​No Access Exploring usefulness of meteorology data for predicting malaria cases in Visakhapatnam, AP

Sehgal Meena, Ghosh Sujit
American Meteorological Society Journals Online, https://doi.org/10.1175/WCAS-D-19-0029.1

Malaria and dengue fever are among the most important vector-borne diseases in the tropics and subtropics. The average weekly meteorological parameters, specifically, minimum temperature, maximum temperature, humidity, rainfall were collected using hundred automated weather station data from Indian Meteorology Department. We obtained district level weekly reported malaria cases from Integrated Disease Surveillance Program (IDSP), Department of Health and Family Welfare, Andhra Pradesh, for three years, 2014 - 16.

We used Generalized Linear Model (GLM) with Poisson distribution and default log-link to estimate model parameters and used quasi-Poisson method with Generalized Additive Model (GAM) that uses non-parametric regression with smoothing splines. It appears that as higher minimum temperatures (e.g.>240C) tend to lead to higher malaria counts but lower values do not seem to impact the malaria counts. On the other hand, higher values of maximum temperature (e.g.>320C) seem to negatively impact the malaria counts. The relationships with rainfall and humidity appears not so strong once we account for smooth (weekly) trends and temperatures as both smooth curves seem to hover around the value of zero across all its value. We noted a rainfall amount between 40mm to 50mm seem to have positive impact on malaria counts.

Our analyses shows, the incremental increase in meteorology parameter does not lead to increase in reported malaria cases in the same manner for all the districts within the same state. This suggests that other factors such as of vegetation, elevation and water index in the environment also influence disease occurrence.

Meteorology data
Vector-borne diseases
Generalized Linear Model
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