Spatio-temporal epidemiology and associated indicators of COVID-19 (wave-I and II) in India

Abstract

The spatio-temporal distribution of COVID-19 across India's states and union territories is not uniform, and the reasons for the heterogeneous spread are unclear. Identifying the space-time trends and underlying indicators influencing COVID-19 epidemiology at micro-administrative units (districts) will help guide public health strategies. The district-wise daily COVID-19 data of cases and deaths from February 2020 to August 2021 (COVID-19 waves-I and II) for the entire country were downloaded and curated from public databases. The COVID-19 data normalized with the projected population (2020) and used for space-time trend analysis shows the states/districts in southern India are the worst hit. Coastal districts and districts adjoining large urban regions of Mumbai, Chennai, Bengaluru, Goa, and New Delhi experienced > 50,001 cases per million population. Negative binomial regression analysis with 21 independent variables (identified through multicollinearity analysis, with VIF < 10) covering demography, socio-economic status, environment, and health was carried out for wave-I, wave-II, and total (wave-I and wave-II) cases and deaths. It shows wealth index, derived from household amenities datasets, has a high positive risk ratio (RR) with COVID-19 cases (RR: 3.577; 95% CI: 2.062-6.205) and deaths (RR: 2.477; 95% CI: 1.361-4.506) across the districts. Furthermore, socio-economic factors such as literacy rate, health services, other workers' rate, alcohol use in men, tobacco use in women, overweight/obese women, and rainfall have a positive RR and are significantly associated with COVID-19 cases/deaths at the district level. These positively associated variables are highly interconnected in COVID-19 hotspot districts. Among these, the wealth index, literacy rate, and health services, the key indices of socio-economic development within a state, are some of the significant indicators associated with COVID-19 epidemiology in India. The identification of district-level space-time trends and indicators associated with COVID-19 would help policymakers devise strategies and guidelines during public health emergencies.

Figure 1

The cumulative COVID-19 cases and deaths in India from April 2020 to August 2021. The timeline for waves I and II is represented on the x-axis. The major lockdown periods are shaded and labeled.

Figure 2

District-wise (A) COVID-19 cases per million population and (B) deaths per million population in India from April 2020 to August 2021. The inset map shows the location of India in Asia. The map was created using the licensed version of ArcGIS 10.4 software by Esri (www.esri.com). The background map represents the World Topographic Map (http://goto.arcgisonline.com/maps/World_Topo_Map).

Figure 3

Spatial distributions of the influential socio-economic, demographic, and health indicators at the district level in India. The map was created using the licensed version of ArcGIS 10.4 software by Esri (www.esri.com).

Figure 4

Distribution of (A) upward trend districts with different significance (P-value) levels and (B) distribution of different categories of hotspot districts identified by space–time pattern analysis for COVID-19 cases in India. The map was created using the licensed version of ArcGIS 10.4 software by Esri (www.esri.com). The background map represents the World Topographic Map (http://goto.arcgisonline.com/maps/World_Topo_Map).