Short-term and long-term modeling of the COVID-19 epidemic in the Hubei Province

Authors: YANG, QIHUI - Kansas State University; YI, CHUNLIN - Kansas State University; VAJDI, ARAM - Kansas State University; Cohnstaedt, Lee; WU, HONGYU - Kansas State University; GUO, XIAOLONG - Kansas State University; SCOGLIO, CATERINA - Kansas State University

Submitted to: Journal of Infectious Diseases and Therapy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/5/2020
Publication Date: 8/13/2020
Citation: Yang, Q., Yi, C., Vajdi, A., Cohnstaedt, L.W., Wu, H., Guo, X., Scoglio, C. 2020. Short-term and long-term modeling of the COVID-19 epidemic in the Hubei Province. Journal of Infectious Diseases and Therapy. 5:563-574. https://doi.org/10.1016/j.idm.2020.08.001.
DOI: https://doi.org/10.1016/j.idm.2020.08.001

Interpretive Summary: As an emerging infectious disease, the 2019 coronavirus disease (COVID-19) has developed into a global pandemic. During the initial spreading of the virus in China, we used mathematical modeling to make short-term forecasts of the daily cases reported in Wuhan City. Our daily forecasts were very accurate and we could make the predictions up to 72 hours before the new cases were reported that day. Second, we used an individual-level network based model to reconstruct the epidemic dynamics in Hubei Province during the early stage and examine the effectiveness of non-pharmaceutical interventions such as social distancing on the viral spread. Our simulation results show the Chinese control measures halted the epidemic in Hubei Province and without them, there would still be transmission. Our model also demonstrates that disease spread is a non-Markovian processes or the current situation is not dependent on the previous situation. This means dynamics can change rapidly and unpredictably which is what was observed during the viral transmission and this produces different outbreak trajectories. This must be accounted for when identifying transmission parameters for the model based on the epidemiology of the virus.

Technical Abstract: As an emerging infectious disease, the 2019 coronavirus disease (COVID-19) has developed into a global pandemic. During the initial spreading of the virus in China, we demonstrated the ensemble Kalman filter performed well as a short-term predictor of the daily cases reported in Wuhan City. Second, we used an individual-level network based model to reconstruct the epidemic dynamics in Hubei Province at its early stage and examine the effectiveness of non-pharmaceutical interventions on the epidemic spreading with various scenarios. Our simulation results show that without continued control measures, the epidemic in Hubei Province could have become persistent. Only by continuing to decrease the infection rate through 1) protective measures and 2) social distancing can the actual epidemic trajectory that happened in Hubei Province be reconstructed in simulation. Finally, we simulate the COVID-19 transmission with non-Markovian processes and show how these models produce different epidemic trajectories, compared to those obtained with Markov processes. Since recent studies show that COVID-19 epidemiological parameters do not follow exponential distributions leading to Markov processes, future works need to focus on non-Markovian models to better capture the COVID-19 spreading trajectories. In addition, shortening the infectious period via early case identification and isolation can slow the epidemic spreading significantly.