Spaulding Research Institute, Spaulding Rehabilitation Hospital and Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
Unidad de Investigación para la Generación y Síntesis de Evidencias en Salud, Universidad San Ignacio de Loyola, Lima, Peru.
PLoS One. 2020 Sep 17;15(9):e0239175. doi: 10.1371/journal.pone.0239175. eCollection 2020.
The COVID-19 outbreak has forced most of the global population to lock-down and has put in check the health services all over the world. Current predictive models are complex, region-dependent, and might not be generalized to other countries. However, a 150-year old epidemics law promulgated by William Farr might be useful as a simple arithmetical model (percent increase [R1] and acceleration [R2] of new cases and deaths) to provide a first sight of the epidemic behavior and to detect regions with high predicted dynamics. Thus, this study tested Farr's Law assumptions by modeling COVID-19 data of new cases and deaths. COVID-19 data until April 10, 2020, was extracted from available countries, including income, urban index, and population characteristics. Farr's law first (R1) and second ratio (R2) were calculated. We constructed epidemic curves and predictive models for the available countries and performed ecological correlation analysis between R1 and R2 with demographic data. We extracted data from 210 countries, and it was possible to estimate the ratios of 170 of them. Around 42·94% of the countries were in an initial acceleration phase, while 23·5% already crossed the peak. We predicted a reduction close to zero with wide confidence intervals for 56 countries until June 10 (high-income countries from Asia and Oceania, with strict political actions). There was a significant association between high R1 of deaths and high urban index. Farr's law seems to be a useful model to give an overview of COVID-19 pandemic dynamics. The countries with high dynamics are from Africa and Latin America. Thus, this is a call to urgently prioritize actions in those countries to intensify surveillance, to re-allocate resources, and to build healthcare capacities based on multi-nation collaboration to limit onward transmission and to reduce the future impact on these regions in an eventual second wave.
新型冠状病毒肺炎疫情迫使全球大部分人口封城，并对世界各地的卫生服务造成冲击。目前的预测模型复杂且依赖于地区，可能无法推广到其他国家。然而，威廉·法尔（William Farr）在 150 年前颁布的传染病定律，或许可以作为一种简单的算术模型（新发病例和死亡人数的增长率 [R1]和加速率 [R2]），有助于我们初步了解疫情的发展态势，并发现疫情发展动态较高的地区。因此，本研究通过对新型冠状病毒肺炎新发病例和死亡数据进行建模，对法尔定律的假设进行了检验。我们从现有的国家中提取了截至 2020 年 4 月 10 日的新型冠状病毒肺炎数据，包括收入、城市指数和人口特征等信息。我们计算了法尔定律的第一（R1）和第二比率（R2）。我们为现有的国家构建了疫情曲线和预测模型，并对 R1 和 R2 与人口统计学数据进行了生态相关性分析。我们从 210 个国家中提取了数据，其中有 170 个国家的数据可以进行估计。大约 42.94%的国家处于初始加速阶段，而 23.5%的国家已经过了高峰期。我们预测到 6 月 10 日，有 56 个国家的病例数将接近零，这些国家大多为亚洲和大洋洲的高收入国家，采取了严格的防疫措施。死亡率的 R1 值较高与城市指数较高之间存在显著相关性。法尔定律似乎是一种有用的模型，可以帮助我们了解新型冠状病毒肺炎疫情的发展动态。疫情发展动态较高的国家来自非洲和拉丁美洲。因此，我们呼吁紧急优先在这些国家采取行动，加强监测，重新分配资源，并在多国合作的基础上建立医疗保健能力，以限制疫情的进一步传播，并减轻疫情对这些地区的未来影响。
