https://www.socictopen.socict.org/files/original/8fa65a16bf9c270c93275f839baeda46.pdf 014976da9ea0c3ea232b401a11a6cb8d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Mathematical Modeling Based Study and Prediction of COVID-19 Epidemic Dissemination Under the Impact of Lockdown in India Creator An entity primarily responsible for making the resource Vipin Tiwari, Namrata Deyal, Nandan S. Bisht Description An account of the resource COVID-19 (SARS-CoV-2) is rapidly spreading in South Asian countries, especially in India. India is the fourth most COVID-19 affected country at present i.e., until July 10, 2020. With limited medical facilities and high transmission rate, the study of COVID-19 progression and its subsequent trajectory needs to be analyzed in India. Epidemiologic mathematical models have the potential to predict the epidemic peak of COVID-19 under different scenarios. Lockdown is one of the most effective mitigation policies adopted worldwide to control the transmission rate of COVID-19 cases. In this study, we use an improvised five compartment mathematical model, i.e., Susceptible (S)-Exposed (E)-Infected (I)-Recovered (R)-Death (D) (SEIRD) to investigate the progression of COVID-19 and predict the epidemic peak under the impact of lockdown in India. The aim of this study is to provide a more precise prediction of epidemic peak and to evaluate the impact of lockdown on epidemic peak shift in India. For this purpose, we examine the most recent data (from January 30, 2020 to July 10, 2020 i.e., 160 days) to enhance the accuracy of outcomes obtained from the proposed model. The model predicts that the total number of COVID-19 active cases would be around 5.8 × 105 on August 15, 2020 under current circumstances. In addition, our study indicates the existence of under-reported cases i.e., 105 during the post-lockdown period in India. Consequently, this study suggests that a nationwide public lockdown would lead to epidemic peak suppression in India. It is expected that the obtained results would be beneficial for determining further COVID-19 mitigation policies not only in India but globally as well. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource India, covid-19, lockdown, underreporting, SEIRD model, epidemic peak Identifier An unambiguous reference to the resource within a given context 10.3389/fphy.2020.586899 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Physics