Sistemas dinámicos forzados: aplicación al estudio de las enfermedades infecciosas. / Driven dymamical systems: application to the study of infectious diseases.

Kaufman, Bruno (2017) Sistemas dinámicos forzados: aplicación al estudio de las enfermedades infecciosas. / Driven dymamical systems: application to the study of infectious diseases. Maestría en Ciencias Físicas, Universidad Nacional de Cuyo, Instituto Balseiro.

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Resumen en español

En este trabajo analizó una colección de bibliografía y datos relativos al comportamiento epidemiológico del virus dengue en la provincia de Misiones, en el norte de Argentina. Se realizaron simulaciones computacionales con modelos de tipo susceptible-infectado-recuperado (SIR) con demografía y población conservada, teniendo en cuenta la conectividad entre poblaciones y la inmigración fronteriza en el termino de transmisión. Además se tuvo en cuenta la estacionalidad en la transmisión debida a la incubación del virus en el vector y la supervivencia del mismo en distintas épocas del año. Realizando simulaciones computacionales se encontró que la estacionalidad de la migración también es importante para reproducir el comportamiento temporal de los casos de dengue reportados. También se planteó un modelo especialmente explicito, haciendo tender los modelos metapoblacionales vistos a un límite contínuo. Finalmente, se implementaron dos algoritmos de ajuste de modelos de campo medio o metapoblacionales a series temporales epidemiológicas, sobre la unidad de procesamiento gráfica (GPU). Se probaron ambos métodos y se encontró que un ajuste por filtrado multi-iterado (MIF) logro buenos resultados, llegando a ajustar un modelo de campo medio SIR demográfíco con estacionalidad de cuatro parámetros a una serie temporal de datos epidemiológicos de la gripe.

Resumen en inglés

In this study, we analyzed a set of literature and relevant data to ascertain key aspects describing the epidemiological behavior of dengue fever in Misiones, in the north of Argentina. Computer simulations were done with Susceptible-Infected-Recovered (SIR) models with demographics and constant population, but taking into account connectivity between cities and border inmigration in the transmission term. The seasonality in transmission due to the incubation of the virus within the mosquito was also taken into account, as well as the vector's survival throughout the year. Computer simulations showed that seasonality in the inmigration term is also very important for reproducing temporal patterns in the observed dengue cases. An spacially explicit model was also developed applying a discrete-to-continuous limit to the previously developed metapopulation models. Finally, we implemented two tting algorithms on General Purpose Graphics Processing Units (GPUs) in order to t mean-eld and metapopulation models to epidemiological time-series data. We tested both methods, and found that a multi-iterated filtering algorithm gives good results tting a four-parameter mean-eld demographic SIR model with seasonal transmission to a flu time-series.

Tipo de objeto:Tesis (Maestría en Ciencias Físicas)
Palabras Clave:Epidemiology; Epidemiología; Infectious diseases; Enfermedades infecciosas; [Dengue; Metapopulation model; Modelo metaploblacional; Iterated filtering; Ajuste por filtro iterado; SIR model; Modelo SIR; Seasonal trasmission; Transmisión estacional]
Referencias:[1] Variaciones a corto y largo plazo de las abundancias de aedes aegypti en la region templada. URL http://server.ege.fcen.uba.ar/gem/html/investigacion_ aedesvariaciones.html. ix, 8, 10 [2] Dengue Guidelines for Diagnosis, Treatment, Prevention and Control. World Health Organization, 2010. URL www.who.int/tdr/publications/documents/ dengue-diagnosis.pdf. 1, 47 [3] Chastel, C. Eventual role of asymptomatic cases of dengue for the introduction and spread of dengue viruses in non-endemic regions. Frontiers in Physiology, 3, 2012. 1 [4] King, A. A., Ionides, E. L., Pascual, M., Bouma, M. J. Inapparent infections and cholera dynamics. Nature, 454, 877-880, 2008. 1, 44 [5] Ministerio de Salud de la Nacion Argentina. Dengue - Guia para el equipo de salud, 4a ed., 2015. URL www.femeba.org.ar/documentos/download/ 2823-dengue-resumen.pdf. 1, 2, 5 [6] Otero, M., Solari, H. G., Schweigmann, N. A stochastic population dynamics model for aedes aegypti: Formulation and application to a city with temperate climate. Bulletin of Mathematical Biology, 68 (8), 19451974, Nov 2006. 1, 4 [7] Chan, M., Johansson, M. A. The incubation periods of dengue viruses. PLoS ONE, 7 (11), 2012. 1, 10 [8] Whitehead, S. S., Blaney, J. E., Durbin, A. P., Murphy, B. R. Prospects for a dengue virus vaccine. Nature Reviews Microbiology, 5 (7), 518528, 2007. 2 [9] Guzman, M. G., Alvarez, M., Halstead, S. B. Secondary infection as a risk factor for dengue hemorrhagic fever/dengue shock syndrome: an historical perspective and role of antibody-dependent enhancement of infection. Archives of Virology, 158 (7), 14451459, Aug 2013. 2 [10] Carbajo, A. E., Schweigmann, N., Curto, S. I., Garin, A. D., Bejaran, R. Dengue transmission risk maps of argentina. Tropical Medicine and International Health, 6 (3), 170183, 2001. 4 [11] Dengue: alertan que hay un 70% mas de casos que en 2009, Abril 2016. URL http://www.lanacion.com.ar/ 1892515-dengue-alertan-que-hay-un-70-mas-de-casos-que-en-2009. 4 [12] Alerta dengue: Argentina enfrenta hoy la peor epidemia de su historia, Agosto 2016. URL https://www.infobae.com/salud/2016/08/18/ alerta-dengue-argentina-enfrenta-hoy-la-peor-epidemia-de-su-historia/. 4 [13] Seijo, A. Dengue 2009: cronologia de una epidemia. 107, 387{389, 10 2009. 4 [14] Coudeville, L., Baurin, N., Vergu, E. Estimation of parameters related to vaccine efficacy and dengue transmission from two large phase iii studies. Vaccine, 34 (50), 64176425, 2016. 4 [15] Argentina no usara la primera vacuna contra el dengue porque su tasa de proteccion es muy baja, Abril 2016. URL http://www.telam.com.ar/notas/201604/ 143574-salud-argentina-dengue-vacuna-organizacion-mundial-de-la-salud. php. 5 [16] Ballarino, F. Anmat aprobo en el pas la primera vacuna que protege contra el dengue, Abril 2017. URL http://www.perfil.com/ciencia/ anmat-aprobo-en-el-pais-la-primera-vacuna-que-protege-contra-el-dengue. phtml. 5 [17] Citcioglu, L. Dengue: Aprobacion de la vacuna en argentina, Julio 2017. URL http://www.stamboulian.com.ar/novedades/ dengue-aprobacion-la-vacuna-argentina/. 5 [18] Flasche, S., Jit, M., Rodriguez-Barraquer, I., Coudeville, L., Recker, M., Koelle, K., et al. The long-term safety, public health impact, and cost-eectiveness of routine vaccination with a recombinant, live-attenuated dengue vaccine (dengvaxia): A model comparison study. PLOS Medicine, 13 (11), 2016. 5 [19] Laneri, K., Paul, R. E., Tall, A., Faye, J., Diene-Sarr, F., Sokhna, C., et al. Dynamical malaria models reveal how immunity buers eect of climate variability. Proceedings of the National Academy of Sciences, 112 (28), 8786-8791, 2015. URL http://www.pnas.org/content/112/28/8786.abstract. 5, 35, 44 [20] Vezzani, D., Carbajo, A. E. Aedes aegypti, aedes albopictus, and dengue in argentina: current knowledge and future directions. Mem Inst Oswaldo Cruz, 103, 2008. URL https://doi.org/10.1590/S0074-0276200Harrington,LauraC. andScott,ThomasW.andLerdthusnee,KriangkraiandColeman,RussellC. andCostero,AdrianaandClark,GaryG.andJones,JamesJ.andKitthawee, SangvornandKittayapong,PattamapornandSithiprasasna,RatanaandEdman, JohnD. 5 [21] Focks, D. A., Haile, D. G., Daniels, E., Mount, G. A. Dynamic life table model for ae aegypti (diptera:culicidae): simulation results and validation. J Med Entomol, 30, 1993. URL https://doi.org/10.1093/jmedent/30.6.1018. 8 [22] Alonso, D., Bouma, M. J., Pascual, M. Epidemic malaria and warmer temperatures in recent decades in an east african highland. Proceedings of the Royal Society of London B: Biological Sciences, 2010. URL http://rspb. royalsocietypublishing.org/content/early/2010/11/09/rspb.2010.2020. 8 [23] Carbajo, A. E., Cardo, M. V., Vezzani, D. Is temperature the main cause of dengue rise in non-endemic countries? the case of argentina. International Journal of Health Geographics, 11 (1), 26, 2012. 10 [24] Tjaden, N. B., Thomas, S. M., Fischer, D., Beierkuhnlein, C. Extrinsic incubation period of dengue: Knowledge, backlog, and applications of temperature dependence. PLoS Neglected Tropical Diseases, 7 (6), 2013. 10, 20 [25] Britton, T., House, T., Lloyd, A. L., Mollison, D., Riley, S., Trapman, P. Five challenges for stochastic epidemic models involving global transmission. Epidemics, 10, 5457, 2015. 11 [26] Harrington, L. C., Scott, T. W., Lerdthusnee, K., Coleman, R. C., Costero, A., Clark, G. G., et al. Dispersal ot the dengue vector aedes aegypti within and between rural communities. The American Journal of Tropical Medicine and Hygiene, 72 (2), 2005. 11 [27] Aron, J. L., May, R. M. The population dynamics of malaria. En: R. M. Anderson (ed.) Population dynamics of infectious diseases: theory and applications. London, UK: Chapman & Hall, 1982. 15 [28] Wearing, H. J., Rohani, P., Keeling, M. J. Appropriate models for the management of infectious diseases. PLoS Med, 2 (7), e174, 2005. 15 [29] Strogatz, S. H. Nonlinear Dynamics and Chaos. Addison-Wesley, 1994. 16, 18 [30] Keeling, M. J., Rohani, P. Modeling Infectious Diseases in Humans and Animals. Princeton University Press, 2008. 16, 19, 34 [31] Coelho, F. C., Codeco, C. T., Struchiner, C. J. Complete treatment of uncertainties in a model for dengue r0 estimation. Cadernos de Saude Publica, 24 (4), 853861, 2008. 19 [32] Goncalves, S., Abramson, G., Gomes, M. F. C. Oscillations in sirs model with distributed delays. The European Physical Journal B, 81 (3), 363371, Apr 2011. 20 [33] Bhadra, A., Ionides, E. L., Laneri, K., Pascual, M., Bouma, M., Dhiman, R. C. Malaria in northwest india: Data analysis via partially observed stochastic dierential equation models driven by Levy noise. Journal of the American Statistical Association, 106 (494), 440{451, 2011. URL https://doi.org/10.1198/jasa. 2011.ap10323. 35, 44, 55 [34] Ellner, S. P., Seifu, Y., Smith, R. H. Fitting population dynamic models to timeseries data by gradient matching. Ecology, 83, 2256{2270, 2002. URL http: //www.jstor.org/stable/2463521. 44 [35] Finkenstadt, B., Grenfell, B. Time series modelling of childhood diseases: a dynamical systems approach. J. R. Statist. Soc. C., Applied Statistics, 49, 187{205, 2000. 44 [36] Ionides, E. L., Bhadra, A., Atchade, Y., King, A. Iterated ltering. The Annals of Statistics, 39 (3), 17761802, 2011. 44, 55, 57, 58, 60 [37] Ionides, E. L., Breto, C., King, A. A. Inference for nonlinear dynamical systems. Proc. Nat. Acad. Sci. USA, 103, 18438{18443, 2006. 44 [38] Breto, C., He, D., Ionides, E. L., King, A. A. Time series analysis via mechanistic models. 3, 319{348, 2009. 44 [39] Bolker, B. M. Ecological Models and Data in R. 508a edon. Princeton University Press, 2008. 44 [40] King, A. A., Ionides, E. L., Breto, C. M., Ellner, S., Kendall, B. pomp: Statistical inference for partially observed Markov processes, 2009. URL http: //pomp.r-forge.r-rproject.org. 44, 55 [41] Grinstead, C. M., Snell, J. L. Introduction to probability. American Mathematical Society, 2006. 47, 48 [42] Endy, T. P., Anderson, K. B., Nisalak, A., Yoon, I.-K., Green, S., Rothman, A. L., et al. Determinants of inapparent and symptomatic dengue infection in a prospective study of primary school children in kamphaeng phet, thailand. PLOS Neglected Tropical Diseases, 5 (3), 1-10, 03 2011. URL https: //doi.org/10.1371/journal.pntd.0000975. 47 [43] NVIDIA. CUDA C Programming Guide, February 2014. URL http://docs. nvidia.com/cuda/cuda-c-programming-guide/. 51 [44] Cook, S. CUDA Programming. A developer's guide to parallel computing with GPUs. Morgan Kaufmann Publishers Inc., 2013. 51 [45] Farber, R. CUDA Application Design and Development. 1a edon. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2011. 51 [46] Nvidia home page. URL http://la.nvidia.com/page/home.html. 51 [47] Arulampalam, M., Maskell, S., Gordon, N., Clapp, T. A tutorial on particle lters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, 50 (2), 174188, 2002. 55, 57, 59 [48] Thrust - parallel algorithms library. URL https://thrust.github.io/. 59, 60 [49] Kaufman, B., Laneri, K. URL https://bitbucket.org/BrunoKaufman/cu_ pomp/src. 60, 61 [50] Flunet. URL http://www.who.int/influenza/gisrs_laboratory/flunet/en/. 67 [51] Influenza (flu), Jul 2016. URL https://www.cdc.gov/flu/about/season/ flu-season.htm. 67, 74 52] Influenza (flu), Nov 2017. URL https://www.cdc.gov/flu/about/disease/ 65over.htm. 67 [53] The world factbook: United states, Nov 2017. URL https://www.cia.gov/ library/publications/the-world-factbook/geos/us.html. 67 [54] Reed, C., Finelli, L., Gambhir, M., Biggersta, M., Cauchemez, S. Estimates of the reproduction number for seasonal, pandemic, and zoonotic in uenza: a systematic review of the literature, Sep 2014. URL https://bmcinfectdis.biomedcentral. com/articles/10.1186/1471-2334-14-480. 78 [55] Influenza (flu), May 2016. URL https://www.cdc.gov/flu/professionals/ acip/clinical.htm. 78
Materias:Física > Sistemas complejos
Divisiones:Gcia. de área de Investigación y aplicaciones no nucleares > Gcia. de Física > Sistemas complejos y altas energías > Física estadística interdisciplinaria
Código ID:651
Depositado Por:Tamara Cárcamo
Depositado En:26 Abr 2018 15:30
Última Modificación:27 Abr 2018 10:49

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