Solution of SIR Infection Equation Using Data Assimilation

H. Isshiki
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Abstract

The new coronavirus infection (COVID-19) is rampant. The most troublesome part of this infection is the time between infection and onset and the infectiveness for several days even in the not-onset state. Therefore, a considerable number of infected persons with infectivity are left unchecked. Therefore, even if the infection status is simulated by the SIR equation or the like, the true values of the infection parameters and the true number of infected persons cannot be grasped. However, it is possible to observe the infection status, and the daily number of infected people and the cumulative number of infected people are announced. These numbers are not true values, but they reflect true values. It is impossible to grasp the true value only by the SIR equation, but it will be possible to estimate the true value by combining it with the observation equation. In short, the data assimilation framework is considered to be effective. We report this effectiveness because we were able to confirm this effectiveness from the numerical results.
用数据同化法求解SIR感染方程
新型冠状病毒感染(COVID-19)肆虐。这种感染最麻烦的部分是感染和发病之间的时间,即使在未发病状态下也有几天的传染性。因此,相当多具有传染性的感染者没有得到控制。因此,即使用SIR方程等模拟感染状态,也无法掌握感染参数的真实值和感染的真实人数。但是,可以观察感染状态,并公布每日感染人数和累计感染人数。这些数字不是真实的值,但它们反映了真实的值。仅凭SIR方程是无法掌握真实值的,但将SIR方程与观测方程相结合,可以估计出真实值。总之,数据同化框架被认为是有效的。我们之所以报告这种有效性,是因为我们能够从数值结果中证实这种有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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