Imperial College London, Business School, London, UK.
Atmospheric, Oceanic and Planetary Physics, Oxford University, Oxford, UK.
Int J Clin Pract. 2021 Apr;75(4):e13836. doi: 10.1111/ijcp.13836. Epub 2020 Dec 21.
Assessing why the spread of the COVID-19 virus slowed down in many countries in March through to May of 2020 is of great significance. The relative role of restrictions on behaviour ("lockdowns") and of a natural slowing for other reasons is difficult to assess when mass testing was not widely done. This paper assesses the evolution of the spread of the COVID-19 virus over this period when there was no data on test results for a large, random sample of the population.
We estimate a version of the susceptible-infected-recovered model applied to data on the numbers who were tested positive in several countries over the period when the virus spread very fast and then its spread slowed sharply. Up to the end of April 2020, test data came from non-random samples of populations who were overwhelmingly those who displayed symptoms. Using data from a period when the criteria used for testing (which was that people had clear symptoms) was relatively consistent is important in drawing out the message from test results. We use this data to assess two things: how large might be the group of those infected who were not recorded and how effective were lockdown measures in slowing the spread of the infection.
We find that to match data on daily new cases of the virus, the estimated model favours high values for the number of people infected but not recorded.
Our findings suggest that the infection may have spread far enough in many countries by April 2020 to have been a significant factor behind the fall in measured new cases. Government restrictions on behaviour-lockdowns-were only one factor behind slowing in the spread of the virus.
评估 2020 年 3 月至 5 月期间 COVID-19 病毒在许多国家传播速度放缓的原因具有重要意义。在未广泛进行大规模检测的情况下，很难评估行为限制（“封锁”）和其他原因导致的自然减缓的相对作用。本文评估了在没有大规模随机人群检测结果数据的情况下，这一时期 COVID-19 病毒传播的演变情况。
我们对几个国家在病毒快速传播然后急剧放缓期间检测呈阳性的人数数据进行了易感-感染-恢复模型的版本估计。截至 2020 年 4 月底，检测数据来自于那些表现出症状的人群的非随机样本。使用在测试中使用的标准（即人们有明显症状）相对一致的时间段的数据对于从检测结果中得出结论非常重要。我们使用这些数据来评估两件事：未记录的感染者群体可能有多大，以及封锁措施在减缓感染传播方面的有效性。
我们发现，要匹配病毒每日新增病例的数据，估计模型倾向于感染但未记录的人数的高值。
我们的研究结果表明，到 2020 年 4 月，感染可能已经在许多国家传播得足够广泛，成为测量新病例下降的一个重要因素。政府对行为的限制——封锁——只是病毒传播放缓的一个因素。
