 150030, Heilongjiang, People's Republic of China
| | - Tao Liu
- Department of Veterinary Surgery, Northeast Agricultural University, Harbin, 150030, Heilongjiang, People's Republic of China
- Heilongjiang Key Laboratory for Laboratory Animals and Comparative Medicine, College of Veterinary Medicine, Northeast Agricultural University, Harbin, 150030, Heilongjiang, People's Republic of China
| | - Xiang Gao
- Department of Veterinary Surgery, Northeast Agricultural University, Harbin, 150030, Heilongjiang, People's Republic of China
- Heilongjiang Key Laboratory for Laboratory Animals and Comparative Medicine, College of Veterinary Medicine, Northeast Agricultural University, Harbin, 150030, Heilongjiang, People's Republic of China
| | - Hong-Bin Wang
- Department of Veterinary Surgery, Northeast Agricultural University, Harbin, 150030, Heilongjiang, People's Republic of China
- Heilongjiang Key Laboratory for Laboratory Animals and Comparative Medicine, College of Veterinary Medicine, Northeast Agricultural University, Harbin, 150030, Heilongjiang, People's Republic of China
| | - Jian-Hua Xiao
- Department of Veterinary Surgery, Northeast Agricultural University, Harbin, 150030, Heilongjiang, People's Republic of China.
- Heilongjiang Key Laboratory for Laboratory Animals and Comparative Medicine, College of Veterinary Medicine, Northeast Agricultural University, Harbin, 150030, Heilongjiang, People's Republic of China.
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Ward MJ, Sorek‐Hamer M, Henke JA, Little E, Patel A, Shaman J, Vemuri K, DeFelice NB. A Spatially Resolved and Environmentally Informed Forecast Model of West Nile Virus in Coachella Valley, California. GEOHEALTH 2023; 7:e2023GH000855. [PMID: 38077289 PMCID: PMC10702611 DOI: 10.1029/2023gh000855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 10/03/2023] [Accepted: 10/05/2023] [Indexed: 01/11/2024]
Abstract
West Nile virus (WNV) is the most significant arbovirus in the United States in terms of both morbidity and mortality. West Nile exists in a complex transmission cycle between avian hosts and the arthropod vector, Culex spp. mosquitoes. Human spillover events occur when humans are bitten by an infected mosquito and predicting these rates of infection and therefore the risk to humans may be associated with fluctuations in environmental conditions. In this study, we evaluate the hydrological and meteorological drivers associated with mosquito biology and viral development to determine if these associations can be used to forecast seasonal mosquito infection rates with WNV in the Coachella Valley of California. We developed and tested a spatially resolved ensemble forecast model of the WNV mosquito infection rate in the Coachella Valley using 17 years of mosquito surveillance data and North American Land Data Assimilation System-2 environmental data. Our multi-model inference system indicated that the combination of a cooler and dryer winter, followed by a wetter and warmer spring, and a cooler than normal summer was most predictive of the prevalence of West Nile positive mosquitoes in the Coachella Valley. The ability to make accurate early season predictions of West Nile risk has the potential to allow local abatement districts and public health entities to implement early season interventions such as targeted adulticiding and public health messaging before human transmission occurs. Such early and targeted interventions could better mitigate the risk of WNV to humans.
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Affiliation(s)
- Matthew J. Ward
- Environmental Medicine and Public HealthIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Meytar Sorek‐Hamer
- Universities Space Research Association (USRA) at NASA Ames Research CenterMoffett FieldCAUSA
| | | | - Eliza Little
- Connecticut Department of Public HealthHartfordCTUSA
| | - Aman Patel
- Environmental Medicine and Public HealthIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Jeffery Shaman
- Columbia Climate SchoolNew YorkNYUSA
- Mailman School of Public HealthNew YorkNYUSA
| | - Krishna Vemuri
- Environmental Medicine and Public HealthIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Nicholas B. DeFelice
- Environmental Medicine and Public HealthIcahn School of Medicine at Mount SinaiNew YorkNYUSA
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3
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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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4
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Ward MJ, Sorek-Hamer M, Vemuri KK, DeFelice NB. Statistical Tools for West Nile Virus Disease Analysis. Methods Mol Biol 2023; 2585:171-191. [PMID: 36331774 DOI: 10.1007/978-1-0716-2760-0_16] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
West Nile virus (WNV) is the most widespread arbovirus in the world and endemic to much of the United States. Its range continues to expand as land use patterns change, creating more habitable environments for the mosquito vector. Though WNV is endemic, the year-to-year risk is highly variable, thus making it difficult to understand the risk for human spillover events. Abatement districts monitor for infected mosquitoes to help understand these potential risks and to help guide our understanding of the risk posed by these observed infected mosquitoes. Creating optimal monitoring networks will provide more informed decision-making tools for abatement districts and policy makers. Investment in these monitoring networks that capture robust observations on mosquito infection rates will allow for environmentally informed inference systems to help guide decision-making and WNV risk. In turn, enhanced decision-making tools allow for faster response times of more targeted and economical surveillance and mosquito population reduction efforts and the overall reduction of WNV transmission. Here we discuss the data streams, their processing, and specifically three ways to calculate WNV infection rates in mosquitoes.
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Affiliation(s)
- Matthew J Ward
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Meytar Sorek-Hamer
- Environmental Analytics Group (USRA), NASA Ames Research Center, Moffett Field, CA, USA
| | - Krishna Karthik Vemuri
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicholas B DeFelice
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Global Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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5
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Rosenbaum AM, Ojo M, Dumenci L, Palumbo AJ, Reed L, Crans S, Williams GM, Gruener J, Indelicato N, Cervantes K. Development of an Index to Measure West Nile Virus Transmission Risk in New Jersey Counties. JOURNAL OF THE AMERICAN MOSQUITO CONTROL ASSOCIATION 2021; 37:216-223. [PMID: 34817604 DOI: 10.2987/21-7029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
We developed an index for use by New Jersey counties to measure West Nile virus (WNV) transmission risk to the human population. We used a latent profile analysis to develop the index, identifying categories of environmental conditions associated with WNV transmission risk to humans. The final model included 4 indicators of transmission risk: mosquito abundance and minimum field infection rate, temperature, and human case count. We used data from 2004 to 2018 from all 21 New Jersey counties aggregated into 11 2-wk units per county per year (N = 3,465). Three WNV risk classes were identified. The Low Risk class had low levels of all variables. The Moderate Risk class had high abundance, average temperature levels, and low levels of the other variables. The High Risk class had substantially above average human case likelihood, average temperature, and high mosquito infection rates. These results suggest the presence of 3 distinct WNV risk profiles, which can be used to guide the development of public health actions intended to mitigate WNV transmission risk to the human population.
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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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Ciota AT, Keyel AC. The Role of Temperature in Transmission of Zoonotic Arboviruses. Viruses 2019; 11:E1013. [PMID: 31683823 PMCID: PMC6893470 DOI: 10.3390/v11111013] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 10/29/2019] [Accepted: 10/30/2019] [Indexed: 12/31/2022] Open
Abstract
We reviewed the literature on the role of temperature in transmission of zoonotic arboviruses. Vector competence is affected by both direct and indirect effects of temperature, and generally increases with increasing temperature, but results may vary by vector species, population, and viral strain. Temperature additionally has a significant influence on life history traits of vectors at both immature and adult life stages, and for important behaviors such as blood-feeding and mating. Similar to vector competence, temperature effects on life history traits can vary by species and population. Vector, host, and viral distributions are all affected by temperature, and are generally expected to change with increased temperatures predicted under climate change. Arboviruses are generally expected to shift poleward and to higher elevations under climate change, yet significant variability on fine geographic scales is likely. Temperature effects are generally unimodal, with increases in abundance up to an optimum, and then decreases at high temperatures. Improved vector distribution information could facilitate future distribution modeling. A wide variety of approaches have been used to model viral distributions, although most research has focused on the West Nile virus. Direct temperature effects are frequently observed, as are indirect effects, such as through droughts, where temperature interacts with rainfall. Thermal biology approaches hold much promise for syntheses across viruses, vectors, and hosts, yet future studies must consider the specificity of interactions and the dynamic nature of evolving biological systems.
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Affiliation(s)
- Alexander T Ciota
- Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA.
- Department of Biomedical Sciences, State University of New York at Albany School of Public Health, Rensselaer, NY 12144, USA.
| | - Alexander C Keyel
- Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA.
- Department of Atmospheric and Environmental Sciences, University at Albany, Albany, NY 12222, USA.
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8
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Barker CM. Models and Surveillance Systems to Detect and Predict West Nile Virus Outbreaks. JOURNAL OF MEDICAL ENTOMOLOGY 2019; 56:1508-1515. [PMID: 31549727 DOI: 10.1093/jme/tjz150] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Indexed: 06/10/2023]
Abstract
Over the past 20 yr, many models have been developed to predict risk for West Nile virus (WNV; Flaviviridae: Flavivirus) disease in the human population. These models have aided our understanding of the meteorological and land-use variables that drive spatial and temporal patterns of human disease risk. During the same period, electronic data systems have been adopted by surveillance programs across much of the United States, including a growing interest in integrated data services that preserve the autonomy and attribution of credit to originating agencies but facilitate data sharing, analysis, and visualization at local, state, and national scales. At present, nearly all predictive models have been limited to the scientific literature, with few having been implemented for use by public-health and vector-control decision makers. The current article considers the development of models for spatial patterns, early warning, and early detection of WNV over the last 20 yr and considers some possible paths toward increasing the utility of these models for guiding interventions.
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Affiliation(s)
- Christopher M Barker
- Department of Pathology, Microbiology, & Immunology, School of Veterinary Medicine, University of California, Davis, Davis, CA
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9
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Li SL, Ferrari MJ, Bjørnstad ON, Runge MC, Fonnesbeck CJ, Tildesley MJ, Pannell D, Shea K. Concurrent assessment of epidemiological and operational uncertainties for optimal outbreak control: Ebola as a case study. Proc Biol Sci 2019; 286:20190774. [PMID: 31213182 PMCID: PMC6599986 DOI: 10.1098/rspb.2019.0774] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Determining how best to manage an infectious disease outbreak may be hindered by both epidemiological uncertainty (i.e. about epidemiological processes) and operational uncertainty (i.e. about the effectiveness of candidate interventions). However, these two uncertainties are rarely addressed concurrently in epidemic studies. We present an approach to simultaneously address both sources of uncertainty, to elucidate which source most impedes decision-making. In the case of the 2014 West African Ebola outbreak, epidemiological uncertainty is represented by a large ensemble of published models. Operational uncertainty about three classes of interventions is assessed for a wide range of potential intervention effectiveness. We ranked each intervention by caseload reduction in each model, initially assuming an unlimited budget as a counterfactual. We then assessed the influence of three candidate cost functions relating intervention effectiveness and cost for different budget levels. The improvement in management outcomes to be gained by resolving uncertainty is generally high in this study; appropriate information gain could reduce expected caseload by more than 50%. The ranking of interventions is jointly determined by the underlying epidemiological process, the effectiveness of the interventions and the size of the budget. An epidemiologically effective intervention might not be optimal if its costs outweigh its epidemiological benefit. Under higher-budget conditions, resolution of epidemiological uncertainty is most valuable. When budgets are tight, however, operational and epidemiological uncertainty are equally important. Overall, our study demonstrates that significant reductions in caseload could result from a careful examination of both epidemiological and operational uncertainties within the same modelling structure. This approach can be applied to decision-making for the management of other diseases for which multiple models and multiple interventions are available.
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Affiliation(s)
- Shou-Li Li
- 1 Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University , University Park, PA , USA.,2 State Key Laboratory of Grassland Agro-ecosystems, and College of Pastoral, Agriculture Science and Technology, Lanzhou University , People's Republic of China
| | - Matthew J Ferrari
- 1 Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University , University Park, PA , USA
| | - Ottar N Bjørnstad
- 1 Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University , University Park, PA , USA
| | - Michael C Runge
- 3 US Geological Survey, Patuxent Wildlife Research Center , Laurel, MD , USA
| | | | - Michael J Tildesley
- 5 Systems Biology and Infectious Disease Epidemiology Research Centre, School of Life Sciences and Mathematics Institute, University of Warwick , Coventry CV4 7AL , UK
| | - David Pannell
- 6 School of Agriculture and Environment, The University of Western Australia (M087) , Crawley, WA 6009 , Australia
| | - Katriona Shea
- 1 Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University , University Park, PA , USA
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10
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Keyel AC, Elison Timm O, Backenson PB, Prussing C, Quinones S, McDonough KA, Vuille M, Conn JE, Armstrong PM, Andreadis TG, Kramer LD. Seasonal temperatures and hydrological conditions improve the prediction of West Nile virus infection rates in Culex mosquitoes and human case counts in New York and Connecticut. PLoS One 2019; 14:e0217854. [PMID: 31158250 PMCID: PMC6546252 DOI: 10.1371/journal.pone.0217854] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 05/19/2019] [Indexed: 01/05/2023] Open
Abstract
West Nile virus (WNV; Flaviviridae: Flavivirus) is a widely distributed arthropod-borne virus that has negatively affected human health and animal populations. WNV infection rates of mosquitoes and human cases have been shown to be correlated with climate. However, previous studies have been conducted at a variety of spatial and temporal scales, and the scale-dependence of these relationships has been understudied. We tested the hypothesis that climate variables are important to understand these relationships at all spatial scales. We analyzed the influence of climate on WNV infection rate of mosquitoes and number of human cases in New York and Connecticut using Random Forests, a machine learning technique. During model development, 66 climate-related variables based on temperature, precipitation and soil moisture were tested for predictive skill. We also included 20-21 non-climatic variables to account for known environmental effects (e.g., land cover and human population), surveillance related information (e.g., relative mosquito abundance), and to assess the potential explanatory power of other relevant factors (e.g., presence of wastewater treatment plants). Random forest models were used to identify the most important climate variables for explaining spatial-temporal variation in mosquito infection rates (abbreviated as MLE). The results of the cross-validation support our hypothesis that climate variables improve the predictive skill for MLE at county- and trap-scales and for human cases at the county-scale. Of the climate-related variables selected, mean minimum temperature from July-September was selected in all analyses, and soil moisture was selected for the mosquito county-scale analysis. Models demonstrated predictive skill, but still over- and under-estimated WNV MLE and numbers of human cases. Models at fine spatial scales had lower absolute errors but had greater errors relative to the mean infection rates.
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Affiliation(s)
- Alexander C. Keyel
- Division of Infectious Disease, Wadsworth Center, New York State Department of Health, Albany, NY, United States of America
- Department of Atmospheric and Environmental Sciences, University at Albany, SUNY, Albany, NY, United States of America
| | - Oliver Elison Timm
- Department of Atmospheric and Environmental Sciences, University at Albany, SUNY, Albany, NY, United States of America
| | - P. Bryon Backenson
- Bureau of Communicable Disease Control, New York State Department of Health, Albany, NY, United States of America
| | - Catharine Prussing
- Department of Biomedical Sciences, University at Albany, SUNY, Albany, NY, United States of America
| | - Sarah Quinones
- Department of Atmospheric and Environmental Sciences, University at Albany, SUNY, Albany, NY, United States of America
| | - Kathleen A. McDonough
- Division of Infectious Disease, Wadsworth Center, New York State Department of Health, Albany, NY, United States of America
- Department of Biomedical Sciences, University at Albany, SUNY, Albany, NY, United States of America
| | - Mathias Vuille
- Department of Atmospheric and Environmental Sciences, University at Albany, SUNY, Albany, NY, United States of America
| | - Jan E. Conn
- Division of Infectious Disease, Wadsworth Center, New York State Department of Health, Albany, NY, United States of America
| | - Philip M. Armstrong
- Center for Vector Biology & Zoonotic Diseases, Department of Environmental Sciences, The Connecticut Agricultural Experimental Station, New Haven, CT, United States of America
| | - Theodore G. Andreadis
- Center for Vector Biology & Zoonotic Diseases, Department of Environmental Sciences, The Connecticut Agricultural Experimental Station, New Haven, CT, United States of America
| | - Laura D. Kramer
- Division of Infectious Disease, Wadsworth Center, New York State Department of Health, Albany, NY, United States of America
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11
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Myer MH, Johnston JM. Spatiotemporal Bayesian modeling of West Nile virus: Identifying risk of infection in mosquitoes with local-scale predictors. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 650:2818-2829. [PMID: 30373059 PMCID: PMC7676626 DOI: 10.1016/j.scitotenv.2018.09.397] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 09/24/2018] [Accepted: 09/28/2018] [Indexed: 05/16/2023]
Abstract
Monitoring and control of West Nile virus (WNV) presents a challenge to state and local vector control managers. Models of mosquito presence and viral incidence have revealed that variations in mosquito autecology and land use patterns introduce unique dynamics of disease at the scale of a county or city, and that effective prediction requires locally parameterized models. We applied Bayesian spatiotemporal modeling to West Nile surveillance data from 49 mosquito trap sites in Nassau County, New York, from 2001 to 2015 and evaluated environmental and sociological predictors of West Nile virus incidence in Culex pipiens-restuans. A Bayesian spike-and-slab variable selection algorithm was used to help select influential independent variables. This method can be used to identify locally-important predictors. The best model predicted West Nile positives well, with an Area Under Curve (AUC) of 0.83 on holdout data. The temporal trend was nonlinear and increased throughout the year. The spatial component identified increased West Nile incidence odds in the northwestern portion of the county, with lower odds in wetlands on the south shore of Long Island. High Normalized Difference Vegetation Index (NDVI) areas, wetlands, and areas of high urban development had negative associations with WNV incidence. In this study we demonstrate a method for improving spatiotemporal models of West Nile virus incidence for decision making at the county and community scale, which empowers disease and vector control organizations to prioritize and evaluate prevention efforts.
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Affiliation(s)
- Mark H Myer
- ORISE Research Participant, U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 960 College Station Rd, Athens, GA 30605, USA
| | - John M Johnston
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 960 College Station Rd, Athens, GA 30605, USA.
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12
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Mori H, Wu J, Ibaraki M, Schwartz FW. Key Factors Influencing the Incidence of West Nile Virus in Burleigh County, North Dakota. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:ijerph15091928. [PMID: 30189592 PMCID: PMC6164257 DOI: 10.3390/ijerph15091928] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 08/31/2018] [Accepted: 09/02/2018] [Indexed: 01/03/2023]
Abstract
The city of Bismarck, North Dakota has one of the highest numbers of West Nile Virus (WNV) cases per population in the U.S. Although the city conducts extensive mosquito surveillance, the mosquito abundance alone may not fully explain the occurrence of WNV. Here, we developed models to predict mosquito abundance and the number of WNV cases, independently, by statistically analyzing the most important climate and virus transmission factors. An analysis with the mosquito model indicated that the mosquito numbers increase during a warm and humid summer or after a severely cold winter. In addition, river flooding decreased the mosquito numbers. The number of WNV cases was best predicted by including the virus transmission rate, the mosquito numbers, and the mosquito feeding pattern. This virus transmission rate is a function of temperature and increases significantly above 20 °C. The correlation coefficients (r) were 0.910 with the mosquito-population model and 0.620 with the disease case model. Our findings confirmed the conclusions of other work on the importance of climatic variables in controlling the mosquito numbers and contributed new insights into disease dynamics, especially in relation to extreme flooding. It also suggested a new prevention strategy of initiating insecticides not only based on mosquito numbers but also 10-day forecasts of unusually hot weather.
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Affiliation(s)
- Hiroko Mori
- Environmental Science Graduate Program, The Ohio State University, Columbus, OH 43210, USA.
| | - Joshua Wu
- College of Public Health, The Ohio State University, Columbus, OH 43210, USA.
| | - Motomu Ibaraki
- School of Earth Sciences, The Ohio State University, Columbus, OH 43210, USA.
| | - Franklin W Schwartz
- School of Earth Sciences, The Ohio State University, Columbus, OH 43210, USA.
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13
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DeFelice NB, Schneider ZD, Little E, Barker C, Caillouet KA, Campbell SR, Damian D, Irwin P, Jones HMP, Townsend J, Shaman J. Use of temperature to improve West Nile virus forecasts. PLoS Comput Biol 2018. [PMID: 29522514 PMCID: PMC5862506 DOI: 10.1371/journal.pcbi.1006047] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Ecological and laboratory studies have demonstrated that temperature modulates West Nile virus (WNV) transmission dynamics and spillover infection to humans. Here we explore whether inclusion of temperature forcing in a model depicting WNV transmission improves WNV forecast accuracy relative to a baseline model depicting WNV transmission without temperature forcing. Both models are optimized using a data assimilation method and two observed data streams: mosquito infection rates and reported human WNV cases. Each coupled model-inference framework is then used to generate retrospective ensemble forecasts of WNV for 110 outbreak years from among 12 geographically diverse United States counties. The temperature-forced model improves forecast accuracy for much of the outbreak season. From the end of July until the beginning of October, a timespan during which 70% of human cases are reported, the temperature-forced model generated forecasts of the total number of human cases over the next 3 weeks, total number of human cases over the season, the week with the highest percentage of infectious mosquitoes, and the peak percentage of infectious mosquitoes that on average increased absolute forecast accuracy 5%, 10%, 12%, and 6%, respectively, over the non-temperature forced baseline model. These results indicate that use of temperature forcing improves WNV forecast accuracy and provide further evidence that temperature influences rates of WNV transmission. The findings provide a foundation for implementation of a statistically rigorous system for real-time forecast of seasonal WNV outbreaks and their use as a quantitative decision support tool for public health officials and mosquito control programs. West Nile virus (WNV) is the leading cause of domestically acquired arthropod-borne viral disease in the United States. Here we show that accurate retrospective forecasts of mosquito infection rates and human WNV cases can be generated for a variety of locations in the U.S. Incorporation of temperature forcing into a baseline dynamic model improves our ability to accurately forecast WNV outbreaks and provides further evidence that temperature modulates rates of WNV transmission. These findings provide a foundation for implementation of a statistically rigorous system for real-time short-term and seasonal forecast of WNV. Such a decision support tool would help public health officials and mosquito control programs target control of infectious mosquito populations, alert the public to future periods of elevated WNV spillover transmission risk, and identify when to intensify blood donor screening.
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Affiliation(s)
- Nicholas B. DeFelice
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
- * E-mail:
| | - Zachary D. Schneider
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Eliza Little
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Christopher Barker
- Center for Vectorborne Diseases, University of California Davis, Davis, California
| | - Kevin A. Caillouet
- St. Tammany Parish Mosquito Abatement District, St. Tammany Parish, Slidell, Louisiana, United States of America
| | - Scott R. Campbell
- Arthropod-Borne Disease Laboratory, Suffolk County Department of Health Services, Yaphank, New York, United States of America
| | - Dan Damian
- Vector Control Division, Maricopa County Environmental Services Department, Phoenix, Arizona, United States of America
| | - Patrick Irwin
- Northwest Mosquito Abatement District, Wheeling, Illinois, United States of America
| | - Herff M. P. Jones
- Iberia Parish Mosquito Abatement District, Iberia Parish, New Iberia, Louisiana, United States of America
| | - John Townsend
- Vector Control Division, Maricopa County Environmental Services Department, Phoenix, Arizona, United States of America
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
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14
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Buczak AL, Baugher B, Moniz LJ, Bagley T, Babin SM, Guven E. Ensemble method for dengue prediction. PLoS One 2018; 13:e0189988. [PMID: 29298320 PMCID: PMC5752022 DOI: 10.1371/journal.pone.0189988] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 12/06/2017] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.e., week in which the maximum weekly number of cases occurred); and 3) total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date. METHODS Our approach used ensemble models created by combining three disparate types of component models: 1) two-dimensional Method of Analogues models incorporating both dengue and climate data; 2) additive seasonal Holt-Winters models with and without wavelet smoothing; and 3) simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations. PRINCIPAL FINDINGS Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week. CONCLUSIONS The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.
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Affiliation(s)
- Anna L. Buczak
- Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America
| | - Benjamin Baugher
- Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America
| | - Linda J. Moniz
- Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America
| | - Thomas Bagley
- Duke University, Durham, North Carolina, United States of America
| | - Steven M. Babin
- Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America
| | - Erhan Guven
- Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America
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15
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Stauffer GE, Miller DA, Williams LM, Brown J. Ruffed grouse population declines after introduction of West Nile virus. J Wildl Manage 2017. [DOI: 10.1002/jwmg.21347] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Glenn E. Stauffer
- Department of Ecosystem Science and ManagementThe Pennsylvania State University419 Forest Resources Building, University ParkPA16802USA
| | - David A.W. Miller
- Department of Ecosystem Science and ManagementThe Pennsylvania State University419 Forest Resources Building, University ParkPA16802USA
| | - Lisa M. Williams
- Bureau of Wildlife ManagementPennsylvania Game Commission2001 Elmerton AvenueHarrisburgPA17110USA
| | - Justin Brown
- Bureau of Wildlife ManagementPennsylvania Game Commission2001 Elmerton AvenueHarrisburgPA17110USA
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16
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Karki S, Westcott NE, Muturi EJ, Brown WM, Ruiz MO. Assessing human risk of illness with West Nile virus mosquito surveillance data to improve public health preparedness. Zoonoses Public Health 2017; 65:177-184. [DOI: 10.1111/zph.12386] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Indexed: 12/17/2022]
Affiliation(s)
- S. Karki
- Department of Pathobiology; University of Illinois; Urbana IL USA
| | - N. E. Westcott
- Illinois State Water Survey; Prairie Research Institute; University of Illinois at Urbana - Champaign; Urbana IL USA
| | - E. J. Muturi
- Crop Bioprotection Research Unit; USDA, ARS; Peoria IL USA
| | - W. M. Brown
- Department of Pathobiology; University of Illinois; Urbana IL USA
| | - M. O. Ruiz
- Department of Pathobiology; University of Illinois; Urbana IL USA
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17
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Little E, Bajwa W, Shaman J. Local environmental and meteorological conditions influencing the invasive mosquito Ae. albopictus and arbovirus transmission risk in New York City. PLoS Negl Trop Dis 2017; 11:e0005828. [PMID: 28832586 PMCID: PMC5584979 DOI: 10.1371/journal.pntd.0005828] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Revised: 09/05/2017] [Accepted: 07/24/2017] [Indexed: 01/03/2023] Open
Abstract
Ae. albopictus, an invasive mosquito vector now endemic to much of the northeastern US, is a significant public health threat both as a nuisance biter and vector of disease (e.g. chikungunya virus). Here, we aim to quantify the relationships between local environmental and meteorological conditions and the abundance of Ae. albopictus mosquitoes in New York City. Using statistical modeling, we create a fine-scale spatially explicit risk map of Ae. albopictus abundance and validate the accuracy of spatiotemporal model predictions using observational data from 2016. We find that the spatial variability of annual Ae. albopictus abundance is greater than its temporal variability in New York City but that both local environmental and meteorological conditions are associated with Ae. albopictus numbers. Specifically, key land use characteristics, including open spaces, residential areas, and vacant lots, and spring and early summer meteorological conditions are associated with annual Ae. albopictus abundance. In addition, we investigate the distribution of imported chikungunya cases during 2014 and use these data to delineate areas with the highest rates of arboviral importation. We show that the spatial distribution of imported arboviral cases has been mostly discordant with mosquito production and thus, to date, has provided a check on local arboviral transmission in New York City. We do, however, find concordant areas where high Ae. albopictus abundance and chikungunya importation co-occur. Public health and vector control officials should prioritize control efforts to these areas and thus more cost effectively reduce the risk of local arboviral transmission. The methods applied here can be used to monitor and identify areas of risk for other imported vector-borne diseases.
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Affiliation(s)
- Eliza Little
- Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Waheed Bajwa
- Office of Vector Surveillance and Control, New York City Department of Health and Mental Hygiene, New York, New York, United States of America
| | - Jeffrey Shaman
- Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
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18
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Myer MH, Campbell SR, Johnston JM. Spatiotemporal modeling of ecological and sociological predictors of West Nile virus in Suffolk County, NY, mosquitoes. Ecosphere 2017; 8:e01854. [PMID: 30147987 PMCID: PMC6104833 DOI: 10.1002/ecs2.1854] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Suffolk County, New York, is a locus for West Nile virus (WNV) infection in the American northeast that includes the majority of Long Island to the east of New York City. The county has a system of light and gravid traps used for mosquito collection and disease monitoring. In order to identify predictors of WNV incidence in mosquitoes and predict future occurrence of WNV, we have developed a spatiotemporal Bayesian model, beginning with over 40 ecological, meteorological, and built-environment covariates. A mixed-effects model including spatially and temporally correlated errors was fit to WNV surveillance data from 2008 to 2014 using the R package “R-INLA,” which allows for Bayesian modeling using the stochastic partial differential equation (SPDE) approach. The integrated nested Laplace approximation (INLA) SPDE allows for simultaneous fitting of a temporal parameter and a spatial covariance, while incorporating a variety of likelihood functions and running in R statistical software on a home computer. We found that land cover classified as open water and woody wetlands had a negative association with WNV incidence in mosquitoes, and the count of septic systems was associated with an increase in WNV. Mean temperature at two-week lag was associated with a strong positive impact, while mean precipitation at no lag and one-week lag was associated with positive and negative impacts on WNV, respectively. Incorporation of spatiotemporal factors resulted in a marked increase in model goodness-of-fit. The predictive power of the model was evaluated on 2015 surveillance results, where the best model achieved a sensitivity of 80.9% and a specificity of 77.0%. The spatial covariate was mapped across the county, identifying a gradient of WNV prevalence increasing from east to west. The Bayesian spatiotemporal model improves upon previous approaches, and we recommend the INLA SPDE methodology as an efficient way to develop robust models from surveillance data to develop and enhance monitoring and control programs. Our study confirms previously found associations between weather conditions and WNV and suggests that wetland cover has a mitigating effect on WNV infection in mosquitoes, while high septic system density is associated with an increase in WNV infection.
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Affiliation(s)
- Mark H Myer
- Oak Ridge Institute for Science and Education, Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency, 960 College Station Road, Athens, Georgia 30605 USA
| | - Scott R Campbell
- Arthropod-Borne Disease Laboratory, Suffolk County Department of Health Services, Yaphank, New York 11980 USA
| | - John M Johnston
- Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency, 960 College Station Road, Athens, Georgia 30605 USA
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19
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Myer MH, Campbell SR, Johnston JM. Spatiotemporal modeling of ecological and sociological predictors of West Nile virus in Suffolk County, NY, mosquitoes. Ecosphere 2017; 8:e01854. [PMID: 30147987 DOI: 10.1002/ecs2.1854e01854-n/a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023] Open
Abstract
Suffolk County, New York, is a locus for West Nile virus (WNV) infection in the American northeast that includes the majority of Long Island to the east of New York City. The county has a system of light and gravid traps used for mosquito collection and disease monitoring. In order to identify predictors of WNV incidence in mosquitoes and predict future occurrence of WNV, we have developed a spatiotemporal Bayesian model, beginning with over 40 ecological, meteorological, and built-environment covariates. A mixed-effects model including spatially and temporally correlated errors was fit to WNV surveillance data from 2008 to 2014 using the R package "R-INLA," which allows for Bayesian modeling using the stochastic partial differential equation (SPDE) approach. The integrated nested Laplace approximation (INLA) SPDE allows for simultaneous fitting of a temporal parameter and a spatial covariance, while incorporating a variety of likelihood functions and running in R statistical software on a home computer. We found that land cover classified as open water and woody wetlands had a negative association with WNV incidence in mosquitoes, and the count of septic systems was associated with an increase in WNV. Mean temperature at two-week lag was associated with a strong positive impact, while mean precipitation at no lag and one-week lag was associated with positive and negative impacts on WNV, respectively. Incorporation of spatiotemporal factors resulted in a marked increase in model goodness-of-fit. The predictive power of the model was evaluated on 2015 surveillance results, where the best model achieved a sensitivity of 80.9% and a specificity of 77.0%. The spatial covariate was mapped across the county, identifying a gradient of WNV prevalence increasing from east to west. The Bayesian spatiotemporal model improves upon previous approaches, and we recommend the INLA SPDE methodology as an efficient way to develop robust models from surveillance data to develop and enhance monitoring and control programs. Our study confirms previously found associations between weather conditions and WNV and suggests that wetland cover has a mitigating effect on WNV infection in mosquitoes, while high septic system density is associated with an increase in WNV infection.
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Affiliation(s)
- Mark H Myer
- Oak Ridge Institute for Science and Education, Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency, 960 College Station Road, Athens, Georgia 30605 USA
| | - Scott R Campbell
- Arthropod-Borne Disease Laboratory, Suffolk County Department of Health Services, Yaphank, New York 11980 USA
| | - John M Johnston
- Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency, 960 College Station Road, Athens, Georgia 30605 USA
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