A predictive model for covid-19 spread applied to six us states

Zeina S. Khan, Frank van Bussel, Fazle Hussain

Research output: Contribution to journalArticlepeer-review


A compartmental epidemic model is proposed to predict the Covid-19 virus spread. It considers: both detected and undetected infected populations, medical quarantine and social sequestration, plus possible reinfection. The coefficients in the model are evaluated by fitting to empirical data for six US states: California, Louisiana, New Jersey, New York State, Texas, and Washington State. The evolution of Covid-19 is fairly similar among the states: differences in contact and detection rates remain below 5%; however, variations are larger in death rate, recovery rate, and stay-at-home effect. The results reveal that outbreaks may have been well underway in several states before first detected and that some western states might have seen more than one influx of the pandemic. For the majority of states the model’s effective reproduction number is slightly above the critical value of one, indicating that Covid-19 will become endemic, spreading for more than two years. Should stay-at-home orders be revoked, most states may experience oscillating yearly infections. Even if additional lockdowns are applied in Texas, and then released according to White House guidelines of 14 days of decreasing cases, a similar endemic situation may occur. Additionally, if lockdowns had been instituted one to three weeks sooner, the number of Covid-19 deaths in New York could have been significantly reduced, but surprisingly not in Texas (perhaps due to aversion to lockdown and absence of a decree).

Original languageEnglish
JournalUnknown Journal
StatePublished - Jun 10 2020

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