Evidence map›Paper›PMID 42819457›Full record

ArticleFrontiers in epidemiology2026

Modeling the effects of pandemics on physiology-related fertility decline in society.

Teddy Lazebnik, Svetlana Bunimovich-Medrazitsky, Ariel Alexi

Abstract read
In one paragraph

Article in Frontiers in epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Teddy LazebnikDepartment of Information Systems, Univeristy of Haifa, Haifa, Israel.
Svetlana Bunimovich-MedrazitskyDepartment of Mathematics, Ariel University, Ariel, Israel.
Ariel AlexiDepartment of Information Science, Bar Ilan University, Ramat Gan, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pandemics throughout history have raised challenges in many forms, such as increased mortality rates, economic instability, and political shifts. Recent pandemics, coupled with globally declining fertility rates, pose unprecedented demographic challenges for humanity. A pandemic is inherently connected to a population's fertility in many ways - psychological, economic, and, most importantly, physiological. Nevertheless, this connection is mostly left unintended, blinding us to the effects of the pandemic-caused physiological fertility decline on the population's growth over time. In this study, we proposed a novel epidemiological model incorporating physiological fertility decline dynamics using an extended Susceptible-Infected-Recovered (SIR) model incorporating age groups, genders, birth and death dynamics, and infection-induced physiological fertility decline. Our model, based on real-world data from 10 large cities globally, demonstrates the impact of infection on fertility, which can persist for generations, creating demographic ripple effects that extend well beyond the immediate pandemic period. That said, the fertility drop is secondary to the pandemic spread, even for relatively high fertility decline due to the pathogen. These results have significant implications for public health policy and population planning. They highlight the need to integrate fertility considerations into pandemic preparedness strategies. Our study provides a quantitative framework for assessing the long-term demographic impacts of pandemics, offering policymakers tools to assess and potentially mitigate the impact of future disease outbreaks at the population level.

Indexed as

demographic fertilitymachine learningpandemic spreadpopulation demographicSIR model

Identifiers

PMID42819457
PMCPMC13624137

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.