Increased Detection coupled with Social Distancing and Health Capacity Planning Reduce the Burden of COVID-19 Cases and Fatalities: A Proof of Concept Study using a Stochastic Computational Simulation Model

Ghosh, Pramit; Basheer, Salah; Paul, Sandip; Chakrabarti, Partha; Sarkar, Jit

doi:10.1101/2020.04.05.20054775

Cited by 10 publications

(8 citation statements)

References 13 publications

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“…The SEIR model, a variant of the SIR model, is one which includes a new compartment called Exposed (E), [13][14][15]. However, more recently an extension of the SEIR model has been proposed by [4,16], where apart from the three compartments, Susceptible (S), Infectious (I), Exposed (E) and Removal (R), two other compartments are included. These compartments are Quarantine (Q) and Hospitalization (H).…”

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: Introductionmentioning

confidence: 99%

An analysis of COVID-19 clusters in India

et al. 2021

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“…Articles are appearing in the the mainstream media explaining the importance of such public health interventions in f lattening the curve. Several research groups are working on the evaluation of such containment measures by predicting the course of COVID-19 cases in India, under various scenarios (Chatterjee et al [2020], Das et al [2020], Das [2020], Ghosh et al [2020]). We have made an endeavour to look at some simple simulations of COVID-19 spread using R, and used those simulations to illustrate how the various public health interventions worked.…”

Section: Abstract Sars-cov2 • Seiqhrf Model • Cluster Analysis 1 Backmentioning

confidence: 99%

“…Variation of SIR model is the SEIR model which includes another compartment as Exposed (E) population in the model (Stehlé et al [2011], Li et al [1999], Röst [2008]). Recently an extension of the SEIR model has been proposed (Churches [2020], Ghosh et al [2020]), where apart from the above mentioned 4 stages, two other stages viz. Quarantine (Q) and Hospitalization (H) are considered to take into account the healthcare capability and the R stage is segregated to usual Recovery (denoted by R) and fatality (denoted by F).…”

Section: Seiqhrf Modelmentioning

confidence: 99%

An Analysis of COVID-19 Clusters in India-Two Case Studies on Nizamuddin and Dharavi.

Sengupta¹,

Ganguli

SenRoy

et al. 2020

Preprint

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Background- With the COVID-19 pandemic wreaking havoc across nations, several research projects are being carried out to study the propagation of the virus. In this study we have made an endeavour to analyse the spread of COVID-19 in the districts of India. Methods- Some districts in India have been much more a ected than the others. A cluster analysis of the worst a ected districts in India provide insight about the similarities between them. The e ects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model. Results - The clustering of hotspot districts in India provide homogeneous clusters of districts that stand out in terms of number of positive COVID-19 cases and covariates like population density and number of COVID-19 special hospitals. The cluster analysis reveal that distribution of number of COVID-19 hospitals in the districts vary from the distribution of con rmed COVID-19 cases. The distribution of hospitals is much less skewed than the population density and 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. Thereby, increasing the risk of the disease spread in the respective states. However, the simulations reveal that the administrative interventions, if implemented strictly, flatten the curve of disease spread. In Dharavi however, as claimed by the Brihanmumbai Municipal Corporation officials, through tracing, tracking, testing and treating, massive breakout of COVID-19 was also brought under control. Conclusions - The study rounds up with two important case studies on Nizamuddin basti and Dharavi slum to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the attendees of the religious events who went back to their respective states, increased the risk of infection manifold. However, Dharavi was one of the few COVID-19 success stories. Through strict testing, treating, tracking and tracing large-scale COVID-19 infection was brought under control.

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“…In the absence of a vaccine or any other proven form of treatment available, the most effective remedy against COVID-19 is social distancing and voluntary isolation (Mackenzie, 2020). To implement these measures, governments across the world have imposed full or partial lockdowns (Hamzelou, 2020; Mitjà, 2020; Ghosh et al , 2020; Vaughan, 2020), which initially began with a lockdown in Wuhan which was the original epicentre of COVID-19 (Lu, 2020). A lockdown as a crisis response towards an epidemic is not unprecedented, especially when the public needs to be isolated from the threat.…”

Section: Introductionmentioning

confidence: 99%

Twitter users’ coping behaviors during the COVID-19 lockdown: an analysis of tweets using mixed methods

Mittal

Ahmed

Mittal

et al. 2021

IDD

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Purpose Using data from Twitter, the purpose of this paper is to assess the coping behaviour and reactions of social media users in response to the initial days of the COVID-19-related lockdown in different parts of the world. Design/methodology/approach This study follows the quasi-inductive approach which allows the development of pre-categories from other theories before the sampling and coding processes begin, for use in those processes. Data was extracted using relevant keywords from Twitter, and a sample was drawn from the Twitter data set to ensure the data is more manageable from a qualitative research standpoint and that meaningful interpretations can be drawn from the data analysis results. The data analysis is discussed in two parts: extraction and classification of data from Twitter using automated sentiment analysis; and qualitative data analysis of a smaller Twitter data sample. Findings This study found that during the lockdown the majority of users on Twitter shared positive opinions towards the lockdown. The results also found that people are keeping themselves engaged and entertained. Governments around the world have also gained support from Twitter users. This is despite the hardships being faced by citizens. The authors also found a number of users expressing negative sentiments. The results also found that several users on Twitter were fence-sitters and their opinions and emotions could swing either way depending on how the pandemic progresses and what action is taken by governments around the world. Research limitations/implications The authors add to the body of literature that has examined Twitter discussions around H1N1 using in-depth qualitative methods and conspiracy theories around COVID-19. In the long run, the government can help citizens develop routines that help the community adapt to a new dangerous environment – this has very effectively been shown in the context of wildfires in the context of disaster management. In the context of this research, the dominance of the positive themes within tweets is promising for policymakers and governments around the world. However, sentiments may wish to be monitored going forward as large-spikes in negative sentiment may highlight lockdown-fatigue. Social implications The psychology of humans during a pandemic can have a profound impact on how COVID-19 shapes up, and this shall also include how people behave with other people and with the larger environment. Lockdowns are the opposite of what societies strive to achieve, i.e. socializing. Originality/value This study is based on original Twitter data collected during the initial days of the COVID-19-induced lockdown. The topic of “lockdowns” and the “COVID-19” pandemic have not been studied together thus far. This study is highly topical.

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Increased Detection coupled with Social Distancing and Health Capacity Planning Reduce the Burden of COVID-19 Cases and Fatalities: A Proof of Concept Study using a Stochastic Computational Simulation Model

Cited by 10 publications

References 13 publications

An analysis of COVID-19 clusters in India

An analysis of COVID-19 clusters in India

An Analysis of COVID-19 Clusters in India-Two Case Studies on Nizamuddin and Dharavi.

Twitter users’ coping behaviors during the COVID-19 lockdown: an analysis of tweets using mixed methods

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