Epidemics
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Revision as of 14:45, 5 November 2008 by Magd (talk | contribs) (→Germann's, in USAGERMANN 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)
Virtual society | Virus spread | Literature | Version |
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Polish virtual society epidemics model | |||
Guinea pigs epidemics model | |||
Virus genetic evolution epidemics model |
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, , where is the instantaneous infection risk of the individual i.
- transmission rate in a household , 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 ), 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, , where 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]
- no seasonal or environmental factors included, no virus evolution effects included
- basic geographic unit corresponds to census tract, it is a community of 2000 individuals: age, household size, and employment status distributions match US census data; agents divided into 5 age groups (0-4, 5-18, 9-29, 30-64, 64+ years) or community with no residents, only a workplace of about 100 individuals; in total, about 180000 communities are included; also travel data for the members of community included (in census tracts info);
- playgroups or daycare centers, schools, workplaces included; also contacts with people from neighborhood, occasional contacts in churches, supermarket, etc, included
- time step = 12h
- transmission model: probability of infection from a single contact, , where is the contact probability (dependent on the age of both contacting people), is the probability of transmission; antiviral treatment of the infectious contact person or the vaccination of the given individual diminish the ;
Total probability of the susceptible person to get infected, , where the product goes over all contacts with infectious individuals, with infection probability at ith contact given by .
- disease history includes latent, incubation, and contagious periods of average durations: 1.2, 1.9, and 4.1 days, respectively.
- model implemented in the molecular dynamics SPaSM code, substituting atoms by agents
Stroud's, in southern California, USA[7]
References
- ↑ 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
- ↑ 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
- ↑ 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
- ↑ 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
- ↑ DUNHAM J B, An Agent-Based Spatially Explicit Epidemiological Model in MASON Journal of Artificial Societies and Social Simulation,2005, 9(1)3
- ↑ 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
- ↑ 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