Cooperation or Confrontation? —— Prediction of COVID-19 Pandemic Situation via Deep Learning (preprint)

The COVID-19 pandemic is the most serious catastrophe since the Second World War. To more accurately observe the epidemic under the influence of policies and provide policy adjustments before the official presidential transition in the United States, we use a three-layer superimposed Long-Short-Term-Memory (LSTM) model to predict the epidemic development trend to mid-January, 2021. The proposed model provides more accuracy and stability relative to Susceptible-Exposed-Infective-Recovered (SEIR), modified stacked au-to-encoder, and single-layer LSTM models. The performance effects of the measures in China and five countries with severe epidemics are analysed and summarised. The model shows that the error rate of China, five countries and the world is less than 1.4%. According to forecasts, the epidemic situations in the United States, India, and Brazil, caused by untimely, inappropriate policies, lax regulations and insufficient public cooperation, remain very severe, with cases continuing to increase by tens of thousands. The number of cumulative confirmed cases worldwide will exceed 84.58 million by mid-January, 2021; however, the mortality rate will gradually decrease. Based on analysis of measures (including China’s effective prevention and control policies), we found that there are performed tremendous different efficiency even using same positive policy for different countries because of various cooperation between people and governments. It is essential to maintain self-protection to prevent the epidemic from deterioration or regenerating, especially, wearing mask and maintaining a safe distance.

Spatiotemporal spread pattern of the COVID-19 cases in China (preprint)

COVID-19 is currently spreading widely around the world, causing huge threats on public safety and global society. This study analyzes the spatiotemporal spread pattern of the COVID-19 in China, reveals China’s epicenters of the epidemic through spatial clustering, and delineates the substantial effect of distance to Wuhan on the COVID-19 spread. The results show that the daily new COVID-19 cases mostly occurred in and around Wuhan before March 6, and then moved to the Grand Bay Area (Shenzhen, Hong Kong and Macau). The total COVID-19 cases in China were mainly distributed in the east of the Huhuanyong Line, where the epicenters account for more than 60% of the country’s total on 24 January and 7 February, half on 31 January, and more than 70% from 14 February. The total cases finally stabilized around 84,000, and the inflection point for Wuhan was on 14 February, one week later than those of Hubei (outside Wuhan) and China (outside Hubei). The generalized additive model-based analysis shows that population density and distance to provincial cities significantly associated with the total number of the cases, while distances to prefecture cities and inter-city traffic stations, and population inflow from Wuhan after 24 January, had no strong relationships with the total number of cases. The results and findings should provide valuable insights for understanding the changes in the COVID-19 transmission and controlling the global COVID-19 spread.