{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T12:39:33Z","timestamp":1787315973247,"version":"build-2736575974"},"reference-count":30,"publisher":"PAGEPress Publications","issue":"2","license":[{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Geospat Health"],"abstract":"<jats:p>Leptospirosis is a serious public health problem in Brazil, which can be observed after flooding events. Using an exploratory mixed clustering method, this ecological study analyzes whether spatial-temporal clustering patterns of leptospirosis occur in Brazil. Data from the Brazilian Unified Health System (SUS) were used to calculate the prevalence of leptospirosis between 2007 and 2017 in all counties of the country. Clustering techniques, including spatial association indicators, were used for analysis and evaluation of disease yearly spatial distribution. Based on Local Indicators of Spatial Association (LISA) with Empirical Bayesian rates detected spatial patterns of leptospirosis ranging from 0.137 (p = 0.001 in 2009) to 0.293 (p = 0.001 in 2008). Over the whole period, the rate was 0.388 (p = 0.001). The main pattern showed permanence of leptospirosis clusters in the South and emergence and permanence of such clusters in northern Brazil. The municipalities with leptospirosis cases and at least one flood occurrence registered in the Brazilian Integrated Disaster Information System were incorporated into the LISA cluster map with Empirical Bayesian rates. These counties were expected to exhibit clustering, not all did. The results of the cluster analysis suggest allocation of health resources in areas with leptospirosis clustering.<\/jats:p>","DOI":"10.4081\/gh.2022.1128","type":"journal-article","created":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T09:03:15Z","timestamp":1669712595000},"source":"Crossref","is-referenced-by-count":13,"title":["Spatio-temporal analysis of leptospirosis in Brazil and its relationship with flooding"],"prefix":"10.4081","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3467-1190","authenticated-orcid":false,"given":"Alice","family":"Nardoni Marteli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8300-846X","authenticated-orcid":false,"given":"Laurindo Antonio","family":"Guasselli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0147-2061","authenticated-orcid":false,"given":"D\u00e9cio","family":"Diament","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8580-5814","authenticated-orcid":false,"given":"Gabriele Oz\u00f3rio","family":"Wink","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3063-2776","authenticated-orcid":false,"given":"Vitor Vieira","family":"Vasconcelos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2549","published-online":{"date-parts":[[2022,11,29]]},"reference":[{"key":"9061","doi-asserted-by":"publisher","DOI":"10.3201\/eid1801.101892"},{"key":"9062","doi-asserted-by":"publisher","DOI":"10.1111\/j.1538-4632.1995.tb00338.x"},{"key":"9063","unstructured":"Anselin, L. 2005. Exploring Spatial Data with GeoDaTM: A Workbook, University of Illinois, 244pp."},{"key":"9064","unstructured":"Anselin L, 2019. Global Spatial Autocorrelation (2) - Bivariate, Differential and EB Rate Moran Scatter Plot. Accessed: June 06, 2022. Available from: https:\/\/geodacenter.github.io\/workbook\/5b_global_adv\/lab5b.html#moran-scatter-plot-for-eb-rates"},{"key":"9065","unstructured":"Anselin L, 2020a. Global Spatial Autocorrelation. Accessed: 06 Febuary, 2022. Available from: https:\/\/geodacenter.github.io\/workbook\/5a_global_auto\/lab5a.html"},{"key":"9066","unstructured":"Anselin L, 2020b. Contiguity-Based Spatial Weights. Accessed: March 01, 2022. Available from: http:\/\/geodacenter.github.io\/workbook\/4a_contig_weights\/lab4a.html"},{"key":"9067","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-33381-3"},{"key":"9068","doi-asserted-by":"publisher","DOI":"10.1590\/S0102-311X2001000700014"},{"key":"9069","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0003898"},{"key":"9070","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0007507"},{"key":"9071","doi-asserted-by":"publisher","DOI":"10.3727\/108354213X13782245307830"},{"key":"9072","unstructured":"DATASUS, 2019a. Ministry of Health Brazil. Sistema de Informa\u00e7\u00e3o de Agravos de Notifica\u00e7\u00e3o. Leptospirose: Casos confirmados por munic\u00edpio de resid\u00eancia de 2007 \u2013 2017. Bras\u00edlia - DF. Accessed: May 20, 2019. Available from: http:\/\/www2.datasus.gov.br\/DATASUS\/index.php?area=0203&id=29892215&VObj=http:\/\/ta%0Abnet.data.gov.br\/cgi\/deftohtm.exe?sinannet\/cnv\/lepto"},{"key":"9073","unstructured":"DATASUS, 2019b. Ministry of Health Brazil. Departamento de Inform\u00e1tica do Sistema \u00danico de Sa\u00fade, Minist\u00e9rio da Sa\u00fade (Brasil). Doen\u00e7as e Agravos de Notifica\u00e7\u00e3o - De 2007 em diante (SINAN) Departamento de Inform\u00e1tica do Sistema \u00danico de Sa\u00fade \u2013 DATASUS. Bras\u00edlia - DF. Accessed: May 20, 2019. Available from: http:\/\/www2.datasus.gov.br\/DATASUS\/index.php"},{"key":"9074","unstructured":"DATASUS, 2019c. Ministry of Health Brazil. Sistema de Informa\u00e7\u00e3o de Agravos de Notifica\u00e7\u00e3o. Popula\u00e7\u00e3o residente por Munic\u00edpio e Ano. Bras\u00edlia, DF. Accessed: May 20, 2019. Available from: http:\/\/www2.datasus.gov.br\/DATASUS\/index.php?area=0206&id=6943&VObj=http:\/\/t"},{"key":"9075","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-35074-3"},{"key":"9076","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0247763"},{"key":"9077","doi-asserted-by":"publisher","DOI":"10.1590\/0102-311x00118417"},{"key":"9078","unstructured":"IBGE, 2019. Cidades. Brazilian Institute of Geography and Statistics, Brasilia \u2013 DF. Accessed: May 20, 2019. Available from: https:\/\/cidades.ibge.gov.br\/brasil\/panorama"},{"key":"9079","doi-asserted-by":"publisher","DOI":"10.1007\/s11069-016-2493-8"},{"key":"9080","doi-asserted-by":"publisher","DOI":"10.4081\/gh.2019.739"},{"key":"9081","doi-asserted-by":"publisher","DOI":"10.4081\/gh.2020.856"},{"key":"9082","doi-asserted-by":"publisher","DOI":"10.1590\/0103-1104202012616"},{"key":"9083","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0006331"},{"key":"9084","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1948.tb00012.x"},{"key":"9085","doi-asserted-by":"publisher","DOI":"10.4081\/gh.2014.35"},{"key":"9086","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0004039"},{"key":"9087","unstructured":"SEDEC, National Secretariat for Civil Defense and Protection, Brazil. Integrated Disaster Information System (S2ID), 2017. Casos registrados por evento (2013-2017). Accessed: November 02, 2019. Available from: https:\/\/s2id.mi.gov.br\/paginas\/relatorios\/"},{"key":"9088","doi-asserted-by":"publisher","DOI":"10.4081\/gh.2020.903"},{"key":"9089","doi-asserted-by":"publisher","DOI":"10.1017\/S0950268815000205"},{"key":"9090","unstructured":"WHO, 2003. Human leptospirosis: guidance for diagnosis, surveillance and control. World Health Organization and International Leptospirosis Society (ILS) ed., Malta."}],"container-title":["Geospatial Health"],"original-title":[],"link":[{"URL":"https:\/\/geospatialhealth.net\/index.php\/gh\/article\/download\/1128\/1200","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/geospatialhealth.net\/index.php\/gh\/article\/download\/1128\/1200","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T03:10:32Z","timestamp":1669777832000},"score":1,"resource":{"primary":{"URL":"https:\/\/geospatialhealth.net\/index.php\/gh\/article\/view\/1128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,29]]},"references-count":30,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,8,29]]}},"URL":"https:\/\/doi.org\/10.4081\/gh.2022.1128","relation":{},"ISSN":["1970-7096","1827-1987"],"issn-type":[{"value":"1970-7096","type":"electronic"},{"value":"1827-1987","type":"print"}],"subject":[],"published":{"date-parts":[[2022,11,29]]}}}