[Estimation of COVID-19 incidence in Shanghai under optimized epidemic prevention and control strategies].

Objective: To quantitatively estimate the incidence of COVID-19 in different backgrounds, including vaccination coverage, non-pharmacological interventions (NPIs) measures, home quarantine willingness and international arrivals, and the demands of healthcare resource in Shanghai in the context of optimized epidemic prevention and control strategies. Methods: Based on the natural history of 2019-nCoV, local vaccination coverage and NPI performance, an age-structured Susceptible-Exposed-Infections-Removed (SEIR) epidemic dynamic model was established for the estimation of the incidence of COVID-19 and demand of hospital beds in Shanghai by using the data on December 1, 2022 as the basis. Results: Based on current vaccination coverage, it is estimated that 180 184 COVID-19 cases would need treatment in hospitals in Shanghai within 100 days. When the booster vaccination coverage reaches an ideal level, the number of the cases needing hospitalization would decrease by 73.20%. School closure or school closure plus workplace closure could reduce the peak demand of regular beds by 24.04% or 37.73%, respectively, compared with the situation without NPI. Increased willingness of home quarantine could reduce the number of daily new cases and delay incidence peak of COVID-19. The number of international arrivals has little impact on the development of the epidemic. Conclusions: According to the epidemiological characteristics of COVID-19 and the actual situation of vaccination in Shanghai, the incidence of COVID-19 and health resource demand might be reduced by increasing vaccination coverage and early implementation of NPI.

Quantifying the Traffic Impacts of the COVID-19 Shutdown

The coronavirus disease (COVID-19) pandemic has significantly disrupted transportation and travel patterns across the US and around the world. A significant driving factor in the significant reduction in travel in the US was the declaration of varying state-, county-, and city-level stay-at-home orders with varying degrees of reduction. However, it is still not clear how significantly any one of those orders contributed to the reduction in travel. This article looks at continuous count data from the Minneapolis-St. Paul, Minnesota, area to quantify the disruption in terms of reductions in traffic volume as well as the abnormality of the disruption to travel patterns. A nearly 50% reduction in total traffic volume is found, and regional trends both in reductions and the gradual recovery toward normal travel patterns are identified. Furthermore, key dates are identified that led to significant reductions in travel, and this disruptive event is compared with other significantly disruptive events in Minnesota for context. It is found that although the stay-at-home order was a significant milestone in the fight against COVID-19, traffic volumes had already reduced significantly before the order went into effect, and traffic volumes had recovered significantly before the order expired. These findings will be helpful in understand the impact of stay-at-home orders on future outbreaks of COVID-19 or other pandemics. (C) 2021 American Society of Civil Engineers.