Epidemics

Virtual society	Virus spread	Literature	Version
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

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

• 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

• 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)