Importancia delos modelos matemáticos en Salud Pública
•Download as PPTX, PDF•
2 likes•1,311 views
Es una conferencia sobre la importancia del modelamiento matemático en Salud Pública y sobre el verdadero valor de esta herramienta para la toma de decisiones ademas de sus ventajas y desventajas, y que requisitos son necesario para juzgar un modelo matemático
Recommended
More Related Content
Similar to Importancia delos modelos matemáticos en Salud Pública
Similar to Importancia delos modelos matemáticos en Salud Pública (20)
Recently uploaded
Recently uploaded (20)
Importancia delos modelos matemáticos en Salud Pública
- 1. Importancia de los Modelos Matemáticos en Salud Pública César V. Munayco, MD, MSc, MPH Doctoral Student Department of Preventive Medicine and Biometrics Uniformed Services University of Health Sciences Bethesda, Maryland, USA. cesar.munayco@usuhs.edu
- 2. Usos de los modelos matemáticos en Salud Pública Informar sobre políticas de Salud Pública Simulación teórica de la patogénesis de una enfermedad Estimar el impacto de intervenciones sanitarias para controlar enfermedades epidémicas como influenza, VIH, etc. Determianr el impacto en la salud y estudios de costo-efectividad de intervenciones Basu S, Andrews J. Complexity in mathematical models of public health policies: a guide for consumers of models. PLoS Med. 2013 Oct;10(10):e1001540.
- 3. ¿Qué es un modelo matemático? A mathematical model is an abstract model that uses mathematical language to describe the behaviour of a system. http://www.sciencedaily.com/articles/m/mathematical_model.htm
- 4. “All models are wrong, but some are useful.” George Box
- 5. “Models should be as simple as possible, but not simpler” Albert Einsten
- 6. Principios del modelamiento matemático Dym CL. Principles of mathematical modeling. 2nd ed. Amsterdam ; Boston: Elsevier Academic Press; 2004. xviii, 303 p. p.
- 7. ¿Cómo se crea un modelo? Kallrath J. Modeling languages in mathematical optimization. Boston: Kluwer Academic Publishers; 2004. xxx, 407 p. p.
- 8. ¿Cómo se crea un modelo? Kallrath J. Modeling languages in mathematical optimization. Boston: Kluwer Academic Publishers; 2004. xxx, 407 p. p.
- 9. Tipo de modelos matemáticos • Deterministic models: the same input will produce the same output. The only uncertainty in a deterministic model is generated by input variation. • Stochastic models: model involves some randomness and will not produce the same output given the same input.
- 10. Modelos determinísticos • Input factors: parameter values, initial conditions • The input factors are uncertain due to • natural variation • error in measurements • lack of current measurement techniques
- 11. Ejemplo SIR model Keeling MJ, Danon L. Mathematical modelling of infectious diseases. British medical bulletin. 2009;92:33-42
- 12. Modelo Complejo Travis C. Porco, Sally M. Blower. Quantifying the Intrinsic Transmission Dynamics of Tuberculosis. Theoretical Population Biology 54, 117132 (1998)
- 13. Fiiting model to the data 2 4 6 8 10 12 14 0 50 100 150 200 250 300 time, day Number of children in bed
- 14. Fiiting model to the data 0 5 10 15 0 50 100 150 200 250 300 B time, day Numbers of Data fitted beta=2.4029, gamma=0.9093, delta=0.4123
- 15. Conceptos
- 16. Ejemplor de R0 Gregory E. Glass. Measuring Disease Dynamics in Populations: Characterizing the Likelihood of Control. On line course. Johns Hopkins University
- 17. Relación entre la tasa de ataque y el R0 Gregory E. Glass. Measuring Disease Dynamics in Populations: Characterizing the Likelihood of Control. On line course. Johns Hopkins University
- 18. Relación entre la inmunidad de grupo y el R0 Gregory E. Glass. Measuring Disease Dynamics in Populations: Characterizing the Likelihood of Control. On line course. Johns Hopkins University
- 19. Inmundidad de grupo y R0
- 20. Inmunidad de grupo *4 doses † Modified from Epid Rev 1993;15: 265-302, Am J Prev Med 2001; 20 (4S): 88-153, MMWR 2000; 49 (SS-9); 27-38
- 21. Generaciones de una epidemia Notes On R0. James Holland Jones. Department of Anthropological Sciences. Stanford University
- 22. Análisis de sensibilidad • The objective of SA is to identify critical inputs (parameters and initial conditions) of a model and quantifying how input uncertainty impacts model outcome(s). • Local sensitivity analysis (LSA): examine change in output values based only on changes in one input factor. • Global sensitivity analysis (GSA): examine change in output values when all parameter values change.
- 27. Implicancias de dos parámetros diferentes Basu S, Andrews J. Complexity in mathematical models of public health policies: a guide for consumers of models. PLoS Med. 2013 Oct;10(10):e1001540.
- 28. Un ejemplo de sobreajuste de un modelo Basu S, Andrews J. Complexity in mathematical models of public health policies: a guide for consumers of models. PLoS Med. 2013 Oct;10(10):e1001540.
- 29. Un ejemplo de sobreajuste de un modelo Basu S, Andrews J. Complexity in mathematical models of public health policies: a guide for consumers of models. PLoS Med. 2013 Oct;10(10):e1001540.
Editor's Notes
- Infectious disease models provide a mathematical representation of the dynamic transmission cycle, involving interactions between infected and susceptible hosts that are generally expressed as a set of coupled ordinary differential equations (ODEs)
- George Edward Pelham Box FRS was a english statistician, who worked in the areas of quality control, time-series analysis, design of experiments, and Bayesian inference.
- A parsimonious approach must be followed. Otherwise, if every mechanism and interaction is included, the resulting mathematical model will be comprised of a large number of variables, parameters, and constraints, most of them uncertain because they are difficult to measure experimentally, or are even completely unknown in many cases Parsimonious principle: It states that among competing hypotheses, the one with the fewest assumptions should be selected. Other, more complicated solutions may ultimately prove correct, but—in the absence of certainty—the fewer assumptions that are made, the better.
- Both a 1-month duration of acute infection with six secondary infections per month (top graph) and a 3-month duration of acute infection with two secondary infections per month (bottom graph) produce the same result of six infections per person during the acute infectious period. But the implications of the two different parameter sets are very different, as early treatment (red dashed line) would be effective in preventing secondary infections only in the latter case. Suppose we have a model of HIV with just two parameters: the number of infections per month during acute HIV infection and the duration of elevated transmission risk during acute infection. But we only have one data point that tells us that a typical infected person causes six secondary infections during their acute infectious Period.