Online prediction of COVID19 dynamics. Belgian case study
Online prediction of COVID19 dynamics. Belgian case study
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COVID19 动态在线预测。
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Nesterov Yurii
中科院分区：
文献类型：
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作者：
Nesterov Yurii
在本文中，我们提出了一个新的传染病发展公理模型，称为HIT，它与COVID19的非常特殊的特征是一致的。这是一个离散时间线性切换模型，用于预测人群中感染总人数和无症状病毒携带者浓度的动态。它的少量参数可以使用可用的关于病毒传播的实时动态数据进行调整。这个模型为我们提供了难得的在线预测未来的可能性。作为一个例子，我们描述了该模型在2020年3月至5月期间对比利时COVID19疫情进行了为期80天的在线分析。在此期间，我们的预测非常准确，通常在0.5%的精确度之内。据我们所知，这是第一个在遏制措施下预测流行病演变的数学模型，这些措施防止了人群中免疫力的发展。
In this paper, we present a new axiomatic model of epidemic development, called HIT, which is consistent with the very special features of COVID19. This is a discrete-time linear switching model for predicting the dynamics of total number of infected persons and concentration of the asymptomatic virus holders in the population. A small number of its parameters can be tuned using the available real-time dynamic data on virus propagation. This model provides us with a rare possibility of online prediction of the future. As an example, we describe an application of this model to the online analysis of COVID19 epidemic in Belgium for eighty days in the period March - May, 2020. During this time, our predictions were exact, typically, within the accuracy of 0.5%. To the best of our knowledge, this is the fi rst mathematical model predicting the evolution of epidemics under containment measures, which prevent development of immunity in the population.