Data_Sheet_1_Estimating the Effects of Public Health Measures by SEIR(MH) Model of COVID-19 Epidemic in Local Geographic Areas.pdf

The COVID-19 pandemic of 2020–21 has been a major challenge to public health systems worldwide. Mathematical models of epidemic are useful tools for assessment of the situation and for providing decision-making support for relevant authorities. We developed and implemented SEIR(MH) model that extends the conventional SEIR model with parameters that define public lockdown (the level and start of lockdown) and the medical system capacity to contain patients. Comparative modeling of four regions in Europe that have similar population sizes and age structures, but different public health systems, was performed: Baden-Württemberg, Lombardy, Belgium, and Switzerland. Modeling suggests that the most effective measure for controlling epidemic is early lockdown (exponential effect), followed by the number of available hospital beds (linear effect if the capacity is insufficient, with diminishing returns when the capacity is sufficient). Dynamic management of lockdown levels is likely to produce better outcomes than strict lockdown.

2020-2021年爆发的COVID-19疫情对全球公共卫生体系构成了重大挑战。流行病学数学模型作为评估疫情状况和为相关当局提供决策支持的实用工具，具有重要意义。本研究团队开发并实施了一种扩展传统SEIR模型（MH模型），其中包含定义公共封锁（封锁级别及起始时间）和医疗系统容纳患者能力的参数。对欧洲四个具有相似人口规模和年龄结构，但公共卫生体系不同的地区（巴登-符腾堡、伦巴第、比利时和瑞士）进行了比较建模。模型研究表明，控制疫情最有效的措施是早期封锁（指数级效应），其次是可用医院床位数（若容量不足，则呈线性效应，当容量充足时，效果递减）。对封锁级别的动态管理可能比严格的封锁产生更佳的成果。