Work

EpiClim: weekly district-wise all-India multi-epidemics climate-health dataset for accelerated GeoHealth research

Year: 2025

Type: article

Abstract: Climate change directly affects public health as it significantly influences the emergence and spread of epidemics. Factors such as temperature, precipitation have a major impact on and promote the growth of prevalent epidemics transmitted by vectors and waterborne epidemics, as seen from historical and contemporary evidence. The collection of high-quality data has become a must to be able to foster the growth of climate health models to understand and predict epidemic outbreaks due to climate variability. Innovative developments in weather and climate science, especially through artificial intelligence-enabled numerical weather prediction, boost confidence in forecasting key variables such as temperature and precipitation. However, a major obstacle to the integration of climate models with health prediction systems lies in the inaccessibility of comprehensive, publicly accessible health datasets for various epidemics. Such data sets, especially on granular spatial and temporal scales, are crucial to advance climate health research and model development. In this paper we present the first weekly district-wise dataset for major epidemics spanning across India from 2009 to 2022. The data utilized in this study has been sourced from the Integrated Disease Surveillance Program, a publically available database maintained by the Government of India. This dataset, named ‘EpiClim: India’s Epidemic-Climate Dataset’, bridges the gap by providing detailed insights into health data tailored for climate-health modeling. The data set offers insight into the temporal and spatial dynamics of epidemics such as dengue, malaria, and acute-diarrheal disease (ADD). We will see in this research work the correlation of various epidemics such as dengue, cholera, ADD, and malaria with temperature For example, dengue and malaria have a good correlation with temperature and cholera, on the other hand, usually outbreaks with anomalies with precipitation showing a good correlation with the precipitation factor. Such datasets open the gates to integrate climate forecasts with epidemic prediction models and create actionable insights for policymakers, public health officials, and researchers. This work lays the foundation for coupling predictive climate health models with numerical weather and climate models, driving an innovation that seeks to understand and mitigate public health crises caused by climate.

Source: Environmental Research Health

DOI: 10.1088/2752-5309/ae30f6