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dc.rights.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.contributor.authorLópez-Montenegro L.E.
dc.contributor.authorPulecio-Montoya A.M.
dc.contributor.authorMarcillo-Hernández G.A.
dc.date.accessioned2024-12-02T20:16:00Z
dc.date.available2024-12-02T20:16:00Z
dc.date.issued2019
dc.identifier.issn15557960
dc.identifier.urihttps://hdl.handle.net/20.500.14112/29006
dc.description.abstractINTRODUCTION Dengue is a disease caused by any one of five virus serotypes and transmitted to humans by the Aedes aegypti mosquito. Climate change and health conditions have combined to make dengue a global public health problem. The situation is especially serious in Colombia, where by week 36 of 2018, dengue incidence was 96 cases per 100,000 population, with a total of 111 deaths. Different mathematical and statistical models have been proposed to understand the dynamics of transmission and consequently to apply control strategies to reduce the number of dengue cases. OBJECTIVE Forecast the number of dengue cases expected in Colombia from 2018 through 2022 with the stochastic Auto-Regressive Integrated Moving Average (ARIMA) model and use the results to adjust the parameters of an ordinary differential equations model in order to determine the disease’s basic reproduction number in the year presenting the highest number of dengue cases. METHODS An ecological time series study was conducted to forecast dengue incidence in Colombia from 2018 through 2022. The data were compiled from Colombia’s National Health Institute series on dengue cases reported by epidemiological week from 2009 to 2017. The stochastic ARIMA time series model was applied. Forecasts were then analyzed, and the year with the highest number of predicted cases was used to adjust the parameters of an ordinary differential equations model (ODE) through nonlinear least squares regression to calculate the vectorial capacity of the transmitting mosquito. RESULTS Forecasts of the total number of dengue cases per year in Colombia for the following five years were: 32,411 (2018), 88,221 (2019), 56,392 (2020), 47,940 (2021), and 77,344 (2022). The highest number of cases was forecast for 2019. Values for the parameters affecting dengue transmission that year (by the year’s four quarters), such as recovery rate (0.0992, 0.0838, 0.1177, and 0.1535, respectively), vectorial capacity of the transmitting mosquito (0.1720, 0.1705, 0.1204, and 0.2147, respectively) and the basic dengue reproduction number (1.73, 2.03, 1.02, and 1.40, respectively) were estimated, indicating that most cases would occur in the second quarter and, since the basic reproduction number values were >1, the disease would persist in the country throughout the entire year. CONCLUSIONS ARIMA model forecasts for 2018 through 2022 predicted the highest incidence of dengue cases in Colombia would occur in 2019. Comparison of ARIMA model forecasts and the ODE model allowed projections of possible variations in dengue cases reported, and the basic reproduction number predicted that dengue would persist throughout 2019. © 2019 MEDICC Medical Education Cooperation with Cuba. All rights reserved.
dc.format7
dc.format.mediumRecurso electrónico
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherMEDICC Medical Education Cooperation with Cuba
dc.rights.uriAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
dc.sourceMEDICC Review
dc.sourceMEDICC Rev.
dc.sourceScopus
dc.titleDengue cases in Colombia: Mathematical forecasts for 2018–2022
datacite.contributorScientific Implementation of General Mathematical Applications (SIGMA), Maria Goretti Center for Advanced Studies (CESMAG), San Juan de Pasto, Colombia
datacite.contributorSIGMA research group, CESMAG, San Juan de Pasto, Colombia
datacite.contributorFORMA Research Group, Mariana University, San Juan de Pasto, Colombia
datacite.contributorLópez-Montenegro L.E., Scientific Implementation of General Mathematical Applications (SIGMA), Maria Goretti Center for Advanced Studies (CESMAG), San Juan de Pasto, Colombia
datacite.contributorPulecio-Montoya A.M., SIGMA research group, CESMAG, San Juan de Pasto, Colombia
datacite.contributorMarcillo-Hernández G.A., FORMA Research Group, Mariana University, San Juan de Pasto, Colombia
datacite.rightshttp://purl.org/coar/access_right/c_abf2
oaire.resourcetypehttp://purl.org/coar/resource_type/c_6501
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.contributor.contactpersonL.E. López-Montenegro
dc.contributor.contactpersonScientific Implementation of General Mathematical Applications (SIGMA), Maria Goretti Center for Advanced Studies (CESMAG), San Juan de Pasto, Colombia
dc.contributor.contactpersonemail: lelopez@iucesmag.edu.co
dc.identifier.instnameUniversidad Mariana
dc.identifier.pissn31373583
dc.identifier.reponameRepositorio Clara de Asis
dc.identifier.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85070506510&partnerID=40&md5=ea110395d45d7c99238fa590904811c5
dc.relation.citationendpage45
dc.relation.citationstartpage38
dc.relation.citationvolume21
dc.relation.iscitedby7
dc.relation.referencesRossati A., Global warming and its health impact, Int J Occupational Environ Med, 8, 1, pp. 7-20, (2017)
dc.relation.referencesMustafa M., Rasotgi V., Jain S., Gupta V., Discovery of fifth serotype of dengue virus (DENV-5): A new public health dilemma in dengue control, Med J Armed Forces India, 71, 1, pp. 67-70, (2015)
dc.relation.referencesPerez-Castro R., Castellanos J.E., Olano V.A., Matiz M.I., Jaramillo J.F., Vargas S.L., Et al., Detection of all four dengue serotypes in Aedes aegypti female mosquitoes collected in a rural area in Colombia, Mems Inst Oswaldo Cruz, 111, 4, pp. 233-240, (2016)
dc.relation.referencesMuller D.A., Depelsenaire A.C., Young P.R., Clinical and laboratory diagnosis of dengue virus infection, J Infect Dis, 215, pp. S89-S95, (2017)
dc.relation.referencesSoo K.M., Khalid B., Ching S.M., Chee H.Y., Meta-analysis of dengue severity during infection by different dengue virus serotypes in primary and secondary infections, PLoS One, 11, 5, (2016)
dc.relation.referencesAnantapreecha S., Chanama S., A-Nuegoonpipat A., Naemkhunthot S., Sa-Ngasang A., Sawanpany-Alert P., Et al., Serological and virological features of dengue fever and dengue haemorrhagic fever in Thailand from 1999 to 2002, Epidemiol Infect, 133, 3, pp. 503-507, (2005)
dc.relation.referencesLopez L.E., Loaiza Munoz A., Olivar Tost G., A mathematical model for transmission of dengue, App Mathematical Sci, 10, 7, pp. 345-355, (2016)
dc.relation.referencesAmaku M., Coutinho F., Raimundo S., Lopez L., Burattini M., Massad E., A comparative analysis of the relative efficacy of vector-control strategies against dengue fever, Bull Math Biol, 76, 3, pp. 697-717, (2014)
dc.relation.referencesKoiller J., Da Silva M.A., Souza M.O., Codeco C., Ig-Gidr A., Sallet G., Aedes, Wolbachia and Dengue, (2014)
dc.relation.referencesBox G.E.P., Jenkins G.M., Reinsel G.C., Ljung G.M., Time Series Analysis: Forecasting and Control, (2015)
dc.relation.referencesWang K.W., Deng C., Li J.P., Zhang Y.Y., Li X.Y., Wu M.C., Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network, Epidemiol Infection, 145, 6, pp. 1118-1129, (2017)
dc.relation.referencesSierra W., Argoty C., Franco H., Varicella incidence rate forecasting in Bogotá DC (Colombia) by stochastic time series analysis, Workshop on Engineering Applications, pp. 647-658, (2017)
dc.relation.referencesPan Y., Zhang M., Chen Z., Zhou M., Zhang Z., An ARIMA based model for forecasting the patient number of epidemic disease, Service Systems and Service Management (ICSSSM),, pp. 1-4, (2016)
dc.relation.referencesDinh T.Q., Le H.V., Cao T.H., Luong Q.C., Diep H.T., Forecasting the magnitude of dengue in Southern Vietnam, Asian Conference on Intelligent Information and Database Systems, pp. 554-563, (2016)
dc.relation.referencesBoletín Epidemiológico Semana 23 Del 2018, (2018)
dc.relation.referencesBoletín Epidemiológico Semana 36 Del 2018, (2018)
dc.relation.referencesPadilla J.C., Lizarazo F.E., Murillo O.L., Mendigana F.A., Pachon E., Vera M.J., Epidemiología de las principales enfermedades transmitidas por vectores en Colombia, 1990‒2016, Biomédica, 37, pp. 27-40, (2017)
dc.relation.referencesCryer J.D., Chan K.S., Time Series Analysis with Applications in R, (2008)
dc.relation.referencesBaayen R.H., Analyzing Linguistic Data: A Practical Introduction to Statistics Using R, (2008)
dc.relation.referencesGarcia Diaz J.C., Predicción en el dominio del tiempo, Análisis De Series Temporales para Inge-Nieros, (2016)
dc.relation.referencesZill D., Cullen M., Ecuaciones Diferenciales, (2013)
dc.relation.referencesBustamam A., Aldila D., Yuwanda A., Understanding dengue control for short-and long-term intervention with a mathematical model approach, J App Math, (2018)
dc.relation.referencesSardar T., Rana S., Chattopadhyay J., A mathematical model of dengue transmission with memory, Comm Nonlinear Sci Numer Simulat, 22, 1-3, pp. 511-525, (2015)
dc.relation.referencesProyecciones Naciona-Les Y Departamentales De Población 2005‒2020. Estudios Post-Censales, (2010)
dc.relation.referencesBasanez M.G., Rodriguez D.J., Dinámica de trans-misión y modelos matemáticos en enfermedades transmitidas por vectores, Entomotrópica, 19, 3, pp. 113-134, (2004)
dc.relation.referencesLiu-Helmersson J., Stenlund H., Wilder-Smith A., Rocklov J., Vectorial capacity of Aedes aegypti: Effects of temperature and implications for global dengue epidemic potential, PLoS One, 9, 3, (2014)
dc.relation.referencesBrauer F., Castillo-Chavez C., Mathematical Models in Population Biology and Epidemiology, (2012)
dc.relation.referencesMATLAB: Application Program Interface Guide, (1996)
dc.relation.referencesCortes F., Turchi Martelli C.M., Arraes de Alencar Ximenes R., Montarrouos U.R., Siqueira Junior J.B., Goncalves Cruz O., Et al., Time series analysis of dengue surveillance data in two Brazilian cities, Acta Trop, 182, pp. 190-197, (2018)
dc.relation.referencesWithanage G.P., Viswakula S.D., Nilmini Silva Gunawardena Y.I., Hapugoda M.D., A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka, Parasit Vectors, 11, 1, (2018)
dc.relation.referencesSepulveda-Salcedo L.S., Vasilieva O., Martinez-Romero H.J., Arias-Castro J.H., Ross McDonald: Un modelo para la dinámica del dengue en Cali, Colombia, Rev Salud Pública, 17, 5, pp. 749-761, (2016)
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsArboviruses
dc.subject.keywordsBasic reproduction number
dc.subject.keywordsClimate
dc.subject.keywordsColombia
dc.subject.keywordsDengue
dc.subject.keywordsModels
dc.subject.keywordsPrognosis
dc.subject.keywordsTheoretical
dc.subject.keywordsColombia
dc.subject.keywordsDengue
dc.subject.keywordsForecasting
dc.subject.keywordsHumans
dc.subject.keywordsIncidence
dc.subject.keywordsInterrupted Time Series Analysis
dc.subject.keywordsModels, Statistical
dc.subject.keywordsPrevalence
dc.subject.keywordsadult
dc.subject.keywordsalgorithm
dc.subject.keywordsArticle
dc.subject.keywordsColombia
dc.subject.keywordsdengue
dc.subject.keywordsdisease transmission
dc.subject.keywordsfemale
dc.subject.keywordshuman
dc.subject.keywordsincidence
dc.subject.keywordsmajor clinical study
dc.subject.keywordsmale
dc.subject.keywordsmathematical analysis
dc.subject.keywordsnonhuman
dc.subject.keywordsprobability
dc.subject.keywordsprognosis
dc.subject.keywordsstochastic model
dc.subject.keywordstime series analysis
dc.subject.keywordsdengue
dc.subject.keywordsforecasting
dc.subject.keywordsprevalence
dc.subject.keywordsstatistical model
dc.type.driverinfo:eu-repo/semantics/article
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.type.redcolhttp://purl.org/redcol/resource_type/ART
dc.type.spaArtículo científico
dc.relation.citationissue2-3


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