{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Pinotti F"],"funding":["MRC CiC 6","NIHR Senior Fellowship and the NIHR Biomedical Research Centre","European Research Council","National Institute for Health Research (NIHR)","Fundação de Amparo à Pesquisa of Rio de Janeiro","Department of Zoology, University of Oxford","Georg und Emily Von Opel Foundation","ERC ‘UNIFLUVAC’","Wellcome Trust","Biotechnology and Biological Sciences Research Council","UKRI GCRF One Health Poultry Hub"],"pagination":["5825"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7954847"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(1)"],"pubmed_abstract":["For endemic pathogens, seroprevalence mimics overall exposure and is minimally influenced by the time that recent infections take to seroconvert. Simulating spatially-explicit and stochastic outbreaks, we set out to explore how, for emerging pathogens, the mix of exponential growth in infection events and a constant rate for seroconversion events could lead to real-time significant differences in the total numbers of exposed versus seropositive. We find that real-time seroprevalence of an emerging pathogen can underestimate exposure depending on measurement time, epidemic doubling time, duration and natural variation in the time to seroconversion among hosts. We formalise mathematically how underestimation increases non-linearly as the host's time to seroconversion is ever longer than the "],"journal":["Scientific reports"],"pubmed_title":["Real-time seroprevalence and exposure levels of emerging pathogens in infection-naive host populations."],"pmcid":["PMC7954847"],"funding_grant_id":["WT109965MA","BB/S011269/1","109965/Z/15/Z","812816","Lectureship","NF-SI-0515-10005"],"pubmed_authors":["Giovanetti M","Thompson C","Lourenco J","Obolski U","Gupta S","Pinotti F","Paton R","Klenerman P","Wikramaratna P"],"additional_accession":[]},"is_claimable":false,"name":"Real-time seroprevalence and exposure levels of emerging pathogens in infection-naive host populations.","description":"For endemic pathogens, seroprevalence mimics overall exposure and is minimally influenced by the time that recent infections take to seroconvert. Simulating spatially-explicit and stochastic outbreaks, we set out to explore how, for emerging pathogens, the mix of exponential growth in infection events and a constant rate for seroconversion events could lead to real-time significant differences in the total numbers of exposed versus seropositive. We find that real-time seroprevalence of an emerging pathogen can underestimate exposure depending on measurement time, epidemic doubling time, duration and natural variation in the time to seroconversion among hosts. We formalise mathematically how underestimation increases non-linearly as the host's time to seroconversion is ever longer than the ","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Mar","modification":"2025-04-04T13:13:14.803Z","creation":"2021-03-18T08:31:02Z"},"accession":"S-EPMC7954847","cross_references":{"pubmed":["33712648"],"doi":["10.1038/s41598-021-84672-1"]}}