The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom.
Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom.
PLoS One. 2023 Aug 16;18(8):e0287397. doi: 10.1371/journal.pone.0287397. eCollection 2023.
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 with 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. While the risk of severe disease following COVID-19 infection is substantially higher for the elderly and 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.
传染病控制的一个关键因素是疫情爆发是否会超过当地医疗能力。对医疗服务的总体需求将取决于疾病的严重程度，但峰值需求的确切时间和规模也取决于从初始感染到发展为严重疾病的时间间隔（或临床时间延迟）。间隔分布范围越广，需求的分布时间可能就越长，但峰值需求越低。因此，在给定发病轨迹的情况下，这些间隔分布对于建模例如住院人数的轨迹非常重要。相反，随着检测率的下降，发病轨迹可能需要通过住院人数的延迟但相对无偏信号来推断。在 COVID-19 大流行期间，医疗需求已经得到了广泛的建模，当地的感染浪潮给医疗服务带来了巨大的压力。虽然这种疾病最初的急性威胁随着疫苗接种和先前感染带来的免疫力增强而减弱，但患病率仍然很高，免疫力下降可能在未来几年带来巨大的压力。在这项工作中，我们提出了一组 COVID-19 病例和随后的严重后果（住院、重症监护室入院和死亡）的间隔分布。这些分布可用于根据感染或病例的轨迹来模拟更现实的住院人数和入住率的场景。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（31724 例住院、3514 例重症监护室入院、8306 例死亡）获得经验分布的方法。我们为个别年龄、性别和居住社区的贫困程度分别提出了分布。虽然 COVID-19 感染后发生严重疾病的风险在老年人和居住在贫困地区的人中要高得多，但住院时间没有明显的依赖关系，这表明严重后果在所有风险群体中同样严重。随着苏格兰和其他国家进入检测不再充足的阶段，这些间隔可能有助于根据严重后果数据对感染模式进行回顾性建模。
