The research of policy responses to COVID-19 and effectiveness evaluation

As of February 2021, the COVID-19 epidemic outbreak has been spreading across nearly 200 countries, causing over 100 million people infected and over 2 million people dead. The governments, private businesses, non-profit organizations, families and individuals in different countries have implemented various policies and strategies to minimize COVID-19 impacts according to their governance mechanisms, economic structures, social systems, and lifestyles. This paper briefly reviews the major epidemics in human history and introduces policy responses to COVID-19 and effectiveness evaluations taken by individual countries. After analyzing policy responses and categorizing them into Asia mode, Western mode and China mode, we summarize a package of policy measures and implement standards, including public protection and epidemic prevention, transportation and travel management, information track and virus detection, personal prevention and quarantine, financial aid and social support. The effectiveness evaluation system comprises 5 indexes and 15 elements. This paper provides suggestions for decision makers and the general public in concerted efforts to overcome COVID-19 and facilitates growth of the community of shared future in public health.

Ultrafast Preliminary Screening of COVID-19 by Machine Learning Analysis of Exhaled NO (preprint)

Background A new coronavirus, SARS-CoV-2, has caused the coronavirus disease-2019 (COVID-19) epidemic. Current diagnostic methods mainly include nucleic acid detection, antibody detection, antigen detection, and chest computed tomography (CT) imaging. Although these methods are crucial for the diagnosis of COVID-19, there is a lack of a rapid and economical method for preliminary screening COVID-19.Methods We measured the FeNO concentrations of 103 subjects without COVID-19 and 46 patients with COVID-19. Using machine learning (ML) method, we build a ML model based on fractional exhaled nitric oxide (FeNO) concentration and features of age, and body size for rapid preliminary screening COVID-19 suspects with low-cost.Findings The statistical analysis t-test show that there is a significant difference between the FeNO of healthy people and patients with COVID-19. The ML model can screen out the patients with COVID-19 or other diseases, which show abnormal FeNO distributions. An area under the curve of 0.982 and a sensitivity 0.917 have been achieved for preliminary screening COVID-19 suspects. This non-invasive detection method which takes in two minutes and costs less than a dollar could provide a direction for the control of the rapid spread COVID-19.Interpretation During the COVID-19 pandemic, large numbers and extensive testing of COVID-19 patients remains a problem. Public healthy efforts to limit SARS-CoV-2 spread need to find a more economical and faster screening method.