General SIR model for visible and hidden epidemic dynamics.

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Frontiers in Artificial Intelligence Pub Date : 2025-02-24 eCollection Date: 2025-01-01 DOI:10.3389/frai.2025.1559880
Igor Nesteruk
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引用次数: 0

Abstract

To simulate hidden epidemic dynamics connected with asymptomatic and unregistered patients, a new general SIR model was proposed. For some cases, the analytical solutions of the set of 5 differential equations were found, which allow simplifying the parameter identification procedure. Two waves of the pertussis epidemic in England in 2023 and 2024 were simulated with the assumption of zero hidden cases. The accumulated and daily numbers of cases and the duration of the second wave were predicted with rather high accuracy. If the trend will not change, the monthly figure of 9 new pertussis cases (as it was in January-February 2023) can be achieved only in May 2025. The proposed approach can be recommended for both simulations and predictions of different epidemics.

可见和隐藏流行病动力学的通用SIR模型。
为了模拟与无症状和未登记患者相关的隐性疫情动态,提出了一种新的通用SIR模型。在某些情况下,找到了5个微分方程的解析解,从而简化了参数辨识过程。在假设隐性病例为零的情况下,模拟了2023年和2024年英国百日咳流行的两波。预测第二波累计病例数、日病例数和持续时间具有较高的准确性。如果这一趋势不改变,只有到2025年5月才能达到每月新增9例百日咳病例(与2023年1月至2月一样)的目标。所提出的方法可推荐用于不同流行病的模拟和预测。
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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来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
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