Comparación de dos modelos matemáticos para estimar los casos de dengue en la provincia del Guayas (Ross-Macdonald, Anderson-May)
Resumen
Las enfermedades infecciosas están influenciadas especialmente por aspectos ecológicos y demográficos, hasta la fecha se han presentado innumerables esfuerzos matemáticos para estudiar la dinámica de la malaria transmitida por vectores infecciosos. A través de conocimientos biológicos y estadísticos, se ha podido demostrar procesos epidemiológicos que conlleven un mayor entendimiento en los patrones de comportamiento del Aedes Aegypti. En este trabajo se realiza modelación retrospectiva a través de los modelos Ross-Macdonald y Anderson-May, que simulan series de tiempo para estimar los casos observados de dengue en la Provincia del Guayas en el periodo 2014-2017. La modelación obtuvo una precisión del 43.3% y 46.5% aplicando un proceso iterativo de los parámetros entomológicos que alimentan a los modelos.
Palabras clave: Modelo Ross-McDonald, Modelo Anderson-May, semana epidemiológica, parámetros entomológicos, simulación por computador
Citas
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Carrascal, D. R. (2013). Adaptation Strategies to Climate Change in the Tropics: Analysis of Two Multifactorial Systems.
Degallier, N., Favier, C., Menkes, C., Lengaigne, M., Ramalho, W. M., Souza, R., Boulanger, J. P. (2010). Toward an early warning system for dengue prevention: Modeling climate impact on dengue transmission. Climatic Change, 98(3), 581-592. https://doi .org/10.1007/s10584-009-9747-3.
Draper, N. R., & Smith, H. (1998). Applied Regression Analysis. Technometrics, 47(3), 706. https://doi .org/10.1198/tech.2005.s303.
Enduri, M. K., & Jalad, S. (2014). Dynamics of Dengue with human and vector mobility. Spatial and Spatto-Temporal Epidemiology. https://doi.org/10.1016/j.sste.2018.03.001.
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Rencher, A. C. (2012). Methods of Multivariate Analysis, Second Edition. IIE Transactions (Vol. 37). https://doi .org/10 .1080/07408170500232784.
Rodgers, J. L., & Nicewander, W. A. (1988). Thirteen Ways to Look at the Correlation Coefficient. The American Statistician, 42(1), 59. https://doi.org/10.2307/2685263.
Ross, R. (1908). Prevention of Malaria in Mauritius. Southern Medical Journal, 3(2).
Sáez, V. S., & Suárez, L. A. C. (2013). El dengue en el nororiente de Venezuela y su incidencia en condiciones extremas de las lluvias durante 2009 y 2010. Investigaciones Geográficas, (59), 171-182. https://doi.org/10.14198/INGE02013.59.10.
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Smith, D. L., Battle, K. E., Hay, S. 1., Barker, C. M., Scott, T. W, & McKenzie, F. E. (2012). Ross, Macdonald, and a theory for the dynamics and control of mosquito-transmitted pathogens. PLoS Pathogens, 8(4). https://doi.org/10.1371/journal.ppat.1002588.
Stewart-Ibarra, A. M., & Lowe, R. (2013). Climate and non-climate drivers of dengue epidemies in southern coastal Ecuador. American Journal of Tropical Medicine and Hygiene, 88(5), 971-981. https: //doi .org/10.4269/aj tmh .12-0478.
Stewart-Ibarra, A. M., Muñoz, Á. G., Ryan, S. J., Ayala, E. B., Borbor-Cordova, M. J., Finkelstein, J. L., Rivero, K. (2014). Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010. BMC lnfectious Diseases, 14(1), 610. https://doi.org110.1186/s12879-014-0610-4.
Sweets, J. A. (1996). Signa' detection theory and ROC analysis in psychology and diagnostics: collected papers. https://doi.org110.10171CB09781107415324.004.
Swets, J. (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285-1293. https://doi.org110.1126/science.3287615.
Tukey, J. W. (1977). Exploratory Data Analysis. Analysis, 2(1999), 688. https://doi.org110.1007/978-1-4419-7976-6.
World Health Organization. (2016). WORLD HEALTH ST ATISTIC S - MONITORING HEALTH FOR THE SDGs. World Health Organization, 1.121. https://doi.org/10.1017/CB09781107415324.004.
Worrall, E., Connor, S. J., & Thomson, M. C. (2007). A model to simulate the impact of timing, coverage and transmission intensity on the effectiveness of indoor residual spraying (IRS) for malaria control. Tropical Medicine and International Health, 12(1), 75-88. https://doi.org/10.1111/j.1365-3156.2006.01772.x
Alonso, D., Bouma, M. J., & Pascual, M. (2011). Epidemic malaria and warmer temperatures in recent decades in an East African highland. Proceedings. Biological Sciences / The Royal Society, 278(1712), 1661-1669. https://doi.org/1 0.1098/rspb.2010.2020.
Anderson, M. P., Woessner, W. W., & Hunt, R. J. (2015). Applied Groundwater Modeling: Simulation of Flow and Advective Transport. Applied Groundwater Modeling. Imps://doi.org/10.1016/13978-0-08-091638- 5.00001-8.
Anderson, R. M., & May, R. M. (1991). Infectious Diseases of Humans: Dynamics and Control (Oxford Univ. Press, Oxford).
Cameron, A. C., & Windmeijer, F. A. G. (1996). An R-squared measure of goodness of fit for some common nonlinear regression models, 0(i), 329-342.
Carrascal, D. R. (2013). Adaptation Strategies to Climate Change in the Tropics: Analysis of Two Multifactorial Systems.
Degallier, N., Favier, C., Menkes, C., Lengaigne, M., Ramalho, W. M., Souza, R., Boulanger, J. P. (2010). Toward an early warning system for dengue prevention: Modeling climate impact on dengue transmission. Climatic Change, 98(3), 581-592. https://doi .org/10.1007/s10584-009-9747-3.
Draper, N. R., & Smith, H. (1998). Applied Regression Analysis. Technometrics, 47(3), 706. https://doi .org/10.1198/tech.2005.s303.
Enduri, M. K., & Jalad, S. (2014). Dynamics of Dengue with human and vector mobility. Spatial and Spatto-Temporal Epidemiology. https://doi.org/10.1016/j.sste.2018.03.001.
Gobierno Provincial del Guayas. (2013). Plan de Ordenamiento Territorial de la Provincia del Guayas 2012 - 2021: Versión 004, 4, 425. Retrieved from http://www.guayas.gob.e/dmdocuments/ley-de-transparcncia/literal-k/Plan-de-Ordcnamiento-T-2013.pdf.
Hanley, A. J., & McNeil, J. B. (1982). The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve. Radiology, 143, 29-36. https://doi.org/10.1148/radiology.143.1.7063747.
Hotez, P. J., Woc-Colburn, L., & Bottazzi, M. E. (2014). Neglected tropical diseases in Central America and Panama: Review of their prevalence, populations at risk and impact on regional development. International Journal for Parasitology, 44(9), 597-603. https://doi .org/10.1016/j ijpara.2014.04 .001.
Ibarra, A. M. S., Ryan, S. J., Beltrá N, E., Mejía, R. L., Silva, M., & Muñoz, N. (2013). Dengue Vector Dynamics (Aedes aegypti) Influenced by Climate and Social Factors in Ecuador: Implications for Targeted Control. PLoS ONE, 8(11). https://doi .org/10.1371/journal.pone. 0078263.
Juan Granda; Subsistema de Sive-Alerta; (2017). Gaceta Epidemiológica Semanal No. 31. Guayaquil. Retrieved from http ://www. sa lud. gob. ec/wp-content/uploads/2013/02/Gac eta-general-SE-31.pdf.
Lowe, R., Stewart-Ibarra, A. n, Petrova, D., García-díez, M., Borbor-cordova, M. J., Mejía, R., ... Rodó, X. (2016). Articles Climate services for health: predicting the evolution of the 2016 dengue season in Machala , Ecuador, 142-151. https://doi.org/10.1016/S2542- 5196(17)30064-5.
Mordecai, E. A., Cohen, J. M., Evans, M. V., Gudapati, P., Johnson, L. R., Lippi, C. A., ... Weikel, D. P. (2017). Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models. PLOS Neglected Tropical Diseases, 11(4), e0005568. https://doi.org110.13711journal.pntd.0005568.
Raquel Lovat; Subsistema de Sive-Alerta. (2015a). Gaceta Epidemiológica Semanal No. 27. Retrieved from http ://instituciones.msp.gob.ec/images/Documentos/gaceta/GACETA_SE_27.pdf
Raquel Lovat; Subsistema de Sive-Alerta. (2015b). Gaceta Epidemiológica Semanal No. 40. Retrieved from http://instituciones.msp.gob.ec/irnages/Documentos/Ministerio/EPIDEMIOLOGIA/gaceta2014/Gaceta N 51_opt.pdf.
Rencher, A. C. (2012). Methods of Multivariate Analysis, Second Edition. IIE Transactions (Vol. 37). https://doi .org/10 .1080/07408170500232784.
Rodgers, J. L., & Nicewander, W. A. (1988). Thirteen Ways to Look at the Correlation Coefficient. The American Statistician, 42(1), 59. https://doi.org/10.2307/2685263.
Ross, R. (1908). Prevention of Malaria in Mauritius. Southern Medical Journal, 3(2).
Sáez, V. S., & Suárez, L. A. C. (2013). El dengue en el nororiente de Venezuela y su incidencia en condiciones extremas de las lluvias durante 2009 y 2010. Investigaciones Geográficas, (59), 171-182. https://doi.org/10.14198/INGE02013.59.10.
Sepúlveda-Salcedo, L. S., Vasilieva, O., Martínez-Romero, H. J., & Arias-Castro, J. II. (2015). Ross McDonald: Un modelo para la dinámica del dengue en Cali, Colombia Ross-Macdonald: A modcl for the dengue dynamic in Cali, Colombia. Rev. Salud Publica, 17(5), 749-761. https://doi.org/10.15446/rsap.v17n5.4468.
Smith, D. L., Battle, K. E., Hay, S. 1., Barker, C. M., Scott, T. W, & McKenzie, F. E. (2012). Ross, Macdonald, and a theory for the dynamics and control of mosquito-transmitted pathogens. PLoS Pathogens, 8(4). https://doi.org/10.1371/journal.ppat.1002588.
Stewart-Ibarra, A. M., & Lowe, R. (2013). Climate and non-climate drivers of dengue epidemies in southern coastal Ecuador. American Journal of Tropical Medicine and Hygiene, 88(5), 971-981. https: //doi .org/10.4269/aj tmh .12-0478.
Stewart-Ibarra, A. M., Muñoz, Á. G., Ryan, S. J., Ayala, E. B., Borbor-Cordova, M. J., Finkelstein, J. L., Rivero, K. (2014). Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010. BMC lnfectious Diseases, 14(1), 610. https://doi.org110.1186/s12879-014-0610-4.
Sweets, J. A. (1996). Signa' detection theory and ROC analysis in psychology and diagnostics: collected papers. https://doi.org110.10171CB09781107415324.004.
Swets, J. (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285-1293. https://doi.org110.1126/science.3287615.
Tukey, J. W. (1977). Exploratory Data Analysis. Analysis, 2(1999), 688. https://doi.org110.1007/978-1-4419-7976-6.
World Health Organization. (2016). WORLD HEALTH ST ATISTIC S - MONITORING HEALTH FOR THE SDGs. World Health Organization, 1.121. https://doi.org/10.1017/CB09781107415324.004.
Worrall, E., Connor, S. J., & Thomson, M. C. (2007). A model to simulate the impact of timing, coverage and transmission intensity on the effectiveness of indoor residual spraying (IRS) for malaria control. Tropical Medicine and International Health, 12(1), 75-88. https://doi.org/10.1111/j.1365-3156.2006.01772.x
