Why gender and sex matter in infectious disease modelling: A conceptual framework
Bibliographic record
Abstract
The COVID-19 pandemic underscored the differential impact of infectious diseases across population groups, with gender and sex identified as important dimensions influencing transmission and health outcomes. Sex-related biological factors, such as differences in immune response and comorbidities, contribute to men's heightened severity risks, while gender norms and roles influence exposure patterns, adherence to prevention measures, and healthcare access, influencing women's higher reported infection rates in certain contexts. Despite widely observed gender/sex disparities, infectious disease models frequently overlook gender and sex as key dimensions, leading to gaps in understanding and potential blind spots in public health interventions. This paper develops a conceptual framework based on the Susceptible-Exposed-Infectious-Recovered/Deceased (SEIR/D) compartmental model to map pathways through which gender and sex may influence susceptibility, exposure, transmission, recovery, and mortality. Using a narrative review of modelling, epidemiological, and clinical studies, this framework identifies and characterises the main social and biological mechanisms on this matter-including gendered occupational exposure, differential adherence to preventive measures, and disparities in healthcare-seeking behaviour-alongside sex-based differences in immune response and disease severity. The framework also examines potential gender-related variations in epidemiological surveillance data, highlighting disparities in testing uptake and hospitalisation referrals that could influence model outputs. By synthesising these insights, this paper provides a theoretical foundation for integrating gender and sex into infectious disease models. It advocates for interdisciplinary collaboration between modellers, social scientists, and clinicians to advance gender- and sex-sensitive modelling approaches. Accounting for gender and sex can enhance predictive accuracy, inform intervention strategies, and promote health equity in pandemic response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".