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

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.

Culex erythrothorax: Insecticide Susceptibility and Mosquito Control

“…Meteorological data was obtained from a weather station that was located 1.3 km east and 3.2 km north of the Hayward Marsh using the US National Centers for Environmental Information database (www.ncdc.noaa.gov/cdo-web; [29]). Cumulative degree-days (DD) for each week was calculated as described previously for Culex mosquitoes [30] by comparing the daily average temperature to a baseline of 10°C (See Equation 1). If the DD calculation resulted in a negative value, zero was used instead.…”

Forecasting incidence of infectious diarrhea using random forest in Jiangsu Province, China

“…The RF model is a new regression method and can address the limitations of ARIMA/X models in the prediction of diarrhea incidence [11][12][13][14]. It can effectively extract non-linear relationships from data.…”

“…Using the RF model, the training set for each tree is randomly selected from the data, and the final predicted value is the average of all CART outputs. RF model has been widely used for infectious-disease prediction such as West Nile virus infection and Bovine viral diarrhea [12,13]. Notably, Michael et al [14] reported that an RF model has advantages over the ARIMA model in predicting avian influenza H5N1 outbreaks.…”

Forecasting incidence of infectious diarrhea using Random forest in Jiangsu province, China

“…Random forest (RF) model can effectively extract nonlinear relationships in data, which starts by creating classification and regression trees and each constituent tree trains on a potentially nonlinear regression space and is then combined with others. It has been widely used in infectious diseases prediction such as to predict West Nile virus infection and avian influenza incidence [10][11].…”