  The COVID-19 pandemic has spurred a large amount of observational studies
reporting linkages between the risk of developing severe COVID-19 or dying from
it, and sex and gender. By reviewing a large body of related literature and
conducting a fine grained analysis based on sex-disaggregated data of 61
countries spanning 5 continents, we discover several confounding factors that
could possibly explain the supposed male vulnerability to COVID-19. We thus
highlight the challenge of making causal claims based on available data, given
the lack of statistical significance and potential existence of biases.
Informed by our findings on potential variables acting as confounders, we
contribute a broad overview on the issues bias, explainability and fairness
entail in data-driven analyses. Thus, we outline a set of discriminatory policy
consequences that could, based on such results, lead to unintended
discrimination. To raise awareness on the dimensionality of such foreseen
impacts, we have compiled an encyclopedia-like reference guide, the Bias
Catalog for Pandemics (BCP), to provide definitions and emphasize realistic
examples of bias in general, and within the COVID-19 pandemic context. These
are categorized within a division of bias families and a 2-level priority
scale, together with preventive steps. In addition, we facilitate the Bias
Priority Recommendations on how to best use and apply this catalog, and provide
guidelines in order to address real world research questions. The objective is
to anticipate and avoid disparate impact and discrimination, by considering
causality, explainability, bias and techniques to mitigate the latter. With
these, we hope to 1) contribute to designing and conducting fair and equitable
data-driven studies and research; and 2) interpret and draw meaningful and
actionable conclusions from these.
