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Modeling and analysis of global epidemiology of avian influenza

Modeling and analysis of global epidemiology of avian influenza. Very Briefly. This paper presents a simulation-based methodology to analyze the spread of H5N1 using stochastic interactions between waterfowl, poultry, and humans

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Modeling and analysis of global epidemiology of avian influenza

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  1. Modeling and analysis of global epidemiology of avian influenza

  2. Very Briefly.. • This paper presents a simulation-based methodology to analyze the spread of H5N1 using stochastic interactions between waterfowl, poultry, and humans • Discusses Avian Influenza’s impacts on human and poultry morbidity and mortality based on results from simulations.

  3. Avian Influenza • Avian influenza caused by H5N1, a highly virulent strain of the influenza-A virus • Virus is endemic to water flow in certain areas • It has a devastating impact on poultry causing 100% mortality within 48 h of infection • Spreads to humans through direct contact with infected poultry • Spreads to other parts of the world by infected migrating waterfowl

  4. SEARUMS • A multi-disciplinary modeling, simulation, and analysis environment that seamlessly integrates knowledge from various fields so that epidemiologists, economists, and disease control centers can collaboratively use it and combat Avian Influenza.

  5. Markov Process • Defined as a sequence of time-dependent random variables X0, X1, X2,., where Xt is a random variable that describes the state of the process at discrete-time t. • Next state to which the process transitions is purely determined by the current state of the system and not its past.

  6. Conceptual, Mathematical Model • 3 Biological entities – Water flow flocks, poultry flocks, human groups. • Life cycle events of individual entities modeled via probabilistic state changes occurring within each Markov process. • Spatial interactions between entities modeled using principles of spherical geometry.

  7. Conceptual, Mathematical Model • SIR life cycles of the three main entities: MPwf, MPpo, and MPhu have been modeled as three different Markov processes represented by the set MP = {MPwf ;MPpo;MPhu} • temporal state of each Markov process is represented by the 5-tuple, Smpt = < Xmpt ; Ympt ; Rmpt ; Impt ; Pmpt >

  8. Migration & Interactions • Migratory behavior of a process mp(mp ϵM) is reflected by periodic, time dependent changes to variables Xmpt and Ympt • Interactions occur when the time-dependent neighborhood of a process Ntmp(mp ϵM) is a non-empty set as per the following equation:

  9. Transmission • transmission of infection is modeled as a change in the measure of infection of the interacting entities as per the following equation:

  10. Markov Process for Waterflow flock

  11. Markov Process for Poultry

  12. Markov Process for Humans

  13. SEARUMS • Phase 1: Development of Eco-description using graphical interface, • Phase 2: Simulation and Data collection phase • Phase 3: Data Visualization and Analysis phase that partially overlaps with Phase 2.

  14. SEARUMS

  15. Experiment

  16. Conclusion • Spread of avian influenza to USA is inevitable. Moreover, the infection will recur year after year based on the cyclical migration patterns of the infected • Controlling the population of infected waterfowl will not slow down intercontinental spread of avian influenza. • Current form with unsustained human-to-human transmissions, an H5N1 pandemic in humans is unlikely

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