COVID-19 and the future of clinical epidemiology.

Clinical epidemiology, the "basic science for clinical medicine"[1], has changed substantially over the last 50 years, moving its focus from clinician driven research and clinical settings to large cohorts and trials, NIH funding, and practice guidelines. The COVID-19 pandemic created major challenges for clinicians who needed to make urgent decisions about the management a new disease and for researchers who needed to understand the clinical syndrome and the questions of greatest importance to the pandemic response. Addressing these challenges reunited clinicians and researchers in collaborative efforts to inform decisions about disease risk, prevention, prognosis and treatment, at least in part because of the shared sense of the need to ration scarce resources, the rapid evolution of understanding of the clinical syndrome, the recognition of widespread uncertainty, and the emphasis on the common good over individual credit. Only time will tell whether the experience during COVID-19 will revive the original practice of clinical epidemiology as "the application by a physician who provides direct patient care, of epidemiologic and biometric methods to the study of diagnostic and therapeutic process in order to effect an improvement in health"[2].

Spatiobehavioral Characteristics - Defining the Epidemiology of New Contagious Diseases at the Earliest Moment Possible.

Spatiobehavioral characteristics are stable for, and hence predictive of, most cases of contagious diseases. They should be acknowledged as a formal way of defining the epidemiology of new contagious diseases at the early stage, enabling health authorities to implement precision control and prevention of the disease at the first moment possible.

Interpreting SARS-CoV-2 seroprevalence, deaths, and fatality rate - Making a case for standardized reporting to improve communication.

The SARS-CoV-2 virus has spread across the world, testing each nation's ability to understand the state of the pandemic in their country and control it. As we looked into the epidemiological data to uncover the impact of the COVID-19 pandemic, we discovered that critical metadata is missing which is meant to give context to epidemiological parameters. In this review, we identify key metadata for the COVID-19 fatality rate after a thorough analysis of mathematical models, serology-informed studies and determinants of causes of death for the COVID-19 pandemic. In doing so, we find reasons to establish a set of standard-based guidelines to record and report the data from epidemiological studies. Additionally, we discuss why standardizing nomenclature is be a necessary component of these guidelines to improve communication and reproducibility. The goal of establishing these guidelines is to facilitate the interpretation of COVID-19 epidemiological findings and data by the general public, health officials, policymakers and fellow researchers. Our suggestions may not address all aspects of this issue; rather, they are meant to be the foundation for which experts can establish and encourage future guidelines throughout the appropriate communities.

Epidemiologic Features of 135 Patients With Coronavirus Disease (COVID-19) in Tianjin, China.

OBJECTIVE: This study describes the epidemiologic features of an outbreak of the coronavirus disease (COVID-19) in Tianjin caused by a novel coronavirus and provides the scientific basis for prevention and control measures. METHODS: Data from COVID-19 cases were collected from daily notifications given to the National Health Commission of the People's Republic of China and Tianjin Health Committee. All of the data were analyzed with SPSS, version 24.0 software (IBM Corp, Armonk, NY). RESULTS: As of February 24, 2020, there have been 135 confirmed cases, 3 deaths, and 87 recoveries in Tianjin, China. The incidence of COVID-19 was 8.65/1 000 000 with a 2.22% case fatality rate. Regarding geographic distribution, the incidence was 8.82 per 1 000 000 in urban areas and 8.00 per 1 000 000 in suburbs. During the early stage of the epidemic, most cases came from urban areas and in patients with a history of sojourning in Hubei Province. The majority of patients were 31-70 years old (75.97%). A familial clustering was the most important characteristic of COVID-19 (accounting for 74.81%). CONCLUSIONS: Current information suggests that people are generally susceptible to COVID-19, which has shown a familial clustering in Tianjin.

Spatial Lifecourse Epidemiology and Infectious Disease Research.

Spatial lifecourse epidemiology aims to utilize advanced spatial, location-aware, and artificial intelligence technologies to investigate long-term effects of measurable biological, environmental, behavioral, and psychosocial factors on individual risk for chronic diseases. It could also further the research on infectious disease dynamics, risks, and consequences across the life course.