[论文解读] Prediction and analysis of Coronavirus Disease 2019

Lin Jia, Kewen Li+3 more|arXiv (Cornell University)|Mar 11, 2020

Data-Driven Disease Surveillance参考文献 6被引用 45

一句话总结

本文通过三种增长模型（Logistic、Bertalanffy、Gompertz）拟合 COVID-19 的传播，并比较它们的拟合性能，以预测武汉、其他中国地区以及中国总体的病例与死亡总数。

ABSTRACT

In December 2019, a novel coronavirus was found in a seafood wholesale market in Wuhan, China. WHO officially named this coronavirus as COVID-19. Since the first patient was hospitalized on December 12, 2019, China has reported a total of 78,824 confirmed CONID-19 cases and 2,788 deaths as of February 28, 2020. Wuhan's cumulative confirmed cases and deaths accounted for 61.1% and 76.5% of the whole China mainland , making it the priority center for epidemic prevention and control. Meanwhile, 51 countries and regions outside China have reported 4,879 confirmed cases and 79 deaths as of February 28, 2020. COVID-19 epidemic does great harm to people's daily life and country's economic development. This paper adopts three kinds of mathematical models, i.e., Logistic model, Bertalanffy model and Gompertz model. The epidemic trends of SARS were first fitted and analyzed in order to prove the validity of the existing mathematical models. The results were then used to fit and analyze the situation of COVID-19. The prediction results of three different mathematical models are different for different parameters and in different regions. In general, the fitting effect of Logistic model may be the best among the three models studied in this paper, while the fitting effect of Gompertz model may be better than Bertalanffy model. According to the current trend, based on the three models, the total number of people expected to be infected is 49852-57447 in Wuhan,12972-13405 in non-Hubei areas and 80261-85140 in China respectively. The total death toll is 2502-5108 in Wuhan, 107-125 in Non-Hubei areas and 3150-6286 in China respetively. COVID-19 will be over p robably in late-April, 2020 in Wuhan and before late-March, 2020 in other areas respectively.

研究动机与目标

• 激励对早期 COVID-19 爆发进行建模并提供短期预测。
• 评估三种经典增长模型在流行病拟合与预测中的应用。
• 评估区域差异（Wuhan、非湖北省中国、China 总体）以及疫情的结束条件。
• 通过追溯拟合 SARS 趋势来验证模型有效性。

提出的方法

• 将三个数学模型（Logistic、Bertalanffy、Gompertz）拟合到截至 2020-02-28 的中国及武汉 COVID-19 数据。
• 比较不同模型和区域的拟合性能，以确定最佳拟合方法。
• 将 SARS 疫情拟合用作对模型的初步有效性检查。

实验结果

研究问题

• RQ1 在早期 COVID-19 病例数据中，Logistic、Bertalanffy 还是 Gompertz 模型提供最佳拟合？
• RQ2 在研究时期内，武汉、非湖北中国和中国的预测感染和死亡总数是多少？
• RQ3 模型拟合在区域之间（Wuhan、非湖北、中国总体）有何差异？

主要发现

• 这三种模型的预测取决于参数和区域而异。
• 在研究的三种模型中，Logistic 模型通常显示出最佳拟合性能。
• 在某些情况下，Gompertz 模型可能比 Bertalanffy 模型拟合得更好。
• 预测总数：武汉感染 49,852–57,447；非湖北感染 12,972–13,405；中国感染 80,261–85,140。
• 预测死亡：武汉 2,502–5,108；非湖北死亡 107–125；中国死亡 3,150–6,286。
• 基于当前趋势，论文指出在武汉疫情可能在 2020 年 4 月下旬结束，而在其他地区可能在 2020 年 3 月下旬之前结束。