Effectiveness of a scenario-based, community-based intervention in containing COVID-19 in China.

Background: Given the significant impact of the more than three-year-long COVID-19 pandemic on people's health, social order, and economic performance, as well as the potential re-emergence of a new variant and the epidemic "Disease X," it is crucial to examine its developmental trends and suggest countermeasures to address community epidemics of severe respiratory infectious diseases. Methods: The epidemiological characterization of various strains of COVID-19 was modeled using an improved Susceptible-Exposed-Infectious-Recovered (SEIR) model to simulate the infections of different strains of COVID-19 under different scenarios, taking as an example an urban area of a prefecture-level city in Shandong Province, China, with a resident population of 2 million. Scenarios 1-5 are scenario-based simulations the Omicron strain, and 6-8 simulate the original COVID-19 strain, with different parameters for each scenario. Scenarios 1 and 6 do not consider community NPIs and represent natural epidemic scenarios. Scenarios 2-4 assess the impact of different NPIs on the original COVID-19 strain. Scenarios 1-4 and 6-8 compare the effects of the same measures on different strains. Scenario 5 simulates the effects of implementing NPIs after an outbreak has spread widely. Compare scenarios 4 and 9 to analyze the effect of high grades versus dynamic clearing of NPIs. By analyzing the time at which the peak number of cases was reached and the maximum number of cases, we were able to calculate the effectiveness of urban community control measures (NPIs) and the impact of vaccination on disease trends. Based on our research into the degree of restriction of social activities in different levels of control areas during real-world epidemics, we categorized the NPIs into three levels, with controls becoming increasingly stringent from levels 1 to 3 as low-, medium-, and high-risk areas are, respectively, controlled. Results: In simulation scenarios 1-5 and 9, where the epidemic strain is Omicron and the susceptible population receives three doses of vaccine, it was found that the real-time peak number of cases in scenario 2, which implemented level 1 controls, was reduced by 18.19%, and in scenario 3, which implemented level 2 controls, it was reduced by 38.94%, compared with scenario 1, where no control measures were taken. Level 1 and level 2 controls do not block transmission but significantly reduce peak incidence and delay the peak time. In scenario 5, even with a high number of initial cases, the implementation of level 3 controls can still control the outbreak quickly, but it requires a longer period of time. However, Omicron has a low rate of severe illness, and the existing beds in City A could largely cope even if the control measures had not been implemented. Analyzing scenarios 4 and 9, level 3 community control and dynamic zeroing of the three zones were similarly successful in interrupting the spread of the epidemic. In simulation scenarios 6-8, where the prevalent strain was the original COVID-19 strain, only level 3 community control was able to rapidly extinguish the outbreak. Unchecked, the outbreak is severe, characterized by high peaks and substantial medical stress. Although level 2 controls reduced real-time incidence and peak new infections by 39.81 and 61.33%, and delayed the peaks by 55 and 52 days, respectively, the high rate of severe illnesses may still overwhelm the medical system. Conclusion: Control effects are related to the level, timing and virus characteristics. Level 3 and dynamic zeroing measures can interrupt community transmission in the early stages of an outbreak. During a pandemic, different NPIs must be implemented, considering the virus's status and cost of control, and ensuring that medical resources are sufficient to maintain medical order.