{"doi":"10.1093/molbev/msz106","title":"Estimating Epidemic Incidence and Prevalence from Genomic Data","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:p>Modern phylodynamic methods interpret an inferred phylogenetic tree as a partial transmission chain providing information about the dynamic process of transmission and removal (where removal may be due to recovery, death, or behavior change). Birth–death and coalescent processes have been introduced to model the stochastic dynamics of epidemic spread under common epidemiological models such as the SIS and SIR models and are successfully used to infer phylogenetic trees together with transmission (birth) and removal (death) rates. These methods either integrate analytically over past incidence and prevalence to infer rate parameters, and thus cannot explicitly infer past incidence or prevalence, or allow such inference only in the coalescent limit of large population size. Here, we introduce a particle filtering framework to explicitly infer prevalence and incidence trajectories along with phylogenies and epidemiological model parameters from genomic sequences and case count data in a manner consistent with the underlying birth–death model. After demonstrating the accuracy of this method on simulated data, we use it to assess the prevalence through time of the early 2014 Ebola outbreak in Sierra Leone.</jats:p>","journal":"Molecular Biology and Evolution","year":2019,"id":643134,"datarank":0.6610078870896381,"base_score":4.406719247264253,"endowment":4.406719247264253,"self_citation_contribution":0.6610078870896381,"citation_network_contribution":0.0,"self_endowment_contribution":0.6610078870896381,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":81,"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":1673224,"name":"Gabriel E Leventhal","orcid":"0000-0002-4463-166X","position":1,"is_corresponding":false},{"id":1673225,"name":"David A Rasmussen","orcid":null,"position":2,"is_corresponding":false},{"id":1646880,"name":"Alexei J Drummond","orcid":null,"position":3,"is_corresponding":false},{"id":109050,"name":"David Welch","orcid":"0000-0003-2784-496X","position":4,"is_corresponding":false},{"id":109909,"name":"Tanja Stadler","orcid":"0000-0001-6431-535X","position":5,"is_corresponding":false},{"id":1673223,"name":"Timothy G Vaughan","orcid":"0000-0001-6220-2239","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Estimating Epidemic Incidence and Prevalence from Genomic Data","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:p>Modern phylodynamic methods interpret an inferred phylogenetic tree as a partial transmission chain providing information about the dynamic process of transmission and removal (where removal may be due to recovery, death, or behavior change). Birth–death and coalescent processes have been introduced to model the stochastic dynamics of epidemic spread under common epidemiological models such as the SIS and SIR models and are successfully used to infer phylogenetic trees together with transmission (birth) and removal (death) rates. These methods either integrate analytically over past incidence and prevalence to infer rate parameters, and thus cannot explicitly infer past incidence or prevalence, or allow such inference only in the coalescent limit of large population size. Here, we introduce a particle filtering framework to explicitly infer prevalence and incidence trajectories along with phylogenies and epidemiological model parameters from genomic sequences and case count data in a manner consistent with the underlying birth–death model. After demonstrating the accuracy of this method on simulated data, we use it to assess the prevalence through time of the early 2014 Ebola outbreak in Sierra Leone.</jats:p>","is_dataset_classified":null,"base_score":4.406719247264253,"endowment":4.406719247264253,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"31058982","pmcid":"PMC6681632","openalex_id":"https://openalex.org/W2944032254","authors":[],"funders":[{"funder_name":"Royal Society of New Zealand","grant_id":"UOA1324","title":null},{"funder_name":"European Commission","grant_id":"335529","title":"New phylogenetic methods for inferring complex population dynamics"},{"funder_name":"Swiss National Science Foundation","grant_id":"162251","title":"Studying the evolutionary adaption of ecological interaction networks"},{"funder_name":"Human Frontiers Science Program","grant_id":"LT000643/2016-L","title":null},{"funder_name":"European Research Council","grant_id":"","title":null},{"funder_name":"Seventh Framework Program","grant_id":"","title":null}],"total_grants":6,"fwci":7.6686,"citation_percentile":0.98384736,"influential_citations":0,"citation_trend":[{"year":2018,"count":2},{"year":2019,"count":5},{"year":2020,"count":8},{"year":2021,"count":32},{"year":2022,"count":9},{"year":2023,"count":7},{"year":2024,"count":5},{"year":2025,"count":9},{"year":2026,"count":4}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://academic.oup.com/mbe/article-pdf/36/8/1804/29098817/msz106.pdf","host_type":"journal"},{"url":"https://academic.oup.com/mbe/article-pdf/36/8/1804/29098817/msz106.pdf","host_type":"publisher"},{"url":"http://academic.oup.com/mbe/advance-article-pdf/doi/10.1093/molbev/msz106/28745689/msz106.pdf","host_type":"publisher"},{"url":"http://academic.oup.com/mbe/article-pdf/36/8/1804/29098817/msz106.pdf","host_type":"publisher"},{"url":"https://doi.org/10.1093/molbev/msz106","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/31058982","host_type":"repository"},{"url":"http://hdl.handle.net/20.500.11850/341457","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6681632","host_type":"repository"},{"url":"https://doi.org/10.3929/ethz-b-000341457","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC6681632","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC6681632?pdf=render","host_type":"Europe_PMC"},{"url":"https://doi.org/10.1101/142570","host_type":""},{"url":"https://dx.doi.org/10.3929/ethz-b-000341457","host_type":""},{"url":"http://dx.doi.org/10.1093/molbev/msz106","host_type":""},{"url":"https://dx.doi.org/10.1101/142570","host_type":""},{"url":"https://dx.doi.org/10.1093/molbev/msz106","host_type":""},{"url":"https://sonar.ch/global/documents/271927","host_type":""},{"url":"http://dx.doi.org/10.1101/142570","host_type":""}],"fields_of_study":["Viral Infections and Outbreaks Research","COVID-19 epidemiological studies","Zoonotic diseases and public health"],"mesh_terms":["Bayes Theorem","Humans","Sierra Leone","Incidence","Prevalence","Molecular Epidemiology","Hemorrhagic Fever, Ebola","Genomics"],"keywords":["Coalescent theory","Biology","Birth–death process","Phylogenetic tree","Inference","Population","Viral phylodynamics","Transmission (telecommunications)","Incidence (geometry)","Evolutionary biology","Most recent common ancestor","Statistics","Demography","Genetics","Computer science","Mathematics","Artificial intelligence","epidemiology","Phylodynamics","Particle Filter","Bayesian Phylogenetics","Molecular Epidemiology","Incidence","Bayes Theorem","Genomics","Hemorrhagic Fever, Ebola","Sierra Leone","phylodynamics; particle filter; epidemiology; Bayesian phylogenetics","Methods","Prevalence","Humans"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. 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