Spatial Aspects of Acute Respiratory Disease Syndrome: An Application of Scan Statistics Using SaTScan in Identification and Analysis of Hotspot in India

Abstract

Respiratory illnesses like acute respiratory disease syndrome (ARDS) have been a persistent issue throughout history, rather than being a contemporary concern and have likely afflicted humans since ancient times, these have become increasingly more prevalent during recent decades and these diseases are among the leading causes of global deaths. This study investigates the spatial distribution of ARDS in India. In the present study, we acquired ARDS disease data from the year 2006 to 2021 and environmental, demographic, and meteorological data from various government sources. Using spatial scan statistics, we detected significant clusters of ARDS cases, highlighting areas with unusually high incidences. To further comprehend the factors contributing to these clusters, we employed regression modeling incorporating a Box-Cox transformation to identify and analyze potential explanatory variables. This transformation was crucial for stabilizing variance and making the data more suitable for regression analysis. Comprehensive and integrated diagnostics are being implemented to validate the model and derive judicious implications. For robust computing, we have used the software MS Excel, MS Solver, R, and Python. Our findings reveal critical insights into the spatial dynamics of ARDS and underline the importance of spatial analysis and appropriate statistical transformations in public health research. These results can guide resource allocation and policy-making aimed at mitigating ARDS in India, ultimately contributing to better health outcomes.