{"doi":"10.1101/2022.04.19.22273976","title":"Do Some Super-Spreaders Spread Better? Effects of individual heterogeneity in epidemiological traits","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>Many high-profile outbreaks are driven by super-spreading, including HIV, MERS, Ebola, and the SARS-Cov-2 pandemic. That super-spreading is a common feature of epidemics is immutable, however, the relative importance of 2super-spreaders to the outcome of an epidemic, and the individual-level traits that lead to super-spreading, is less clear. For example, an individual may contribute disproportionately to transmission by way of an extremely high contact rate or by way of low recovery, but how these two super-spreaders differ in their effect on epidemiological dynamics is unclear. Furthermore, epidemiological traits may often covary with one another in ways that promote or inhibit super-spreading. What patterns of covariation, and between what traits, are most likely to lead to large epidemics driven by super-spreading? Using stochastic individual-based simulations of an SIR epidemiological model, we explore how variation and covariation between transmission-related traits (contact rate and infectiousness) and duration-related traits (virulence and recovery) of infected individuals affects super-spreading and peak epidemic size. We show that covariation matters when contact rate and infectiousness covary: peak epidemic size is largest when they covary positively and smallest when they covary negatively. We did not see that more super-spreading always leads to larger epidemics, rather, we show that the relationship between super-spreading and peak epidemic size is dependent on which traits are covarying. This suggests that there may not necessarily be any general relationship between the frequency of super-spreading and the size of an epidemic.</jats:p>","journal":null,"year":null,"id":662506,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":561767,"name":"Sarah A. Budischak","orcid":"0000-0002-7382-169X","position":1,"is_corresponding":false},{"id":334850,"name":"Meggan E. Craft","orcid":"0000-0001-5333-8513","position":2,"is_corresponding":false},{"id":814136,"name":"Kristian M. Forbes","orcid":"0000-0002-2112-2707","position":3,"is_corresponding":false},{"id":981302,"name":"Richard Hall","orcid":"0000-0002-3264-3217","position":4,"is_corresponding":false},{"id":1127742,"name":"David Nguyen","orcid":"0000-0003-0426-3169","position":5,"is_corresponding":false},{"id":1729527,"name":"Clay E. Cressler","orcid":null,"position":6,"is_corresponding":false},{"id":1729525,"name":"Alexis S. Beagle","orcid":"0000-0002-2101-9371","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Do Some Super-Spreaders Spread Better? Effects of individual heterogeneity in epidemiological traits","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>Many high-profile outbreaks are driven by super-spreading, including HIV, MERS, Ebola, and the SARS-Cov-2 pandemic. That super-spreading is a common feature of epidemics is immutable, however, the relative importance of 2super-spreaders to the outcome of an epidemic, and the individual-level traits that lead to super-spreading, is less clear. For example, an individual may contribute disproportionately to transmission by way of an extremely high contact rate or by way of low recovery, but how these two super-spreaders differ in their effect on epidemiological dynamics is unclear. Furthermore, epidemiological traits may often covary with one another in ways that promote or inhibit super-spreading. What patterns of covariation, and between what traits, are most likely to lead to large epidemics driven by super-spreading? Using stochastic individual-based simulations of an SIR epidemiological model, we explore how variation and covariation between transmission-related traits (contact rate and infectiousness) and duration-related traits (virulence and recovery) of infected individuals affects super-spreading and peak epidemic size. We show that covariation matters when contact rate and infectiousness covary: peak epidemic size is largest when they covary positively and smallest when they covary negatively. We did not see that more super-spreading always leads to larger epidemics, rather, we show that the relationship between super-spreading and peak epidemic size is dependent on which traits are covarying. This suggests that there may not necessarily be any general relationship between the frequency of super-spreading and the size of an epidemic.</jats:p>","is_dataset_classified":null,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19162232","pmcid":null,"openalex_id":"https://openalex.org/W4281395940","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2024,"count":1},{"year":2025,"count":1}],"oa_status":"green","license":"cc-by-nd","oa_locations":[{"url":"https://www.medrxiv.org/content/medrxiv/early/2022/04/23/2022.04.19.22273976.full.pdf","host_type":"repository"},{"url":"https://www.medrxiv.org/content/medrxiv/early/2022/04/23/2022.04.19.22273976.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2022.04.19.22273976","host_type":"publisher"},{"url":"https://doi.org/10.1101/2022.04.19.22273976","host_type":"repository"},{"url":"https://europepmc.org/article/PPR/PPR497101","host_type":"Europe_PMC"},{"url":"https://europepmc.org/api/fulltextRepo?pprId=PPR497101&type=FILE&fileName=EMS149640-pdf.pdf&mimeType=application/pdf","host_type":"Europe_PMC"}],"fields_of_study":["COVID-19 epidemiological studies","Viral Infections and Outbreaks Research","Mathematical and Theoretical Epidemiology and Ecology Models"],"mesh_terms":[],"keywords":["Transmission (telecommunications)","Transmission rate","Biology","Pandemic","Demography","Evolutionary biology","Statistics","Coronavirus disease 2019 (COVID-19)","Mathematics","Computer science","Disease","Medicine"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-12T15:26:52.276026Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}