The pitfalls of pandemic data for which bias and/or misinterpretation are inherent

Accurate and near real-time data on the course and evolution of the COVID-19 pandemic have been instrumental in informing public health mitigation strategies and policy worldwide. Although many aspects of the pandemic have been tracked across numerous types of data, including rates of infection, hospitalizations and deaths, Christina Pagel and Christian Yates argue in a Perspective that the inherent biases and pitfalls in interpretation in each data source need to be recognized and accounted for. “Because choosing the right mitigation policies relies on an accurate assessment of the current state of the local epidemic, the potential ramifications of misinterpreting data are serious,” write the authors. Pagel and Yates provide an overview of the ways in which COVID-19 is currently being tracked worldwide, like through case rates, for example, and highlight the sources of potential bias inherent within related data. What’s more, the authors discuss the data not currently being reliably captured, particularly incidences of Long Covid and breakthrough cases among vaccinated individuals. According to Pagel and Yates, using all available data to quantify the pandemic is crucial to address it, and relying too much on a single data source or a limited selection of aggregated data risks misunderstanding the state of the epidemic.