An analysis of COVID-19 clusters in India

Abstract

Background In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. Methods A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. Results The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. Conclusions The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

Uncertainty in the Spread of COVID-19: An Analysis in the Context of India

“…We inspired by Baruh's work [24]. He used his own statistical method based on exponential model to study the pandemic COVID-19 situation in India, USA and world.…”

“…Jahan et al (23) studied the entry and initial spread of COVID-19 in India. Sengupta et al (24) researched a cluster study of COVID-19 in India.…”

Clusters of COVID-19 Indicators in India: Characterization, Correspondence and Change Analysis

“…We now summarize the results and key findings of the paper as follows: We applied suitable techniques to cluster over 700 time-series of case rate across districts and states of India in a coherent manner and identified four prominent shape patterns that emerged during a period of more than 300 days. Thus, we gained additional insights for states such as Uttar Pradesh as compared to the previous work [ 25 ] where the study was on a shorter duration and data was not normalized. Our novel cluster similarity (edge-weight) metric and the application of cluster correspondence using maximum edge-weighted matching was very effective in mapping between similar clusters of case rates of states and districts.…”

Seroprevalence of COVID-19 infection among vaccine naïve population after the second surge (June 2020) in a rural district of South India: A community-based cross-sectional study

“…Pandemic fatigue, emergence of mutant strains, political and religious mass gatherings and vaccine complacency resulted in a massive second surge. The slow rising curve of new cases in February 2021 soon transformed into a steep uphill by April, 2021 [ 2 ]. On 19 April 2021, the number of new cases was about 0.3 million, which was already three times that of the first wave around the same month the previous year [ 1 ].…”