Epidemics

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Pandemics history info on the Internet

Past Pandemic Timeline in Nature

H5N1 outbreaks in birds and other animals in Nature

Human cases of avian flu in Nature


Influenza report 2006 - on-line book

Review of epidemics models

Ferguson's, in France[1]

Quite large amount of data on epidemics in France from Sentinel and Centre National de Reference de la Grippe, region Sud, Laboratoire de Virology.

  • workplaces not explicitly included
  • transmission model: probability of the infection of the individual i, p_i=1-\exp^{-\lambda_{i,y}(t) \Delta T}, where \lambda_i is the instantaneous infection risk of the individual i.
  • transmission rate in a household = \beta^{a}_{hous} f(t)/n, where a - age (Adult(>=18) or Child), f(t) - relative infectiousness according to time t since infection, n - the size of the household.
  • transmission rates for: within household infection (A,C), within school, within community (A->A,C->C,A<->C). Hence, the overall risk of infection includes: the household risk, the within school risk, the community risk
  • time step = 6h
  • annual variations of influenza infections modeled via: the strength of transmission during the year and the relative contribution of children to transmission during the year (included in \lambda_{i,y}(t)), where y - year

Ferguson's, in UK and USA[2]

  • households, schools, workplaces included in the model
  • commuting data also included (the average distances traveled from hh to wp); the UK distance distribution more-less like power law, that of USA - surprisingly a bit different
  • air travel data included
  • time step = 6h
  • transmission model: probability of the infection of the individual i, p_i=1-\exp^{-\lambda_i(t) \Delta T}, where \lambda_i is the instantaneous infection risk of the individual i.

Ferguson's, in Thailand[3]

  • households, schools, workplaces included in the model
  • travel data - not enormous, but some, generalized into probabilities of commuting within district
  • seasonality not included (available data suggested that climate changes do not influence the infectivity rate)


Ferguson's in UK[4]

Dunham's general model - MASON[5]

Germann's, in USA[6]

Stroud's, in southern California, USA[7]

References

  1. CAUCHEMEZ S, Valleron A J, Boelle P Y, Flahault A, and Ferguson N M, Estimating the impact of school closure on influenza transmission from Sentinel data, Nature, 452 (2008), pp.750-754
  2. FERGUSON N M, Cummings D A T, Fraser C, Cajka J C, Cooley P C, and Burke D S, Strategies for mitigating an influenza pandemic, Nature, 442 (2006), pp. 448-452
  3. FERGUSON N M, Cummings D, Cauchemez S, Fraser C, Riley S, Meeyai A, Iamsirithaworn S, and Burke D, Strategies for containing an emerging influenza pandemic in Southeast Asia, Nature, 437 (2005), pp. 209-214
  4. FERGUSON N M, Donnelly C, and Anderson R A, Transmission intensity and impact of control policies on the foot and mouth epidemic in Great Britain, Nature, 413 (2001), pp. 542-548
  5. DUNHAM J B, An Agent-Based Spatially Explicit Epidemiological Model in MASON Journal of Artificial Societies and Social Simulation,2005, 9(1)3
  6. GERMANN T C, Kadau K, Longini I, and Macken C, Mitigation strategies for pandemic influenza in the United States, Proc. Nat. Acad. Sci., 103 (2006), pp.5935-5940
  7. STROUD P, Del Valle S, Sydoriak S, Riese J and Mniszewski S (2007). Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society,Journal of Artificial Societies and Social Simulation 10(4)9