Exploring how sex and gender analysis is transforming infectious disease research and public health responses through innovative modeling and inclusive methodologies.
When COVID-19 swept across the globe, a curious pattern emerged that puzzled scientists and public health experts alike: the virus was not affecting everyone equally. Men, particularly older men, were dying at significantly higher rates than women, while women were showing higher infection rates in many regions. This was not merely a biological fluke—it represented the complex interplay of our biological makeup and socially constructed roles that had been largely overlooked in infectious disease research for decades. The pandemic spotlighted what specialized researchers had known for years: that sex and gender are critical determinants in how diseases spread, who gets sick, and who survives.
The integration of sex and gender analysis represents nothing short of a paradigm shift in our approach to infectious diseases. From modeling disease transmission to designing public health interventions, researchers are increasingly recognizing that these factors cannot be treated as mere variables to be controlled for, but as fundamental organizers of health outcomes.
This article explores how this evolving field is transforming our understanding of infectious diseases and why it may hold the key to more effective and equitable public health responses in the future.
Understanding the biological and socio-cultural factors that shape disease outcomes
Refers to biological attributes including chromosomes, gene expression, hormone function, and reproductive anatomy. These biological differences influence immune response, with females typically mounting more robust immune reactions to infection but also being more susceptible to autoimmune diseases 1 5 .
Encompasses socially constructed roles, behaviors, and identities that can influence exposure patterns, healthcare-seeking behavior, and adherence to preventive measures 1 .
The interaction between these dimensions creates what researchers call distinct health landscapes for different groups. For example, during the COVID-19 pandemic, gender norms meant that women were more likely to work in frontline healthcare roles and bear caregiving responsibilities, increasing their exposure risk, while biological sex differences contributed to men's heightened vulnerability to severe disease once infected 1 .
Following the thalidomide tragedy which caused severe birth defects, the U.S. FDA implemented policies in 1977 that effectively prohibited women of childbearing potential from participating in early-phase clinical trials 5 .
Researchers were mandated to include women in NIH-funded studies, but compliance remained inconsistent due to persistent misconceptions about female hormonal cycles creating problematic variability in research results 5 .
Meta-analyses of murine studies have challenged assumptions about female variability, and there is increasing recognition of the importance of sex and gender considerations in research design and analysis.
A pivotal development in the field came from researchers who developed a comprehensive conceptual framework based on the Susceptible-Exposed-Infectious-Recovered/Deceased (SEIR/D) compartmental model to systematically map pathways through which gender and sex influence infectious disease dynamics 1 . This framework represents one of the most ambitious attempts to formally integrate both biological and social factors into infectious disease modeling.
Compartmental framework for modeling disease transmission
Researchers conducted a narrative review of modeling, epidemiological, and clinical studies to identify key mechanisms through which sex and gender influence disease susceptibility, exposure, transmission, recovery, and mortality.
They categorized these mechanisms into two groups:
The team modified the standard SEIR/D model to incorporate these identified pathways, creating a more nuanced modeling approach that could account for sex and gender differences at multiple stages of disease progression.
The framework also examined how gender-related variations in epidemiological surveillance data—such as testing uptake and hospitalization referrals—could influence model outputs and create blind spots in public health understanding 1 .
The research demonstrated that failure to account for sex and gender dimensions creates significant gaps in understanding transmission dynamics and potential blind spots in public health interventions.
Primarily linked to gender norms and roles influencing exposure patterns
Connected to sex-related biological factors, such as immune response differences
Both biological and social mechanisms must be considered simultaneously
This framework provides researchers with a systematic approach for integrating gender and sex considerations into infectious disease models, potentially enhancing predictive accuracy and promoting health equity in pandemic response 1 .
Understanding patterns through comprehensive data analysis
| Burden Metric | Global Figure | Highest Burden Region | Highest Burden Age Group |
|---|---|---|---|
| Deaths | 619,130 | Sub-Saharan Africa | 35-44 years |
| DALYs* | 28,782,771 | Sub-Saharan Africa | 35-44 years |
| ASDR** Trend | Decreasing modestly | Eastern Europe (increasing) | - |
These figures highlight significant global disparities in how sexual health risks affect different populations, with low- to middle-income regions bearing nearly 80% of the total burden. Understanding these patterns is essential for developing targeted public health strategies.
| Outcome Measure | Sex Disparity | Contributing Factors |
|---|---|---|
| Infection Rates | Higher in women in certain contexts | Gendered occupational exposure, caregiving roles |
| Disease Severity | Higher in men | Sex-based immune response differences, comorbidities |
| Mortality | Higher in men | Combination of biological and behavioral factors |
| Testing Uptake | Varies by gender | Healthcare-seeking behavior, access |
Tools for advancing sex and gender-based infectious disease research
| Reagent/Method | Function/Application | Considerations for Sex/Gender Research |
|---|---|---|
| Cell Lines | In vitro studies of infection mechanisms | Include both male and female-derived cells when possible |
| Animal Models | Preclinical testing of treatments and vaccines | Balance male and female subjects; report data by sex |
| SEIR/D Models | Compartmental modeling of disease transmission | Incorporate sex and gender pathways as modifiable parameters |
| Gender Measures | Assessing social determinants of health | Use validated instruments that capture gender-related variables |
| Sex-Disaggregated Data | Epidemiological analysis | Ensure sufficient sample sizes for both sexes in all age groups |
| Harmonized Demographic Forms | Inclusive participant characterization | Include comprehensive gender identity measures beyond binary categories 4 |
The tools for studying sex and gender in infectious diseases range from biological reagents to analytical frameworks and data collection instruments. Proper implementation requires careful consideration at each stage of research design and analysis.
Modern techniques such as integrative data analysis allow researchers to pool raw data from multiple studies, enabling more powerful examinations of gender identity effects on health outcomes even when individual studies may not have sufficient statistical power for such analyses on their own 4 .
Advanced techniques enable more nuanced analysis of sex and gender effects
Emerging trends and transformative approaches shaping the field
Develop more sophisticated compartmental models that explicitly incorporate sex and gender as modifiable parameters rather than fixed covariates 1 .
Apply artificial intelligence and machine learning techniques to identify subtle interactions between biological and social factors that may be difficult to detect using traditional statistical methods.
Create data harmonization protocols that allow for pooling and comparative analysis of sex and gender data across different studies and populations 4 .
Public health messaging that considers gender-related differences in information processing and health behaviors.
Treatment guidelines that consider sex-specific pharmacokinetics and pharmacodynamics, particularly for antimicrobial therapies.
Appropriately represent transgender, non-binary, and gender diverse individuals in study populations 4 .
Implement guidelines such as the Sex and Gender Equity in Research (SAGER) guidelines, which provide comprehensive procedures for reporting sex and gender information throughout scientific publications .
Consider how sex and gender interact with other social determinants such as race, socioeconomic status, and geographic location to shape health outcomes 7 .
The integration of sex and gender analysis into infectious disease research represents more than a specialized niche—it constitutes a fundamental refinement of our scientific approach to understanding and combating pathogens. As the COVID-19 pandemic made abundantly clear, pathogens do not affect all people equally, and our defenses must account for this variability to be effective.
Enhanced through comprehensive consideration of sex and gender variables
More effective and fair health interventions for all population groups
The future of this field lies not merely in adding sex as a biological variable or gender as a demographic category, but in fundamentally rethinking how we model disease transmission, design clinical trials, develop therapeutics, and implement public health interventions. This approach promises not only greater scientific rigor but also more equitable health outcomes for all population groups.
As research continues to evolve, the systematic inclusion of sex and gender considerations may well prove to be one of the most important advancements in our collective ability to predict, prevent, and respond to infectious disease threats in the 21st century. The pathogens we face do not affect a generic human body—they encounter bodies shaped by both complex biological systems and social structures, and our science must reflect this reality to be truly effective.