The reported COVID-19 cases in the United States of America have crossed over 10 million and a large number of infected cases are undetected whose estimation can be done if country-wide antibody testing is performed. In this study, we estimate this undetected fraction of the population by a modeling and simulation approach. We employ an epidemic model SIPHERD in which three categories of infection carriers, symptomatic, purely asymptomatic, and exposed are considered with different transmission rates that are taken dependent on the social distancing conditions, and the detection rate of the infected carriers is taken dependent on the tests done per day. The model is first validated for Germany and South Korea and then applied for prediction of the total number of confirmed, active and dead, and daily new positive cases in the United States. Our study predicts the possible outcomes of the infection if social distancing conditions are relaxed or kept stringent. We estimate that around 30.1 million people are already infected, and in the absence of any vaccine, 66.2 million (range: 64.3-68.0) people, or 20% (range: 19.4-20.5) of the population will be infected by mid-February 21 if social distancing conditions are not made stringent. We find the infection-to-fatality ratio to be 0.65% (range: 0.63-0.67).
Keywords: Coronavirus; computer modeling; epidemiology; pandemics.
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