论文标题
旨在预测Covid-19感染波：随机步行蒙特卡洛模拟方法
Towards predicting COVID-19 infection waves: A random-walk Monte Carlo simulation approach
论文作者
论文摘要
现象学和确定性模型通常用于估计流行病的传输参数以及其生长轨迹的预测。这样的分析通常基于单个峰值爆发动力学。鉴于目前的COVID-19大流行，需要更好地理解观察到的流行病生长具有多个峰结构，最好使用第一原理方法。按照我们以前的工作[Physica A 574，126014（2021）]的界限，在这里我们应用2D随机步行蒙特卡洛计算，以更好地理解Covid-19通过接触相互作用扩散。锁定场景和所有其他控制干预措施都是通过流动性限制和随机相互作用种群内感染率的调节而施加的。随着时间的推移，易感，感染和恢复的种群会随着时间的流逝而追踪，每天的感染率无需求助于微分方程的解决方案。 这些模拟是针对与四个国家，印度，塞尔维亚，南非和美国相对应的人口密度进行的。在所有情况下，我们的结果都捕获了观察到的感染增长率。更重要的是，模拟模型被证明可以以合理的精度预测感染的次生和第三波浪。多个波结构的这种预测性提供了一种简单有效的工具，该工具可用于计划在当前大流行期间的缓解策略。
Phenomenological and deterministic models are often used for the estimation of transmission parameters in an epidemic and for the prediction of its growth trajectory. Such analyses are usually based on single peak outbreak dynamics. In light of the present COVID-19 pandemic, there is a pressing need to better understand observed epidemic growth with multiple peak structures, preferably using first-principles methods. Along the lines of our previous work [Physica A 574, 126014 (2021)], here we apply 2D random-walk Monte Carlo calculations to better understand COVID-19 spread through contact interactions. Lockdown scenarios and all other control interventions are imposed through mobility restrictions and a regulation of the infection rate within the stochastically interacting population. The susceptible, infected and recovered populations are tracked over time, with daily infection rates obtained without recourse to the solution of differential equations. The simulations were carried out for population densities corresponding to four countries, India, Serbia, South Africa and USA. In all cases our results capture the observed infection growth rates. More importantly, the simulation model is shown to predict secondary and tertiary waves of infections with reasonable accuracy. This predictive nature of multiple wave structures provides a simple and effective tool that may be useful in planning mitigation strategies during the present pandemic.
