Forecast of peak infection and estimate of excess deaths in COVID-19 transmission and prevalence in Taiyuan City,2022 to 2023

In this paper,with the method of epidemic dynamics,we assess the spread and prevalence of COVID-19 after the policy adjustment of prevention and control measure in December 2022 in Taiyuan City in China,and estimate the excess population deaths caused by COVID-19.Based on the transmission mechanism of COVID-19 among individuals,a dynamic model with heterogeneous contacts is established to describe the change of control measures and the population's social behavior in Taiyuan city.The model is verified and simulated by basing on reported case data from November 8th to December 5th,2022 in Taiyuan city and the statistical data of the questionnaire survey from December 1st to 23rd,2022 in Neijiang city.Combining with reported numbers of permanent residents and deaths from 2017 to 2021 in Taiyuan city,we apply the dynamic model to estimate theoretical population of 2022 under the assumption that there is no effect of COVID-19.In addition,we carry out sensitivity analysis to determine the propagation character of the Omicron strain and the effect of the control measures.As a result of the study,it is concluded that after adjusting the epidemic policy on December 6th,2022,three peaks of infection in Taiyuan are estimated to be from December 22nd to 31st,2022,from May 10th to June 1st,2023,and from September 5th to October 13th,2023,and the corresponding daily peaks of new cases can reach 400000,44000 and 22000,respectively.By the end of 2022,excess deaths can range from 887 to 4887,and excess mortality rate can range from 3.06%to 14.82%.The threshold of the infectivity of the COVID-19 variant is estimated 0.0353,that is if the strain infectivity is above it,the epidemic cannot be control with the previous normalization measures.

Why is it difficult to accurately predict the COVID-19 epidemic?

Since the COVID-19 outbreak in Wuhan City in December of 2019,numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported.These model predictions have shown a wide range of variations.In our study,we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations.Using the Akaike Information Criterion(AIC)for model selection,we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data.This indicates that predictions using more complex models may not be more reliable compared to using a simpler model.We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23,2020.We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic,and modeling the potential of a second outbreak after the return-to-work in the city.