Exploring the Usefulness of Meteorological Data for Predicting Malaria Cases in Visakhapatnam, Andhra Pradesh

Abstract

AbstractMalaria and dengue fever are among the most important vectorborne diseases in the tropics and subtropics. Average weekly meteorological parameters—specifically, minimum temperature, maximum temperature, humidity, and rainfall—were collected using data from 100 automated weather stations from the Indian Space Research Organization. We obtained district-level weekly reported malaria cases from the Integrated Disease Surveillance Program (IDSP), Department of Health and Family Welfare, Andhra Pradesh, India, for three years, 2014–16. We used a generalized linear model with Poisson distribution and default logarithm-link to estimate model parameters, and we used a quasi-Poisson method with a generalized additive model that uses nonparametric regression with smoothing splines. It appears that higher minimum temperatures (e.g., >24°C) tend to lead to higher malaria counts but lower values do not seem to have an impact on the malaria counts. On the other hand, higher values of maximum temperature (e.g., >32°C) seem to negatively affect the malaria counts. The relationships with rainfall and humidity appear to be not as strong once we account for smooth (weekly) trends and temperatures; both smooth curves seem to hover around zero across all of their values. We note that a rainfall amount between 40 and 50 mm seems to have a positive impact on malaria counts. Our analyses show that the incremental increase in meteorological parameters does not lead to an increase in reported malaria cases in the same manner for all of the districts within the same state. This suggests that other factors such as vegetation, elevation, and water index in the environment also influence disease occurrence.