Recently, a novel coronavirus pneumonia (2019–nCoV) outbreak occurred in Wuhan, China, rapidly spreading first to the whole country, and then globally, causing widespread concern. From the perspectives of early warning and identification of risk, risk monitoring, and analysis, as well as risk management and handling, we propose corresponding solutions and recommendations, which include institutional cooperation, and to inform national and international policy-makers.

This study first analyzes the national and global infection status of the Coronavirus Disease that emerged in 2019 (COVID-19). It then uses the trend comparison method to predict the inflection point and Key Point of the COVID-19 virus by comparison with the severe acute respiratory syndrome (SARS) graphs, followed by using the Autoregressive Integrated Moving Average model, Autoregressive Moving Average model, Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors, and Holt Winter’s Exponential Smoothing to predict infections, deaths, and GDP in China. Finally, it discusses and assesses the impact of these results. This study argues that even if the risks and impacts of the epidemic are significant, China’s economy will continue to maintain steady development.

Covid-19 is primarily spread through contact with the virus, which may survive on surfaces with a lifespan of hours or even days if not sanitized. To curb its spread, it is hence of vital importance to detect those who have been in contact with the virus for a sustained period of time, the so-called close contacts . Most of the existing digital approaches for contact tracing focus only on direct face-to-face contacts. There has been little work on detecting indirect environmental contact, which is to detect people coming into a contaminated area with the live virus, i.e., an area last visited by an infected person within the virus lifespan. In this work, we study automatic Internet of Things (IoT) contact tracing when the virus has a lifespan, which may depend on the disinfection frequency at a location. Leveraging the ubiquity of WiFi signals, we propose vContact, a novel, private, pervasive, and fully distributed WiFi-based IoT contact tracing approach. Users carrying an IoT device (phone, wearable, dongle, etc.) continuously scan WiFi access points (APs) and store their hashed IDs. Given a confirmed case, the signals are then uploaded to a server for other users to match in their local IoT devices for virus exposure notification. vContact is not based on device pairing, and no information of other users is stored locally. The confirmed case does not need to have the device for it to work properly. As WiFi data are sampled sporadically and asynchronously, vContact uses novel and effective signal processing approaches and a similarity metric to align and match signals at any time. We conduct extensive indoor and outdoor experiments to validate vContact performance. Our results demonstrate that vContact is effective and accurate for contact detection. The precision, recall, and F1-score of contact detection are high (up to 90%) for close contact proximity (2 m). Its performance is robust against AP numbers, AP changes, and phone heterogeneity. Having implemented vContact as an Android software development kit and installed it on phones and smart watches, we present a case study to demonstrate the validity and implementability of our design in notifying its users about their exposure to the virus with a specific lifespan.

Covid-19 is primarily spread through contact with the virus which may survive on surfaces with lifespan of more than hours. To curb its spread, it is hence of vital importance to detect and quarantine those who have been in contact with the virus for sustained period of time, the so-called close contacts. In this work, we study, for the first time, automatic contact detection when the virus has a lifespan. Leveraging upon the ubiquity of WiFi signals, we propose a novel, private, and fully distributed WiFi-based approach called vContact. Users installing an app continuously scan WiFi and store its hashed IDs. Given a confirmed case, the signals of the major places he/she visited are then uploaded to a server and matched with the stored signals of users to detect contact. vContact is not based on phone pairing, and no information of any other users is stored locally. The confirmed case does not need to have installed the app for it to work properly. As WiFi data are sampled sporadically, we propose efficient signal processing approaches and similarity metric to align and match signals of any time. We conduct extensive indoor and outdoor experiments to evaluate the performance of vContact. Our results demonstrate that vContact is efficient and robust for contact detection. The precision and recall of contact detection are high (in the range of 50-90%) for close contact proximity (2m). Its performance is robust with respect to signal lengths (AP numbers) and phone heterogeneity. By implementing vContact as an app, we present a case study to demonstrate the validity of our design in notifying its users their exposure to virus with lifespan.