The number of new infections per day is a key quantity for effective epidemic management. It can be estimated relatively directly by testing of random population samples. Without such direct epidemiological measurement, other approaches are required to infer whether the number of new cases is likely to be increasing or decreasing: for example, estimating the pathogen effective reproduction number, R, using data gathered from the clinical response to the disease. For Covid-19 (SARS-CoV-2) such R estimation is heavily dependent on modelling assumptions, because the available clinical case data are opportunistic observational data subject to severe temporal confounding. Given this difficulty it is useful to retrospectively reconstruct the time course of infections from the least compromised available data, using minimal prior assumptions. A Bayesian inverse problem approach applied to UK data on first wave Covid-19 deaths and the disease duration distribution suggests that fatal infections were in decline before full UK lockdown (24 March 2020), and that fatal infections in Sweden started to decline only a day or two later. An analysis of UK data using the model of Flaxman et al. (2020, Nature 584) gives the same result under relaxation of its prior assumptions on R, suggesting an enhanced role for non pharmaceutical interventions (NPIs) short of full lock down in the UK context. Similar patterns appear to have occurred in the subsequent two lockdowns.
翻译:每天新感染的人数是有效流行病管理的关键,可以通过随机人口样本的检测来相对直接估计。如果没有这种直接的流行病学测量,就需要采取其他方法来推断新病例的数量是否可能在增加或减少:例如,利用临床对疾病的反应所收集的数据估计病原体有效生殖数,R,R,使用从临床对疾病的反应中收集的数据。对于Covid-19(萨斯-科沃-2)来说,这种R估计在很大程度上取决于模型假设,因为现有的临床病例数据是机会性观察数据,容易在时间上出现严重混乱。鉴于这一困难,利用最低的先前假设,从最不易受损的可用数据追溯性地重建感染的时间过程是有用的。对联合王国关于第一波乔维-19死亡和疾病持续时间分布的数据采用的巴耶斯反问题方法表明,致命感染在联合王国全面封闭之前(2020年3月24日)就已经下降,瑞典致命感染仅仅在一天或两天后开始下降。利用Flaxman等人的模式对联合王国的数据进行了分析(20,自然584)显示,在放松其先前关于R型现有数据时程的假设之后,同样的结果也是一样,表明联合王国在随后的完全停止。