{"doi":"10.1214/25-ba1548","title":"Bayesian Analysis of Growth Curves for Epidemiological Longitudinal Studies","abstract":"Monitoring the spread of infectious diseases during an epidemic or pandemic presents a great challenge to public health systems worldwide. To facilitate informed decision-making, concerned parties usually rely on short-term daily or weekly projections generated via predictive modeling. Traditional epidemiological models, particularly growth models, are frequently employed with the assumption that epidemics or pandemics exhibit a single-peaked pattern in case numbers or mortality rates. However, this may not hold true for all diseases, as evidenced by the prolonged and multi-peaked nature of infectious diseases like COVID-19. Additionally, government interventions have led to time-varying disease transmission rates, making the observed data multi-modal. In response to these complexities, this paper presents BAGELS, a generalized Bayesian stochastic growth model augmented by a change-point detection mechanism to account for multiple peaks. A trans-dimensional reversible jump Markov chain Monte Carlo algorithm is used to sample the posterior distributions of interpretable epidemiological parameters while estimating the number and locations of change-points. We demonstrate the effectiveness of BAGELS through extensive evaluations using both simulated data and COVID-19 data from major U.S. states. Not only does this work offer a robust tool for longitudinal epidemiological studies, but it also advances the Bayesian change-point detection methodology for growth curves.","journal":"Bayesian Analysis","year":2025,"id":572600,"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":0.953,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1262660,"name":"Yanghong Guo","orcid":null,"position":1,"is_corresponding":false},{"id":372059,"name":"Yanxun Xu","orcid":"0000-0001-5554-8637","position":2,"is_corresponding":false},{"id":352535,"name":"Qiwei Li","orcid":"0000-0002-1020-3050","position":3,"is_corresponding":false},{"id":744830,"name":"Tejasv Bedi","orcid":"0000-0001-7532-4075","position":0,"is_corresponding":true}],"reference_count":33,"raw_metadata":null,"created_at":"2026-07-19T02:57:27.876396Z","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":[]}