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Network-based Modeling of COVID-19 Dynamics: Early Pandemic Spread in India
, Rupam Bhattachayya, Shariq Mohammed, Veera Baladandayuthapani
Published in
Volume: 1
Modeling the dynamics of COVID-19 pandemic spread is a challenging and relevant problem. Established models for the epidemic spread such as compartmental epidemiological models e.g. Susceptible-Infected-Recovered (SIR) models and its variants, have been discussed extensively in the literature and utilized to forecast the growth of the pandemic across different hot-spots in the world. The standard formulations of SIR models rely upon summary-level data, which may not be able to fully capture the complete dynamics of the pandemic growth. Since the disease spreads from carriers to susceptible individuals via some form of contact, it inherently relies upon a network of individuals for its growth, with edges established via direct interaction, such as shared physical proximity. Using individual-level COVID-19 data from the early days (January 30 to April 15, 2020) of the pandemic in India and under a network-based SIR model framework, we performed state-specific forecasting under multiple scenarios characterized by the basic reproduction number of COVID-19 across 34 Indian states and union territories. We validated our short-term projections using observed case counts and the long-term projections using national sero-survey findings. Based on healthcare availability data, we also performed projections to assess the burdens on the infrastructure along the spectrum of the pandemic growth. We have developed an interactive dashboard summarizing our results. Our predictions successfully identified the initial hot-spots of India such as Maharashtra and Delhi and those that emerged later, such as Madhya Pradesh and Kerala. These models have the potential to inform appropriate policies for isolation and mitigation strategies to contain the pandemic, through a phased approach by appropriate resource prioritization and allocation.Competing Interest StatementThe authors have declared no competing interest.Funding StatementS.B. is supported by DST INSPIRE Faculty Award Grant No. 04/2015/002165 and also by IIM Indore Young Faculty Research Chair Award grant. S.M. was partially supported by Precision Health at the University of Michigan. V.B. was supported by NIH grants R01-CA160736 and P30 CA 46592 and NSF DMS grant 1463233 and start-up funds from the University of Michigan School of Public Health. Author DeclarationsI confirm all relevant ethical guidelines have been followed and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:NAAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesIndividual-level, location-informed COVID-19 data for Indian states and union territories were obtained from the Kaggle database available at \urlhttps://www.kaggle.com/sudalairajkumar/covid19-in-india. Data on state-wide daily infections is available from \urlhttps://api.covid19india.org/. https://www.kaggle.com/sudalairajkumar/covid19-in-india https://api.covid19india.org/ https://bayesrx.shinyapps.io/COV-N/
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