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An analysis of COVID-19 clusters in India: Two case studies on Nizamuddin and Dharavi

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 interve...

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Autores principales: Sengupta, Pooja, Ganguli, Bhaswati, SenRoy, Sugata, Chatterjee, Aditya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011051/
https://www.ncbi.nlm.nih.gov/pubmed/33789619
http://dx.doi.org/10.1186/s12889-021-10491-8
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author Sengupta, Pooja
Ganguli, Bhaswati
SenRoy, Sugata
Chatterjee, Aditya
author_facet Sengupta, Pooja
Ganguli, Bhaswati
SenRoy, Sugata
Chatterjee, Aditya
author_sort Sengupta, Pooja
collection PubMed
description 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.
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spelling pubmed-80110512021-03-31 An analysis of COVID-19 clusters in India: Two case studies on Nizamuddin and Dharavi Sengupta, Pooja Ganguli, Bhaswati SenRoy, Sugata Chatterjee, Aditya BMC Public Health Research Article 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. BioMed Central 2021-03-31 /pmc/articles/PMC8011051/ /pubmed/33789619 http://dx.doi.org/10.1186/s12889-021-10491-8 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Sengupta, Pooja
Ganguli, Bhaswati
SenRoy, Sugata
Chatterjee, Aditya
An analysis of COVID-19 clusters in India: Two case studies on Nizamuddin and Dharavi
title An analysis of COVID-19 clusters in India: Two case studies on Nizamuddin and Dharavi
title_full An analysis of COVID-19 clusters in India: Two case studies on Nizamuddin and Dharavi
title_fullStr An analysis of COVID-19 clusters in India: Two case studies on Nizamuddin and Dharavi
title_full_unstemmed An analysis of COVID-19 clusters in India: Two case studies on Nizamuddin and Dharavi
title_short An analysis of COVID-19 clusters in India: Two case studies on Nizamuddin and Dharavi
title_sort analysis of covid-19 clusters in india: two case studies on nizamuddin and dharavi
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011051/
https://www.ncbi.nlm.nih.gov/pubmed/33789619
http://dx.doi.org/10.1186/s12889-021-10491-8
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