![]() Furthermore, it calls into question analyses performed to date, which do not account for a number of known data gaps. It also describes other cross-cutting implications of COVID-19 data for policy, practice and research, including reported deaths, missing information, implementation of policy, and unpredictable population behaviour. 1) and provides examples from a number of countries of possible barriers leading to inaccurate data on reported COVID-19 cases. This paper focuses on the testing and reporting cycle (Fig. It is important to understand the limitations of available COVID-19 data in order to properly inform decision making, especially at the outset as a novel infectious disease. While the work of this team is ongoing, this paper limits the findings from the inception of the pandemic to the end of 2020. ![]() Through this work, we identified gaps in the accuracy of reported numbers of COVID-19 cases and deaths, which make cross-country comparisons of the raw data, indexes using the raw data, and policy outcomes challenging. ![]() Policymakers and the public have been using metrics such as number of cases, number of deaths and testing capacity to make policy or programme decisions or to decide whether to trust the actions of their governments, respectively.Īn international team of researchers has been collecting data on physical distancing policies and contextual factors, such as health and political systems and demographics, to expedite knowledge translation (which means applying high-quality research evidence to processes of decision making) on the effect of policies and their influence on the epidemiology of COVID-19. Comparison is a prime requisite for evaluating the effectiveness of implementation of various policies between countries. These factors pose challenges for comparison among countries. Also relevant is the carrying capacity and infrastructure of health systems. Some hypothesized sociodemographic factors for increased exposure and severity of COVID-19 include living in a long-term care facility or being institutionalized, age (older), gender (mixed findings), having comorbidities (including high blood pressure, diabetes, obesity, immunocompromised status, tobacco smoking) and social vulnerabilities including race or ethnicity. Timing and synergies of policies and sociodemographic and political factors play a role in the effectiveness of these policies. There are many aspects of distancing, such as recommendations for maintaining a physical distance in public, banning group gatherings (the maximum number and where they take place), or complete lockdowns, that complicate their assessment. The best time to institute physical distancing policies and what happens when and how they are eased remain unclear. While physical distancing policies have been the mainstay in the battle against COVID-19, there has been a call to understand which forms of physical distancing policies are effective so that targeted and less disruptive measures can be taken in further waves of this pandemic and future pandemics. Out of all the strategies implemented to date, physical distancing policies have emerged as one of the more effective NPIs to battle COVID-19. Most critically, scientists have lacked data to conduct analyses on non-pharmaceutical interventions (NPIs), including policies and strategies that governments have engaged to mitigate the situation, and how these have varied across regions, presumably affecting both short- and long-term outcomes. Since the emergence of coronavirus disease 2019 (COVID-19) in Wuhan, China, the world has faced serious data issues, ranging from a lack of transparency on the emergence, spread and nature of the virus to an absence of grounded comparative analyses, with temporal differences considered, about emerging social and economic challenges.
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