Mixing Between Adjacent Regions in Spatiotemporal Models
The Introduction to Compartment Models describes how an infectious disease evolves in time. STEM also allows users to represent the geographic distribution of people (or animals) and how they move about in space. A compartment model that deals only with the trajectory of a disease in time implicitly assumes that the population (or populations) in question is so well mixed that there is no need to model the spatial distribution of people. However, for very large scale simulations, the details of population distribution, transportation, trade, even wild bird migration can all be important factors in understanding the evolution of an infectious disease in space and time. The following page describes how Transportation Models are implemented in STEM.
Without going into the complete details of a SpatioTemporal Compartment model, let’s look only at the interaction term defined in equation <1a>. Imagine that this equation is being used to define only the rate at which infectious people at some particular location j cause susceptible people at the same location j to become infectious. We might then write the same equation as:
Let the rate of infection in the population 𝑗 form mixing with population 𝑘 be:
Then the rate of new infections (incidence rate) in population 𝑘 is:
𝑚𝑗𝑘 is the mixing rate between 𝑗 and 𝑘 (note that 𝑚𝑗𝑗=1 ) 𝐼𝑘 is the number of infectious in population 𝑘 𝐾 is the total number of populations 𝑁𝑘 is the population in patch 𝑘 𝛽 is the transmission coefficient
Under these assumptions, the within patch reproductive number is:
where the sum is taken over all sites k. The vectors that connect different locations are represented by the set of mjk. Connections need not be only between adjacent locations. For example, air travel map provide connections between distant sites.
Today STEM disease models use several types of edges to model transportation.
- Common Border Edges with Common Border Labels are used to compute the circulation of people between neighbors by mixing as described above.
Common Border Edges are based only on geometrical and geographic facts.
- Road Transportation Edges are connections weighted by the number of roads connecting two regions
- Air Transportation Edges represent transportation by Air Travel
In the future STEM will also provide Edges that model motion specific to particular populations or species. These new Mixing and Migration'' edges will associate a transportation rate with the population by name.
- Mixing Edges represent circulation of the population with each edge associated with a particular population by name. Today this is done with (bidirectional) Common Border Edges (and the computation simply assumes a human population).
- Migration Edges represent migration of people over time. With these directed edges, users can specify migration rates or create their own migration model.
Not all administrative regions are the same size. The STEM model for mixing or circulation of a population scales the mixing rate by an effective depth λ for each region. This is analogous assuming a skin depth or average mixing length scale. The figure below shows a simple example of two adjacent nearly square regions of area Ai and Aj respectively.
Today stem uses the simplest possible approximation for the characteristic mixing in this model, the fraction of people leaving site j for any neighboring site i becomes:
Where the average mixing length, 𝜆o, is determined as part of an optimization process. Typically we find 𝜆o ≲ 45km.
In reality, the polygons in STEM are quite varied in shape. The figure below shows two adjacent regions (again with simple shapes) associated with a common border of length ℓij
In this example a better approximation for the fraction of people leaving site j for any neighboring site k is given by:
The STEM Common Border edge data sets now include the correct common border length, ℓij, for each edge and the approximation above will soon be implemented.
Migration Between Regions
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