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Berman JD, Abadi AM, Bell JE. Existing Challenges and Opportunities for Advancing Drought and Health Research. Curr Environ Health Rep 2024; 11:255-265. [PMID: 38568401 DOI: 10.1007/s40572-024-00440-z] [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] [Accepted: 03/08/2024] [Indexed: 05/12/2024]
Abstract
PURPOSE OF REVIEW Drought is one of the most far-reaching natural disasters, yet drought and health research is sparse. This may be attributed to the challenge of quantifying drought exposure, something complicated by multiple drought indices without any designed for health research. The purpose of this general review is to evaluate current drought and health literature and highlight challenges or scientific considerations when performing drought exposure and health assessments. RECENT FINDINGS The literature revealed a small, but growing, number of drought and health studies primarily emphasizing Australian, western European, and US populations. The selection of drought indices and definitions of drought are inconsistent. Rural and agricultural populations have been identified as vulnerable cohorts, particularly for mental health outcomes. Using relevant examples, we discuss the importance of characterizing drought and explore why health outcomes, populations of interest, and compound environmental hazards are crucial considerations for drought and health assessments. As climate and health research is prioritized, we propose guidance for investigators performing drought-focused analyses.
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Affiliation(s)
- Jesse D Berman
- Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Mayo Mail Code #807, 420 Delaware Street SE, Minneapolis, MN, 55455, USA.
| | - Azar M Abadi
- Department of Environmental Health Sciences, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, 35233, USA
| | - Jesse E Bell
- Department of Environmental, Agricultural, and Occupational Health, Medical Center College of Public Health, University of Nebraska, Omaha, NE, USA
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, USA
- Daugherty Water for Food Global Institute, University of Nebraska, Lincoln, NE, USA
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2
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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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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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McCarter MSJ, Self S, Dye-Braumuller KC, Lee C, Li H, Nolan MS. The utility of a Bayesian predictive model to forecast neuroinvasive West Nile virus disease in the United States of America, 2022. PLoS One 2023; 18:e0290873. [PMID: 37682897 PMCID: PMC10490885 DOI: 10.1371/journal.pone.0290873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 08/17/2023] [Indexed: 09/10/2023] Open
Abstract
Arboviruses (arthropod-borne-viruses) are an emerging global health threat that are rapidly spreading as climate change, international business transport, and landscape fragmentation impact local ecologies. Since its initial detection in 1999, West Nile virus has shifted from being a novel to an established arbovirus in the United States of America. Subsequently, more than 25,000 cases of West Nile neuro-invasive disease have been diagnosed, cementing West Nile virus as an arbovirus of public health importance. Given its novelty in the United States of America, high-risk ecologies are largely underdefined making targeted population-level public health interventions challenging. Using the Centers for Disease Control and Prevention ArboNET neuroinvasive West Nile virus data from 2000-2021, this study aimed to predict neuroinvasive West Nile virus human cases at the county level for the contiguous USA using a spatio-temporal Bayesian negative binomial regression model. The model includes environmental, climatic, and demographic factors, as well as the distribution of host species. An integrated nested Laplace approximation approach was used to fit our model. To assess model prediction accuracy, annual counts were withheld, forecasted, and compared to observed values. The validated models were then fit to the entire dataset for 2022 predictions. This proof-of-concept mathematical, geospatial modelling approach has proven utility for national health agencies seeking to allocate funding and other resources for local vector control agencies tackling West Nile virus and other notifiable arboviral agents.
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Affiliation(s)
- Maggie S. J. McCarter
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, United States of America
| | - Stella Self
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, United States of America
| | - Kyndall C. Dye-Braumuller
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, United States of America
| | - Christopher Lee
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, United States of America
| | - Huixuan Li
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, United States of America
| | - Melissa S. Nolan
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, United States of America
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5
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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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Keyel AC, Kilpatrick AM. Better null models for assessing predictive accuracy of disease models. PLoS One 2023; 18:e0285215. [PMID: 37146010 PMCID: PMC10162537 DOI: 10.1371/journal.pone.0285215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 04/17/2023] [Indexed: 05/07/2023] Open
Abstract
Null models provide a critical baseline for the evaluation of predictive disease models. Many studies consider only the grand mean null model (i.e. R2) when evaluating the predictive ability of a model, which is insufficient to convey the predictive power of a model. We evaluated ten null models for human cases of West Nile virus (WNV), a zoonotic mosquito-borne disease introduced to the United States in 1999. The Negative Binomial, Historical (i.e. using previous cases to predict future cases) and Always Absent null models were the strongest overall, and the majority of null models significantly outperformed the grand mean. The length of the training timeseries increased the performance of most null models in US counties where WNV cases were frequent, but improvements were similar for most null models, so relative scores remained unchanged. We argue that a combination of null models is needed to evaluate the forecasting performance of predictive models for infectious diseases and the grand mean is the lowest bar.
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Affiliation(s)
- Alexander C Keyel
- Division of Infectious Diseases, 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
| | - A Marm Kilpatrick
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, Santa Cruz, CA, United States of America
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7
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Abadi AM, Gwon Y, Gribble MO, Berman JD, Bilotta R, Hobbins M, Bell JE. Drought and all-cause mortality in Nebraska from 1980 to 2014: Time-series analyses by age, sex, race, urbanicity and drought severity. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 840:156660. [PMID: 35710006 DOI: 10.1016/j.scitotenv.2022.156660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 06/08/2022] [Accepted: 06/08/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Climate change will increase drought duration and severity in many regions around the world, including the Central Plains of North America. However, studies on drought-related health impacts are still sparse. This study aims to explore the potential associations between drought and all-cause mortality in Nebraska from 1980 to 2014. METHODS The Evaporative Demand Drought Index (EDDI) were used to define short-, medium- and long-term drought exposures, respectively. We used a Bayesian zero-inflated censored negative binomial (ZICNB) regression model to estimate the overall association between drought and annual mortality first in the total population and second in stratified sub-populations based on age, race, sex, and the urbanicity class of the counties. RESULTS The main findings indicate that there is a slightly negative association between all-cause mortality and all types of droughts in the total population, though the effect is statistically null. The joint-stratified analysis renders significant results for a few sub-groups. White population aged 25-34 and 45-64 in metro counties and 45-54 in non-metro counties were the population more at risk in Nebraska. No positive associations were observed in any race besides white. Black males aged 20-24 and white females older than 85 showed protective effect against drought mainly in metro counties. We also found that more sub-populations had higher rates of mortality with longer-term droughts compared to shorter-term droughts (12-month vs 1- or 6-month timescales), in both metro and non-metro counties, collectively. CONCLUSION Our results suggest that mortality in middle aged white population in Nebraska shows a greater association with drought. Moreover, women aged 45-54 were more affected than men in non-metro counties. With a projected increase in the frequency and severity of drought due to climate change, understanding these relationships between drought and human health will better inform drought mitigation planning to reduce potential impacts.
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Affiliation(s)
- Azar M Abadi
- Department of Environmental, Agricultural, and Occupational Health, College of Public Health, University of Nebraska Medical Center, Omaha, NE, USA; Daugherty Water for Food Global Institute, University of Nebraska, Lincoln, NE, USA.
| | - Yeongjin Gwon
- Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, NE, USA.
| | - Matthew O Gribble
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA.
| | - Jesse D Berman
- Division of Environmental Health Sciences, University of Minnesota School of Public Health, Minneapolis, MN, USA.
| | - Rocky Bilotta
- ISciences, L.L.C. and the National Oceanographic and Atmospheric Administration's National Centers for Environmental Information, Asheville, NC, USA.
| | - Mike Hobbins
- Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA; NOAA Physical Sciences Laboratory, Boulder, CO, USA.
| | - Jesse E Bell
- Department of Environmental, Agricultural, and Occupational Health, College of Public Health, University of Nebraska Medical Center, Omaha, NE, USA; Daugherty Water for Food Global Institute, University of Nebraska, Lincoln, NE, USA; School of Natural Resources, University of Nebraska-Lincoln, Lincoln, 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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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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Löwen Levy Chalhoub F, Maia de Queiroz-Júnior E, Holanda Duarte B, Eielson Pinheiro de Sá M, Cerqueira Lima P, Carneiro de Oliveira A, Medeiros Neves Casseb L, Leal das Chagas L, Antônio de Oliveira Monteiro H, Sebastião Alberto Santos Neves M, Facundo Chaves C, Jean da Silva Moura P, Machado Rapello do Nascimento A, Giesbrecht Pinheiro R, Roberio Soares Vieira A, Bergson Pinheiro Moura F, Osvaldo Rodrigues da Silva L, Nogueira Farias da Escóssia K, Caranha de Sousa L, Leticia Cavalcante Ramalho I, Williams Lopes da Silva A, Maria Simōes Mello L, Felix de Souza F, das Chagas Almeida F, dos Santos Rodrigues R, do Vale Chagas D, Ferreira-de-Brito A, Ribeiro Leite Jardim Cavalcante K, Angélica Monteiro de Mello Mares-Guia M, Martins Guerra Campos V, Rodrigues da Costa Faria N, Adriano da Cunha e Silva Vieira M, Cesar Lima de Mendonça M, Camila Amorim de Alvarenga Pivisan N, de Oliveira Moreno J, Aldessandra Diniz Vieira M, Gonçalves de Aguiar Gomes R, Montenegro de Carvalho Araújo F, Henrique de Oliveira Passos P, Garkauskas Ramos D, Pecego Martins Romano A, Carício Martins L, Lourenço-de-Oliveira R, Maria Bispo de Filippis A, Pauvolid-Corrêa A. West Nile Virus in the State of Ceará, Northeast Brazil. Microorganisms 2021; 9:1699. [PMID: 34442778 PMCID: PMC8401605 DOI: 10.3390/microorganisms9081699] [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: 07/24/2021] [Revised: 08/04/2021] [Accepted: 08/05/2021] [Indexed: 01/07/2023] Open
Abstract
In June 2019, a horse with neurological disorder was diagnosed with West Nile virus (WNV) in Boa Viagem, a municipality in the state of Ceará, northeast Brazil. A multi-institutional task force coordinated by the Brazilian Ministry of Health was deployed to the area for case investigation. A total of 513 biological samples from 78 humans, 157 domestic animals and 278 free-ranging wild birds, as well as 853 adult mosquitoes of 22 species were tested for WNV by highly specific serological and/or molecular tests. No active circulation of WNV was detected in vertebrates or mosquitoes by molecular methods. Previous exposure to WNV was confirmed by seroconversion in domestic birds and by the detection of specific neutralizing antibodies in 44% (11/25) of equids, 20.9% (14/67) of domestic birds, 4.7% (13/278) of free-ranging wild birds, 2.6% (2/78) of humans, and 1.5% (1/65) of small ruminants. Results indicate that not only equines but also humans and different species of domestic animals and wild birds were locally exposed to WNV. The detection of neutralizing antibodies for WNV in free-ranging individuals of abundant passerine species suggests that birds commonly found in the region may have been involved as amplifying hosts in local transmission cycles of WNV.
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Affiliation(s)
- Flávia Löwen Levy Chalhoub
- Laboratório de Flavivírus, Fundação Oswaldo Cruz (Fiocruz), Ministério da Saúde (MS), Rio de Janeiro, RJ 21040-900, Brazil; (F.L.L.C.); (M.A.M.d.M.M.-G.); (V.M.G.C.); (N.R.d.C.F.); (M.C.L.d.M.); (A.M.B.d.F.)
| | - Eudson Maia de Queiroz-Júnior
- Agência de Defesa Agropecuária do Estado do Ceará (ADAGRI), Fortaleza, CE 60811-520, Brazil; (E.M.d.Q.-J.); (A.W.L.d.S.); (J.d.O.M.)
| | - Bruna Holanda Duarte
- Secretaria Estadual de Saúde do Estado do Ceará (SES-CE), Fortaleza, CE 60060-440, Brazil; (B.H.D.); (A.R.S.V.); (F.B.P.M.); (L.O.R.d.S.); (K.N.F.d.E.); (L.C.d.S.); (N.C.A.d.A.P.); (R.G.d.A.G.)
| | - Marcos Eielson Pinheiro de Sá
- Departamento de Serviços Técnicos, Secretaria de Defesa Agropecuária, Ministério da Agricultura Pecuária e Abastecimento (MAPA), Brasília, DF 70043-900, Brazil;
| | | | - Ailton Carneiro de Oliveira
- Centro Nacional de Pesquisa para Conservação das Aves Silvestres (CEMAVE), Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio), Ministério do Meio Ambiente (MMA), Cabedelo, PB 58108-012, Brazil;
| | - Lívia Medeiros Neves Casseb
- Seção de Arbovirologia e Febres Hemorrágicas, Instituto Evandro Chagas (IEC), MS, Ananindeua, PA 67030-000, Brazil; (L.M.N.C.); (L.L.d.C.); (H.A.d.O.M.); (L.C.M.)
| | - Liliane Leal das Chagas
- Seção de Arbovirologia e Febres Hemorrágicas, Instituto Evandro Chagas (IEC), MS, Ananindeua, PA 67030-000, Brazil; (L.M.N.C.); (L.L.d.C.); (H.A.d.O.M.); (L.C.M.)
| | - Hamilton Antônio de Oliveira Monteiro
- Seção de Arbovirologia e Febres Hemorrágicas, Instituto Evandro Chagas (IEC), MS, Ananindeua, PA 67030-000, Brazil; (L.M.N.C.); (L.L.d.C.); (H.A.d.O.M.); (L.C.M.)
| | - Maycon Sebastião Alberto Santos Neves
- Laboratório de Mosquitos Transmissores de Hematozoários, Fiocruz, MS, Rio de Janeiro, RJ 21040-900, Brazil; (M.S.A.S.N.); (A.F.-d.-B.); (R.L.-d.-O.)
| | | | - Paulo Jean da Silva Moura
- Secretaria Municipal de Saúde de Boa Viagem (SMS-Boa Viagem), Boa Viagem, CE 63870-000, Brazil; (P.J.d.S.M.); (F.F.d.S.); (F.d.C.A.); (R.d.S.R.); (D.d.V.C.); (M.A.D.V.)
| | - Aline Machado Rapello do Nascimento
- Coordenação-Geral de Vigilância das Arboviroses (CGARB), Departamento de Imunização e Doenças Transmissíveis (DEIDT), Secretaria de Vigilância em Saúde (SVS), MS, Brasília, DF 70058-900, Brazil; (A.M.R.d.N.); (R.G.P.); (M.A.d.C.e.S.V.); (P.H.d.O.P.); (D.G.R.); (A.P.M.R.)
| | - Rodrigo Giesbrecht Pinheiro
- Coordenação-Geral de Vigilância das Arboviroses (CGARB), Departamento de Imunização e Doenças Transmissíveis (DEIDT), Secretaria de Vigilância em Saúde (SVS), MS, Brasília, DF 70058-900, Brazil; (A.M.R.d.N.); (R.G.P.); (M.A.d.C.e.S.V.); (P.H.d.O.P.); (D.G.R.); (A.P.M.R.)
| | - Antonio Roberio Soares Vieira
- Secretaria Estadual de Saúde do Estado do Ceará (SES-CE), Fortaleza, CE 60060-440, Brazil; (B.H.D.); (A.R.S.V.); (F.B.P.M.); (L.O.R.d.S.); (K.N.F.d.E.); (L.C.d.S.); (N.C.A.d.A.P.); (R.G.d.A.G.)
| | - Francisco Bergson Pinheiro Moura
- Secretaria Estadual de Saúde do Estado do Ceará (SES-CE), Fortaleza, CE 60060-440, Brazil; (B.H.D.); (A.R.S.V.); (F.B.P.M.); (L.O.R.d.S.); (K.N.F.d.E.); (L.C.d.S.); (N.C.A.d.A.P.); (R.G.d.A.G.)
| | - Luiz Osvaldo Rodrigues da Silva
- Secretaria Estadual de Saúde do Estado do Ceará (SES-CE), Fortaleza, CE 60060-440, Brazil; (B.H.D.); (A.R.S.V.); (F.B.P.M.); (L.O.R.d.S.); (K.N.F.d.E.); (L.C.d.S.); (N.C.A.d.A.P.); (R.G.d.A.G.)
| | - Kiliana Nogueira Farias da Escóssia
- Secretaria Estadual de Saúde do Estado do Ceará (SES-CE), Fortaleza, CE 60060-440, Brazil; (B.H.D.); (A.R.S.V.); (F.B.P.M.); (L.O.R.d.S.); (K.N.F.d.E.); (L.C.d.S.); (N.C.A.d.A.P.); (R.G.d.A.G.)
| | - Lindenberg Caranha de Sousa
- Secretaria Estadual de Saúde do Estado do Ceará (SES-CE), Fortaleza, CE 60060-440, Brazil; (B.H.D.); (A.R.S.V.); (F.B.P.M.); (L.O.R.d.S.); (K.N.F.d.E.); (L.C.d.S.); (N.C.A.d.A.P.); (R.G.d.A.G.)
| | | | - Antônio Williams Lopes da Silva
- Agência de Defesa Agropecuária do Estado do Ceará (ADAGRI), Fortaleza, CE 60811-520, Brazil; (E.M.d.Q.-J.); (A.W.L.d.S.); (J.d.O.M.)
| | - Leda Maria Simōes Mello
- Laboratório Central do Estado do Ceará (LACEN-CE), Fortaleza, CE 60120-002, Brazil; (I.L.C.R.); (L.M.S.M.); (F.M.d.C.A.)
| | - Fábio Felix de Souza
- Secretaria Municipal de Saúde de Boa Viagem (SMS-Boa Viagem), Boa Viagem, CE 63870-000, Brazil; (P.J.d.S.M.); (F.F.d.S.); (F.d.C.A.); (R.d.S.R.); (D.d.V.C.); (M.A.D.V.)
| | - Francisco das Chagas Almeida
- Secretaria Municipal de Saúde de Boa Viagem (SMS-Boa Viagem), Boa Viagem, CE 63870-000, Brazil; (P.J.d.S.M.); (F.F.d.S.); (F.d.C.A.); (R.d.S.R.); (D.d.V.C.); (M.A.D.V.)
| | - Raí dos Santos Rodrigues
- Secretaria Municipal de Saúde de Boa Viagem (SMS-Boa Viagem), Boa Viagem, CE 63870-000, Brazil; (P.J.d.S.M.); (F.F.d.S.); (F.d.C.A.); (R.d.S.R.); (D.d.V.C.); (M.A.D.V.)
| | - Diego do Vale Chagas
- Secretaria Municipal de Saúde de Boa Viagem (SMS-Boa Viagem), Boa Viagem, CE 63870-000, Brazil; (P.J.d.S.M.); (F.F.d.S.); (F.d.C.A.); (R.d.S.R.); (D.d.V.C.); (M.A.D.V.)
| | - Anielly Ferreira-de-Brito
- Laboratório de Mosquitos Transmissores de Hematozoários, Fiocruz, MS, Rio de Janeiro, RJ 21040-900, Brazil; (M.S.A.S.N.); (A.F.-d.-B.); (R.L.-d.-O.)
| | | | - Maria Angélica Monteiro de Mello Mares-Guia
- Laboratório de Flavivírus, Fundação Oswaldo Cruz (Fiocruz), Ministério da Saúde (MS), Rio de Janeiro, RJ 21040-900, Brazil; (F.L.L.C.); (M.A.M.d.M.M.-G.); (V.M.G.C.); (N.R.d.C.F.); (M.C.L.d.M.); (A.M.B.d.F.)
| | - Vinícius Martins Guerra Campos
- Laboratório de Flavivírus, Fundação Oswaldo Cruz (Fiocruz), Ministério da Saúde (MS), Rio de Janeiro, RJ 21040-900, Brazil; (F.L.L.C.); (M.A.M.d.M.M.-G.); (V.M.G.C.); (N.R.d.C.F.); (M.C.L.d.M.); (A.M.B.d.F.)
| | - Nieli Rodrigues da Costa Faria
- Laboratório de Flavivírus, Fundação Oswaldo Cruz (Fiocruz), Ministério da Saúde (MS), Rio de Janeiro, RJ 21040-900, Brazil; (F.L.L.C.); (M.A.M.d.M.M.-G.); (V.M.G.C.); (N.R.d.C.F.); (M.C.L.d.M.); (A.M.B.d.F.)
| | - Marcelo Adriano da Cunha e Silva Vieira
- Coordenação-Geral de Vigilância das Arboviroses (CGARB), Departamento de Imunização e Doenças Transmissíveis (DEIDT), Secretaria de Vigilância em Saúde (SVS), MS, Brasília, DF 70058-900, Brazil; (A.M.R.d.N.); (R.G.P.); (M.A.d.C.e.S.V.); (P.H.d.O.P.); (D.G.R.); (A.P.M.R.)
- Coordenação de Epidemiologia, Secretaria de Estado da Saúde do Piauí, Teresina, PI 64018-000, Brazil
| | - Marcos Cesar Lima de Mendonça
- Laboratório de Flavivírus, Fundação Oswaldo Cruz (Fiocruz), Ministério da Saúde (MS), Rio de Janeiro, RJ 21040-900, Brazil; (F.L.L.C.); (M.A.M.d.M.M.-G.); (V.M.G.C.); (N.R.d.C.F.); (M.C.L.d.M.); (A.M.B.d.F.)
| | - Nayara Camila Amorim de Alvarenga Pivisan
- Secretaria Estadual de Saúde do Estado do Ceará (SES-CE), Fortaleza, CE 60060-440, Brazil; (B.H.D.); (A.R.S.V.); (F.B.P.M.); (L.O.R.d.S.); (K.N.F.d.E.); (L.C.d.S.); (N.C.A.d.A.P.); (R.G.d.A.G.)
| | - Jarier de Oliveira Moreno
- Agência de Defesa Agropecuária do Estado do Ceará (ADAGRI), Fortaleza, CE 60811-520, Brazil; (E.M.d.Q.-J.); (A.W.L.d.S.); (J.d.O.M.)
| | - Maria Aldessandra Diniz Vieira
- Secretaria Municipal de Saúde de Boa Viagem (SMS-Boa Viagem), Boa Viagem, CE 63870-000, Brazil; (P.J.d.S.M.); (F.F.d.S.); (F.d.C.A.); (R.d.S.R.); (D.d.V.C.); (M.A.D.V.)
| | - Ricristhi Gonçalves de Aguiar Gomes
- Secretaria Estadual de Saúde do Estado do Ceará (SES-CE), Fortaleza, CE 60060-440, Brazil; (B.H.D.); (A.R.S.V.); (F.B.P.M.); (L.O.R.d.S.); (K.N.F.d.E.); (L.C.d.S.); (N.C.A.d.A.P.); (R.G.d.A.G.)
| | | | - Pedro Henrique de Oliveira Passos
- Coordenação-Geral de Vigilância das Arboviroses (CGARB), Departamento de Imunização e Doenças Transmissíveis (DEIDT), Secretaria de Vigilância em Saúde (SVS), MS, Brasília, DF 70058-900, Brazil; (A.M.R.d.N.); (R.G.P.); (M.A.d.C.e.S.V.); (P.H.d.O.P.); (D.G.R.); (A.P.M.R.)
| | - Daniel Garkauskas Ramos
- Coordenação-Geral de Vigilância das Arboviroses (CGARB), Departamento de Imunização e Doenças Transmissíveis (DEIDT), Secretaria de Vigilância em Saúde (SVS), MS, Brasília, DF 70058-900, Brazil; (A.M.R.d.N.); (R.G.P.); (M.A.d.C.e.S.V.); (P.H.d.O.P.); (D.G.R.); (A.P.M.R.)
| | - Alessandro Pecego Martins Romano
- Coordenação-Geral de Vigilância das Arboviroses (CGARB), Departamento de Imunização e Doenças Transmissíveis (DEIDT), Secretaria de Vigilância em Saúde (SVS), MS, Brasília, DF 70058-900, Brazil; (A.M.R.d.N.); (R.G.P.); (M.A.d.C.e.S.V.); (P.H.d.O.P.); (D.G.R.); (A.P.M.R.)
| | - Lívia Carício Martins
- Seção de Arbovirologia e Febres Hemorrágicas, Instituto Evandro Chagas (IEC), MS, Ananindeua, PA 67030-000, Brazil; (L.M.N.C.); (L.L.d.C.); (H.A.d.O.M.); (L.C.M.)
| | - Ricardo Lourenço-de-Oliveira
- Laboratório de Mosquitos Transmissores de Hematozoários, Fiocruz, MS, Rio de Janeiro, RJ 21040-900, Brazil; (M.S.A.S.N.); (A.F.-d.-B.); (R.L.-d.-O.)
| | - Ana Maria Bispo de Filippis
- Laboratório de Flavivírus, Fundação Oswaldo Cruz (Fiocruz), Ministério da Saúde (MS), Rio de Janeiro, RJ 21040-900, Brazil; (F.L.L.C.); (M.A.M.d.M.M.-G.); (V.M.G.C.); (N.R.d.C.F.); (M.C.L.d.M.); (A.M.B.d.F.)
| | - Alex Pauvolid-Corrêa
- Laboratório de Flavivírus, Fundação Oswaldo Cruz (Fiocruz), Ministério da Saúde (MS), Rio de Janeiro, RJ 21040-900, Brazil; (F.L.L.C.); (M.A.M.d.M.M.-G.); (V.M.G.C.); (N.R.d.C.F.); (M.C.L.d.M.); (A.M.B.d.F.)
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843-4458, USA
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