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Infectious disease and group size: more than just a numbers game
Open accessAbstract
Increased risk of infectious disease is assumed to be a major cost of group living, yet empirical evidence for this effect is mixed. We studied whether larger social groups are more subdivided structurally. If so, the social subdivisions that form in larger groups may act as barriers to the spread of infection, weakening the association between group size and infectious disease. To investigate this ‘social bottleneck’ hypothesis, we examined the association between group size and four network structure metrics in 43 vertebrate and invertebrate species. We focused on metrics involving modularity, clustering, distance and centralization. In a meta-analysis of intraspecific variation in social networks, modularity showed positive associations with network size, with a weaker but still positive effect in cross-species analyses. Network distance also showed a positive association with group size when using intraspecific variation. We then used a theoretical model to explore the effects of subgrouping relative to other effects that influence disease spread in socially structured populations. Outbreaks reached higher prevalence when groups were larger, but subgrouping reduced prevalence. Subgrouping also acted as a ‘brake’ on disease spread between groups. We suggest research directions to understand the conditions under which larger groups become more subdivided, and to devise new metrics that account for subgrouping when investigating the links between sociality and infectious disease risk.
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“High-density living and increased contact can increase pathogen transmission rates among hosts (Nunn et al. 2015).”
From Red fox viromes in urban and rural landscapes (2020) · cites this paper as Nunn et al. 2015
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Cites
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256
The delayed rise of present-day mammals
Bininda-Emonds, O. R. P., Cardillo, M., Jones, K. E., MacPhee, R. D. E., Beck, R. M. D., Grenyer, R., Price, S. A., Vos, R. A., Gittleman, J. L., & Purvis, A. (2007) · Nature · doi:10.1038/nature05634 · Cited by 2,082
Dynamics and Control of Diseases in Networks with Community Structure
Salathé, M., & Jones, J. H. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000736 · Cited by 586
Flagged first, then by citations
Research works printed but not linked: 67
- 10.2307/1936609 · DOI
- 10.1016/S0065-3454(08)60405-2 · DOI
- 10.2307/1941192 · DOI · Paper page
- 10.1146/annurev.ecolsys.34.030102.151725 · DOI
- 10.1645/GE-310R · DOI
- 10.1093/beheco/arh028 · DOI
- 10.2307/2389485 · DOI
- 10.1007/s00265-005-0005-z · DOI
- 10.1093/molbev/msp018 · DOI
- 10.1093/beheco/4.1.36 · DOI
- 10.1126/science.290.5494.1168 · DOI
- 10.1111/j.0014-3820.2002.tb00859.x · DOI
- 10.1098/rspb.2001.1950 · DOI
- 10.1006/jtbi.2001.2524 · DOI
- Schmid-Hempel P, 1998, Parasites in social insects
- 10.1098/rstb.2014.0102 · DOI
- 10.1098/rstb.2014.0108 · DOI
- 10.1007/s00442-007-0834-8 · DOI
- 10.1007/s002650050523 · DOI
- 10.1051/apido:19870401 · DOI
- 10.1093/jee/49.6.831 · DOI
- 10.1163/156853992X00011 · DOI
- 10.1093/beheco/6.2.159 · DOI
- 10.2307/3565589 · DOI
- 10.1098/rsbl.2007.0393 · DOI
- 10.1086/666081 · DOI
- 10.1017/S0031182012002259 · DOI
- Cohen J, 1988, Statistical power analysis for the behavioral sciences
- 10.1007/s10682-011-9526-2 · DOI
- 10.1098/rsif.2014.0349 · DOI
- 10.1371/journal.pone.0095039 · DOI
- 10.7208/chicago/9780226090009.001.0001 · DOI
- 10.1007/978-3-662-43550-2 · DOI · Paper page
- 10.1098/rstb.2014.0107 · DOI
- 10.1073/pnas.0712149105 · DOI
- R Development Core Team, 2014, R: A language and environment for statistical computing
- Csardi G, 2006, The igraph software package for complex network research, InterJournal Complex Syst., 1695
- 10.1103/PhysRevE.60.7332 · DOI
- 10.1007/s00265-008-0600-x · DOI
- 10.1016/j.socnet.2010.03.006 · DOI
- 10.1007/BF01386390 · DOI
- Opsahl T, 2009, Structure and evolution of weighted networks
- 10.1073/pnas.96.8.4472 · DOI
- 10.1007/s10329-009-0153-2 · DOI
- Borenstein M, 2005, Comprehensive meta-analysis
- Lajeunesse M, 2009, Meta Analysis and the Comparative Phylogenetic Method, Am. Nat., 174, 369, 10.1086/603628 · DOI
- Lajeunesse MJ, 2011, phyloMeta: a program for phylogenetic comparative analyses with meta-analysis, Bioinformatics, 27, 2603, 10.1093/bioinformatics/btr438 · DOI
- Orme D Freckleton R Thomas G Petzoldt T Fritz S& Isaac N. 2011 caper: comparative analyses of phylogenetics and evolution in R. See http://R-Forge.R-project.org/projects/caper/. · DOI
- 10.1038/44766 · DOI · Paper page
- 10.1086/343873 · DOI · Paper page
- 10.1093/bioinformatics/btl505 · DOI · Paper page
- 10.1073/pnas.0605858103 · DOI
- 10.1007/s10682-007-9180-x · DOI
- 10.1016/j.anbehav.2009.02.023 · DOI
- 10.1016/0020-7101(88)90067-0 · DOI
- 10.2307/1403510 · DOI
- 10.1046/j.1365-2664.2000.00553.x · DOI
- Burnham KP, 2002, Model selection and multimodel inference: a practical information-theoretic approach
- 10.18637/jss.v019.i09 · DOI
- Gramacy RB, 2012, Categorical inputs, sensitivity analysis, optimization and importance tempering with tgp version 2, an R package for treed Gaussian process models, J. Stat. Soft., 33, 1
- Wlasiuk G, 2010, Promiscuity and the rate of molecular evolution at primate immunity genes, Evolution, 64, 2204
- Nunn CL, 2002, Spleen size, disease risk and sexual selection: a comparative study in primates, Evol. Ecol. Res., 4, 91
- 10.1038/nature05260 · DOI
- 10.1111/j.1365-2583.2006.00682.x · DOI
- 10.1016/j.cub.2007.06.008 · DOI
- 10.1007/s00040-013-0282-x · DOI
- 10.1371/journal.pone.0021677 · DOI
Cited by
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Tibetan Macaque Social Style: Covariant and Quasi-independent Evolution
Balasubramaniam, K., Ogawa, H., Li, J.-H., Ionica, C., & Berman, C. M. (2019) · The Behavioral Ecology of the Tibetan Macaque · doi:10.1007/978-3-030-27920-2_8 · Cited by 6
Social context alters host behavior and infection risk
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“Group density is a prominent factor that affects key animal behaviors, such as foraging (26–28), mating (29) and predator avoidance (27, 28, 30), and affects parasite and disease transmission (31).”
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Marescot, L., Franz, M., Benhaiem, S., Hofer, H., Scherer, C., East, M., & Kramer-Schadt, S. (2020) · bioRxiv · doi:10.1101/2020.04.11.036806 · Cited by 5
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Costs and constraints of cellular immune activity during development in an arboreal primate
González, N. T., Freedberg, L., Higham, J., Vogel, E. R., & Cords, M. (2025) · Royal Society Open Science · doi:10.1098/rsos.241659 · Cited by 4
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Mariencheck, C. L. (2024) · Evolutionary Anthropology Issues News and Reviews · doi:10.1002/evan.22038 · Cited by 6
“78,79 Sex differences in immunity might influence social grouping and behavior to minimize transmission (e.g., the fission – fusion subgrouping that minimizes virus transmission 80 ).”
Gerken, K. N., Owuor, K. O., Ndenga, B., Wambua, S., Winter, C., Chemutai, S., Omukuti, R., Arabu, D., Miring’u, I., Wilson, W. C., et al. (2024) · Pathogens · doi:10.3390/pathogens13060488 · Cited by 4
“As RVFV becomes more endemic and occurs more frequently, the ability to recognize clinical signs in affected animals is likely to wane further as populations have different sizes and higher baseline prevalence [27].”
BackgroundThe role of social attraction and social avoidance in shaping modular networks
Romano, V., Puga-Gonzalez, I., Macintosh, A. J., & Sueur, C. (2024) · Royal Society Open Science · doi:10.1098/rsos.231619 · Cited by 4
“In animal societies, it has been suggested that increased network modularity, which is typically associated with larger groups of vertebrates and invertebrates, might decrease costly social connections, such as those involving pathogen transmission [26,27].”
BackgroundCuervo, P., Beldomenico, P., Sánchez, A., Pietrobon, E., Valdez, S., & Racca, A. (2018) · Journal of Experimental Zoology Part A Ecological and Integrative Physiology · doi:10.1002/jez.2218 · Cited by 4
Nitsch, A., Lummaa, V., Ketola, T., Honkola, T., Vesakoski, O., & Briga, M. (2025) · iScience · doi:10.1016/j.isci.2025.112530 · Cited by 3
Song, Y., & Yang, Q. (2024) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2024.111772 · Cited by 3
Social network analysis uncovers hidden social complexity in giant pandas
Connor, T., Frank, K., Qiao, M.-J., Scribner, K., Hou, J., Zhang, J.-D., Wilson, A. E., Hull, V., Li, R.-G., & Liu, J. (2023) · Ursus · doi:10.2192/ursus-d-22-00011.1 · Cited by 3
Social behavior impacts parasite infection risk in equid groups.
Joly, C., Rachid, L., Ortega, A., & Richard, F.-J. (2023) · Applied Animal Behaviour Science · doi:10.1016/j.applanim.2023.106091 · Cited by 3
Lynsdale, C. L., Seltmann, M. W., Mon, N. O., Aung, H. H., Nyein, U. K., Htut, W., Lahdenperä, M., & Lummaa, V. (2022) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-022-03192-8 · Cited by 7
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Self-citationLaker, P. R., Hoppitt, W., Weiss, M. N., & Madden, J. (2021) · Proceedings of the Royal Society B · doi:10.1098/rspb.2020.2614 · Cited by 3
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Kamiński, M., Chyb, A., Matson, K. D., & Minias, P. (2024) · Integrative Zoology · doi:10.1111/1749-4877.12899 · Cited by 2
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“[1,2]), but there is evidence that individuals in larger groups have access to more information and resources (e.”
BackgroundThe ontogeny of selection on genetic diversity in harvester ants
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“In larger social groups, increased network modularity reduced disease transmission [86,87] but the extent to which genetic diversity might amplify this effect has not been tested.”
BackgroundLutermann, H. (2022) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2022.879031 · Cited by 2
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Fernandes, F., Abreu, S. D. S., & Cruz, L. D. (2020) · Parasitology · doi:10.1017/s0031182020002231 · Cited by 2
“the formation of sub-groups within a large group, may decrease transmission risk and parasite success (Griffin and Nunn, 2012; Nunn et al., 2015).”
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Malloy, M., Cahn, A., & Koller, J. (2020) · International Conference on Internet of Things and Intelligence System · doi:10.48550/arxiv.2006.09326 · Cited by 2
“with social and encounter networks, there is work that studies the eects of graph modularity on the spread of infectious disease [17, 29, 30].”
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Yellow fever outbreak temporarily changes dispersal patterns in an endangered primate.
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Nicholls, C. R., Cantor, M., Möller, L., & Parra, G. J. (2026) · Mammal Review · doi:10.1111/mam.70020 · Cited by 1
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Negrey, J. D., Angedakin, S., Reddy, R. B., Sandel, A., Jombela, S., Akamumpa, E., Aliganyira, C., Birungi, C., Kalunga, D., Kamugyisha, B., et al. (2025) · Biological Conservation · doi:10.1016/j.biocon.2025.111225 · Cited by 1
Fisher, D. N., Bechsgaard, J., & Bilde, T. (2024) · Heredity · doi:10.1038/s41437-024-00724-y · Cited by 1
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Fisher, D. N., Bechsgaard, J., & Bilde, T. (2024) · bioRxiv · doi:10.1101/2024.05.21.595160 · Cited by 1
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BackgroundIndirect genetic effects should make group size more evolvable than expected
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BackgroundGroup-size effects on virus prevalence depend on the presence of an invasive species
Moore, A. T., O'Brien, V., & Brown, C. (2021) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-021-03040-1 · Cited by 1
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BackgroundVoelkl, B. (2020) · Mathematical biosciences and engineering : MBE · doi:10.3934/mbe.2020271 · Cited by 1
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Campbell, S. J., Ashley, W., Gil-Fernández, M., Newsome, T., Giallonardo, F. D., Ortiz-Baez, A., Mahar, J., Towerton, A. L., Gillings, M., Holmes, E., et al. (2020) · bioRxiv · doi:10.1101/2020.06.15.153858 · Cited by 1
“High-density living and increased host contact can increase 97 pathogen transmission rates among hosts (29).”
BackgroundPopulation-Level Disease Dynamics Reflect Individual Heterogeneities in Transmission
Siva-Jothy, J. A., White, L. A., Craft, M. E., & Vale, P. (2019) · bioRxiv · doi:10.1101/735480 · Cited by 1
Friend or foe? The dynamics of social life
Macedo, R., & DuVal, E. (2018) · Animal Behaviour · doi:10.1016/j.anbehav.2018.06.022 · Cited by 1
The role of population structure and size in determining bat pathogen richness
Lucas, T. (2016) · doi:10.6084/m9.figshare.4668388.v1 · Cited by 1
“Furthermore, multicollinearity between many traits also makes it hard to clearly distinguish which factors are important (Nunn et al. 2015).”
BackgroundLeu, S., & Godfrey, S. (2018) · Behaviour · doi:10.1163/1568539x-00003507 · Cited by 1
Social response of healthy honeybees towards Nosema ceranae-infected workers: care or kill?
Biganski, S., Kurze, C., Müller, M. Y., & Moritz, R. (2017) · Apidologie · doi:10.1007/s13592-017-0557-8
“Despite numerous advantages of living in large social groups, a major disadvantage is arguably the facilitation of pathogen transmission due to the high density of group members (Cremer et al. 2007; Nunn et al. 2015).”
BackgroundSocial interaction, and not group size, predicts parasite burden in mammals
Lucatelli, J., Mariano‐Neto, E., & Japyassú, H. F. (2020) · Evolutionary Ecology · doi:10.1007/s10682-020-10086-6 · Cited by 18
Sopilko, N. G., Shcherbakova, V. D., Mugue, N., & Galoyan, E. (2026) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-026-03789-3
“Simultaneously, greater visibility for predators (Vine 1971 ; Ruxton and Johnsen 2016), increased intraspecific competition (Rita et al. 1996), higher risk of infection, and parasite transmission (Nunn et al. 2015 ) may reduce individual fitness within aggregations.”
Pereira, H., Zepellini, C. G., & Alves, R. R. D. N. (2026) · Acta Tropica · doi:10.1016/j.actatropica.2026.108272
Effects of multiple stressors on the behavior of fish in the presence and absence of a predator
Munson, A., Cortese, D., Tiddy, I. C., Norman, H., & Killen, S. S. (2026) · Behavioral Ecology · doi:10.1093/beheco/arag083
“However, group living can increase disease transfer (Nunn et al. 2015) and competition for resources (Ward and Webster 2016).”
Experience-dependent modulation of collective behavior in larval zebrafish
Harpaz, R., Phillips-Batra, M., Goel, R., Bind, M.-A. C., Fishman, M., & Engert, F. (2026) · Nature Communications · doi:10.1038/s41467-026-74872-6
“prominent factor that affects key animal behaviors, such as foraging 34 – 36 , mating 37 , and predator avoidance 35,36,38 , and affects parasite and disease transmission 39 .”
Multilayer contagions in animal groups
Silk, M. J., & Fefferman, N. H. (2026) · Animal Behaviour · doi:10.1016/j.anbehav.2026.123525
Langgeng, A., Sigaud, M., Prameswari, W., Priambada, N., Rianti, P., Moore, R., MacIntosh, A., & Matsuda, I. (2026) · bioRxiv · doi:10.1016/j.ijppaw.2026.101242
“As a result, parasite assemblages in managed populations often undergo qualitative reorganization rather than uniform declines in infection or diversity (Stringer and Linklater, 2014; Nunn et al., 2015; Herrera et al., 2019).”
Evolutionary Complexity of Primate Immune System Uncovered by the Extensive Phylogenomic Sampling
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“Interestingly, group size—a key component of social systems—did not show a significant influence on the evolution of the primate immune system, despite the fact that species with larger populations face a higher risk of infectious disease transmission (Nunn et al. 2015).”
Fitri-Suhaimi, M. N., Frias, L., Zimmermann, E., Lambut, P., Tangah, J., Bernard, H., Kumar, V., & Matsuda, I. (2026) · bioRxiv · doi:10.1016/j.ijppaw.2026.101221
“Factors such as group structure, contact patterns, and social hierarchies all play key roles in determining infection dynamics (Nunn et al., 2015).”
Kürschner, T., Radchuk, V., Planillo, A., Scherer, C., Blaum, N., Staubach, C., Thulke, H., & Kramer-Schadt, S. (2026) · Ecosphere · doi:10.1002/ecs2.70539
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Wijayatilake, N., Bansal, S., Pedersen, A., & Silk, M. (2025) · bioRxiv · doi:10.1098/rspb.2026.1424
“Our findings complement epidemiological studies showing that modular structures facilitate rapid transmission within strongly connected groups while reducing between-group spread, effectively acting as a ‘brake’ on overall transmission [44 ,46 ,57 ,58 ].”
Effects of biological and environmental factors on orthoebolavirus serology in bats in GuineaPreprint
Champagne, M., Cappelle, J., Caron, A., Pouliquen, T., Samoura, A., Doumbouya, M. I., Thaurignac, G., Soumah, A., Ayouba, A., Keita, A., et al. (2025) · bioRxiv · doi:10.1101/2023.08.23.554395
“Large social groups have been shown to allow higher rates of intra-species contacts which, for some pathogens, might facilitate transmission and increase prevalence (38).”
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Xu, Z.-H., Wu, C.-F., Yang, Y., Castellano-Navarro, A., Macanás-Martínez, E., Liu, B.-J., Chen, T., Ma, X.-C., & MacIntosh, A. J. J. (2025) · American Journal of Primatology · doi:10.1002/ajp.70037
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Valdes, L., Ng, W.-H., & Mcart, S. (2025) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-024-03542-8
“Indeed, theoretical models suggest that infections will spread more slowly across larger, more modular social networks (Naug and Camazine 2002 ; Nunn et al. 2015 ; Pie et al. 2004), although empirical support for this hypothesis is limited (but see Stroeymeyt et al. 2018).”
InfluentialEmergence of keystone individuals in the social networks of the ant Camponotus fellah
Kovács, B., & Jordán, F. (2024) · Insectes Sociaux · doi:10.1007/s00040-024-00963-6 · Cited by 1
“Higher level quantifications of group structure may help to reveal ecological (Verdolin et al. 2021) and epidemiological (Nunn et al. 2015; Stroeymeyt et al. 2018) aspects of sociality.”
Self-citationBalasubramaniam, K., Aiempichitkijkarn, N., Kaburu, S., Marty, P., Beisner, B., Bliss-Moreau, E., Arlet, M. E., Atwill, E., & McCowan, B. (2021) · bioRxiv · doi:10.1101/2021.07.19.452944
Modeling partial lockdowns in multiplex networks using partition strategies
Plazas, A., Malvestio, I., Starnini, M., & Díaz-Guilera, A. (2020) · Applied Network Science · doi:10.1007/s41109-021-00366-7
Ferreira, M., & Carolina, S. (2020) · doi:10.17169/refubium-26567
Dejene, S. W. (2017) · doi:10.18174/420891
“Degree measures the importance of a node according to the number of direct contacts a node has with other nodes within a network (Wasserman and Faust, 1994; Nunn et al., 2015).”
BackgroundMethodInfluential
Metadata
- Authors
- Nunn, C. L., Jordán, F., McCabe, C. M., Verdolin, J. L., & Fewell, J. H.
- Affiliations
- Duke University (US)
- University of Trento (IT)
- Harvard University (US)
- Arizona State University (US)
- Corresponding author
- Nunn, Charles L.
- Venue
- Philosophical Transactions of the Royal Society B: Biological Sciences
- Publisher
- The Royal Society
- Published
- 2015-05-26
- Volume
- 370
- Issue
- 1669
- Pages
- 20140111
- ISSN
- 0962-8436 (ISSN-L)
- 1471-2970
- PMC
- PMC4410377
- OpenAlex
- W1966269069
- Cited by
- 192
- References
- 0
- Citations by year
- 2026: 11 · 2025: 9 · 2024: 16 · 2023: 12 · 2022: 13 · 2021: 27 · 2020: 27 · 2019: 15 · 2018: 26 · 2017: 17 · 2016: 14 · 2015: 5
- FWCI
- 2.50
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Philosophical Transactions of the Royal Society B Biological Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Animal Behavior and Reproduction
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical and Theoretical Epidemiology and Ecology Models
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- infectious diseases, group size, social networks, network modularity, network distance, network centralization, network clustering, disease spread, sociality, animal social structure, vertebrates, invertebrates
- MeSH terms
- Animals, Behavior, Animal, Communicable Diseases, Invertebrates, Models, Biological, Population Density, Risk Factors, Social Behavior, Species Specificity, Vertebrates
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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