Behavior also differs across age groups in ways that affects susceptibility. In high population density areas, younger people have contacts more frequently and at closer quarters – e.g., on urban transport, in offices, and on playgrounds. Older people who are homebound and do not have as many contacts have a lower risk of being near an infectious person, but when near such a person, may have a higher risk of getting infected because of weaker immune systems.

In SEIR models, one way to simulate these differences in behavioral and biological factors of susceptibility is to introduce varying transmission probabilities defined in advance for different age compartments. With more computing power available in recent years, such age‐structured models have become more common.

When simulating the effectiveness of public health policies, as much heterogeneity in risk of infection is desirable as is reasonable to model. Location is another important source of such variation. Urban areas can be quarantined or social distancing measures imposed, which cut the contact rates in the population. But these are more difficult to impose in rural areas where there are less defined modes of entry and exit and the population is harder to reach with public health communication. Therefore, while in rural areas the average number of effective contacts per person might be low, the public health measures might have low impact. And as occurs for older people, rural people on average may have poorer health status. They may also have poor access to antivirals. In the pandemic influenza case, since the entire country is susceptible to pandemic influenza and rapid transmission via air and road networks is probable, modeling simultaneous SEIR progress in multiple cities that exchange susceptible and infectious people is desirable. However, this requires mainframe computing resources if the time step of a day is considered.

**_Physical distribution and structure of populations_** : In comparison with non‐ spatial SEIR, _stochastic­spatial_ models have more success in incorporating population and location heterogeneity. They also have a different intent and modeling philosophy. A stochastic‐spatial epidemic model simulates the epidemic process as a series of random events in space and time with the probability of specific events defined by the model parameters. The two event probabilities of interest are the probability of effective contact ( _c_ ), and probability of transmission on contact ( _x_ ). The former varies at the individual level based on factors – location in space, and characteristics of that location. The latter varies in reality based on age, public health context, individual behavior, etc., and is usually a constant in analysis as it is difficult to model.

In a recent stochastic‐spatial model of pandemic flu spread in rural SE Asia the location contexts are called ‘mixing groups’ (e.g., offices, schools, and households), and for each such context a specific probability of effective contact per day is defined<sup>3</sup> . The age‐ distribution of individuals, number of mixing groups and their average sizes, as well as their clustering are set so as to mimic a real population (in this case rural Thailand). Average inter‐mixing group distances –important to generate the spread across the simulated space – are based on GIS data. At the time of model generation, 500,000 individuals are distributed according to an algorithm in close contact mixing groups (households, schools, and workplaces), as well as casual/social contact mixing groups (temples, markets, shops). The people have effective contacts based on the assumed average rates for that mixing group context. For example, the authors assume that the probability of two children making at least one effective contact per day in a household mixing group is 0.635.

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