Time intervals between COVID-19 cases, and more severe outcomes
Time intervals between COVID-19 cases, and more severe outcomes
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DOI：
10.1101/2022.10.31.22281769
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
A. Wood;R. Kao
中科院分区：
文献类型：
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作者：
A. Wood;R. Kao
传染病控制的一个关键因素是爆发的风险超过了当地的医疗能力。对医疗服务的总体需求将取决于疾病的严重程度，但高峰需求的确切时间和规模也取决于初始感染和严重疾病发展之间的时间间隔（或临床时间延迟）。更广泛的间隔分布可能会在更长的时间内吸引需求，但具有较低的峰值需求。因此，这些间隔分布在给定发病率的轨迹的情况下对例如入院的轨迹建模是重要的。相反，随着检测率的下降，可能需要通过延迟但相对公正的入院信号来推断发病率轨迹。在COVID-19大流行期间，医疗需求被广泛模拟，局部感染浪潮对医疗服务造成严重压力。虽然这种疾病造成的最初急性威胁已经从疫苗接种和先前感染的免疫力积累中消退，但流行率仍然很高，免疫力减弱可能导致未来几年的巨大压力。在这项工作中，然后，我们提出了一组区间分布，为COVID-19病例和随后的严重结果;入院，ICU入院和死亡。这些可以用于在给定感染或病例的轨迹的情况下对入院和入住的更现实的场景进行建模。我们提出了一种使用2020年9月至2022年1月期间苏格兰COVID-19结局数据（N = 31724例住院，N = 3514例ICU住院，N = 8306例死亡）获得经验分布的方法。我们提出了单独的个人年龄，性别和剥夺居住社区的分布。我们发现，虽然老年人或居住在高度贫困地区的人感染COVID-19后患严重疾病的风险要高得多，但住院时间没有显示出强烈的依赖性，这表明严重后果在风险群体中同样严重。随着苏格兰和其他国家进入一个检测不再丰富的阶段，这些时间间隔可能有助于对感染模式进行回顾性建模，并提供严重结果的数据。
A critical factor in infectious disease control is the risk of an outbreak overwhelming local healthcare capacity. The overall demand on healthcare services will depend on disease severity, but the precise timing and size of peak demand also depends on the time interval (or clinical time delay) between initial infection, and development of severe disease. A broader distribution of intervals may draw that demand out over a longer period, but have a lower peak demand. These interval distributions are therefore important in modelling trajectories of e.g. hospital admissions, given a trajectory of incidence. Conversely, as testing rates decline, an incidence trajectory may need to be inferred through the delayed, but relatively unbiased signal of hospital admissions. Healthcare demand has been extensively modelled during the COVID-19 pandemic, where localised waves of infection have imposed severe stresses on healthcare services. While the initial acute threat posed by this disease has since subsided from immunity buildup from vaccination and prior infection, prevalence remains high and waning immunity may lead to substantial pressures for years to come. In this work, then, we present a set of interval distributions, for COVID-19 cases and subsequent severe outcomes; hospital admission, ICU admission, and death. These may be used to model more realistic scenarios of hospital admissions and occupancy, given a trajectory of infections or cases. We present a method for obtaining empirical distributions using COVID-19 outcomes data from Scotland between September 2020 and January 2022 (N = 31724 hospital admissions, N = 3514 ICU admissions, N = 8306 mortalities). We present separate distributions for individual age, sex, and deprivation of residing community. We show that, while the risk of severe disease following COVID-19 infection is substantially higher for the elderly or those residing in areas of high deprivation, the length of stay shows no strong dependence, suggesting that severe outcomes are equally severe across risk groups. As Scotland and other countries move into a phase where testing is no longer abundant, these intervals may be of use for retrospective modelling of patterns of infection, given data on severe outcomes.