Study of the dispersion of the chagas disease through cellular automata
Main Article Content
Abstract
Chagas disease has become a public health problem throughout Latin America. The reason is the scarce knowledge of the human-vector-reservoir dynamics, influenced by climate change and alternate routes of contagion (transfusional, oral, congenital and probably sexual). Therefore, a thorough study of this
dynamic would provide relevant information to formulate proposals aimed at a definitive control of vectors and reservoirs. For this purpose, the study of this dynamics by Systems of Partial Differential Equations has not delivered a plausible proposal that fully complies with this objective, due to the same complexity of the dynamics. That is why Cellular Automata simulation technique is proposed to the scientific community, which is able to incorporate in a simple way the details of the dynamics and promises to give definitive lights for its understanding.
References
Alkmim. (2013). Trypanosoma cruzi experimental congenital transmission associated with TcV and TcI subpatent maternal parasitemia.
Bice, D., & Zeledón, R. (1970). Comparison of infectivity of strains of Trypanoso macruzi (Chagas, 1909). J Parasitol, 56, 663-670.
Boggio, G. (2000). Modelo de regresión logística aplicado a un estudio sobre enfermedad de Chagas. Rio de Jainero: Universidade Federal do Rio de Janeiro.
Chopard, B., & Droz, M. (1999). Cellular Automata Modeling of Physical System. Cambridge University Cambridge.
Chimelli, L., & Scaravilli, F. (1997). Trypanosomiasis. Brain Pathol, 7, 599-611.
Coffieldet. (2013). Mathematical models for neglected tropical diseases: essential tools for control and elimination. USA: Elservier.
Spagnuolo. (2007). Congenital Trypanosomacruzi infectionin a non-endemic area. Case report: Transactions of the Royal Society of Tropical Medicine and Hygiene, 101, 1161 – 1162
Dias, J. C. P. (1979). Mecanismos de transmissão. En Z. Brener & Z. Andrade (eds), Trypanosomacruzi e Doença de Chagas, Guanabara Koogan, (pp. 152-174). Rio de Janeiro.
Hernández, G., & Torres, L. (1994). Autómatas celulares estocásticos. Lecturas Matemáticas, 15, 167-191.
Inaba, H., & Sekine, H. (2004). A mathematical model for Chagas disease with infection-age- dependent infectifity. Mathematical Biosciences,
190(1), 39-69. doi:10.1016 j.mbs.2004.02.004
Lambert, R. C., Kolibras, K. N., et al. (2008). The potencial for emergence of Chagas disease in the United States. Geospatial Health, 2(2).
Mice. (2001). Experimental Transmission of Trypanosoma cruzi Through the Genitalia of Albino. Universidade Federal do Rio de Janeiro: UFRJ
Musso, V. (2008). Desarrollo de Software para la Simulación Espacio-Temporal de la Dinámica Poblacional de Roedores Transmisores de la Fiebre Hemorrágica. Recuperado de http://rdu.unc.edu.ar/bitstream/handle/11086/11/14319.pdf?sequence=1
OMS. (2003). Cambio Climático y salud humana. -Riesgos y respuestas. Resumen. Ginebra-Suiza.
Oquendo, W. F., & Muñoz, J. D. (2008). Simulación de la Propagación de una Epidemia Utilizando un Autómata Celular de Difusión Bidimensional. Revista Colombiana de Física, 40(2).
Sarmiento. (2004). Trypanosoma Cruzi-associated cerebrovascular disese: a case-control study in eastern Colombia. Journal of Neurological Sciences, 217, 61-64.
Scorza, C., Herrera, L., & Urdaneta-Morales, S. (1996). Trypanosomacruzi: histopathology in mice infected with strains isolated from Didelphismarsupialisfrom the valley of Caracas (Venezuela). Acta Cient Venez, 47, 244-247.
Xiaoyi, H., & Li-Shi, Luo. (1997). Theory of the lattice Boltzmann method: From the Boltzmann equation to the lattice Boltzmann aquation. Phys. Rev. E, 56, 6811-6817.