COVID19 epidemic modelling and the effect of public health interventions in India-SEIQHRF model

Sengupta, Pooja; Ganguli, Bhaswati; Chatterjee, Amlan; SenRoy, Sugata; Chatterjee, Moumita

doi:10.1101/2020.09.16.20182915

Cited by 1 publication

(3 citation statements)

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“…The probabilistic model can be further improved to adequately model reallife scenarios. We have thus used the model to explore the effects numerous public health interventions and policies through scenario analyses, [5].…”

Section: Seiqhrf Modelmentioning

confidence: 99%

“…Also note that case fatalities are restricted only to occur among those in the H compartment, that is, those requiring hospitalisation (irrespective of whether they can get it or not). A similar method for modelling spread of COVID-19 has been used by [5].…”

Section: Seiqhrf Modelmentioning

confidence: 99%

“…Articles are being added to the expanding body of work on COVID-19 in flattening the curve. Many recent research papers have discussed the assessment of these containment policies through prediction of the path of COVID-19 cases in India, for different scenarios [1][2][3][4][5]. Some recent papers have discussed in detail the outbreak of the disease across the globe.…”

Section: Introductionmentioning

confidence: 99%

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An analysis of COVID-19 clusters in India

et al. 2021

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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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Section: Seiqhrf Modelmentioning

confidence: 99%

Section: Seiqhrf Modelmentioning

confidence: 99%