Including gender-specific features in epidemic modeling: the case of the second wave of COVID-19 in Italy

Biological and behavioral differences between genders influence infectious disease dynamics. Yet, most epidemiological models overlook these aspects in favor of age stratification alone. Here, we systematically evaluate the impact of incorporating gender-specific features into an age-structured epidemic compartmental model, calibrated to COVID-19 mortality data from the second wave in Italy (Autumn 2020-Winter 2021). We develop eight model versions representing different combinations of three data-driven features: gender-stratified contact matrices derived from CoMix data, gender-specific infection fatality ratios (IFR), and gender-dependent transmission rates linked to behavioral differences. We calibrate these models against aggregated mortality data and evaluate their performance on data disaggregated by gender, age, and both. Our results demonstrate that models incorporating gender-stratified contact patterns significantly outperform those relying solely on age, improving the accuracy of the fit even when analyzing age-disaggregated data alone. Furthermore, the inclusion of gender-specific IFR is essential for reproducing the empirically higher mortality rates observed in males. While phenomenological behavioral adjustments improve the fit for specific subgroups, such as older males, we observe trade-offs where maximizing performance for one demographic group occasionally reduces accuracy for another. Overall, our findings highlight that integrating gender data--particularly regarding contact patterns--is a critical step toward increasing the realism and precision of epidemiological models, even when outcome data is not fully disaggregated. Author summaryDue to a combination of behavioral and biological factors, such as the adoption of protective measures and immune responses, infectious diseases affect men and women differently. However, the mathematical models used to understand and predict epidemics often overlook these aspects, focusing primarily on age. Here, we investigate whether explicitly including gender-specific data improves the accuracy of epidemic simulations. Using data from the second wave of the COVID-19 pandemic in Italy, we develop and compare several models that incorporated gender differences in social interaction patterns, biological risk of death, and protective behaviors. We find that men and women exhibit distinct social mixing patterns, and including this information is the most important factor for accurately reproducing the overall number of deaths. Additionally, accounting for the higher biological risk faced by males is essential for capturing gender-specific mortality trends. We also observe that because men had higher mortality rates, the models naturally prioritized fitting male data, sometimes at the expense of accuracy for females. Our work demonstrates that gender-agnostic models may miss crucial details. We conclude that incorporating gender-specific data is vital for creating more realistic models and designing inclusive public health policies that protect all demographic groups effectively.