Abstract
A quantitative COVID-19 model that incorporates hidden asymptomatic patients is developed, and an analytic solution in parametric form is given. The model incorporates the impact of lock-down and resulting spatial migration of population due to announcement of lock-down. A method is presented for estimating the model parameters from real-world data, and it is shown that the various phases in the observed epidemiological data are captured well. It is shown that increase of infections slows down and herd immunity is achieved when active symptomatic patients are 10-25% of the population for the four countries we studied. Finally, a method for estimating the number of asymptomatic patients, who have been the key hidden link in the spread of the infections, is presented.
Publication types
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Research Support, Non-U.S. Gov't
MeSH terms
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Asymptomatic Infections / epidemiology
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COVID-19 / epidemiology
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COVID-19 / immunology
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COVID-19 / pathology*
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COVID-19 / virology
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France / epidemiology
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Humans
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Immunity, Herd*
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Italy / epidemiology
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Japan / epidemiology
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Models, Theoretical*
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Quarantine
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SARS-CoV-2 / isolation & purification
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Switzerland / epidemiology
Grants and funding
Sankhya Sutra Labs provided support for the study in the form of salaries for authors [SB and SA]. The specific roles of these authors are articulated in the ‘author contributions’ section. Science and Engineering Research Board (SERB) provided additional funding to MV. No additional funding was received for this research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.