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A novel approach of adapting social distancing consideration into a SEIR model is presented, where susceptible, exposed and unidentified compartments are collated under the umbrella of the social-distanced compartment. Another key characteristic of the model is the inclusion of the nature of social distancing to be contingent on the rate of change of the active cases. The methodology and the results exhibiting an excellent fit to the data (upto 3rd March 2021) are presented, in case of the COVID-19 outbreak in India. The model attributed the apparently extensive social distancing, to the socio-geographical factors, unique to India. Also the data exhibited greater rate of infection from a diagnosed case as compared to undetected infection. Finally, it is demonstrated that a very conservative estimate of undiagnosed cases is at least $75%$ of the total number of cases.
The outbreak of the novel coronavirus, COVID-19, has been declared a pandemic by the WHO. The structures of social contact critically determine the spread of the infection and, in the absence of vaccines, the control of these structures through large
We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several of these (epidemic) models to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases tha
In the absence of neither an effective treatment or vaccine and with an incomplete understanding of the epidemiological cycle, Govt. has implemented a nationwide lockdown to reduce COVID-19 transmission in India. To study the effect of social distanc
In this paper, we introduce a novel modeling framework for incorporating fear of infection and frustration with social distancing into disease dynamics. We show that the resulting SEIR behavior-perception model has three principal modes of qualitativ
The SARS-CoV-2 infectious outbreak has rapidly spread across the globe and precipitated varying policies to effectuate physical distancing to ameliorate its impact. In this study, we propose a new hybrid machine learning model, SIRNet, for forecastin