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

Abstract

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.

Socio-economic analysis of short-term trends of COVID-19: modeling and data analytics

“…The NPIs should also reduce death by about 95%; SIR model was adopted for the simulations [53]. The other studies, reported in the Table 7, which involve lockdown in their analysis exhibit similar conclusions in terms of infection and death reduction after or while applying lockdown measures, based on statistics or simulations [1,18,57,69].…”

“…According to the analysis of the references [ 1 , 51 – 55 , 57 , 69 ], it was remarkable that the application of lockdown policies enhanced the slow-down of the spread of the pandemic in space and time. For instance, based on real-time statistics in Libanon, Kharroubi et Saleh [ 52 ] demonstrated the success of the lockdown measures on the containment of the disease.…”

“…HCI and age variables: the coupled effect of health care system and elderly According to the literature, the health care system was considered by means of different indicators such as ICU level [11], Prevention and Control (P&C) capacity [71], the number of hospitals and the 4 T's (tracing, tracking, testing and treating) [69]. In this work, and as previously introduced, HCI index was adopted as a public health policy indicator.…”

“…x c [67] x x [68] x x [69] x [70] x [71] x [72] x x temperature becomes meaningless compared to HDI and could not describe COVID-19 transmission [64]. In another hand, Ahmed et al [65] included GDP and life expectancy among numerous other environmental factors (temperature, humidity…).…”

Campanha de vacinação em massa contra a COVID-19 em comunidades vulneráveis no município do Rio de Janeiro

“…Tal fato pode ser explicado pelo risco aumentado de contaminação, causado por uma maior exposição provocada pela falta de reservas financeiras da população, que dificulta na adesão de medidas preventivas não farmacológicas, como isolamento social, uso de máscaras e higienização das mãos e, também, devido ao acesso limitado aos serviços de saúde (SANTOS et al, 2022). Alguns exemplos, seriam as comunidades carentes na Índia (SENGUPTA et al, 2021), e no Quênia (KIM et al, 2022).…”