Modeling COVID-19 latent prevalence to assess a public health intervention at a state and regional scale

Philip J. Turk; Shih-Hsiung Chou; Marc A. Kowalkowski; Pooja P. Palmer; Jennifer S. Priem; Melanie D. Spencer; Yhenneko J. Taylor; Andrew D. McWilliams

All other things being equal, if residents continue to observe the stay-at-home orders, maintaining attention to social distancing and increased personal hygiene, then this wave of the COVID-19 outbreak would essentially be over by mid-July. It is possible that we could see continued flattening and shrinking of the infection curve in which case our forecast results would adapt commensurately. It is also possible that infection prevalence could oscillate at a low level over time, in which case more advanced modeling and methods would be needed. Our results highlight the importance of incorporating local context into pandemic forecast modeling, as well as the need to remain vigilant and informed by the data as we enter into a critical period of the outbreak. While there will regrettably still be tragic loss of life and many NC citizens infected by coronavirus, this scenario pales in comparison to what could have been a far worse conclusion. assembled references; YT, SHC, MS, and AW performed critical revision of the manuscript for important intellectual content; PP collected the data and drafted the bibliography; and AW supervised the study.