季节性影响包括COVID-19在内的呼吸道感染严重程度,通过马尔科夫链建模量化
Rob C van Wijk1, Laurynas Mockeliunas1, Caryn M Upton2
1Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
这项研究使用马尔科夫模型分析COVID-19大流行期间的呼吸道感染 (RTIs). 流行病浪潮增加了RTI风险,而SARS-CoV-2抗体和疫苗接种改善了症状恢复.
科学领域:
- 传染性疾病 传染性疾病
- 流行病学 流行病学
- 数学建模的数学建模
背景情况:
- 呼吸道感染 (RTIs) 构成了全球重大健康挑战,季节性影响其发病率和严重程度.
- 描述RTI风险因素是复杂的,特别是在流行病期间.
研究的目的:
- 用马尔科夫模型来描述RTI发生和严重程度的概率.
- 探索各种协同变量的对RTI进展和分辨率的影响.
主要方法:
- 利用马尔科夫模型与四个症状严重程度状态的健康评分 (HSs).
- 分析了HS之间的过渡概率,包括人口统计,病史,接种疫苗 (BCG,COVID-19,流感),SARS-CoV-2血清学和流行病波作为共同变量.
- 在日历时间内执行模型诊断,以评估报告准确性.
主要成果:
- 疫情浪潮增加的感染压力增加了RTI症状发展的风险.
- 存在SARS-CoV-2抗体,可以防止RTI的发展,并促进症状的缓解.
- BCG (再接种) 疫苗,SARS-CoV-2或流感疫苗可以减少从轻度症状过渡到健康状态.
- 在非洲族裔和男性生物性别的参与者中观察到更高的症状缓解概率.
- 据估计,在第一波期间,COVID-19病例报告不足2.76倍.
结论:
- 马尔科夫链模型有效地捕捉了RTI发展和严重性的风险因素,包括流行病学知情的感染压力.
- 调查结果反映了南非COVID-19流行病的初始阶段.
- 这项研究强调了SARS-CoV-2抗体和针对RTIs的疫苗的保护作用.
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