$α$-Satellite: An AI-driven System and Benchmark Datasets for Hierarchical Community-level Risk Assessment to Help Combat COVID-19 (preprint)

The novel coronavirus and its deadly outbreak have posed grand challenges to human society: as of March 26, 2020, there have been 85,377 confirmed cases and 1,293 reported deaths in the United States; and the World Health Organization (WHO) characterized coronavirus disease (COVID-19) - which has infected more than 531,000 people with more than 24,000 deaths in at least 171 countries - a global pandemic. A growing number of areas reporting local sub-national community transmission would represent a significant turn for the worse in the battle against the novel coronavirus, which points to an urgent need for expanded surveillance so we can better understand the spread of COVID-19 and thus better respond with actionable strategies for community mitigation. By advancing capabilities of artificial intelligence (AI) and leveraging the large-scale and real-time data generated from heterogeneous sources (e.g., disease related data from official public health organizations, demographic data, mobility data, and user geneated data from social media), in this work, we propose and develop an AI-driven system (named $\alpha$-Satellite}, as an initial offering, to provide hierarchical community-level risk assessment to assist with the development of strategies for combating the fast evolving COVID-19 pandemic. More specifically, given a specific location (either user input or automatic positioning), the developed system will automatically provide risk indexes associated with it in a hierarchical manner (e.g., state, county, city, specific location) to enable individuals to select appropriate actions for protection while minimizing disruptions to daily life to the extent possible. The developed system and the generated benchmark datasets have been made publicly accessible through our website. The system description and disclaimer are also available in our website.

COVID-19 in a Designated Infectious Diseases HospitalOutside Hubei Province,China (preprint)

Background A new type of novel coronavirus infection (COVID-19) occurred in Wuhan, Hubei Province. Previous investigations reported patients in Wuhan city often progressed into severe or critical and had a high mortality rate.The clinical characteristics of affected patients outside the epicenter of Hubei province are less well understood. Methods All confirmed COVID-19 case treated in the Third People's Hospital of Shenzhen,from January 11, 2020 to February 6, 2020, were included in this study. We analyzed the epidemiological and clinical features of these cases to better inform patient management in normal hospital settings. Results Among the 298 confirmed cases, 233(81.5%) had been to Hubei while 42(14%) had not clear epidemiological history. Only 192(64%) cases presented with fever as initial symptom. The lymphocyte count decreased in 38% patients after admission. The number (percent) of cases classified as non-severe and severe was 240(80.6%) and 58(19.4%) respectively. Thirty-two patients (10.7%) needed ICU care. Compared to the non-severe cases, severe cases were associated with older age, underlying diseases, as well as higher levels of CRP, IL-6 and ESR. The median (IRQ) duration of positive viral test were 14(10-19). Slower clearance of virus was associated with higher risk of progression to severe clinical condition. As of February 14, 2020, 66(22.1%) patients were discharged and the overall mortality rate remains 0. Conclusions In a designated hospital outside the Hubei Province, COVID-19 patients were mainly characterized by mild symptoms and could be effectively manage by properly using the existing hospital system.