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

Publication date: Dec 20, 2025

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.

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