{"doi":"10.12688/gatesopenres.13261.1","title":"Estimating HIV, HCV and HSV2 incidence from emergency department serosurvey","abstract":"<ns3:p> <ns3:bold>Background:</ns3:bold> Our understanding of pathogens and disease transmission has improved dramatically over the past 100 years, but coinfection, how different pathogens interact with each other, remains a challenge. Cross-sectional serological studies including multiple pathogens offer a crucial insight into this problem. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> We use data from three cross-sectional serological surveys (in 2003, 2007 and 2013) in a Baltimore emergency department to predict the prevalence for HIV, hepatitis C virus (HCV) and herpes simplex virus, type 2 (HSV2), in a fourth survey (in 2016). We develop a mathematical model to make this prediction and to estimate the incidence of infection and coinfection in each age and ethnic group in each year. </ns3:p> <ns3:p> <ns3:bold>Results:</ns3:bold> Overall we find a much stronger age cohort effect than a time effect, so that, while incidence at a given age may decrease over time, individuals born at similar times experience a more constant force of infection over time. </ns3:p> <ns3:p> <ns3:bold>Conclusions:</ns3:bold> These results emphasise the importance of age-cohort counselling and early intervention while people are young. Our approach adds value to data such as these by providing age- and time-specific incidence estimates which could not be obtained any other way, and allows forecasting to enable future public health planning. </ns3:p>","journal":"Gates Open Research","year":2021,"id":226049,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.7274,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":108425,"name":"Oliver Laeyendecker","orcid":"0000-0002-6429-4760","position":1,"is_corresponding":false},{"id":828776,"name":"Louise Dyson","orcid":"0000-0001-9788-4858","position":2,"is_corresponding":false},{"id":246864,"name":"Yu‐Hsiang Hsieh","orcid":"0000-0002-1616-8014","position":3,"is_corresponding":false},{"id":246863,"name":"Eshan U. Patel","orcid":"0000-0003-2174-5004","position":4,"is_corresponding":false},{"id":242115,"name":"Richard E. Rothman","orcid":"0000-0002-1017-9505","position":5,"is_corresponding":false},{"id":541247,"name":"Gabor D. Kelen","orcid":"0000-0002-3236-8286","position":6,"is_corresponding":false},{"id":227510,"name":"Thomas C. Quinn","orcid":"0000-0002-0404-1315","position":7,"is_corresponding":false},{"id":274661,"name":"T. Déirdre Hollingsworth","orcid":"0000-0001-5962-4238","position":8,"is_corresponding":false},{"id":274656,"name":"Simon E. F. Spencer","orcid":"0000-0002-8375-5542","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":null,"created_at":"2026-07-18T23:54:30.292454Z","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":[]}