Spatiotemporal pattern recognition and dynamical analysis of COVID-19 in Shanghai, China.

Shanghai suffered a large outbreak of Omicron mutant of COVID-19 at the beginning of March 2022. To figure out the spatiotemporal patterns of the epidemic, a retrospective statistical investigation, coupled with a dynamic model, is implemented in this study. The hotspots of SARS-CoV-2 transmissions are identified, and strong aggregative effects in the decay stage are found. Besides, the visualization of disease diffusion is provided to show how COVID-19 disease invades all districts of Shanghai in the early stage. Furthermore, the calculations from the dynamic model manifest the effect of detections to suppress the epidemic dissemination. These results reveal the strategies to improve the spatial control of disease.

Mathematical analysis for COVID-19 resurgence in the contaminated environment.

A mathematical model is proposed that incorporates the key routes of COVID-19 resurgence: human-to-human transmission and indirect transmission by inhaling infectious aerosols or contacting public facilities with the virus. The threshold condition for the disease invasion is established, and the relationships among the basic reproduction number, peak value and final size are formulated. The model is validated by matching the model with the data on cases of COVID-19 resurgence in April of 2020 from Heilongjiang province in China, which indicates that the predictive values from the mathematical model fit the real data very well. Based upon the computations from the model and analytical formulae, we reveal how the indirect transmission from environmental pathogens contribute to the disease outbreak and how the input of asymptomatic individuals affect the disease spread. These findings highlight the importance of mass detection and environmental disinfection in the control of COVID resurgence.