The study, led by Teddy Lazebnik of the University of Haifa and Jönköping University, together with Svetlana Bunimovich-Medrazitsky of Ariel University and Ariel Alexi of Bar-Ilan University, addresses a gap that has long been overlooked in pandemic preparedness planning. While demographers and economists have extensively studied how pandemics change the decision to have children—through economic uncertainty, social disruption, and shifting risk perceptions—far less attention has been paid to how the pathogens themselves damage the biological machinery of reproduction. The new work builds a bridge between these two worlds by embedding fertility physiology directly into the mathematics of epidemic spread.

At the heart of the model is an extended version of the classic Susceptible-Infected-Recovered (SIR) framework, the workhorse of mathematical epidemiology since the 1920s. In its basic form, the SIR model sorts a population into three compartments—susceptible, infected, and recovered—and tracks how individuals flow between them through infection and recovery. The researchers expanded this structure considerably. Their version incorporates two genders, three age groups (children, adults, and the elderly), birth and death dynamics, an exposed state to capture incubation periods, and separate asymptomatic and symptomatic infectious states. Crucially, adults who recover from symptomatic infection face a defined probability of developing physiological fertility impairment, which reduces their residual reproductive capacity by a fixed proportion.

The biological motivation is grounded in clinical evidence from the COVID-19 pandemic. Studies cited by the authors found that severe COVID-19 infection was associated with marked decreases in ovarian reserve, hormonal disturbances, and endometrial abnormalities in women—key factors contributing to infertility. In men, the virus significantly reduced sperm quality and caused sex hormone disruption, with effects on sperm concentration and motility that were most pronounced during the illness stage. Research also showed that SARS-CoV-2 can infect the ovary, uterus, vagina, and placenta through the ACE2 receptor, potentially causing infertility, menstrual disorders, and fetal distress. These are not merely statistical correlations; they represent a physiological pathway by which a pathogen can permanently alter an individual’s reproductive capacity.

To translate this biology into population-level dynamics, the researchers formulated a system of ordinary differential equations describing how individuals move through epidemiological states within each gender and age group. The birth function in their model is particularly elegant: it calculates the average fertility potential of adult male-female pairings, weighting fertility-impaired individuals at a fraction of normal fertility, and caps total births by the smaller adult gender group. The team proved several mathematical properties of the system, including that solutions exist and remain unique, that all state variables stay non-negative, and that the population cannot blow up in finite time—ensuring the model behaves sensibly across its parameter space.

Because the coupled system does not admit a closed-form analytical solution, the researchers solved it numerically using an agent-based simulation approach. Each agent in the simulation carries an age, gender, epidemiological state, and fertility status, and the population evolves through discrete daily time steps over a ten-year horizon. Fertility impairment is treated as an absorbing state during adulthood: once an adult enters the fertility-impaired compartment, they remain there until aging out of the reproductive window. This assumption reflects the study’s focus on long-term physiological damage rather than transient illness effects.

To ground the simulations in reality, the team calibrated the model using demographic data from ten major cities worldwide, including Delhi, Shanghai, Paris, Istanbul, London, Toronto, Tel Aviv, New York, São Paulo, and Berlin. These cities span an enormous range of baseline fertility rates—from Tel Aviv’s 3.0 children per woman to Shanghai’s 1.28—and vary widely in age structure, from Delhi’s 37.2 percent children to Shanghai’s 52.1 percent elderly. The epidemiological parameters were drawn from published COVID-19 literature, with infection rates, recovery rates, and immunity decay calibrated to observed pandemic behavior. The fertility reduction parameter was set conservatively at 4 to 6 percent, benchmarked against the roughly 4 percent drop in U.S. births observed in 2020 and similar cross-country declines at the pandemic’s trough.

The simulation results reveal a striking asymmetry between the immediate and long-term consequences of a pandemic. When the researchers compared scenarios with and without physiological fertility impairment, the impaired scenario produced substantially larger and more persistent declines in total fertility rate. Moreover, the decline did not simply mirror the epidemic curve—it continued to grow for years after the pandemic peaked, eventually reaching a plateau in most cities. Some cities, such as São Paulo, showed continued upward trends in fertility decline even at the end of the simulation horizon, suggesting that the demographic ripple effects of a single outbreak can extend well beyond the immediate crisis period.

Sensitivity analysis identified which parameters matter most for the magnitude of fertility decline. The recovery rate from disease emerged as the single most significant predictor, followed by the proportional fertility reduction caused by the pathogen and the average infection rate—all statistically significant at the 5 percent level. This finding carries a clear policy implication: interventions that accelerate treatment and shorten disease duration may protect demographic stability more effectively than measures targeting infection rates alone. The analysis also revealed highly non-linear interactions between parameters, particularly between the exposed-to-infected transition rate and the infection rate, where moderate transition speeds combined with high infection rates produced the steepest fertility declines.

Perhaps the most sobering result concerns cities that already have low baseline fertility. The model showed that pandemics disproportionately affect regions with already declining fertility trends, amplifying existing demographic pressures rather than simply adding a uniform shock. In cities like Shanghai, where the total fertility rate already sits well below replacement level, even a moderate infection-induced fertility decline compounds an existing trajectory toward population contraction. The authors conclude that policymakers should explicitly incorporate fertility considerations into pandemic preparedness strategies, treating the physiological reproductive consequences of infection as a first-order demographic concern rather than a secondary afterthought. As the world confronts the twin challenges of emerging pathogens and unprecedented fertility decline, this framework offers a quantitative tool for anticipating—and potentially mitigating—the long-term demographic costs of the next pandemic.

Subject of Research: Mathematical modeling of infection-induced physiological fertility decline during pandemics

Article Title: Modeling the effects of pandemics on physiology-related fertility decline in society

Article References: Lazebnik, T., Bunimovich-Medrazitsky, S., & Alexi, A. (2026). Modeling the effects of pandemics on physiology-related fertility decline in society. Frontiers in Epidemiology, 6, Article 1740829. https://doi.org/10.3389/fepid.2026.1740829

Image Credits: AI Generated

DOI: 10.3389/fepid.2026.1740829

Keywords: pandemic, fertility decline, SIR model, epidemiology, COVID-19, demographics, total fertility rate, agent-based simulation, reproductive health, population dynamics, mathematical modeling, public health policy