{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Huber JH"],"funding":["national institute of allergy and infectious diseases","national science foundation","NIAID NIH HHS","bill and melinda gates foundation","university of notre dame"],"pagination":["58"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8860266"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["21(1)"],"pubmed_abstract":["<h4>Background</h4>Inference of person-to-person transmission networks using surveillance data is increasingly used to estimate spatiotemporal patterns of pathogen transmission. Several data types can be used to inform transmission network inferences, yet the sensitivity of those inferences to different data types is not routinely evaluated.<h4>Methods</h4>The influence of different combinations of spatial, temporal, and travel-history data on transmission network inferences for Plasmodium falciparum malaria were evaluated.<h4>Results</h4>The information content of these data types may be limited for inferring person-to-person transmission networks and may lead to an overestimate of transmission. Only when outbreaks were temporally focal or travel histories were accurate was the algorithm "],"journal":["Malaria journal"],"pubmed_title":["Inferring person-to-person networks of Plasmodium falciparum transmission: are analyses of routine surveillance data up to the task?"],"pmcid":["PMC8860266"],"funding_grant_id":["1132226","K23 AI076614","Peggy Notebaert Premier Fellowship","Richard","AI101012","K24 AI144048","DGE-1841556"],"pubmed_authors":["Huber JH","Greenhouse B","Hsiang MS","Murphy M","Nhlabathi N","Nielsen R","Lerch A","Dlamini N","Vilakati S","Ntshalintshali N","Perkins TA"],"additional_accession":[]},"is_claimable":false,"name":"Inferring person-to-person networks of Plasmodium falciparum transmission: are analyses of routine surveillance data up to the task?","description":"<h4>Background</h4>Inference of person-to-person transmission networks using surveillance data is increasingly used to estimate spatiotemporal patterns of pathogen transmission. Several data types can be used to inform transmission network inferences, yet the sensitivity of those inferences to different data types is not routinely evaluated.<h4>Methods</h4>The influence of different combinations of spatial, temporal, and travel-history data on transmission network inferences for Plasmodium falciparum malaria were evaluated.<h4>Results</h4>The information content of these data types may be limited for inferring person-to-person transmission networks and may lead to an overestimate of transmission. Only when outbreaks were temporally focal or travel histories were accurate was the algorithm ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Feb","modification":"2025-04-22T12:52:33.74Z","creation":"2024-11-15T17:04:47.312Z"},"accession":"S-EPMC8860266","cross_references":{"pubmed":["35189905"],"doi":["10.1186/s12936-022-04072-2"]}}