Flexible, Freely Available Stochastic Individual Contact Model for Exploring COVID-19 Intervention and Control Strategies: Development and Simulation
Flexible, Freely Available Stochastic Individual Contact Model for Exploring COVID-19 Intervention and Control Strategies: Development and Simulation
复制标题
用于探索 COVID-19 干预和控制策略的灵活、免费的随机个人接触模型：开发和模拟
DOI：
10.2196/preprints.18965
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发表时间：
2020
影响因子：
8.5
通讯作者：
Louisa R Jorm
中科院分区：
文献类型：
--
作者：
T. Churches;Louisa R Jorm
整个2020年3月，世界各国领导人都在就如何以及何时实施公共卫生干预措施以抗击冠状病毒病（COVID-19）做出重要决定。他们迫切需要工具来帮助他们探索在其流行病规模和传播的具体情况下最有效的方法，以及可行的干预方案。我们试图快速开发一种灵活、免费的模拟模型，供建模者和研究人员使用，以调查在不同时间点实施的各种公共卫生干预措施如何改变COVID-19流行曲线的形状。方法COVOID （COVID-19开源感染动力学）是一种随机个体接触模型（ICM），它扩展了开源EpiModel包为R统计计算环境提供的ICM。为了展示其用途并为2020年3月30日的紧急决策提供信息，我们使用各种模型类型和新场景对其他研究人员报告的类似干预场景进行了建模。这些情景包括隔离病例、适度的社交距离，以及在假设的10万人口中，在不同的时间段实施更严格的人口“封锁”。2020年4月30日，我们模拟了澳大利亚悉尼东部三个连续的地区（人口287,344）在五种不同干预方案下的流行曲线，这些地区截至2020年4月30日记录了5.3%的澳大利亚COVID-19病例，并将模型预测与这些地区的观察到的流行曲线进行了比较。结果COVOID将种群的每个成员分配到7个隔间中的一个。不同隔间内个体相互作用的次数以及每次相互作用时传播感染的概率可以改变，以模拟干预的效果。利用2020年3月30日的COVOID，我们能够将疫情应对模式复制到其他人报告的特定社会距离干预情景中。2020年3月1日至4月30日期间，悉尼三个地区的模拟曲线在峰值病例数、病例总数和持续时间方面与观察到的流行曲线相似，这些曲线代表了实际实施的公共卫生措施，包括病例隔离、增加检测和社会距离措施。COVOID允许对许多潜在的干预方案进行快速建模，可以针对不同的环境进行定制，并且只需要标准的计算基础设施。它复制了其他需要非常详细的人口水平数据的模型所产生的流行病曲线，其预测的流行病曲线使用了模拟已颁布的公共卫生措施的参数，其形式与在澳大利亚悉尼实际观察到的相似。我们的团队和合作者目前正在开发一个扩展的开源COVOID包，其中包括一套工具，可以使用几种模型来探索干预场景。
Background Throughout March 2020, leaders in countries across the world were making crucial decisions about how and when to implement public health interventions to combat the coronavirus disease (COVID-19). They urgently needed tools to help them to explore what will work best in their specific circumstances of epidemic size and spread, and feasible intervention scenarios. Objective We sought to rapidly develop a flexible, freely available simulation model for use by modelers and researchers to allow investigation of how various public health interventions implemented at various time points might change the shape of the COVID-19 epidemic curve. Methods “COVOID” (COVID-19 Open-Source Infection Dynamics) is a stochastic individual contact model (ICM), which extends the ICMs provided by the open-source EpiModel package for the R statistical computing environment. To demonstrate its use and inform urgent decisions on March 30, 2020, we modeled similar intervention scenarios to those reported by other investigators using various model types, as well as novel scenarios. The scenarios involved isolation of cases, moderate social distancing, and stricter population “lockdowns” enacted over varying time periods in a hypothetical population of 100,000 people. On April 30, 2020, we simulated the epidemic curve for the three contiguous local areas (population 287,344) in eastern Sydney, Australia that recorded 5.3% of Australian cases of COVID-19 through to April 30, 2020, under five different intervention scenarios and compared the modeled predictions with the observed epidemic curve for these areas. Results COVOID allocates each member of a population to one of seven compartments. The number of times individuals in the various compartments interact with each other and their probability of transmitting infection at each interaction can be varied to simulate the effects of interventions. Using COVOID on March 30, 2020, we were able to replicate the epidemic response patterns to specific social distancing intervention scenarios reported by others. The simulated curve for three local areas of Sydney from March 1 to April 30, 2020, was similar to the observed epidemic curve in terms of peak numbers of cases, total numbers of cases, and duration under a scenario representing the public health measures that were actually enacted, including case isolation and ramp-up of testing and social distancing measures. Conclusions COVOID allows rapid modeling of many potential intervention scenarios, can be tailored to diverse settings, and requires only standard computing infrastructure. It replicates the epidemic curves produced by other models that require highly detailed population-level data, and its predicted epidemic curve, using parameters simulating the public health measures that were enacted, was similar in form to that actually observed in Sydney, Australia. Our team and collaborators are currently developing an extended open-source COVOID package comprising of a suite of tools to explore intervention scenarios using several categories of models.