Palabras clave
Infecciones por Arbovirus
Vigilancia en Salud Pública
Predicción
Aprendizaje Automático
Bibliometría
Cómo citar
Resumen
Introducción: los métodos de aprendizaje automático permiten manejar datos estructurados y no estructurados para construir modelos predictivos y apoyar la toma de decisiones. Objetivo: identificar los métodos de aprendizaje automático aplicados para predecir el comportamiento epidemiológico de enfermedades arbovirales utilizando datos de vigilancia epidemiológica. Metodología: se realizó búsqueda en EMBASE y PubMed, análisis bibliométrico y síntesis de la información. Resultados: se seleccionaron 41 documentos, todos publicados en la última década. La palabra clave más frecuente fue dengue. La mayoría de los autores (88,3 %) participó en un artículo de investigación. Se encontraron16 métodos de aprendizaje automático, el más frecuente fue Red Neuronal Artificial seguido de Máquinas de Vectores de Soporte. Conclusiones: en la última década se incrementó la publicación de trabajos que pretenden predecir el comportamiento epidemiológico de arbovirosis por medio de diversos métodos de aprendizaje automático que incorporan series de tiempo de los casos, variables climatológicas, y otras fuentes de información de datos abiertos.
Referencias
World Health Organization. A Global Brief on Vector-Borne Diseases. WHO; 2014. Disponible en: https://apps.who.int/iris/bitstream/handle/10665/111008/WHO_DCO_WHD_2014.1_eng.pdf
Warpeha KM, Munster V, Mullié C, Chen SH. Editorial: Emerging Infectious and Vector-Borne Diseases: A Global Challenge. Front. Public Health [Internet]. 2020; 8 (214): 1-2. doi: https://doi.org/10.3389%2Ffpubh.2020.00214
Wilder-Smith A, Gubler DJ, Weaver SC, Monath TP, Heymann DL, Scott TW. Epidemic arboviral diseases: priorities for research and public health. Lancet Infect Dis [Internet]. 2017; 17(3): e101-e106. doi: https://doi.org/10.1016/s1473-3099(16)30518-7
Padilla JC, Lizarazo FE, Murillo OL, Mendigaña FA, Pachón E, Vera MJ. Epidemiología de las principales enfermedades transmitidas por vectores en Colombia, 1990-2016. Biomédica (Bogotá) [Internet]. 2017; 37(Sup2): 27-40. doi: https://doi.org/10.7705/biomedica.v37i0.3769
Instituto Nacional de Salud de Colombia. Vectores de Dengue – Chikungunya , Estado Actual. INS; 2014. Disponible en: https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/IA/INS/Zika-vector-22-mayo-2015-entomologia-vector.pdf
Mora-Salamanca AF, Porras-Ramírez A, De la Hoz Restrepo FP. Estimating the burden of arboviral diseases in Colombia between 2013 and 2016. Int J Infect Dis [Internet]. 2020; 97: 81-89. doi: https://doi.org/10.1016/j.ijid.2020.05.051
Hall HI, Correa A, Yoon PW, Braden CR, CDC. Lexicon, Definitions, and Conceptual Framework for Public Health Surveillance. MMWR Suppl. 2012. 2012; 61(3): 10-14.
Govindaraju V, Raghavan V, Rao CR. Handbook of Statistics Big Data Analytics. 2015. Vol 33.
Gandomi A, Haider M. Beyond the hype: Big data concepts, methods, and analytics. Int J Inf Manage [Internet]. 2015; 35(2): 137-144. doi: https://doi.org/10.1016/j.ijinfomgt.2014.10.007
Martínez Sesmero JM. “Big data”; application and use for the health system. Farm Hosp [Internet]. 2015; 39(2): 69-70. doi: http://dx.doi.org/10.7399/fh.2015.39.2.8835
Mcafee A, Brynjolfsson E. Spotlight on Big Data Big Data: The Management Revolution. Harv Bus Rev. 2012; (October): 1-9. Disponible en: http://tarjomefa.com/wp-content/uploads/2017/04/6539-English-TarjomeFa-1.pdf
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffman TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ. 2021; 372(71). doi: https://doi.org/10.1136/bmj.n71
Goodman LB, Whittaker GR. Public health surveillance of infectious diseases: beyond point mutations. The Lancet Microbe. 2021; 2(2): e53-e54. doi: https://doi.org/10.1016/S2666-5247(21)00003-3
Bowman LR, Runge-Ranzinger S, McCall PJ. Assessing the Relationship between Vector Indices and Dengue Transmission: A Systematic Review of the Evidence. PLoS Negl Trop Dis. 2014; 8(5). doi: https://doi.org/10.1371/journal.pntd.0002848
Benedum CM, Seidahmed OME, Eltahir EAB, Markuzon N. Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore. PLoS Negl Trop Dis [Internet]. 2018; 12(12): 1-18. doi: https://doi.org/10.1371/journal.pntd.0006935
Carroll L, Au A, Detwiler LT, Fu T, Painter I, Abernethy N. Visualization and Analytics Tools for Infectious Disease Epidemiology: A Systematic Review. J Biomed Inf [Internet]. 2014; 51: 287-298. doi: https://doi.org/10.1016/j.jbi.2014.04.006
Dwolatzky B, Trengove E, Struthers H, McIntyre JA, Martinson NA. Linking the global positioning system (GPS) to a personal digital assisstant (PDA) to support tuberculosis control in South Africa: A pilot study. Int J Health Geogr [Internet]. 2006; 5(34): 1-6. doi: https://doi.org/10.1186/1476-072x-5-34
Janies DA, Treseder T, Alexandrov B, Habib F, Chen JJ, Ferreira R,,et al. The Supramap project: Linking pathogen genomes with geography to fight emergent infectious diseases. Cladistics [Internet]. 2011; 27(1): 61-66. doi: https://doi.org/10.1111%2Fj.1096-0031.2010.00314.x
Grubaugh ND, Ladner JT, Kraemer MUG, Dudas G, Tan AL, Gangavarapu K, et al. Genomic epidemiology reveals multiple introductions of Zika virus into the United States. Nature [Internet]. 2017; 546: 401-405. doi: https://doi.org/10.1038%2Fnature22400
Dellicour S, Rose R, Faria NR, Vieira LFP, Bourhy H, Gilbert M, et al. Using Viral Gene Sequences to Compare and Explain the Heterogeneous Spatial Dynamics of Virus Epidemics. Mol Biol Evol [Internet]. 2017; 34(10): 2563-2571. doi: https://doi.org/10.1093/molbev/msx176
Hansen TE, Hourcade JP, Segre A, Hlady C, Polgreen P, Wyman C. Interactive visualization of hospital contact network data on multi-touch displays. Proc 3rd Mex Work Hum Comput Interact. 2010; 1: 15-22.
Althouse BM, Ng YY, Cummings DAT. Prediction of dengue incidence using search query surveillance. PLoS Negl Trop Dis [Internet]. 2011; 5(8): 1-7. doi: https://doi.org/10.1371%2Fjournal.pntd.0001258
Teng Y, Bi D, Xie G, Jin Y, Huang Y, Lin B, et al. Dynamic forecasting of zika epidemics using google trends. PLoS One [Internet]. 2017; 12(1): 1-10. doi: https://doi.org/10.1371/journal.pone.0165085
Daughton AR, Paul MJ. Identifying Protective Health Behaviors on Twitter: Observational Study of Travel Advisories and Zika Virus. J Med Internet Res [Internet]. 2019; 21(5):13090. doi: https://doi.org/10.2196%2F13090
McGough SF, Brownstein JS, Hawkins JB, Santillana M. Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data. PLoS Negl Trop Dis [Internet]. 2017;11(1):1-15. doi: https://doi.org/10.1371/journal.pntd.0005295
Nsoesie EO, Flor L, Hawkins J, Maharana A, Skotnes T, Marinho F, et al. Social Media as a Sentinel for Disease Surveillance: What Does Sociodemographic Status Have to Do with It? Plos Curr [Internet]. 2016;7. doi: https://doi.org/10.1371%2Fcurrents.outbreaks.cc09a42586e16dc7dd62813b7ee5d6b6
Flahault A, Geissbuhler A, Guessous I, Guérin PJ, Bolon I, Salathé M, et al. Precision global health in the digital age. Swiss Med Wkly [Internet]. 2017;147. doi: https://doi.org/10.4414/smw.2017.14423
Chuang TW, Wimberly MC. Remote Sensing of Climatic Anomalies and West Nile Virus Incidence in the Northern Great Plains of the United States. PLoS One [Internet]. 2012; 7(10): 1-10. doi: https://doi.org/10.1371/journal.pone.0046882
Ruiz-Moreno D. Assessing Chikungunya risk in a metropolitan area of Argentina through satellite images and mathematical models. BMC Infect Dis [Internet]. 2016; 16(1): 1-12. doi: https://doi.org/10.1186/s12879-016-1348-y
Wu CH, Kao SC, Shih CH, Kan MH. Open data mining for Taiwan’s dengue epidemic. Acta Trop [Internet]. 2018; 183: 1-7. doi: https://doi.org/10.1016/j.actatropica.2018.03.017
Guo P, Liu T, Zhang Q, Wang L, Xiao J, Zhang Q, et al. Developing a dengue forecast model using machine learning: A case study in China. PLoS Negl Trop Dis [Internet]. 2017; 11(10): 1-22. doi: https://doi.org/10.1371/journal.pntd.0005973
Baquero OS, Santana LMR, Chiaravalloti-Neto F. Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models. PLoS One [Internet]. 2018; 13(4): 1-12. doi: https://doi.org/10.1371/journal.pone.0195065
Jain R, Sontisirikit S, Iamsirithaworn S, Prendinger H. Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data. BMC Infect Dis [Internet]. 2019; 19(1): 1-16. doi: https://doi.org/10.1186%2Fs12879-019-3874-x
Aswi A, Cramb SM, Moraga P, Mengersen K. Bayesian spatial and spatio-temporal approaches to modelling dengue fever: A systematic review. Epidemiol Infect [Internet]. 2019; 147: 33. doi: https://doi.org/10.1017/s0950268818002807
Akter R, Hu W, Gatton M, Bambrick H, Cheng J, Tong S. Climate variability, socio-ecological factors and dengue transmission in tropical Queensland, Australia: A Bayesian spatial analysis. Environ Res [Internet]. 2021; 195: 110285. doi: https://doi.org/10.1016/j.envres.2020.110285
Ho SH, Speldewinde P, Cook A. Predicting arboviral disease emergence using Bayesian networks: A case study of dengue virus in Western Australia. Epidemiol Infect [Internet]. 2017; 145(1): 54-66. doi: https://doi.org/10.1017/S0950268816002090
Martínez-Bello D, López-Quílez A, Prieto AT. Spatiotemporal modeling of relative risk of dengue disease in Colombia. Stoch Environ Res Risk Assess [Internet]. 2017; 32(6): 1587-1601. doi: https://doi.org/10.1007/s00477-017-1461-5
Deo R. Machine Learning in Medicine. Circulation [Internet]. 2015; 132(20): 1920–1930. doi: https://doi.org/10.1161/CIRCULATIONAHA.115.001593
Zhao N, Charland K, Carabali M, Nsoesie EO, Maheu-Giroux M, Rees E, et al. Machine learning and dengue forecasting : Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia. PLoS Negl Trop Dis [Internet]. 2020; 14(9): 1-16. doi: https://doi.org/10.1371/journal.pntd.0008056
Liu K, Zhang M, Xi G, Deng A, Song T, Li Q, et al. Enhancing fine-grained intra-urban dengue forecasting by integrating spatial interactions of human movements between urban regions. PLoS Negl Trop Dis [Internet]. 2020; 14(12): 1-22. doi: https://doi.org/10.1371/journal.pntd.0008924
Sippy R, Farrell DF, Lichtenstein DA, Nightingale R, Harris MA, Toth J, et al. Severity index for suspected arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection. PLoS Negl Trop Dis [Internet]. 2020; 14(2): 1-20. doi: https://doi.org/10.1371/journal.pntd.0007969
Jiang D, Hao M, Ding F, Fu J, Li M. Mapping the transmission risk of Zika virus using machine learning models. Acta Trop [Internet]. 2018; 185: 391-399. doi: https://doi.org/10.1016/j.actatropica.2018.06.021
Salim NAM, Wah YB, Reeves C, Smith M, Yaacob WFW, Mudin RN, et al. Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques. Sci Rep [Internet]. 2021; 11(1): 1-9. doi: https://doi.org/10.1038/s41598-020-79193-2
Mussumeci E, Coelho FC. Large-scale multivariate forecasting models for Dengue - LSTM versus random forest regression. Spat Spatiotemporal Epidemiol [Internet]. 2020;35:100372. doi: https://doi.org/10.1016/j.sste.2020.100372
Xu J, Xu K, Li Z, Meng F, Tu T, Xu L, et al. Forecast of dengue cases in 20 chinese cities based on the deep learning method. Int J Environ Res Public Health [Internet]. 2020; 17(2): 453. doi: https://doi.org/10.3390/ijerph17020453
Bomfim R, Pei S, Shaman J, Yamana T, Makse HA, Andrade JS Jr, et al. Predicting dengue outbreaks at neighbourhood level using human mobility in urban areas. Interface [Internet]. 2020; 17(171): 20200691. doi: http://dx.doi.org/10.1098/rsif.2020.0691
Akhtar M, Kraemer MUG, Gardner LM. A dynamic neural network model for predicting risk of Zika in real time. BMC Med [Internet]. 2019; 17(1). doi: http://dx.doi.org/10.1186/s12916-019-1389-3
Starczewski JT. Efficient triangular type-2 fuzzy logic systems. Int J Approx Reason [Internet]. 2009; 50(5): 799-811. doi: http://dx.doi.org/10.1016/j.ijar.2009.03.001
Torres C, Barguil S, Melgarejo M, Olarte A. Fuzzy model identification of dengue epidemic in Colombia based on multiresolution analysis. Artif Intell Med [Internet]. 2014; 60(1): 41-51. doi: http://dx.doi.org/10.1016/j.artmed.2013.11.008
Adak S, Jana S. A model to assess dengue using type 2 fuzzy inference system. Biomed Signal Process Control [Internet]. 2021; 63:102121. doi: http://dx.doi.org/10.1016/j.bspc.2020.102121
Wu CH, Kao SC. Knowledge discovery in open data for epidemic disease prediction. Heal Policy Technol [Internet]. 2021; 10(1): 126-134. doi: http://dx.doi.org/10.1016/j.hlpt.2021.01.001
Stolerman LM, Maia PD, Nathan Kutz J. Forecasting dengue fever in Brazil: An assessment of climate conditions. PLoS One [Internet]. 2019; 14(8). doi: http://dx.doi.org/10.1371/journal.pone.0220106
Chen Y, Ong JHY, Rajarethinam J, Yap G, Ng LC, Cook AR. Neighbourhoodlevel real-time forecasting of dengue cases in tropical urban Singapore. BMC Med [Internet]. 2018; 16(1): 1-13. doi: http://dx.doi.org/10.1186/s12916-018-1108-5
Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S,Yap G, et al. Three-month real-time dengue forecast models: An early warning system for outbreak alerts and policy decision support in Singapore. Environ Health Perspect. 2016; 124(9): 1369-1375. doi: http://dx.doi.org/10.1289/ehp.1509981
Campbell KM, Haldeman K, Lehnig C, Munayco CV, Halsey ES, Laguna-Torres VA, et al. Weather regulates location, timing, and intensity of dengue virus transmission between humans and mosquitoes. PLoS Negl Trop Dis [Internet]. 2015; 9(7): 1-26. doi: https://doi.org/10.1371/journal.pntd.0003957
Ong J, Liu X, Rajarethinam J, Kok SY, Liang S, Tang CS, et al. Mapping dengue risk in Singapore using Random Forest. PLoS Negl Trop Dis [Internet]. 2018; 12(6): 1-12. doi: https://doi.org/10.1371/journal.pntd.0006587
Polwiang S. The time series seasonal patterns of dengue fever and associated weather variables in Bangkok (2003-2017). BMC Infect Dis [Internet]. 2020; 20(1): 1-10. doi: https://doi.org/10.1186/s12879-020-4902-6
Tuladhar R, Singh A, Banjara MR, Gautam I, Dhimal M, Varma A, et al. Effect of meteorological factors on the seasonal prevalence of dengue vectors in upland hilly and lowland Terai regions of Nepal. Parasites and Vectors [Internet]. 2019; 12(1):1-15. doi: https://doi.org/10.1186/s13071-019-3304-3
Yuan HY, Liang J, Lin PS, Sucipto K, Tsegaye MM, Wen TH, et al. The effects of seasonal climate variability on dengue annual incidence in Hong Kong: A modelling study. Sci Rep [Internet]. 2020; 10(1): 1-10. doi: https://doi.org/10.1038/s41598-020-60309-7
Nejad YF, Varathan KD. Identification of significant climatic risk factors and machine learning models in dengue outbreak prediction. BMC Med Inform Decis Mak [Internet]. 2021; 21(1): 1-12. doi: https://doi.org/10.1186/s12911-021-01493-y
McGough SF, Clemente L, Kutz JN, Santillana M. A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles. J R Soc Interface [Internet]. 2021; 18(179): 20201006. doi: https://doi.org/10.1098/rsif.2020.1006
Benedum CM, Shea KM, Jenkins HE, Kim LY, Markuzon N. Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore. PLoS Negl Trop Dis [Internet]. 2020; 14(10): 1-26. doi: https://doi.org/10.1371/journal.pntd.0008710
Carvajal TM, Viacrusis KM, Hernandez LFT, Ho HT, Amalin DM, Watanabe K. Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines. BMC Infect Dis [Internet]. 2018; 18(1): 1-15. doi: https://doi.org/10.1186/s12879-018-3066-0
Liu K, Yin L, Zhang M, Kang M, Deng AP, Li QL, et al. Facilitating fine-grained intra-urban dengue forecasting by integrating urban environments measured from street-view images. Infect Dis Poverty [Internet]. 2021;10(40):1-16. doi: https://doi.org/10.1186/s40249-021-00824-5
Liu X, Rajarethinam J, Shi Y, Liang S, Yap G, Ng LC. Development of predictive dengue risk map using Random Forest. Int J Infect Dis [Internet]. 2016; 45: 346. doi: https://doi.org/10.1016/j.ijid.2016.02.746
Hsu JC, Hsieh CL, Lu CY. Trend and geographic analysis of the prevalence of dengue in Taiwan, 2010–2015. Int J Infect Dis [Internet]. 2017; 54: 43-49. doi: https://doi.org/10.1016/j.ijid.2016.11.008
Ho TS, Weng TC, Wang JD, Han HC, Cheng HC, Yang CC, et al. Comparing machine learning with case- control models to identify confirmed dengue cases. PLoS Negl Trop Dis [Internet]. 2020; 14(11): 1-21. doi: https://doi.org/10.1371/journal.pntd.0008843
Xiao J, Liu T, Lin H, Zhu G, Zeng W, Li X, et al. Weather variables and the El Niño Southern Oscillation may drive the epidemics of dengue in Guangdong Province, China. Sci Total Environ [Internet]. 2018; 624: 926-934. doi: https://doi.org/10.1016/j.scitotenv.2017.12.200
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Derechos de autor 2022 Sonia Isabel Polo-Triana, Yuly Andrea Ramírez-Sierra, Javier Eduardo Arias-Osorio, Ruth Aralí Martínez-Vega, Henry Lamos-Díaz