Predicción con modelo ARIMA en series temporales de Salmonella spp en Chile entre 2014-2022

Resumen

Introducción. Las Enfermedades Transmitidas por Alimentos ( ETA), constituyen un problema de salud pública de relevancia mundial, siendo motivo de vigilancia epidemiológica y son el resultado del consumo de alimentos que contienen toxinas o microorganismos patógenos vivos. El objetivo de este estudio es analizar las series temporales de Salmonella spp. para el periodo 2014-2022 en Chile y desarrollar un modelo predictivo de Media Móvil Autorregresiva  (ARIMA). Métodos. Se realizó una descomposición de la serie para estudiar tendencia y estacionalidad. Se utilizó la prueba de Dickey-Fuller para estacionalidad y Kruskall-Wallis para comparación de grupos. Se aplicó el modelo ARIMA para realizar una predicción de casos en un año adelante. Resultados. La serie de estudio para Salmonella spp. tuvo un comportamiento estacional sin diferencias significativas entre grupos (periodos). El modelo ARIMA tuvo un buen desempeño para predecir casos en una serie continua. Conclusiones. El análisis de series temporales en epidemiología es una herramienta valiosa para prever futuros brotes o epidemias en el territorio nacional. El modelo ARIMA tiene un buen desempeño en la serie estacional analizada para Salmonella spp en muestras de deposición.

Palabras clave: salmonella, series temporales, ARIMA

Descargas

La descarga de datos todavía no está disponible.

Citas

Akil, L., & Ahmad, H. A. (2016). Salmonella infections modelling in Mississippi using neural network and geographical information system (GIS). BMJ Open, 6(3), e009255. https://doi.org/10.1136/BMJOPEN-2015-009255

Benedick, P. lou, Robert, J., & Traon, Y. le. (2021). A Systematic Approach for Evaluating Artificial Intelligence Models in Industrial Settings. Sensors (Basel, Switzerland), 21(18). https://doi.org/10.3390/S21186195

Calle, A., Carrascal, A. K., Patiño, C., Carpio, C., Echeverry, A., & Brashears, M. (2021). Seasonal effect on Salmonella, Shiga toxin-producing E. coli O157:H7 and non-O157 in the beef industry in Colombia, South America. Heliyon, 7(7), e07547. https://doi.org/10.1016/J.HELIYON.2021.E07547

Chao, D. L., Roose, A., Roh, M., Kotloff, K. L., & Proctor, J. L. (2019). The seasonality of diarrheal pathogens: A retrospective study of seven sites over three years. PLOS Neglected Tropical Diseases, 13(8), e0007211. https://doi.org/10.1371/JOURNAL.PNTD.0007211

Chile, BCN. B. del C. N. de. (2020). Reportes Estadísticos Biblioteca del Congreso Nacional de Chile. https://www.bcn.cl/siit/reportescomunales/

Delahoy, M. J., Cárcamo, C., Huerta, A., Lavado, W., Escajadillo, Y., Ordoñez, L., Vasquez, V., Lopman, B., Clasen, T., Gonzales, G. F., Steenland, K., & Levy, K. (2021). Meteorological factors and childhood diarrhea in Peru, 2005–2015: a time series analysis of historic associations, with implications for climate change. Environmental Health: A Global Access Science Source, 20(1), 1–10. https://doi.org/10.1186/S12940-021-00703-4/TABLES/3

Du, Y., Wang, H., Cui, W., Zhu, H., Guo, Y., Dharejo, F. A., & Zhou, Y. (2021). Foodborne Disease Risk Prediction Using Multigraph Structural Long Short-term Memory Networks: Algorithm Design and Validation Study. JMIR Medical Informatics, 9(8). https://doi.org/10.2196/29433

Flores Monter, Y. M., Chaves, A., Arellano-Reynoso, B., López-Pérez, A. M., Suzán-Azpiri, H., & Suzán, G. (2021). Edaphoclimatic seasonal trends and variations of the Salmonella spp. infection in Northwestern Mexico. Infectious Disease Modelling, 6, 805. https://doi.org/10.1016/J.IDM.2021.05.002

Fong, K., & Wang, S. (2016). Heat resistance of Salmonella enterica is increased by pre-adaptation to peanut oil or sub-lethal heat exposure. Food Microbiology, 58, 139–147. https://doi.org/10.1016/J.FM.2016.04.004

Gut, A. M., Vasiljevic, T., Yeager, T., & Donkor, O. N. (2018). Salmonella infection – Prevention and treatment by antibiotics and probiotic yeasts: A review. Microbiology (United Kingdom), 164(11), 1327–1344. https://doi.org/10.1099/MIC.0.000709/CITE/REFWORKS

Huang, L. (2004). Thermal Resistance of Listeria monocytogenes, Salmonella Heidelberg, and Escherichia coli O157:H7 at Elevated Temperatures. Journal of Food Protection, 67(8), 1666–1670. https://doi.org/10.4315/0362-028X-67.8.1666

Lamas, A., Miranda, J. M., Regal, P., Vázquez, B., Franco, C. M., & Cepeda, A. (2018). A comprehensive review of non-enterica subspecies of Salmonella enterica. Microbiological Research, 206, 60–73. https://doi.org/10.1016/J.MICRES.2017.09.010

Li, X., Singh, N., Beshearse, E., Blanton, J. L., DeMent, J., & Havelaar, A. H. (2021). Spatial Epidemiology of Salmonellosis in Florida, 2009–2018. Frontiers in Public Health, 8, 1001. https://doi.org/10.3389/FPUBH.2020.603005/BIBTEX

Martin-Moreno, J. M., Alegre-Martinez, A., Martin-Gorgojo, V., Alfonso-Sanchez, J. L., Torres, F., & Pallares-Carratala, V. (2022). Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic. International Journal of Environmental Research and Public Health, 19(9), 5546. https://doi.org/10.3390/IJERPH19095546

Naumova, E. N., Jagai, J. S., Matyas, B., DeMaria, A., MacNeill, I. B., & Griffiths, J. K. (2007). Seasonality in six enterically transmitted diseases and ambient temperature. Epidemiology and Infection, 135(2), 281. https://doi.org/10.1017/S0950268806006698

Odeyemi, O. A. (2016). Public health implications of microbial food safety and foodborne diseases in developing countries. Food & Nutrition Research, 60(1). https://doi.org/10.3402/FNR.V60.29819

Payedimarri, A. B., Concina, D., Portinale, L., Canonico, M., Seys, D., Vanhaecht, K., & Panella, M. (2021). Prediction models for public health containment measures on covid-19 using artificial intelligence and machine learning: A systematic review. International Journal of Environmental Research and Public Health, 18(9), 4499. https://doi.org/10.3390/IJERPH18094499/S1

Popa, G. L., & Popa, M. I. (2021). Salmonella spp. infection - a continuous threat worldwide. Germs, 11(1), 88. https://doi.org/10.18683/GERMS.2021.1244

Pública, I. de S. (2019). Boletin Epidemiológico Trimestral. Brotes de Enfermedades Transmitidas por Alimentos (ETA). Ministerio de Salud, Chile, 1(1), 12. http://epi.minsal.cl/wp-content/uploads/2020/02/BET_ETA_2019.pdf

Sánchez-Vargas, F. M., Abu-El-Haija, M. A., & Gómez-Duarte, O. G. (2011). Salmonella infections: An update on epidemiology, management, and prevention. Travel Medicine and Infectious Disease, 9(6), 263–277. https://doi.org/10.1016/J.TMAID.2011.11.001

Thomas, J. L., Slawson, R. M., & Taylor, W. D. (2013). Salmonella serotype diversity and seasonality in urban and rural streams. Journal of Applied Microbiology, 114(3), 907–922. https://doi.org/10.1111/JAM.12079

Vereen, E., Lowrance, R. R., Jenkins, M. B., Adams, P., Rajeev, S., & Lipp, E. K. (2013). Landscape and seasonal factors influence Salmonella and Campylobacter prevalence in a rural mixed use watershed. Water Research, 47(16), 6075–6085. https://doi.org/10.1016/J.WATRES.2013.07.028

Wang, H., Cui, W., Guo, Y., Du, Y., & Zhou, Y. (2021). Machine Learning Prediction of Foodborne Disease Pathogens: Algorithm Development and Validation Study. JMIR Medical Informatics, 9(1). https://doi.org/10.2196/24924

Weller, D. L., Love, T. M. T., Belias, A., & Wiedmann, M. (2020). Predictive Models May Complement or Provide an Alternative to Existing Strategies for Assessing the Enteric Pathogen Contamination Status of Northeastern Streams Used to Provide Water for Produce Production. Frontiers in Sustainable Food Systems, 4, 151. https://doi.org/10.3389/FSUFS.2020.561517/BIBTEX

Xu, Z., Hu, W., Zhang, Y., Wang, X., Zhou, M., Su, H., Huang, C., Tong, S., & Guo, Q. (2015). Exploration of diarrhoea seasonality and its drivers in China. Scientific Reports, 5. https://doi.org/10.1038/SREP08241

Yang, C. C. (2022). Explainable Artificial Intelligence for Predictive Modeling in Healthcare. Journal of Healthcare Informatics Research, 6(2), 228. https://doi.org/10.1007/S41666-022-00114-1

Yang, Y., Khoo, W. J., Zheng, Q., Chung, H. J., & Yuk, H. G. (2014). Growth temperature alters Salmonella Enteritidis heat/acid resistance, membrane lipid composition and stress/virulence related gene expression. International Journal of Food Microbiology, 172, 102–109. https://doi.org/10.1016/J.IJFOODMICRO.2013.12.006

Publicado
2023-01-23
Cómo citar
Ayala-Aldana, N., Monleon-Getino, A., Canela-Soler, J., & Retamal-Contreras, E. (2023). Predicción con modelo ARIMA en series temporales de Salmonella spp en Chile entre 2014-2022. Ciencia Latina Revista Científica Multidisciplinar, 7(1), 1337-1351. https://doi.org/10.37811/cl_rcm.v7i1.4484
Sección
Artículos

Artículos más leídos del mismo autor/a