{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T18:56:01Z","timestamp":1772218561504,"version":"3.50.1"},"reference-count":63,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["IF2018\/CP1384\/IST-ID\/175\/2018"],"award-info":[{"award-number":["IF2018\/CP1384\/IST-ID\/175\/2018"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["DSAIPA\/DS\/0115\/2020"],"award-info":[{"award-number":["DSAIPA\/DS\/0115\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UID\/ECI\/04028\/2020"],"award-info":[{"award-number":["UID\/ECI\/04028\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>During the SARS-CoV-2 pandemic, governments and public health authorities collected massive amounts of data on daily confirmed positive cases and incidence rates. These data sets provide relevant information to develop a scientific understanding of the pandemic\u2019s spatiotemporal dynamics. At the same time, there is a lack of comprehensive approaches to describe and classify patterns underlying the dynamics of COVID-19 incidence across regions over time. This seriously constrains the potential benefits for public health authorities to understand spatiotemporal patterns of disease incidence that would allow for better risk communication strategies and improved assessment of mitigation policies efficacy. Within this context, we propose an exploratory statistical tool that combines functional data analysis with unsupervised learning algorithms to extract meaningful information about the main spatiotemporal patterns underlying COVID-19 incidence on mainland Portugal. We focus on the timeframe spanning from August 2020 to March 2022, considering data at the municipality level. First, we describe the temporal evolution of confirmed daily COVID-19 cases by municipality as a function of time, and outline the main temporal patterns of variability using a functional principal component analysis. Then, municipalities are classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. Our findings reveal disparities in disease dynamics between northern and coastal municipalities versus those in the southern and hinterland. We also distinguish effects occurring during the 2020\u20132021 period from those in the 2021\u20132022 autumn-winter seasons. The results provide proof-of-concept that the proposed approach can be used to detect the main spatiotemporal patterns of disease incidence. The novel approach expands and enhances existing exploratory tools for spatiotemporal analysis of public health data.<\/jats:p>","DOI":"10.1371\/journal.pone.0297772","type":"journal-article","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T13:40:50Z","timestamp":1706794850000},"page":"e0297772","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7890-7708","authenticated-orcid":true,"given":"Manuel","family":"Ribeiro","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0677-079X","authenticated-orcid":true,"given":"Leonardo","family":"Azevedo","sequence":"additional","affiliation":[]},{"given":"Andr\u00e9 Peralta","family":"Santos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5939-2577","authenticated-orcid":true,"given":"Pedro","family":"Pinto Leite","sequence":"additional","affiliation":[]},{"given":"Maria Jo\u00e3o","family":"Pereira","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2024,2,1]]},"reference":[{"key":"pone.0297772.ref001","first-page":"157","article-title":"WHO declares COVID-19 a pandemic","author":"D Cucinotta","year":"2020","journal-title":"Acta Biomedica"},{"key":"pone.0297772.ref002","article-title":"COVID-19 seasonality in temperate countries","volume":"206","author":"F D\u2019Amico","year":"2022","journal-title":"Environ Res"},{"key":"pone.0297772.ref003","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s00168-021-01071-0","article-title":"The geography of COVID-19 in Sweden","volume":"68","author":"R Florida","year":"2022","journal-title":"Annals of Regional Science"},{"key":"pone.0297772.ref004","doi-asserted-by":"crossref","DOI":"10.1016\/j.sste.2022.100493","article-title":"Spatiotemporal clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland","volume":"41","author":"M Siljander","year":"2022","journal-title":"Spat Spatiotemporal Epidemiol"},{"key":"pone.0297772.ref005","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1093\/eurpub\/ckab192","article-title":"Community socioeconomic deprivation and SARS-CoV-2 infection risk: findings from Portugal","volume":"32","author":"JPM Magalh\u00e3es","year":"2022","journal-title":"Eur J Public Health"},{"key":"pone.0297772.ref006","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1080\/17441692.2020.1783340","article-title":"The comparative politics of COVID-19: The need to understand government responses","author":"SL Greer","year":"2020","journal-title":"Global Public Health"},{"key":"pone.0297772.ref007","first-page":"361","volume-title":"Coronavirus Politics: The Comparative Politics and Policy of COVID-19","author":"A Peralta-Santos","year":"2021"},{"key":"pone.0297772.ref008","doi-asserted-by":"crossref","DOI":"10.1016\/j.spasta.2021.100544","article-title":"Spatio-temporal modelling of COVID-19 incident cases using Richards\u2019 curve: An application to the Italian regions","volume":"49","author":"M Mingione","year":"2022","journal-title":"Spat Stat"},{"key":"pone.0297772.ref009","doi-asserted-by":"crossref","first-page":"140033","DOI":"10.1016\/j.scitotenv.2020.140033","article-title":"Spatial analysis and GIS in the study of COVID-19. 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R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.; 2021."},{"key":"pone.0297772.ref028","unstructured":"Ramsay JO, Graves S, Hooker G. fda: Functional Data Analysis. 2021. https:\/\/CRAN.R-project.org\/package=fda"},{"key":"pone.0297772.ref029","unstructured":"Giraldo R, Delicado P, Mateu J. geofd: Spatial Prediction for Function Value Data. 2020. https:\/\/CRAN.R-project.org\/package=geofd"},{"key":"pone.0297772.ref030","unstructured":"Wickham H. ggplot2. 2nd ed. New York, NY: Springer New York; 2016."},{"key":"pone.0297772.ref031","unstructured":"Instituto Nacional de Estat\u00edstica I\u2013P. Statistics Portugal, Population and housing census\u20142021. Lisbon; 2022 Nov."},{"key":"pone.0297772.ref032","doi-asserted-by":"crossref","DOI":"10.2807\/1560-7917.ES.2022.27.23.2100497","article-title":"Impact of stringent non-pharmaceutical interventions applied during the second and third COVID-19 epidemic waves in Portugal, 9 November 2020 to 10 February 2021: an ecological study","volume":"27","author":"AR Torres","year":"2022","journal-title":"Eurosurveillance"},{"key":"pone.0297772.ref033","first-page":"291","article-title":"The Unlikely Saviour: Portugal\u2019s National Health System and the Initial Impact of the COVID-19 Pandemic?","volume":"63","author":"J Varanda","year":"2020","journal-title":"Development (Basingstoke)"},{"key":"pone.0297772.ref034","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.1038\/s41591-020-1112-0","article-title":"Magnitude, demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries","volume":"26","author":"V Kontis","year":"2020","journal-title":"Nat Med"},{"key":"pone.0297772.ref035","doi-asserted-by":"crossref","DOI":"10.1002\/9781119387916","volume-title":"Geostatistical Functional Data Analysis","author":"J Mateu","year":"2022"},{"key":"pone.0297772.ref036","doi-asserted-by":"crossref","DOI":"10.1201\/9781315117416","volume-title":"Introduction to Functional Data Analysis","author":"P Kokoszka","year":"2017"},{"key":"pone.0297772.ref037","doi-asserted-by":"crossref","DOI":"10.1007\/b98888","volume-title":"Functional Data Analysis","author":"JO Ramsay","year":"2005","edition":"2"},{"key":"pone.0297772.ref038","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-98185-7","volume-title":"Functional Data Analysis with R and MATLAB","author":"J Ramsay","year":"2009"},{"key":"pone.0297772.ref039","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1002\/env.1003","article-title":"Statistics for spatial functional data: Some recent contributions","volume":"21","author":"P Delicado","year":"2010","journal-title":"Environmetrics"},{"key":"pone.0297772.ref040","first-page":"1","article-title":"Review of functional data analysis","author":"J-L Wang","year":"2015","journal-title":"Annual Review of Statistics"},{"key":"pone.0297772.ref041","first-page":"49","author":"CA Calder","year":"2009","journal-title":"Kriging and Variogram Models"},{"key":"pone.0297772.ref042","doi-asserted-by":"crossref","first-page":"5","DOI":"10.20344\/amp.18528","article-title":"SARS-CoV-2 Seroprevalence following a Large-Scale Vaccination Campaign in Portugal: Results of the National Serological Survey, September\u2014November 2021","volume":"36","author":"I Kislaya","year":"2023","journal-title":"Acta Med Port"},{"key":"pone.0297772.ref043","doi-asserted-by":"crossref","DOI":"10.1186\/s12879-020-05537-y","article-title":"Comparison of spatiotemporal characteristics of the COVID-19 and SARS outbreaks in mainland China","volume":"20","author":"X Zhang","year":"2020","journal-title":"BMC Infect Dis"},{"key":"pone.0297772.ref044","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pntd.0008875","article-title":"Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level","volume":"14","author":"R Ram\u00edrez-Aldana","year":"2020","journal-title":"PLoS Negl Trop Dis"},{"key":"pone.0297772.ref045","article-title":"Why Italy first? 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Functional data analysis: Exploratory tools on Covid-19 pandemic. AIP Conference Proceedings. 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