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通过强制 SEIRD 模型监测意大利 COVID-19 传播。

Monitoring Italian COVID-19 spread by a forced SEIRD model.

机构信息

Department of Computer Science and Engineering, University of Bologna, Bologna, Italy.

Department of Mathematics, University of Bologna, Bologna, Italy.

出版信息

PLoS One. 2020 Aug 6;15(8):e0237417. doi: 10.1371/journal.pone.0237417. eCollection 2020.

DOI:10.1371/journal.pone.0237417
PMID:32760133
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7410324/
Abstract

Due to the recent evolution of the COVID-19 outbreak, the scientific community is making efforts to analyse models for understanding the present situation and for predicting future scenarios. In this paper, we propose a forced Susceptible-Exposed-Infected-Recovered-Dead (fSEIRD) differential model for the analysis and forecast of the COVID-19 spread in Italian regions, using the data from the Italian Protezione Civile (Italian Civil Protection Department) from 24/02/2020. In this study, we investigate an adaptation of fSEIRD by proposing two different piecewise time-dependent infection rate functions to fit the current epidemic data affected by progressive movement restriction policies put in place by the Italian government. The proposed models are flexible and can be quickly adapted to monitor various epidemic scenarios. Results on the regions of Lombardia and Emilia-Romagna confirm that the proposed models fit the data very accurately and make reliable predictions.

摘要

由于 COVID-19 疫情的最新演变，科学界正在努力分析模型，以了解当前形势并预测未来情景。在本文中，我们提出了一个强制易感-暴露-感染-恢复-死亡（fSEIRD）微分模型，用于分析和预测意大利各地区 COVID-19 的传播情况，使用的是意大利民防局（意大利民事保护部门）从 2020 年 2 月 24 日起的数据。在这项研究中，我们通过提出两个不同的分段时间相关感染率函数来研究 fSEIRD 的适应性，以适应意大利政府实施的渐进性限制措施对当前传染病数据的影响。所提出的模型是灵活的，可以快速适应各种传染病的监测情景。伦巴第大区和艾米利亚-罗马涅大区的结果证实，所提出的模型非常准确地拟合了数据，并做出了可靠的预测。

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