Department of Environmental Science, Policy and Management, University of California, Berkeley, CA 94708-3114, USA.
School of Mathematical Sciences, University of KwaZulu-Natal, Durban, 4000, South Africa.
J Transl Med. 2021 Mar 16;19(1):109. doi: 10.1186/s12967-021-02736-2.
No versatile web app exists that allows epidemiologists and managers around the world to comprehensively analyze the impacts of COVID-19 mitigation. The http://covid-webapp.numerusinc.com/ web app presented here fills this gap.
Our web app uses a model that explicitly identifies susceptible, contact, latent, asymptomatic, symptomatic and recovered classes of individuals, and a parallel set of response classes, subject to lower pathogen-contact rates. The user inputs a CSV file of incidence and, if of interest, mortality rate data. A default set of parameters is available that can be overwritten through input or online entry, and a user-selected subset of these can be fitted to the model using maximum-likelihood estimation (MLE). Model fitting and forecasting intervals are specifiable and changes to parameters allow counterfactual and forecasting scenarios. Confidence or credible intervals can be generated using stochastic simulations, based on MLE values, or on an inputted CSV file containing Markov chain Monte Carlo (MCMC) estimates of one or more parameters.
We illustrate the use of our web app in extracting social distancing, social relaxation, surveillance or virulence switching functions (i.e., time varying drivers) from the incidence and mortality rates of COVID-19 epidemics in Israel, South Africa, and England. The Israeli outbreak exhibits four distinct phases: initial outbreak, social distancing, social relaxation, and a second wave mitigation phase. An MCMC projection of this latter phase suggests the Israeli epidemic will continue to produce into late November an average of around 1500 new case per day, unless the population practices social-relaxation measures at least 5-fold below the level in August, which itself is 4-fold below the level at the start of July. Our analysis of the relatively late South African outbreak that became the world's fifth largest COVID-19 epidemic in July revealed that the decline through late July and early August was characterised by a social distancing driver operating at more than twice the per-capita applicable-disease-class (pc-adc) rate of the social relaxation driver. Our analysis of the relatively early English outbreak, identified a more than 2-fold improvement in surveillance over the course of the epidemic. It also identified a pc-adc social distancing rate in early August that, though nearly four times the pc-adc social relaxation rate, appeared to barely contain a second wave that would break out if social distancing was further relaxed.
Our web app provides policy makers and health officers who have no epidemiological modelling or computer coding expertise with an invaluable tool for assessing the impacts of different outbreak mitigation policies and measures. This includes an ability to generate an epidemic-suppression or curve-flattening index that measures the intensity with which behavioural responses suppress or flatten the epidemic curve in the region under consideration.
目前还没有一个通用的网络应用程序能够让世界各地的流行病学家和管理者全面分析 COVID-19 缓解措施的影响。本文介绍的 http://covid-webapp.numerusinc.com/ 网络应用程序填补了这一空白。
我们的网络应用程序使用了一种模型，该模型明确标识了易感、接触、潜伏、无症状、有症状和康复人群的类别，以及一组与之平行的反应类别，这些类别受到较低的病原体接触率的影响。用户输入一个包含发病率的 CSV 文件，如果有兴趣，还可以输入死亡率数据。我们提供了一组默认参数，也可以通过输入或在线输入进行覆盖，并可以使用最大似然估计 (MLE) 对用户选择的参数子集进行拟合。可以指定模型拟合和预测间隔，并且参数的变化允许进行反事实和预测场景分析。可以使用基于 MLE 值的随机模拟或包含一个或多个参数的马尔可夫链蒙特卡罗 (MCMC) 估计的 CSV 文件生成置信或可信区间。
我们展示了如何从 COVID-19 疫情在以色列、南非和英国的发病率和死亡率中提取社交距离、社交放松、监测或毒力转换（即时间变化的驱动因素）等功能。以色列疫情爆发经历了四个不同阶段：初始爆发、社交距离、社交放松和第二轮缓解阶段。对后者阶段的 MCMC 预测表明，除非以色列人口实施社交放松措施的力度至少是 8 月份的 5 倍，而 8 月份本身是 7 月初的 4 倍，否则以色列疫情将持续到 11 月底，每天平均新增病例数约为 1500 例。我们对相对较晚爆发的南非疫情的分析表明，7 月底和 8 月初的疫情下降阶段的社交距离驱动因素的作用强度是社交放松驱动因素的两倍以上。我们对相对较早的英国疫情的分析表明，监测工作的改善程度超过了疫情的两倍。它还确定了 8 月初的人均社交距离（pc-adc）率，尽管接近 pc-adc 社交放松率的两倍，但似乎几乎无法遏制如果进一步放松社交距离可能爆发的第二轮疫情。
我们的网络应用程序为没有流行病学建模或计算机编码专业知识的政策制定者和卫生官员提供了一个宝贵的工具，用于评估不同疫情缓解政策和措施的影响。这包括生成一个疫情抑制或曲线变平指数的能力，该指数可衡量行为反应在考虑区域内抑制或变平疫情曲线的强度。
