Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 3 complete abstracts (584 words), 9 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
Sudden outbreaks of a disease in a country or region not previously recognized in that area, or a rapid increase in the number of new cases of a previous existing endemic disease. Epidemics can also refer to outbreaks of disease in animal or plant populations.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Where it fits in the MeSH classification
- Broader topics
- Disease Outbreaks
- Narrower topics
- Opioid Epidemic; Pandemics
Research landscape
PubMed indexes 162,225 records tagged with the MeSH heading “Epidemics” (counts retrieved 2026-10-31). 146,810 of them (90.5%) were published from 2020 onward, and the busiest period so far is 2020 onward with 146,810 records.
Reviews account for 20,316 records (12.5%) and meta-analyses for 1,463. Randomized controlled trials number 1,244 (0.8%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 3,572 |
| 2000-2009 | 86 |
| 2010-2019 | 11,757 |
| 2020 onward | 146,810 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 20,316 | 12.5% |
| Meta-analyses | 1,463 | 0.9% |
| Randomized controlled trials | 1,244 | 0.8% |
Method: PubMed query "Epidemics"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-31.
Reading the evidence in public health and nutrition
Population and nutrition studies mostly observe what people eat, breathe or are exposed to and then look for links with health, which shows association and not proof of cause. Self-reported diet is imprecise, people who eat or live differently also differ in other ways, and effects measured across a population may not apply to one person. Check the study design, how exposure was measured, what else was accounted for and the size of the effect, not only whether it was statistically significant.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| The theory of epidemics with altruism | 2026 | Research article Design not recorded in structured metadata | 3 | cc by PMID 41758666 · DOI · PMC12956880 |
| Graph neural network surrogates to leverage mechanistic expert knowledge towards reliable and immediate pandemic response | 2026 | Research article Design not recorded in structured metadata | 3 | cc by PMID 41688640 · DOI · PMC12904856 |
| Optimizing infectious disease mitigation under dynamic conditions | 2026 | Research article Design not recorded in structured metadata | 2 | cc by PMID 42546195 · DOI · PMC13462870 |
The theory of epidemics with altruism [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Social distancing can mitigate the spread of diseases in humans and animals.
- Closing excerpt
- The resulting behavior, while emerging from a complex optimization problem, is simple enough that it could have evolved as a behavioral response in social animals, as well as being easy to communicate and understand for humans.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (199 words)
Social distancing can mitigate the spread of diseases in humans and animals. Social distancing allows susceptible individuals to protect themselves (and others) but confers no personal benefit for infected individuals if recovery provides immunity. However, individuals are likely to be at least weakly altruistic and may be interested in protecting others when infected. A strongly altruistic population where individuals value others as equal to themselves would be expected to self-isolate when infected. This would strongly suppress the disease, avoid Herd Immunity, and vastly improve outcomes. Still, little is known about how weaker altruism affects behavior during epidemics. Here we show using game theory that even extremely weakly altruistic individuals, valuing their own lives equivalent to roughly 100,000 others, can rationally achieve almost identical outcomes. Individuals self-isolate in order to avoid setting off chains of infections that they would perceive as costly to them even at such small altruism. Our results are robust to a moderate fraction of asymptomatic cases or completely selfish individuals. The resulting behavior, while emerging from a complex optimization problem, is simple enough that it could have evolved as a behavioral response in social animals, as well as being easy to communicate and understand for humans.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41758666 · DOI · PMC12956880
Graph neural network surrogates to leverage mechanistic expert knowledge towards reliable and immediate pandemic response [2]
Abstract reading summary — source excerpts
- Opening excerpt
- During the COVID-19 crisis, mechanistic models have guided evidence-based decision making.
- Closing excerpt
- These results show how GNN surrogates can translate complex metapopulation models into immediate, reliable tools for pandemic response.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (195 words)
During the COVID-19 crisis, mechanistic models have guided evidence-based decision making. However, time-critical decisions in a dynamical environment limit the time available to gather supporting evidence. We address this bottleneck by developing a graph neural network (GNN) surrogate of an age-structured and spatially resolved mechanistic metapopulation simulation model. This combined approach complements classical modeling approaches which are mostly mechanistic and purely data-driven machine learning approaches which are often black box. Our design of experiments spans outbreak and persistent-threat regimes, up to three contact change points, and age-structured contact matrices on a spatial graph with 400 nodes representing German counties. We benchmark multiple GNN layers and identify an ARMAConv-based architecture that offers a strong accuracy–runtime trade-off. Across horizons of 30–90 day simulation and prediction, allowing up to three contact change points, the surrogate model attains 10–27% mean absolute percentage error (MAPE) while delivering (near) constant runtime with respect to the forecast horizon. Our approach accelerates evaluation by up to 28 670 times compared with the mechanistic model, allowing responsive decision support in time-critical scenarios and straightforward web integration. These results show how GNN surrogates can translate complex metapopulation models into immediate, reliable tools for pandemic response.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41688640 · DOI · PMC12904856
Optimizing infectious disease mitigation under dynamic conditions [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Mitigation measures are essential for controlling the spread of infectious diseases during pandemics and epidemics, but they impose considerable societal, individual, and economic costs.
- Closing excerpt
- Overall, our framework is easily applicable to general and complex settings and thereby presents a versatile tool to explore optimal mitigation strategies for endemic and pandemic infectious disease.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (190 words)
Mitigation measures are essential for controlling the spread of infectious diseases during pandemics and epidemics, but they impose considerable societal, individual, and economic costs. We developed a general framework that combines simulation of disease dynamics with optimal control to determine mitigation strategies that balance infection and mitigation costs. Optimizing this trade-off, we identified three surprising effects: first, assuming a constant reproduction number [Formula: see text], the optimal response is typically "all-or-nothing": depending on disease severity, either strict mitigation or none at all is optimal, with intermediate levels emerging only in restricted regimes that we characterize analytically. Second, under seasonal variations, optimal mitigation is stricter during winter. Interestingly, a single wave of infections still arises in spring, replacing the autumn/winter waves known for classical influenza. Third, during steady vaccination campaigns, even optimal mitigation can result in transient infection waves. Finally, we quantify the cost of delayed mitigation onset and show that even short delays can substantially increase total costs-if the disease is severe. Overall, our framework is easily applicable to general and complex settings and thereby presents a versatile tool to explore optimal mitigation strategies for endemic and pandemic infectious disease.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42546195 · DOI · PMC13462870
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis [4]
- Research article Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis [5]
- Research article Gastrointestinal Manifestations of SARS-CoV-2 Infection and Virus Load in Fecal Samples From a Hong Kong Cohort: Systematic Review and Meta-analysis [6]
- Research article COVID-19 patients' clinical characteristics, discharge rate, and fatality rate of meta-analysis [7]
- Research article Prevalence of mental health problems during the COVID-19 pandemic: A systematic review and meta-analysis [8]
- Research article A systematic review and meta-analysis of longitudinal cohort studies comparing mental health before versus during the COVID-19 pandemic in 2020 [9]
Full-text reviews on EvidenceLib
Open-access review articles about this topic, reproduced in full with authors, licence and references.
Demographic, regional and temporal trends of hyperuricemia epidemics in mainland China from 2000 to 2019: a systematic review and meta-analysis
Yang Li, Ziyan Shen, Bowen Zhu et al. · Global Health Action 2021 · CC BY 4.0
References
- Lynch MP, Schnyder SK, Molina JJ, Yamamoto R, Turner MS. The theory of epidemics with altruism. . 2026. PMID 41758666 · DOI · PMC12956880
- Schmidt A, Zunker H, Heinlein A, Kühn MJ. Graph neural network surrogates to leverage mechanistic expert knowledge towards reliable and immediate pandemic response. . 2026. PMID 41688640 · DOI · PMC12904856
- Müller L, Sartori F, Dehning J, Eggl MF, Priesemann V. Optimizing infectious disease mitigation under dynamic conditions. . 2026. PMID 42546195 · DOI · PMC13462870
- Chu DK, Akl EA, Duda S, Solo K, Yaacoub S, Schünemann HJ, et al.. Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis. Lancet. 2020. PMID 32497510 · DOI · PMC7263814
- Salari N, Hosseinian-Far A, Jalali R, Vaisi-Raygani A, Rasoulpoor S, Mohammadi M, et al.. Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. Global Health. 2020. PMID 32631403 · DOI · PMC7338126
- Cheung KS, Hung IFN, Chan PPY, Lung KC, Tso E, Liu R, et al.. Gastrointestinal Manifestations of SARS-CoV-2 Infection and Virus Load in Fecal Samples From a Hong Kong Cohort: Systematic Review and Meta-analysis. Gastroenterology. 2020. PMID 32251668 · DOI · PMC7194936
- Li LQ, Huang T, Wang YQ, Wang ZP, Liang Y, Huang TB, et al.. COVID-19 patients' clinical characteristics, discharge rate, and fatality rate of meta-analysis. J Med Virol. 2020. PMID 32162702 · DOI · PMC7228329
- Wu T, Jia X, Shi H, Niu J, Yin X, Xie J, et al.. Prevalence of mental health problems during the COVID-19 pandemic: A systematic review and meta-analysis. J Affect Disord. 2021. PMID 33310451 · DOI · PMC7710473
- Robinson E, Sutin AR, Daly M, Jones A. A systematic review and meta-analysis of longitudinal cohort studies comparing mental health before versus during the COVID-19 pandemic in 2020. J Affect Disord. 2022. PMID 34600966 · DOI · PMC8578001
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-11-04; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-11-04.
See how EvidenceLib is built. Spotted an error? Email support@evidencehelix.com.
Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.