Background and Objectives

SARS-CoV-2 emerged in December 2019 and rapidly spread into a global pandemic. Designing optimal community responses (social distancing, vaccination) is dependent on the stage of the disease progression, discovery of asymptomatic individuals, changes in virulence of the pathogen, and current levels of herd immunity. Community strategies may have severe and undesirable social and economic side effects. Modeling is the only available scientific approach to develop effective strategies that can minimize these unwanted side effects while retaining the effectiveness of the interventions.

Methods

We extended the agent-based model, SpatioTemporal Human Activity Model (STHAM), for simulating SARS-CoV-2 transmission dynamics.

Results

Here we present preliminary STHAM simulation results that reproduce the overall trends observed in the Wasatch Front (Utah, United States of America) for the general population. The results presented here clearly indicate that human activity patterns are important in predicting the rate of infection for different demographic groups in the population.

Conclusions

Future work in pandemic simulations should use empirical human activity data for agent-based techniques.

中文翻译：

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人类活动模式对 SARS-CoV-2 传播建模的影响

背景和目标

SARS-CoV-2 于 2019 年 12 月出现并迅速蔓延为全球大流行。设计最佳社区反应（社交距离、疫苗接种）取决于疾病进展的阶段、无症状个体的发现、病原体毒力的变化以及当前的群体免疫水平。社区战略可能会产生严重和不良的社会和经济副作用。建模是制定有效策略的唯一可用科学方法，可以最大限度地减少这些不需要的副作用，同时保持干预措施的有效性。

方法

我们扩展了基于代理的模型，即时空人类活动模型 (STHAM)，用于模拟 SARS-CoV-2 传播动态。

结果

在这里，我们展示了初步 STHAM 模拟结果，该结果再现了在瓦萨奇阵线（美国犹他州）中观察到的一般人群的总体趋势。这里呈现的结果清楚地表明，人类活动模式对于预测人口中不同人口群体的感染率很重要。

结论

大流行模拟的未来工作应该将经验人类活动数据用于基于代理的技术。