Tackling the politicisation of COVID-19 data reporting through open access data sharing

Public health policies are only as good as the quality of the data on which they are based. Policy decisions that are so crucial to containing an emerging pathogen are challenged by the sparsity of data on which to optimise them. The earlier, and more completely, the data can be compiled, the better the robustness of risk estimates, forecasting, and modelling. Within 3 weeks of the announcement by WHO of an anomalous cluster of severe coronavirus cases in Wuhan, China, the Johns Hopkins University (JHU) Center for Systems Science and Engineering (CSSE) launched the COVID-19 Dashboard. With the utmost transparency, Dong and colleagues¹ detail the challenges faced and the solutions developed in providing a central, user-friendly database for publicly available epidemiological data compiled from over 3500 locations across 195 countries and regions. The reliable granular surveillance of reported SARS-CoV-2 cases, deaths and case fatalities, and vaccine doses administered in many instances, has provided an evidence-base for determining effective local, national, and international control measures. Methodological innovations in the data curation process proved fundamental to overcoming anomalies that arose from a myriad of sources, including the politicisation of COVID-19 risk assessments.

The JHU CSSE Dashboard was not susceptible to the same misalignment of incentives as some government officials who were more concerned about short-term economic repercussions of measures to curtail COVID-19 transmission than accurate risk evaluations. Regression analysis of country-specific death rates among 137 countries, showed that approximately 400 000 deaths were estimated to be unaccounted for during the first year of the pandemic, most likely among autocratic governments.² During the early stages of the pandemic, the Chinese Government limited knowledge of the emerging disease and downplayed its severity.³ The Chinese Government did not allow the media to use terms like fatal and lockdown.³ Houthi rebels in Yemen relied on under-reporting cases to avoid accountability and maintain economic activity, leading to the reporting of only four COVID-19 cases and one death in the highly populated Sana'a City over the first year of the pandemic.⁴ Most countries report cases and deaths that are both probable and confirmed by testing. However, in Russia, only COVID-19 confirmed deaths are included in official counts, despite low supplies of PCR tests, leading to vast under-reporting of deaths.⁵ Similarly, some Brazilian hospitals have been implicated in the under-reporting of COVID-19 deaths in response to government pressure to avoid triggering the apparent need for lockdown measures.⁶ When official and aggregate sources were available, the JHU CSSE team overcame such challenges by implementing innovative anomaly detection processes and data fusion approaches in the data coalition process.

Political polarisation has threatened the reliability of data supplied by US Government agencies. The Trump White House Administration advised hospitals to send data on SARS-CoV-2 and intensive care unit capacities to a private company, bypassing the US Center for Disease Control and Prevention (CDC).⁷ Concerningly, a relationship was exposed between the private contractor and the Trump family's corporation.⁸ The switch to sending data to a private contractor led to a hiatus in publicly available data from the US CDC.⁹ Moreover, the transition was accompanied by sporadic updates, with many irregularities in the data and inconsistencies in the definition of metrics from the contracted private company.⁷ Even the appearance of such impropriety undermines public confidence in the accuracy of such data and the willingness to adhere to government recommendations regarding COVID-19 specifically and potentially to health policies more broadly. However, the objectivity of the JHU CSSE Dashboard as an independent data source remained steadfast. The data validation undertaken consisted of computational scanning for large deviations across temporal trajectories, with manual review and confirmation of any anomalies appearing in the data. For instances of anomalous spikes in data due to a backlog in reporting, the respective health agencies were contacted to correctly distribute reporting dates retrospectively. Furthermore, the data collation for the JHU CSSE Dashboard maintains consistent definitions for case and death counts (using those specified by the US CDC) and includes both probable and confirmed numbers in the overall count. As new COVID-19 variants continue to emerge, human behaviour shifts, and policies evolve, the JHU CSSE COVID-19 Dashboard will continue to be a highly informative tool for decision makers and the public alike.