ODE-based metapopulation model

Introduction

This model incorporates spatial resolution into an ODE-based metapopulation model by including the effects of other regions into the force of infection term of the ODEs. A population is divided into several subpopulations, each representing spatially seperated regions which interact on some level. Each subpopulation is further divided into epidemiological compartments and eventually into age groups. Commuting between regions is governed by a commuting matrix, which describes the fraction of individuals that commute from one region to another.

Note

In comparison to the Graph-based metapopulation model, commuting is not performed explicitly, i.e., individuals are not exchanged between subpopulations, but rather their theoretical impact to transmission dynamics in other locations is considered, leading to a large system of ODEs which can be solved using standard numerical integration methods. For a dense network, this approach scales better with the number of regions than the graph-based approach. Currently, the implicit mobility approach is only available for the SEIR model.

Simulation

As the model is given as a large system of ODEs, the simulation can be performed by solving the system using a standard ODE solver. The model is build on the same setup as the simpler ODE-models, so we refer to ODE-based models for more details.

How to: Set up and run the ODE-based metapopulation model

To set up a simulation of the ODE metapopulation model, you need to initialize the model with the desired number of regions and age groups, e.g., 3 regions and 1 age group:

mio::oseirmetapop::Model<ScalarType> model(3, 1)

Set a population with the number of individuals in each region, age group and epidemiological compartment, e.g.:

model.populations[{mio::oseirmetapop::Region(0), mio::AgeGroup(0), mio::oseirmetapop::InfectionState::Susceptible}] = 900;
model.populations[{mio::oseirmetapop::Region(0), mio::AgeGroup(0), mio::oseirmetapop::InfectionState::Exposed}]     = 100;
model.populations[{mio::oseirmetapop::Region(1), mio::AgeGroup(0), mio::oseirmetapop::InfectionState::Susceptible}] = 1000;
model.populations[{mio::oseirmetapop::Region(2), mio::AgeGroup(0), mio::oseirmetapop::InfectionState::Susceptible}] = 1000;

and the epidemiological parameters, e.g.:

model.parameters.template get<ContactPatterns<>>().get_cont_freq_mat()[0].get_baseline().setConstant(2.7);

model.parameters.set<mio::oseirmetapop::TimeExposed<>>(3.335);
model.parameters.set<mio::oseirmetapop::TimeInfected<>>(8.097612257);
model.parameters.set<mio::oseirmetapop::TransmissionProbabilityOnContact<>>(0.07333);

Currently, the contact matrix is set globally, so it is the same for every region. The other parameters can be set for every region individually by

model.parameters.get<mio::oseirmetapop::TimeExposed<>>()[{mio::regions::Region(0), mio::AgeGroup(0)}] = 3.

Construct an Eigen::MatrixXd of size \(n_{regions} \times n_{regions}\) which describes the fraction of individuals commuting from one region to another. The matrix should satisfy the sum of each row equal to 1.0, e.g.:

Eigen::MatrixXd commuting_strengths(3, 3);
commuting_strengths << 0.4, 0.3, 0.3, 0.2, 0.7, 0.1, 0.4, 0.1, 0.5;

Set the commuting strengths matrix via the set_commuting_strengths method to ensure that the population after commuting is correctly updated:

model.set_commuting_strengths(commuting_strengths);

If no mobility data is provided, you should still run model.set_commuting_strengths() which sets the matrix to an identity matrix, reflecting no mobility between regions. Without setting this, you will encounter errors during the simulation.

Finally, to run the simulation from t0 to tmax with a time step of dt, use the following command:

simulate(t0, tmax, dt, model);