Dynamic variations in and prediction of COVID-19 with omicron in the four first-tier cities of mainland China, Hong Kong, and Singapore

BACKGROUND: The COVID-19 pandemic, which began in late 2019, has resulted in the devastating collapse of the social economy and more than 10 million deaths worldwide. A recent study suggests that the pattern of COVID-19 cases will resemble a mini-wave rather than a seasonal surge. In general, COVID-19 has more severe impacts on cities than on rural areas, especially in cities with high population density. METHODS: In this study, the background situation of COVID-19 transmission is discussed, including the population number and population density. Moreover, a widely used time series autoregressive integrated moving average (ARIMA) model is applied to simulate and forecast the COVID-19 variations in the six cities. We comprehensively analyze the dynamic variations in COVID-19 in the four first-tier cities of mainland China (BJ: Beijing, SH: Shanghai, GZ: Guangzhou and SZ: Shenzhen), Hong Kong (HK), China and Singapore (SG) from 2020 to 2022. RESULTS: The major results show that the six cities have their own temporal characteristics, which are determined by the different control and prevention measures. The four first-tier cities of mainland China (i.e., BJ, SH, GZ, and SZ) have similar variations with one wave because of their identical “Dynamic COVID-19 Zero” strategy and strict Non-Pharmaceutical Interventions (NPIs). HK and SG have multiple waves primarily caused by the input cases. The ARIMA model has the ability to provide an accurate forecast of the COVID-19 pandemic trend for the six cities, which could provide a useful approach for predicting the short-term variations in infectious diseases.Accurate forecasting has significant value for implementing reasonable control and prevention measures. CONCLUSIONS: Our main conclusions show that control and prevention measures should be dynamically adjusted and organically integrated for the COVID-19 pandemic. Moreover, the mathematical models are proven again to provide an important scientific basis for disease control.