Artificial Intelligence Forecasting of Covid-19 in China

By Zixin Hu, Qiyang Ge, Shudi Li, Li Jin, M. Xiong. International Journal of Educational Excellence, 2020

330 citations recorded by the source corpus.

BACKGROUND An alternative to epidemiological models for transmission dynamics of Covid-19 in China, we propose the artificial intelligence (AI)-inspired methods for real-time forecasting of Covid-19 to estimate the size, lengths and ending time of Covid-19 across China. METHODS We developed a modified stacked auto-encoder for modeling the transmission dynamics of the epidemics. We applied this model to real-time forecasting the confirmed cases of Covid-19 across China. The data were collected from January 11 to February 27, 2020 by WHO. We used the latent variables in the auto-encoder and clustering algorithms to group the provinces/cities for investigating the transmission structure. RESULTS We forecasted curves of cumulative confirmed cases of Covid-19 across China from Jan 20, 2020 to April 20, 2020. Using the multiple-step forecasting, the estimated average errors of 6-step, 7-step, 8-step, 9-step and 10-step forecasting were 1.64%, 2.27%, 2.14%, 2.08%, 0.73%, respectively. We predicted that the time points of the provinces/cities entering the plateau of the forecasted transmission dynamic curves varied, ranging from Jan 21 to April 19, 2020. The 34 provinces/cities were grouped into 9 clusters. CONCLUSIONS The accuracy of the AI-based methods for forecasting the trajectory of Covid-19 was high. We predicted that the epidemics of Covid-19 will be over by the middle of April. If the data are reliable and there are no second transmissions, we can accurately forecast the transmission dynamics of the Covid-19 across the provinces/cities in China. The AI-inspired methods are a powerful tool for helping public health planning and policymaking.

Subfields: Epidemiology, Modeling and Simulation, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/1a59ca238c64e4bae304240d7df787cf0345d50e

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In Radiology, Nuclear Medicine and Imaging

1. Ling Shao
2. Quoc-Viet Pham
3. Jianbing Shen
4. Tao Zhou
5. Huan Fu
6. Deng-Ping Fan
7. Ge-Peng Ji

Citations in the corpus

• Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19 (2021, neutral): “Hu et al. [27] utilized AI-driven meth-ods for real-time forecasting of COVID-19 outbreak within China based on lengths, size estimations, and ending time of the outbreak based on data obtained from WHO (collected between 19 January 2020 and 16 February 2020).”
• A Time-dependent SIR model for COVID-19 (2020, neutral): “, [4], [5], [6], and [7] 固定某些關 鍵參數如 transmission rate 與 recovering rate ，我們的 transmission rate 與 recovering rate 會基於過往的資料進 transmission rate 驟降。除 了基於各式傳染病模型的論文，也有許多預測 COVID-19 趨勢論文採用 data-driven 或是 curve fitting 方法 [8], [9], and [10] 其中，上述微分方程組仍滿足 Eq.”
• Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, background): “Hu et al. (2020b)† have compiled data collected by World Health Organization (WHO) and other actors across the period from January 11 - February 27, 2020 to develop a dataset of accumulated and new confirmed cases in 31 provinces and cities of China.”
• Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts (2020, methodology): “, the number of infected cases, ending time and trajectory of the COVID-19 pandemic) of the COVID-19 outbreak in China [15].”
• Covid-19 spread: Reproduction of data and prediction using a SIR model on Euclidean network. (2020, background): “Considerable number of analysis of the available data of the number of cases and deaths have been attempted recently, and a few data driven models have also been proposed [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] .”
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images (2020, background): “[15] categorized these applications into three scales, including patient scale (e.g., medical imaging for diagnosis [37] , [38] ), molecular scale (e.g., protein structure prediction [39] ), and societal scale (e.g., epidemiology [40] ).”