Mapping of space–time patterns of infectious disease using spatial statistical models: a case study of COVID-19 in India

Abstract

Introduction

Mapping of infectious diseases like COVID-19 is the foremost importance for diseases control and prevention. This study attempts to identify the spatio-temporal pattern and evolution trend of COVID-19 at the district level in India using spatial statistical models.

Materials and methods

Active cases of eleven time-stamps (30 March–2 December, 2020) with an approximately 20-day interval are considered. The study reveals applications of spatial statistical tools, i.e. optimised hotspot and outlier analysis (which follow Gi* and Moran I statistics) and emerging hotspot with the base of space time cube, are effective for the spatio-temporal evolution of disease clusters.

Results

The result shows the overall increasing trend of COVID-19 infection with a Mann–Kendall trend score of 2.95 (p = 0.0031). The spatial clusters of high infection (hotspots) and low infection (coldspots) change their location over time but are limited to the districts of the south-western states (Kerala, Karnataka, Andhra Pradesh, Maharashtra, Gujarat) and the north-eastern states (West Bengal, Jharkhand, Assam, Tripura, Manipur, etc.) respectively.

Conclusions

A total of eight types of patterns are identified, but the most concerning types are consecutive (7.24% of districts), intensifying (15.13% districts) and persistent (24.34% of districts) which will help health policy makers and the government to prioritize-based resource allocation and control measures.

Keywords:

• COVID-19
• spatial statistics
• emerging hotspot
• space–time cube
• Mann–Kendall
• Moran’s I

Disclosure statement

No potential conflict of interest was reported by the author(s).

Annexure 1: Hotspots and coldspots pattern and its descriptions.