{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T15:39:02Z","timestamp":1779291542702,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T00:00:00Z","timestamp":1717027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad Peruana Uni\u00f3n"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Amid the COVID-19 pandemic, understanding the spatial and temporal dynamics of the disease is crucial for effective public health interventions. This study aims to analyze COVID-19 data in Peru using a Bayesian spatio-temporal generalized linear model to elucidate mortality patterns and assess the impact of vaccination efforts. Leveraging data from 194 provinces over 651 days, our analysis reveals heterogeneous spatial and temporal patterns in COVID-19 mortality rates. Higher vaccination coverage is associated with reduced mortality rates, emphasizing the importance of vaccination in mitigating the pandemic\u2019s impact. The findings underscore the value of spatio-temporal data analysis in understanding disease dynamics and guiding targeted public health interventions.<\/jats:p>","DOI":"10.3390\/e26060474","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T08:15:54Z","timestamp":1717056954000},"page":"474","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1697-0514","authenticated-orcid":false,"given":"C\u00e9sar Ra\u00fal Castro","family":"Galarza","sequence":"first","affiliation":[{"name":"Escuela de Posgrado, Universidad Peruana Uni\u00f3n, Lima 15468, Peru"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6276-1145","authenticated-orcid":false,"given":"Omar Nolberto D\u00edaz","family":"S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Escuela de Posgrado, Universidad Peruana Uni\u00f3n, Lima 15468, Peru"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2982-5383","authenticated-orcid":false,"given":"Jonatha Sousa","family":"Pimentel","sequence":"additional","affiliation":[{"name":"Department of Statistics, Federal University of Pernambuco, Recife 50740-540, PE, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3668-6860","authenticated-orcid":false,"given":"Rodrigo","family":"Bulh\u00f5es","sequence":"additional","affiliation":[{"name":"Department of Statistics, Federal University of Bahia, Salvador 40170-110, BA, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0847-0552","authenticated-orcid":false,"given":"Javier Linkolk","family":"L\u00f3pez-Gonzales","sequence":"additional","affiliation":[{"name":"Escuela de Posgrado, Universidad Peruana Uni\u00f3n, Lima 15468, Peru"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1248-9910","authenticated-orcid":false,"given":"Paulo Canas","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Department of Statistics, Federal University of Bahia, Salvador 40170-110, BA, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1620\/tjem.250.271","article-title":"The coronavirus disease 2019 (COVID-19) pandemic","volume":"250","author":"Baloch","year":"2020","journal-title":"Tohoku J. 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