Computational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamics

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-09-15T00:32:24Z
Fecha de publicación2020-06-24
ResumenThe study of infectious disease behavior has been a scientific concern for many years as early identification of outbreaks provides great advantages including timely implementation of public health measures to limit the spread of an epidemic. We propose a methodology that merges the predictions of (i) a computational model with machine learning, (ii) a projection model, and (iii) a proposed smoothed endemic channel calculation. The predictions are made on weekly acute respiratory infection (ARI) data obtained from epidemiological reports in Mexico, along with the usage of key terms in the Google search engine. The results obtained with this methodology were compared with state-of-the-art techniques resulting in reduced root mean squared percentage error (RMPSE) and maximum absolute percent error (MAPE) metrics, achieving a MAPE of 21.7%. This methodology could be extended to detect and raise alerts on possible outbreaks on ARI as well as for other seasonal infectious diseases.es_ES
Doihttps://doi.org/10.3390/ijerph17124540es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/3865
Idiomaenes_ES
EditorialMultidisciplinary Digital Publishing Institutees_ES
RelaciónInternational Journal of Environmental Research and Public Healthes_ES
URL relacionadohttps://doi.org/10.3390/ijerph17124540es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteInternational Journal of Environmental Research and Public Health
Palabra claveMean absolute percentage errores_ES
Palabra claveOutbreakes_ES
Palabra claveInfectious disease (medical specialty)es_ES
Palabra claveMean squared errores_ES
Palabra claveComputer sciencees_ES
Palabra claveStatisticses_ES
Palabra claveEpidemiologyes_ES
Palabra claveDiseasees_ES
Palabra claveMedicinees_ES
Palabra claveMachine learninges_ES
Palabra claveEconometricses_ES
Palabra claveMathematicses_ES
ClasificaciónData-Driven Disease Surveillancees_ES
TítuloComputational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamicses_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorBandala, Daniel Alejandro González
AutorCuevas‐Tello, Juan C.
AutorNoyola, Daniel E.
AutorComas‐García, Andreu
AutorGarcía‐Sepúlveda, Christian A.
AutorBandala, Daniel Alejandro Gonzálezes_ES
AutorCuevas‐Tello, Juan C.es_ES
AutorNoyola, Daniel E.es_ES
AutorComas‐García, Andreues_ES
AutorGarcía‐Sepúlveda, Christian A.es_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número12es_ES
Rango de páginas4540-4540es_ES
URL relacionadahttps://doi.org/10.3390/ijerph17124540
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen17es_ES

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