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.

Alexander C Keyel,Kathleen A Mcdonough,P Bryon Backenson,Mathias Vuille,Catharine Prussing,Sarah Quinones,Jan E Conn,Philip M Armstrong,Oliver Elison Timm,Laura D Kramer,Theodore G Andreadis,Jeffrey Shaman

doi:10.1371/journal.pone.0217854

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.

Highlights

West Nile virus (WNV) has caused 46,086 diagnosed cases in the United States, with over 2000 human deaths (1999–2016) [1]
We evaluated model fit by examining 1) the root mean squared error (RMSE), 2) Root Mean Squared Error (RMSE) scaled by the mean value, 3) the coefficient of determination, R2, or the percent of variation explained by the model in the validation set, [105], 4) the Spearman Rank correlation coefficient between the predicted and observed values, and 5) the Pearson correlation coefficient between the predicted and observed values
We present first a summary of the random forest model fitting results followed by results that address the question of which–if any—climate covariates improve the model skill

Summary

Introduction

West Nile virus (WNV) has caused 46,086 diagnosed cases in the United States, with over 2000 human deaths (1999–2016) [1]. Avian hosts are thought to be responsible for the majority of WNV amplification [13,14], species vary widely from non-infectious (e.g., Rock Pigeons, Columba livia [14]) to superspreaders (e.g., American Robins, Turdus migratorius [15]). Climatic conditions may facilitate WNV through 1) increased mosquito abundances (e.g., [16], 2) increased viral replication rates [17,18,19], and 3) changing the interactions between mosquitoes and their hosts. Some of these changes could be indirect, such as by affecting timing of breeding or migration for key amplifying species [20,21]

Methods

Results

Discussion

Conclusion