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: 4 complete abstracts (676 words), 10 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
Epidemics of infectious disease that have spread to many countries, often more than one continent, and usually affecting a large number of people.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Pandemic.
Where it fits in the MeSH classification
- Broader topics
- Epidemics
Research landscape
PubMed indexes 146,021 records tagged with the MeSH heading “Pandemics” (counts retrieved 2026-10-31). 141,047 of them (96.6%) were published from 2020 onward, and the busiest period so far is 2020 onward with 141,047 records.
Reviews account for 19,036 records (13.0%) and meta-analyses for 1,413. Randomized controlled trials number 1,197 (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 | 62 |
| 2000-2009 | 41 |
| 2010-2019 | 4,871 |
| 2020 onward | 141,047 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 19,036 | 13.0% |
| Meta-analyses | 1,413 | 1.0% |
| Randomized controlled trials | 1,197 | 0.8% |
Method: PubMed query "Pandemics"[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 |
|---|---|---|---|---|
| H5N1 2.3.4.4b: a review of mammalian adaptations and risk of pandemic emergence | 2025 | Review article Design not recorded in structured metadata | 33 | cc by PMID 40465371 · DOI · PMC12137919 |
| Assessing phylogenetic confidence at pandemic scales | 2025 | Research article Design not recorded in structured metadata | 11 | cc by PMID 41193798 · DOI · PMC12611777 |
| Assessing the risk of diseases with epidemic and pandemic potential in a changing world | 2025 | Research article Design not recorded in structured metadata | 10 | cc by PMID 40700491 · DOI · PMC12285694 |
| 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 |
H5N1 2.3.4.4b: a review of mammalian adaptations and risk of pandemic emergence [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Avian influenza viruses can cause severe disease when they spill over into mammalian and human hosts.
- Closing excerpt
- 4b outbreak and the molecular mechanisms behind these mutations to assess the pandemic risk of this virus.
- 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 (140 words)
Avian influenza viruses can cause severe disease when they spill over into mammalian and human hosts. H5N1 clade 2.3.4.4b has spread globally since 2021, decimating avian species, and has spilled over into mammalian species, causing sporadic infections and fatal outbreaks in sea lions, cats, mink and dairy cattle. Increased human cases of H5N1 are fuelling concern that H5N1 could soon adapt to become a new pandemic virus. Adaptive mutations have emerged following spillover, which support H5N1 outbreaks in mammalian populations and include changes to the PB2 such as E627K, D701N, M631L and T271A. Further changes to haemagglutinin, altering binding preference to human-like α2,6 sialic acid receptors have yet to be seen. Here, we review the adaptations that have emerged in mammals throughout the 2.3.4.4b outbreak and the molecular mechanisms behind these mutations to assess the pandemic risk of this virus.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 40465371 · DOI · PMC12137919
Assessing phylogenetic confidence at pandemic scales [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Phylogenetics has a central role in evolutionary biology and genomic epidemiology1.
- Closing excerpt
- Our method introduces a new approach to assessing phylogenetic confidence, enhancing the interpretability of pandemic-scale phylogenetic analyses and improving our ability to prepare for and respond to future pandemics.
- 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 (233 words)
Phylogenetics has a central role in evolutionary biology and genomic epidemiology1. Assessing phylogenetic confidence and reliability is therefore crucial and the methods that do this, such as those derived from Felsenstein's bootstrap2, are among the most widely used in modern science. However, these methods require enormous computational capacity, and are unsuitable for large datasets. Furthermore, most of these methods emerge from a focus on the membership of clades (groupings of taxa), which makes their results difficult to interpret in the context of genomic epidemiology. Here we propose subtree pruning and regrafting-based tree assessment (SPRTA), an efficient and interpretable approach to assess confidence in phylogenetic trees. SPRTA shifts the paradigm of phylogenetic support measurement from evaluating the confidence in clades to evolution histories and phylogenetic placement-for example, assessing whether a lineage evolved from another considered lineage, which is particularly valuable in genomic epidemiology. We use SPRTA to investigate a global public SARS-CoV-2 phylogenetic tree relating more than two million genomes, highlighting plausible alternative evolutionary origins of many SARS-CoV-2 variants, assessing reliability in the Pango outbreak lineage classification system3, and demonstrating the effect of phylogenetic uncertainty on inferred mutation rates. Our results show that SPRTA enables pandemic-scale and detailed probabilistic assessment of transmission and mutational histories. Our method introduces a new approach to assessing phylogenetic confidence, enhancing the interpretability of pandemic-scale phylogenetic analyses and improving our ability to prepare for and respond to future pandemics.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41193798 · DOI · PMC12611777
Assessing the risk of diseases with epidemic and pandemic potential in a changing world [3]
Abstract reading summary — source excerpts
- Opening excerpt
- How do human activities contribute to the emergence of zoonotic diseases that can lead to epidemics and pandemics?
- Closing excerpt
- This study also presents a global risk map and an epidemic risk index that combines countries' specific risk with their capacities for preparing and responding to zoonotic threats.
- 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 (108 words)
How do human activities contribute to the emergence of zoonotic diseases that can lead to epidemics and pandemics? Our analysis of common drivers of the World Health Organization's priority diseases suggests that climate conditions, including higher temperatures, higher annual precipitation levels, and water deficits, elevate the risk of disease outbreaks. In addition, land-use changes, human encroachment on forested areas, increased population and livestock density, and biodiversity loss contribute to this risk, with biodiversity loss showing a complex and nonlinear relationship. This study also presents a global risk map and an epidemic risk index that combines countries' specific risk with their capacities for preparing and responding to zoonotic threats.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 40700491 · DOI · PMC12285694
Graph neural network surrogates to leverage mechanistic expert knowledge towards reliable and immediate pandemic response [4]
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
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 [5]
- Research article Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis [6]
- Research article Clinical manifestations, risk factors, and maternal and perinatal outcomes of coronavirus disease 2019 in pregnancy: living systematic review and meta-analysis [7]
- Research article Prevalence and impact of cardiovascular metabolic diseases on COVID-19 in China [8]
- 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 [9]
- Research article COVID-19 patients' clinical characteristics, discharge rate, and fatality rate of meta-analysis [10]
Full-text reviews on EvidenceLib
Open-access review articles about this topic, reproduced in full with authors, licence and references.
COVID-19 Pandemic and Overall Mental Health of Healthcare Professionals Globally: A Meta-Review of Systematic Reviews
Muhammad Chutiyami, Allen M. Y. Cheong, Dauda Salihu et al. · Frontiers in Psychiatry 2022 · CC BY 4.0
Changes in Access to Health Services during the COVID-19 Pandemic: A Scoping Review
Georgina Pujolar, Aida Oliver-Anglès, Ingrid Vargas et al. · International Journal of Environmental Research and Public Health 2022 · CC BY 4.0
Estimation of Behavioral Addiction Prevalence During COVID-19 Pandemic: A Systematic Review and Meta-analysis
Zainab Alimoradi, Aida Lotfi, Chung-Ying Lin et al. · Current Addiction Reports 2022 · CC BY 4.0
The impact of the COVID-19 pandemic on maternal and perinatal health: a scoping review
Bethany Kotlar, Emily Michelle Gerson, Sophia Petrillo et al. · Reproductive Health 2021 · CC BY 4.0
The Impact of the COVID-19 Pandemic on Immunization Campaigns and Programs: A Systematic Review
Zohra S Lassi, Rabia Naseem, Rehana A Salam et al. · International Journal of Environmental Research and Public Health 2021 · CC BY 4.0
References
- Capelastegui F, Goldhill DH. H5N1 2.3.4.4b: a review of mammalian adaptations and risk of pandemic emergence. . 2025. PMID 40465371 · DOI · PMC12137919
- De Maio N, Ly-Trong N, Martin S, Minh BQ, Goldman N. Assessing phylogenetic confidence at pandemic scales. . 2025. PMID 41193798 · DOI · PMC12611777
- Fanelli A, Cescatti A, Ciscar JC, Dubois G, Ibarreta D, Lowe R, et al.. Assessing the risk of diseases with epidemic and pandemic potential in a changing world. . 2025. PMID 40700491 · DOI · PMC12285694
- 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
- 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
- Allotey J, Stallings E, Bonet M, Yap M, Chatterjee S, Kew T, et al.. Clinical manifestations, risk factors, and maternal and perinatal outcomes of coronavirus disease 2019 in pregnancy: living systematic review and meta-analysis. BMJ. 2020. PMID 32873575 · DOI · PMC7459193
- Li B, Yang J, Zhao F, Zhi L, Wang X, Liu L, et al.. Prevalence and impact of cardiovascular metabolic diseases on COVID-19 in China. Clin Res Cardiol. 2020. PMID 32161990 · DOI · PMC7087935
- 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
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.
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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.