Lec18-Modeling

Lec18-Modeling - Introduction to infectious disease...

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1 Introduction to infectious disease modelling Jamie Lloyd-Smith Epi 220, Fall 2010 UCLA • Why model? • Some terminology • Microparasite models (the SIR family) • Modeling transmission R 0 • Macroparasite models Basic results from SIR models Outline • Elements of more advanced models • Types of data that modelers can use • Linking infectious diseases to virology, evolution, ecology, and beyond • Convince you that models are useful and relevant. • Teach enough that you can be an intelligent consumer of modeling studies. • Lay groundwork for you to collaborate with modelers in future – or even do it yourself! Goals Why do we model infectious diseases? 1. Gain insight into mechanisms influencing disease spread, and link individual scale ‘clinical’ knowledge with population-scale patterns. 2. Focus thinking : model formulation forces clear statement of assumptions, hypotheses. 3. Derive new insights and hypotheses from mathematical analysis or simulation. 4 Establish relative importance of different processes and parameters Following Heesterbeek & Roberts (1995) 4. Establish of different processes and parameters, to focus research or management effort. 5. Thought experiments and “what if” questions, since real experiments are often logistically or ethically impossible. 6. Explore management options . Note the absence of prediction. Models are highly simplified representations of very complex systems, and parameter values are difficult to estimate. exact quantitative predictions are virtually impossible. Why do we model?
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2 Epidemic models: the role of data Why work with data? Basic aim is to describe real patterns, solve real problems. Test assumptions! Get more attention for your work jobs, fame, fortune, etc influence public health policy Challenges of working with data Hard to get good data sets. The real world is messy! And sometimes hard to understand. Statistical methods for non-linear models can be complicated. What about pure theory? Valuable for clarifying concepts, developing methods, integrating ideas. (My opinion) The world needs a few brilliant theorists, and many strong applied modellers. Modeling terminology State variables : quantities that describe the entities of interest for your model. e.g. population size, allele frequency, disease prevalence Parameters : quantities that govern the dynamics, but don’t describe the state of the system and (typically) don’t change over time. e.g. per capita birth rate, carrying capacity, mean lifespan Infectious disease epidemiology Incidence: number of new infections per unit time. Prevalence: proportion of population that is infected at a particular time. Attack rate: proportion of susceptible individuals in a given setting that become infected.
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Lec18-Modeling - Introduction to infectious disease...

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