The coronavirus disease 2019 (COVID-19) pandemic requires urgent implementation of effective community-engaged strategies to enhance education, awareness, and inclusion of underserved communities in prevention, mitigation, and treatment efforts. The Texas Community-Engagement Alliance Consortium was established with support from the United States’ National Institutes of Health (NIH) to conduct community-engaged projects in selected geographic locations with a high proportion of medically underserved minority groups with a disproportionate burden of COVID-19 disease and hospitalizations. The purpose of this paper is to describe the development of the Consortium. The Consortium organized seven projects with focused activities to address COVID-19 clinical and vaccine trials in highly affected counties, as well as critical statewide efforts. Five Texas counties (Bexar, Dallas, Harris, Hidalgo, and Tarrant) were chosen by NIH because of high concentrations of underserved minority communities, existing community infrastructure, ongoing efforts against COVID-19, and disproportionate burden of COVID-19. Policies and practices can contribute to disparities in COVID-19 risk, morbidity, and mortality. Community engagement is an essential element for effective public health strategies in medically underserved minority areas. Working with partners, the Consortium will use community engagement strategies to address COVID-19 disparities.

Based on the public data from the health departments of Tianjin and Shenzhen, we conducted a comparative analysis of the coronavirus disease 2019 (COVID-19) epidemic situation between these 2 cities. The aim of this study was to evaluate the role of public data in epidemic prevention and control of COVID-19, providing a scientific advice for the subsequent mitigation and containment of COVID-19 prevalence.

To describe the laboratory findings of cases of death with coronavirus disease 2019 (COVID-19) and to establish a scoring system for predicting death, we conducted this single-centre, retrospective, observational study including 336 adult patients (≥18 years old) with severe or critically ill COVID-19 admitted in two wards of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology in Wuhan, who had definite outcomes (death or discharge) between 1 February 2020 and 13 March 2020. Single variable and multivariable logistic regression analyses were performed to identify mortality-related factors. We combined multiple factors to predict mortality, which was validated by receiver operating characteristic curves. As a result, in a total of 336 patients, 34 (10.1%) patients died during hospitalisation. Through multivariable logistic regression, we found that decreased lymphocyte ratio (Lymr, %) (odds ratio, OR 0.574, P < 0.001), elevated blood urea nitrogen (BUN) (OR 1.513, P = 0.009), and raised D-dimer (DD) (OR 1.334, P = 0.002) at admission were closely related to death. The combined prediction model was developed by these factors with a sensitivity of 100.0% and specificity of 97.2%. In conclusion, decreased Lymr, elevated BUN, and raised DD were found to be in association with death outcomes in critically ill patients with COVID-19. A scoring system was developed to predict the clinical outcome of these patients.

In December, 2019, an infectious outbreak of unknown cause occurred in Wuhan, which attracted intense attention. Shortly after the virus was identified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the epidemic of coronavirus disease 2019 (COVID-19) broke out, and an information storm occurred. At that time, 2 important aspects, that is, the stages of spread and the components of the epidemic, were unclear. Answers to the questions (1) what are the sources, (2) how do infections occur, and (3) who will be affected should be clarified as the outbreak continues to evolve. Furthermore, components of the epidemic and the stages of spread should be explored and discussed. Based on information of SARS, Middle East respiratory syndrome (MERS), and COVID-19, the components of the epidemic (the sources, the routes of infection, and the susceptible population) will be discussed, as well as the role of natural and social factors involved. Epidemiologic characteristics of patients will be traced based on current information.