Prediction of Daily New COVID-19 Cases - Difficulties and Possible Solutions

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Abstract
Epidemiological compartmental models, such as SEIR (Susceptible, Exposed, Infectious, and Recovered) models, have been generally used in analyzing epidemiological data and forecasting the trajectory of transmission of infectious diseases such as COVID-19. Experience shows that accurately forecasting the trajectory of COVID-19 transmission curve is a big challenge to researchers in the field of epidemiological modeling. Multiple factors (such as social distancing, vaccinations, public health interventions, and new COVID-19 variants) can affect the trajectory of COVID-19 transmission. In the past years, we used a new compartmental model, l-i SEIR model, to analyze the COVID-19 transmission trend in the United States. The letters l and i are two parameters in the model representing the average time length of the latent period and the average time length of infectious period. The l-i SEIR model takes into account of the temporal heterogeneity of infected individuals and thus improves the accuracy in forecasting the trajectory of transmission of infectious diseases. This paper describes how these multiple factors mentioned above could significantly change COVID-19 transmission trends, why accurately forecasting COVID-19 transmission trend is difficult, what the strategies we have used to improve the forecast outcome, and some of successful examples that we have obtained.

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