[Paper Review] Prediction and analysis of Coronavirus Disease 2019

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

Data-Driven Disease Surveillance6 references45 citations

TL;DR

The paper fits COVID-19 spread using three growth models (Logistic, Bertalanffy, Gompertz) and compares their fitting performance to predict case and death totals in Wuhan, other China regions, and China overall.

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.

Motivation & Objective

• Motivate modeling of the early COVID-19 outbreak and provide short-term forecasts.
• Evaluate three classical growth models for epidemic fitting and prediction.
• Assess regional differences (Wuhan, non-Hubei China, China) and end conditions of the outbreak.
• Validate models by retrospectively fitting SARS trends to test model validity.

Proposed method

• Fit three mathematical models (Logistic, Bertalanffy, Gompertz) to COVID-19 data from China and Wuhan as of Feb 28, 2020.
• Compare fitting performance across models and regions to identify best-fitting approach.
• Use SARS epidemic fitting as a preliminary validity check of the models.

Experimental results

Research questions

• RQ1 Which of the Logistic, Bertalanffy, or Gompertz models provides the best fit to early COVID-19 case data?
• RQ2 What are the predicted total infections and deaths in Wuhan, non-Hubei China, and China as of the study period?
• RQ3 How do model fits differ between regions (Wuhan vs. non-Hubei vs. China overall)?

Key findings

• The three models yield different predictions depending on parameters and region.
• The Logistic model generally shows the best fitting performance among the three models studied.
• The Gompertz model may fit better than the Bertalanffy model in some cases.
• Predicted totals: Wuhan infections 49,852–57,447; non-Hubei infections 12,972–13,405; China infections 80,261–85,140.
• Predicted deaths: Wuhan 2,502–5,108; non-Hubei deaths 107–125; China deaths 3,150–6,286.
• The paper suggests the outbreak could end late-April 2020 in Wuhan and before late-March 2020 in other areas, based on current trends.