论文标题
人口敏感疾病传播中的时空动力学：COVID-19在纽约传播作为案例研究
Spatiotemporal dynamics in demography-sensitive disease transmission: COVID-19 spread in NY as a case study
论文作者
论文摘要
高度传染性的新型冠状病毒的快速传播通过跨针对地理的几种数据引导的方法来表示，以了解何时将受到控制，并可以放松施加的锁定措施。但是，这些流行病学模型主要基于使用大量病例和死亡的训练数据，因为它们不考虑主要导致疾病扩散的时空种群动态。在这里，提出了一个随机细胞自动机的预测模型，能够准确地描述依赖人口统计学的人群动态对疾病传播的影响。将冠状病毒在纽约州的传播作为一个案例研究，计算框架的结果非常符合对受感染案件和死亡的实际计数，如在整个组织中报告的。这些预测表明，在长达180天内，以某种形式进行延长的锁定可以显着降低第二波爆发的风险。此外，即使不太严格的社会距离准则和强加的社会距离，医疗测试的可用性增加也能够减少感染患者的数量。将这种随机方法与人口统计学因素（例如年龄比率，预先存在的健康状况）配备，可以鲁棒化该模型，以预测未来爆发的传播性，然后才能转化为流行病。
The rapid transmission of the highly contagious novel coronavirus has been represented through several data-guided approaches across targeted geographies, in an attempt to understand when the pandemic will be under control and imposed lockdown measures can be relaxed. However, these epidemiological models predominantly based on training data employing number of cases and fatalities are limited in that they do not account for the spatiotemporal population dynamics that principally contributes to the disease spread. Here, a stochastic cellular automata enabled predictive model is presented that is able to accurate describe the effect of demography-dependent population dynamics on disease transmission. Using the spread of coronavirus in the state of New York as a case study, results from the computational framework remarkably agree with the actual count for infected cases and deaths as reported across organizations. The predictions suggest that an extended lockdown in some form, for up to 180 days, can significantly reduce the risk of a second wave of the outbreak. In addition, increased availability of medical testing is able to reduce the number of infected patients, even when less stringent social distancing guidelines and imposed. Equipping this stochastic approach with demographic factors such as age ratio, pre-existing health conditions, robustifies the model to predict the transmittivity of future outbreaks before they transform into an epidemic.
