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
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 conduct...
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| Veröffentlicht in: | PloS one 2019-06, Vol.14 (6), p.e0217854 |
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| creator | Keyel, Alexander C Elison Timm, Oliver Backenson, P Bryon Prussing, Catharine Quinones, Sarah McDonough, Kathleen A Vuille, Mathias Conn, Jan E Armstrong, Philip M Andreadis, Theodore G Kramer, Laura D |
| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0217854 |
| format | Article |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0217854</identifier><identifier>PMID: 31158250</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Animal populations ; Animals ; Aquatic insects ; Biology and life sciences ; Birds ; Census of Population ; Climate ; Climate change ; Climate models ; Connecticut - epidemiology ; Correlation analysis ; Culex - virology ; Culicidae ; Dependence ; Discriminant analysis ; Earth Sciences ; Ecology and Environmental Sciences ; Environmental effects ; Environmental science ; Epidemics ; Forests ; Geography ; Health ; Human populations ; Humans ; Hydrologic models ; Hydrology ; Infection ; Infections ; Infectious diseases ; Land cover ; Land use ; Learning algorithms ; Machine learning ; Medicine and health sciences ; Minimum temperatures ; Models, Biological ; Mosquitoes ; New York - epidemiology ; Patient outcomes ; People and places ; Physical Sciences ; Precipitation ; Predictions ; Reptiles & amphibians ; Research and Analysis Methods ; Seasonal temperatures ; Seasons ; Sewage treatment ; Soil analysis ; Soil moisture ; Soil temperature ; Soil testing ; Studies ; Temperature ; Temporal variations ; Variables ; Vector-borne diseases ; Virus diseases ; Viruses ; Wastewater ; Wastewater treatment ; Wastewater treatment plants ; West Nile fever ; West Nile Fever - epidemiology ; West Nile Fever - virology ; West Nile virus ; West Nile virus - physiology ; Zoonoses</subject><ispartof>PloS one, 2019-06, Vol.14 (6), p.e0217854</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Keyel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2019 Keyel et al 2019 Keyel et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-7638d751cab01d03982ffd580b6f8e5f0c68bebc5eb94ea965cf30d731e1e7aa3</citedby><cites>FETCH-LOGICAL-c692t-7638d751cab01d03982ffd580b6f8e5f0c68bebc5eb94ea965cf30d731e1e7aa3</cites><orcidid>0000-0001-5256-6274 ; 0000-0001-6425-6839</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546252/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546252/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31158250$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Shaman, Jeffrey</contributor><creatorcontrib>Keyel, Alexander C</creatorcontrib><creatorcontrib>Elison Timm, Oliver</creatorcontrib><creatorcontrib>Backenson, P Bryon</creatorcontrib><creatorcontrib>Prussing, Catharine</creatorcontrib><creatorcontrib>Quinones, Sarah</creatorcontrib><creatorcontrib>McDonough, Kathleen A</creatorcontrib><creatorcontrib>Vuille, Mathias</creatorcontrib><creatorcontrib>Conn, Jan E</creatorcontrib><creatorcontrib>Armstrong, Philip M</creatorcontrib><creatorcontrib>Andreadis, Theodore G</creatorcontrib><creatorcontrib>Kramer, Laura D</creatorcontrib><title>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</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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.</description><subject>Animal populations</subject><subject>Animals</subject><subject>Aquatic insects</subject><subject>Biology and life sciences</subject><subject>Birds</subject><subject>Census of Population</subject><subject>Climate</subject><subject>Climate change</subject><subject>Climate models</subject><subject>Connecticut - epidemiology</subject><subject>Correlation analysis</subject><subject>Culex - virology</subject><subject>Culicidae</subject><subject>Dependence</subject><subject>Discriminant analysis</subject><subject>Earth Sciences</subject><subject>Ecology and Environmental Sciences</subject><subject>Environmental effects</subject><subject>Environmental science</subject><subject>Epidemics</subject><subject>Forests</subject><subject>Geography</subject><subject>Health</subject><subject>Human populations</subject><subject>Humans</subject><subject>Hydrologic models</subject><subject>Hydrology</subject><subject>Infection</subject><subject>Infections</subject><subject>Infectious diseases</subject><subject>Land cover</subject><subject>Land use</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Medicine and health sciences</subject><subject>Minimum temperatures</subject><subject>Models, Biological</subject><subject>Mosquitoes</subject><subject>New York - epidemiology</subject><subject>Patient outcomes</subject><subject>People and places</subject><subject>Physical Sciences</subject><subject>Precipitation</subject><subject>Predictions</subject><subject>Reptiles & amphibians</subject><subject>Research and Analysis Methods</subject><subject>Seasonal temperatures</subject><subject>Seasons</subject><subject>Sewage treatment</subject><subject>Soil analysis</subject><subject>Soil moisture</subject><subject>Soil temperature</subject><subject>Soil testing</subject><subject>Studies</subject><subject>Temperature</subject><subject>Temporal variations</subject><subject>Variables</subject><subject>Vector-borne diseases</subject><subject>Virus diseases</subject><subject>Viruses</subject><subject>Wastewater</subject><subject>Wastewater treatment</subject><subject>Wastewater treatment plants</subject><subject>West Nile fever</subject><subject>West Nile Fever - epidemiology</subject><subject>West Nile Fever - virology</subject><subject>West Nile virus</subject><subject>West Nile virus - 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Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Keyel, Alexander C</au><au>Elison Timm, Oliver</au><au>Backenson, P Bryon</au><au>Prussing, Catharine</au><au>Quinones, Sarah</au><au>McDonough, Kathleen A</au><au>Vuille, Mathias</au><au>Conn, Jan E</au><au>Armstrong, Philip M</au><au>Andreadis, Theodore G</au><au>Kramer, Laura D</au><au>Shaman, Jeffrey</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>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</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2019-06-03</date><risdate>2019</risdate><volume>14</volume><issue>6</issue><spage>e0217854</spage><pages>e0217854-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31158250</pmid><doi>10.1371/journal.pone.0217854</doi><tpages>e0217854</tpages><orcidid>https://orcid.org/0000-0001-5256-6274</orcidid><orcidid>https://orcid.org/0000-0001-6425-6839</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2019-06, Vol.14 (6), p.e0217854 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2234461758 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS); EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Animal populations Animals Aquatic insects Biology and life sciences Birds Census of Population Climate Climate change Climate models Connecticut - epidemiology Correlation analysis Culex - virology Culicidae Dependence Discriminant analysis Earth Sciences Ecology and Environmental Sciences Environmental effects Environmental science Epidemics Forests Geography Health Human populations Humans Hydrologic models Hydrology Infection Infections Infectious diseases Land cover Land use Learning algorithms Machine learning Medicine and health sciences Minimum temperatures Models, Biological Mosquitoes New York - epidemiology Patient outcomes People and places Physical Sciences Precipitation Predictions Reptiles & amphibians Research and Analysis Methods Seasonal temperatures Seasons Sewage treatment Soil analysis Soil moisture Soil temperature Soil testing Studies Temperature Temporal variations Variables Vector-borne diseases Virus diseases Viruses Wastewater Wastewater treatment Wastewater treatment plants West Nile fever West Nile Fever - epidemiology West Nile Fever - virology West Nile virus West Nile virus - physiology Zoonoses |
| title | 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 |
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