An Analysis of COVID-19 Clusters in India-Two Case Studies on Nizamuddin and Dharavi.
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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
Posted 24 Sep, 2020
Received 19 Oct, 2020
On 19 Oct, 2020
On 28 Sep, 2020
Received 26 Sep, 2020
Invitations sent on 25 Sep, 2020
On 25 Sep, 2020
On 02 Sep, 2020
On 01 Sep, 2020
On 01 Sep, 2020
On 01 Sep, 2020
An Analysis of COVID-19 Clusters in India-Two Case Studies on Nizamuddin and Dharavi.
Posted 24 Sep, 2020
Received 19 Oct, 2020
On 19 Oct, 2020
On 28 Sep, 2020
Received 26 Sep, 2020
Invitations sent on 25 Sep, 2020
On 25 Sep, 2020
On 02 Sep, 2020
On 01 Sep, 2020
On 01 Sep, 2020
On 01 Sep, 2020
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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.