Queen Square Institute of Neurology, University College London, London, UK
Institute of Global Health, University College London, London, UK.
BMJ Glob Health. 2020 Dec;5(12). doi: 10.1136/bmjgh-2020-003978.
Recent reports using conventional Susceptible, Exposed, Infected and Removed models suggest that the next wave of the COVID-19 pandemic in the UK could overwhelm health services, with fatalities exceeding the first wave. We used Bayesian model comparison to revisit these conclusions, allowing for heterogeneity of exposure, susceptibility and transmission. We used dynamic causal modelling to estimate the evidence for alternative models of daily cases and deaths from the USA, the UK, Brazil, Italy, France, Spain, Mexico, Belgium, Germany and Canada over the period 25 January 2020 to 15 June 2020. These data were used to estimate the proportions of people (i) not exposed to the virus, (ii) not susceptible to infection when exposed and (iii) not infectious when susceptible to infection. Bayesian model comparison furnished overwhelming evidence for heterogeneity of exposure, susceptibility and transmission. Furthermore, both lockdown and the build-up of population immunity contributed to viral transmission in all but one country. Small variations in heterogeneity were sufficient to explain large differences in mortality rates. The best model of UK data predicts a second surge of fatalities will be much less than the first peak. The size of the second wave depends sensitively on the loss of immunity and the efficacy of Find-Test-Trace-Isolate-Support programmes. In summary, accounting for heterogeneity of exposure, susceptibility and transmission suggests that the next wave of the SARS-CoV-2 pandemic will be much smaller than conventional models predict, with less economic and health disruption. This heterogeneity means that seroprevalence underestimates effective herd immunity and, crucially, the potential of public health programmes.
最近的报告使用传统的易感、暴露、感染和清除模型表明，下一波 COVID-19 大流行在英国可能会使医疗服务不堪重负，死亡人数超过第一波。我们使用贝叶斯模型比较来重新审视这些结论，允许暴露、易感性和传播存在异质性。我们使用动态因果建模来估计来自美国、英国、巴西、意大利、法国、西班牙、墨西哥、比利时、德国和加拿大的每日病例和死亡的替代模型的证据，这些数据的时间范围为 2020 年 1 月 25 日至 2020 年 6 月 15 日。这些数据用于估计以下人群的比例：（i）未接触病毒，（ii）接触病毒时不易感染，（iii）感染时不易传播。贝叶斯模型比较为暴露、易感性和传播的异质性提供了压倒性的证据。此外，在所有国家中，除了一个国家之外，封锁和人群免疫力的建立都有助于病毒的传播。异质性的微小变化足以解释死亡率的巨大差异。英国数据的最佳模型预测第二波死亡人数将远远低于第一波高峰。第二波的大小取决于免疫力的丧失和 Find-Test-Trace-Isolate-Support 计划的效果。总之，考虑到暴露、易感性和传播的异质性表明，下一波 SARS-CoV-2 大流行将比传统模型预测的小得多，对经济和健康的破坏也将小得多。这种异质性意味着血清流行率低估了有效的群体免疫，而且至关重要的是，公共卫生计划的潜力。
