Analizando la evolución del modelado de enfermedades infecciosas

Félix Sebastián Rincón Tobo; Javier Antonio Ballesteros Ricaurte; Angela Maria Gonzalez Amarillo

doi:10.22490/21456453.2281

Vol. 10 Núm. 1 (2019)

Publicado 18-12-2018

Licencia

Derechos de autor 2018 Revista de Investigación Agraria y Ambiental

Cuando RIAA recibe la postulación de un original por parte de su autor, ya sea a través de correo electrónico o postal, considera que puede publicarse en formatos físicos y/o electrónicos y facilitar su inclusión en bases de datos, hemerotecas y demás sistemas y procesos de indexación. RIAA autoriza la reproducción y citación del material de la revista, siempre y cuando se indique de manera explícita el nombre de la revista, los autores, el título del artículo, volumen, número y páginas. Las ideas y conceptos expresados en los artículos son responsabilidad de los autores y en ningún caso reflejan las políticas institucionales de la UNAD

Artículos de Investigación

Analizando la evolución del modelado de enfermedades infecciosas

DOI: https://doi.org/10.22490/21456453.2281

Félix Sebastián Rincón Tobo Universidad Pedagógica y Tecnológica de Colombia

Javier Antonio Ballesteros Ricaurte Universidad Pedagógica y Tecnológica de Colombia

Angela Maria Gonzalez Amarillo Universidad Nacional Abierta y a Distancia

PDF

HTML

XML

Resumen
Referencias

El interés global por conocer y controlar las enfermedades que afectan a humanos y animales ha permitido modelar enfermedades mediante diversos métodos (modelos matemáticos, estocásticos, discretos) que se aplican actualmente para predecir la propagación de nuevas epidemias, reducir el contagio de enfermedades infecciosas, evaluar el impacto que tendrán las diferentes estrategias de control de enfermedades y mejorar las condiciones de vida de los individuos. Actualmente, nuevas técnicas y herramientas se están implementando para modelar enfermedades infecciosas, el presente documento describe conceptos de esta área, así como las tendencias y retos existentes, finalmente se ofrecen al lector algunos criterios a considerar para la selección de un modelo epidemiológico.

Palabras clave: Control de epidemias, enfermedades infecciosas, impacto, modelo epidemiológico

Aldila D., Götz T. & Soewono E. (2013). An optimal control problem arising from a dengue disease transmission model. Mathematical Biosciences; 242(1): 9–16. http://doi.org/http://dx.doi.org/10.1016/j.mbs.2012.11.014

Álvarez J., Bezos J., de la Cruz M.L., Casal C., Romero B., Domínguez L., et al. (2014). Bovine tuberculosis: Within-herd transmission models to support and direct the decision-making process. Res Vet Sci; 97, Supple, S61 – S68. http://doi.org/http://dx.doi.org/10.1016/j.rvsc.2014.04.00

Atkins K.E., Shim E., Pitzer V.E. & Galvani A.P. (2012). Impact of rotavirus vaccination on epidemiological dynamics in England and Wales. Vaccine; 30(3): 552–564. http://doi.org/http://dx.doi.org/10.1016/j.vaccine.2011.11.064

Bacaër N. (2011). A Short History of Mathematical Population Dynamics. New York, Springer. http://doi.org/10.1007/978-0-85729-115-8

Backer J.A., van Roermund H.J., Fischer E.A., van Asseldonk M.A. & Bergevoet, R.H. ()2015. Controlling highly pathogenic avian influenza outbreaks: An epidemiological and economic model analysis. Prev Vet Med; 121(1–2): 142–150. http://doi.org/http://dx.doi.org/10.1016/j.prevetmed.2015.06.006

Bekara M.A., Courcoul A., Bénet J. & Durand B. (2014). Modeling tuberculosis dynamics, detection and control in cattle herds. PloS One; 9(9), e108584. http://doi.org/10.1371/journal.pone.0108584

Binder B.J., Ross J.V. & Simpson M.J. (2012). A hybrid model for studying spatial aspects of infectious diseases. ANZIAM Journal; 54(1-2): 37–49. http://doi.org/10.1017/S1446181112000296Cai Y., Wang Z. & Wang W. (2016). Endemic dynamics in a host-parasite epidemiological model within spatially heterogeneous environment. Applied Mathematics Letters; 61: 129-136. http://doi.org/http://dx.doi.org/10.1016/j.aml.2016.05.011

Casabán M., Cortés J., Navarro-Quiles A., Romero J., Roselló M. & Villanueva R. (2016). A comprehensive probabilistic solution of random SIS-type epidemiological models using the random variable transformation technique. Communications in Nonlinear Science and Numerical Simulation; 32: 199–210. http://doi.org/http://dx.doi.org/10.1016/j.cnsns.2015.08.009

Casabán M., Cortés J., Romero J. & Roselló M. (2015). Probabilistic solution of random SI-type epidemiological models using the Random Variable Transformation technique. Communications in Nonlinear Science and Numerical Simulation; 24(1–3): 86–97. http://doi.org/http://dx.doi.org/10.1016/j.cnsns.2014.12.016

Cauchemez S. & Ferguson N.M. (2008). Likelihood-based estimation of continuous-time epidemic models from time-series data: Application to measles transmission in London. Journal of the Royal Society Interface; 5(25): 885–897. http://doi.org/10.1098/rsif.2007.1292

Cortés J., Sánchez-Sánchez A., Santonja F. & Villanueva R.J. (2013). Non-parametric probabilistic forecasting of academic performance in Spanish high school using an epidemiological modelling approach. Applied Mathematics and Computation; 221: 648–661. http://doi.org/http://dx.doi.org/10.1016/j.amc.2013.06.070

Daniel W.B., Hengartner N.W., Rivera M.K., Powell D.R. & McPherson T.N. (2013). An epidemiological model of spatial coupling for trips longer than the infectious period. Mathematical Biosciences; 242(1): 1–8. http://doi.org/http://dx.doi.org/10.1016/j.mbs.2012.11.002

Fan K., Zhang Y. & Gao S. (2016). On a new eco-epidemiological model for migratory birds with modified Leslie-Gower functional schemes. Advances in Difference Equations; 97(1). http://doi.org/10.1186/s13662-016-0825-3

Fischer E.A.J., van Roermund H.J.W., Hemerik L., van Asseldonk M.A.P.M. & de Jong M.C.M. (2005). Evaluation of surveillance strategies for bovine tuberculosis (Mycobacterium bovis) using an individual based epidemiological model. Prev Vet Med; 67(4): 283–301. http://doi.org/http://dx.doi.org/10.1016/j.prevetmed.2004.12.002

Gaucel S., Laroche B., Ezanno P., Vergu E. & Touzeau S. (2009). Using singular perturbations to reduce an epidemiological model: Application to bovine viral diarrhoea virus within-herd spread. J Theor Biol; 258(3): 426–436. http://doi.org/http://dx.doi.org/10.1016/j.jtbi.2008.08.011

Hernández M. (2007). Epidemiología: diseño y análisis de estudios. 2nd ed. Editorial Médica Panamericana.

Holko A., Mȩdrek M., Pastuszak Z. & Phusavat K. (2016). Epidemiological modeling with a population density map-based cellular automata simulation system. Expert Systems with Applications; 48: 1–8. http://doi.org/http://dx.doi.org/10.1016/j.eswa.2015.08.018

Kang G.J., Gunaseelan L. & Abbas K.M. (2014). Epidemiological modeling of bovine brucellosis in India. In Big Data (Big Data), IEEE International Conference on 2014; 6–10. http://doi.org/10.1109/BigData.2014.7004420

Kang G., Gunaseelan L. & Abbas K. (2015). Epidemiological dynamics of bovine brucellosis in India. Annals of Global Health; 81(1): 127–128. http://doi.org/http://dx.doi.org/10.1016/j.aogh.2015.02.793

Kar T.K. & Jana S. (2013). A theoretical study on mathematical modelling of an infectious disease with application of optimal control. Biosystems; 111(1): 37–50. http://doi.org/http://dx.doi.org/10.1016/j.biosystems.2012.10.003

Keeling M. J. & Rohani P. (2011). Modeling infectious diseases in Humans and Animals. New Jersey, USA: Princeton University Press. http://www.scopus.com/inward/record.url?eid=2-s2.0 84884049994&partnerID=40&md5=c999c61563de33ed4e8a6171c364ad25. Consultado 20 Jul, 2017.

Kermack W.O. & McKendrick A.G. (1927). A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences; 115(772): 700–721. http://doi.org/10.1098/rspa.1927.0118

Ligarte M.D., Goicoa T., Ibáñez B. & Militino, A.F. (2009). Evaluating the performance of spatio-temporal Bayesian models in disease mapping. Environmetrics; 20(6): 647–665. http://doi.org/10.1002/env.969

Marutschke D.M. & Ogawa H. (2014). Clustering Scientific Publication Trends in Cultural Context Using Epidemiological Model Parameters. Procedia Technology; 18: 90–95. http://doi.org/http://dx.doi.org/10.1016/j.protcy.2014.11.018

Milne C.E., Gunn G.J., Entrican G. & Longbottom D. (2009). Epidemiological modelling of chlamydial abortion in sheep flocks. Vet Microbiol; 135(1–2): 128–133. http://doi.org/http://dx.doi.org/10.1016/j.vetmic.2008.09.032

Morris R.S. (2015). Diseases, dilemmas, decisions—Converting epidemiological dilemmas into successful disease control decisions. Prev Vet Med; 122(1–2): 242–252. http://doi.org/http://dx.doi.org/10.1016/j.prevetmed.2015.05.003

Njagarah J.B. & Nyabadza F. (2014). A metapopulation model for cholera transmission dynamics between communities linked by migration. Applied Mathematics and Computation; 241: 317–331. http://doi.org/http://dx.doi.org/10.1016/j.amc.2014.05.036

Öztürk Y. & Gülsu M. (2015). Numerical solution of a modified epidemiological model for computer viruses. Applied Mathematical Modelling; 39(23–24): 7600–7610. http://doi.org/http://dx.doi.org/10.1016/j.apm.2015.03.023

Pedersen S. (2015). Algunos Modelos matemáticos para (algunas) enfermedades contagiosas: transmisión, infección, tratamiento [Tesis de Licenciatura]. Buenos Aires, Universidad de Buenos Aires.

Pérez J. M. (2010). Inteligencia computacional inspirada en la vida. SPICUM – Universidad de Málaga.

Pittavino M., Ferreri L., Giacobini M., Bertolotti L., Rosati S. & Venturino E. (2014). A CAEV epidemiological model for goat breeding. Applied Mathematics and Computation; 227: 156–163. http://doi.org/http://dx.doi.org/10.1016/j.amc.2013.11.030

Rao D.M. (2016). Efficient parallel simulation of spatially-explicit agent-based epidemiological models. Journal of Parallel and Distributed Computing; 93–94: 102–119. http://doi.org/http://dx.doi.org/10.1016/j.jpdc.2016.04.004

Rist C.L., Ngonghala C.N., Garchitorena A., Brook C.E., Ramananjato R., Miller A.C., et al. (2015). Modeling the burden of poultry disease on the rural poor in Madagascar. One Health; 1: 60–65.

http://doi.org/http://dx.doi.org/10.1016/j.onehlt.2015.10.002

Rodrigues H.S., Monteiro M.T.T. & Torres D.F.M. (2014). Vaccination models and optimal control strategies to dengue. Mathematical Biosciences; 247: 1–12. http://doi.org/http://dx.doi.org/10.1016/j.mbs.2013.10.006

Rossi G., De Leo G.A., Pongolini S., Natalini S., Vincenzi S. & Bolzoni L. (2015). Epidemiological modelling for the assessment of bovine tuberculosis surveillance in the dairy farm network in Emilia-Romagna (Italy). Epidemics; 11: 62–70. http://doi.org/10.1016/j.epidem.2015.02.007

Sahneh F.D., Vajdi A., Melander J. & Scoglio C.M. (2017). Contact Adaption during Epidemics: A Multilayer Network Formulation Approach. IEEE Transaction on Network Science and Engineering. DOI 10.1109/TNSE.2017.2770091

Santman-Berends I.M.G.A., Mars M.H., van Duijn L. & van Schaik G. (2015). Evaluation of the epidemiological and economic consequences of control scenarios for bovine viral diarrhea virus in dairy herds. J Dairy Sci; 98(11): 7699–7716. http://doi.org/http://dx.doi.org/10.3168/jds.2014-9255

Sato A.H., Ito I., Sawai H. & Iwata K. (2015). An epidemic simulation with a delayed stochastic SIR model based on international socioeconomic-technological databases. Big Data (Big Data), 2015 IEEE International Conference on: 2732–2741. http://doi.org/10.1109/BigData.2015.7364074

Savall J.F., Bidot C., Leblanc-Maridor M., Belloc C. & Touzeau S. (2016). Modelling Salmonella transmission among pigs from farm to slaughterhouse: Interplay between management variability and epidemiological uncertainty. Int J Food Microbiol; 229: 33–43. http://doi.org/http://dx.doi.org/10.1016/j.ijfoodmicro.2016.03.020

Sieber M., Malchow H. & Hilker F.M. (2014). Disease-induced modification of prey competition in eco-epidemiological models. Ecological Complexity; 18: 74–82. http://doi.org/10.1016/j.ecocom.2013.06.002

Ssematimba A., Jores J. & Mariner J.C. (2015). Mathematical modelling of the transmission dynamics of contagious bovine pleuropneumonia reveals minimal target profiles for improved vaccines and diagnostic assays. PLoS ONE 2015; 10(2). http://doi.org/10.1371/journal.pone.0116730

Staubach C., Schmid V., Knorr-Held L. & Ziller M. (2002). A Bayesian model for spatial wildlife disease prevalence data. Prev Vet Med; 56(1): 75–87. http://doi.org/10.1016/S0167-5877(02)00125-3

University of Chicago. (1989). Hipócrates. Hippocratic writings. On airs, waters and places. http://classics.mit.edu/Hippocrates/airwatpl.2.2.html. Consultado 23 Jul, 2017.

Van Effelterre T., Marano C. & Jacobsen K.H. (2016). Modeling the hepatitis A epidemiological transition in Thailand. Vaccine; 34(4): 555–562. http://doi.org/http://dx.doi.org/10.1016/j.vaccine.2015.11.052

Van Hulzen K.J.E., Koets A.P., Nielen M., Heuven H.C.M., Van Arendonk J.A.M.& Klinkenberg D. (2014). The effect of genetic selection for Johne’s disease resistance in dairy cattle: Results of a genetic-epidemiological model. J Dairy Sci; 97(3): 1762–1773. http://doi.org/10.3168/jds.2013-7032

Vynnycky E. & White R. (2010). An Introduction to Infectious Disease Modelling. 1nd ed. OUP Oxford.

Weidemann F., Dehnert M., Koch J., Wichmann O. & Höhle M. (2014). Modelling the epidemiological impact of rotavirus vaccination in Germany – A Bayesian approach. Vaccine; 32(40): 5250–5257. http://doi.org/http://dx.doi.org/10.1016/j.vaccine.2014.06.090

Yakob L., Riley T.V., Paterson D.L. & Clements A.C.A. (2013). Clostridium difficile exposure as an insidious source of infection in healthcare settings: An epidemiological model. BMC Infectious Diseases; 13(1). http://doi.org/10.1186/1471-2334-13-376

Zaman G., Kang Y.H. & Jung I.H. (2009). Optimal treatment of an SIR epidemic model with time delay. Biosystems; 98(1): 43–50. http://doi.org/http://dx.doi.org/10.1016/j.biosystems.2009.05.006

Zhang Q., Jiang D., Liu Z., O’Regan D. (2016). Asymptotic behavior of a three species eco-epidemiological model perturbed by white noise. Journal of Mathematical Analysis and Applications; 433(1): 121–148. http://doi.org/http://dx.doi.org/10.1016/j.jmaa.2015.07.025

Licencia

Derechos de autor 2018 Revista de Investigación Agraria y Ambiental

Cómo citar

Rincón Tobo, F. S., Ballesteros Ricaurte, J. A., & Gonzalez Amarillo, A. M. (2018). Analizando la evolución del modelado de enfermedades infecciosas. Revista De Investigación Agraria Y Ambiental, 10(1), 27-42. https://doi.org/10.22490/21456453.2281

Descargar cita

Almétricas

Métricas

Archivos descargados

1,705

| |