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dabeleWadimKoslowmknaranja
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155 add vaccination model from paper (#233)
Added the new SECIRVVS model which accounts for three different paths or subpopulations. One naive population and two populations having different levels of immunity. These levels can be obtained by vaccination schemes. Infection, hospitalization etc. are possible for all subpopulations but with different probabilities. An undergone infection makes the individual go to the subpopulation which has the highest immunity level. (Re)infection is possible for all individuals. Co-authored-by: kosl_wa <[email protected]> Co-authored-by: Martin J. Kühn <[email protected]>
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Lines changed: 12491 additions & 1417 deletions

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‎cpp/CMakeLists.txt‎

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@@ -74,6 +74,7 @@ add_subdirectory(memilio)
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if (MEMILIO_BUILD_MODELS)
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add_subdirectory(models/abm)
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add_subdirectory(models/secir)
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add_subdirectory(models/ode_secirvvs)
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add_subdirectory(models/ide_seir)
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add_subdirectory(models/ode_seir)
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endif()

‎cpp/examples/parameter_study_secir.cpp‎

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@@ -19,8 +19,9 @@
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*/
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#include "secir/secir_parameters_io.h"
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#include "secir/parameter_space.h"
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#include "secir/parameter_studies.h"
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#include "memilio/compartments/parameter_studies.h"
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#include "memilio/mobility/mobility.h"
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#include "memilio/io/result_io.h"
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/**
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* @brief creates xml file with a single run parameter study with std 0 (used to save parameters of individual runs)
@@ -68,8 +69,10 @@ write_single_run_result(const int run,
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std::transform(graph.nodes().begin(), graph.nodes().end(), std::back_inserter(ids), [](auto& node) {
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return node.id;
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});
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auto num_groups = (int)(size_t)graph.nodes()[0].property.get_simulation().get_model().parameters.get_num_groups();
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BOOST_OUTCOME_TRY(
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mio::save_result(all_results, ids, mio::path_join(abs_path, ("Results_run" + std::to_string(run) + ".h5"))));
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mio::save_result(all_results, ids, num_groups,
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mio::path_join(abs_path, ("Results_run" + std::to_string(run) + ".h5"))));
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return mio::success();
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}
@@ -163,17 +166,20 @@ int main()
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//create study
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auto num_runs = size_t(1);
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mio::ParameterStudy<mio::SecirSimulation<>> parameter_study(model, t0, tmax, 0.2, num_runs);
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mio::ParameterStudy<mio::SecirSimulation<>> parameter_study(model, t0, tmax, num_runs);
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//run study
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int run = 0;
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auto lambda = [&run](auto&& graph) {
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int run = 0;
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auto sample_graph = [](auto&& graph) {
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return mio::draw_sample(graph);
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};
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auto handle_result = [&run](auto&& graph) {
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auto write_result_status = write_single_run_result(run++, graph);
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if (!write_result_status) {
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std::cout << "Error writing result: " << write_result_status.error().formatted_message();
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}
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};
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parameter_study.run(lambda);
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parameter_study.run(sample_graph, handle_result);
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return 0;
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}

‎cpp/examples/read_graph.cpp‎

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@@ -18,8 +18,10 @@
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* limitations under the License.
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*/
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#include "memilio/mobility/mobility.h"
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#include "secir/secir_parameters_io.h"
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#include "memilio/io/mobility_io.h"
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#include "memilio/compartments/parameter_studies.h"
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#include "secir/parameter_space.h"
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#include "secir/secir_parameters_io.h"
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#include <data_dir.h>
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#include <iostream>
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‎cpp/memilio/CMakeLists.txt‎

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@@ -2,6 +2,9 @@ configure_file(config_internal.h.in memilio/config_internal.h)
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add_library(memilio
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config.h
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data/analyze_result.h
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data/analyze_result.cpp
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epidemiology/age_group.h
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epidemiology/populations.h
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epidemiology/damping.cpp
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epidemiology/damping.h
@@ -15,16 +18,22 @@ add_library(memilio
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epidemiology/dynamic_npis.cpp
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epidemiology/regions.h
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epidemiology/regions.cpp
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epidemiology/simulation_day.h
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epidemiology/holiday_data_de.ipp
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compartments/compartmentalmodel.h
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compartments/simulation.h
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compartments/parameter_studies.h
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io/io.h
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io/io.cpp
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io/hdf5_cpp.h
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io/json_serializer.h
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io/json_serializer.cpp
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io/mobility_io.h
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io/mobility_io.cpp
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io/result_io.h
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io/result_io.cpp
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io/epi_data.h
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io/epi_data.cpp
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math/euler.cpp
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math/euler.h
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math/smoother.h

‎cpp/memilio/compartments/compartmentalmodel.h‎

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@@ -58,7 +58,7 @@ template <class T>
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using has_apply_constraints_member_function = is_expression_valid<details::apply_constraints_expr_t, T>;
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/**
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* @brief ComppartmentalModel is a template for a compartmental model for an
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* @brief CompartmentalModel is a template for a compartmental model for an
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* array of initial populations and a parameter set
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*
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* The Populations must be a concrete class derived from the Populations template,
@@ -71,9 +71,10 @@ using has_apply_constraints_member_function = is_expression_valid<details::apply
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* studies
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*
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*/
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template <class Pop, class Params>
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template <class Comp, class Pop, class Params>
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struct CompartmentalModel {
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public:
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using Compartments = Comp;
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using Populations = Pop;
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using ParameterSet = Params;
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cpp/models/secir/parameter_studies.h renamed to cpp/memilio/compartments/parameter_studies.h

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@@ -20,8 +20,6 @@
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#ifndef PARAMETER_STUDIES_H
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#define PARAMETER_STUDIES_H
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23-
#include "secir/secir.h"
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#include "secir/parameter_space.h"
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#include "memilio/utils/time_series.h"
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#include "memilio/mobility/mobility.h"
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#include "memilio/compartments/simulation.h"
@@ -89,33 +87,18 @@ class ParameterStudy
8987
m_graph.add_node(0, model);
9088
}
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92-
/**
93-
* @brief create study for single compartment model with normal distributions.
94-
* Sets all parameters to normal distribution with specified relative deviation.
95-
* @param params SecirParams object
96-
* @param t0 start time of simulations
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* @param tmax end time of simulations
98-
* @param dev_rel relative deviation of parameters distributions
99-
* @param num_runs number of runs in ensemble run
100-
*/
101-
ParameterStudy(typename Simulation::Model const& model, double t0, double tmax, double dev_rel, size_t num_runs)
102-
: ParameterStudy(model, t0, tmax, num_runs)
103-
{
104-
set_params_distributions_normal(m_graph.nodes()[0].property, t0, tmax, dev_rel);
105-
}
106-
10790
/*
10891
* @brief Carry out all simulations in the parameter study.
10992
* Save memory and enable more runs by immediately processing and/or discarding the result.
11093
* @param result_processing_function Processing function for simulation results, e.g., output function.
11194
* Receives the result after each run is completed.
11295
*/
113-
template<class HandleSimulationResultFunction>
114-
void run(HandleSimulationResultFunction result_processing_function)
96+
template <class SampleGraphFunction, class HandleSimulationResultFunction>
97+
void run(SampleGraphFunction sample_graph, HandleSimulationResultFunction result_processing_function)
11598
{
11699
// Iterate over all parameters in the parameter space
117100
for (size_t i = 0; i < m_num_runs; i++) {
118-
auto sim = create_sampled_simulation();
101+
auto sim = create_sampled_simulation(sample_graph);
119102
sim.advance(m_tmax);
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121104
result_processing_function(std::move(sim).get_graph());
@@ -124,15 +107,16 @@ class ParameterStudy
124107

125108
/*
126109
* @brief Carry out all simulations in the parameter study.
127-
* Convinience function for a few number of runs, but uses a lot of memory.
110+
* Convenience function for a few number of runs, but uses a lot of memory.
128111
* @return vector of results of each run.
129112
*/
130-
std::vector<mio::Graph<mio::SimulationNode<Simulation>, mio::MigrationEdge>> run()
113+
template <class SampleGraphFunction>
114+
std::vector<mio::Graph<mio::SimulationNode<Simulation>, mio::MigrationEdge>> run(SampleGraphFunction sample_graph)
131115
{
132116
std::vector<mio::Graph<mio::SimulationNode<Simulation>, mio::MigrationEdge>> ensemble_result;
133117
ensemble_result.reserve(m_num_runs);
134118

135-
run([&ensemble_result](auto&& r) {
119+
run(sample_graph, [&ensemble_result](auto&& r) {
136120
ensemble_result.emplace_back(std::move(r));
137121
});
138122

@@ -219,47 +203,18 @@ class ParameterStudy
219203

220204
private:
221205
//sample parameters and create simulation
222-
mio::GraphSimulation<mio::Graph<mio::SimulationNode<Simulation>, mio::MigrationEdge>> create_sampled_simulation()
206+
template <class SampleGraphFunction>
207+
mio::GraphSimulation<mio::Graph<mio::SimulationNode<Simulation>, mio::MigrationEdge>>
208+
create_sampled_simulation(SampleGraphFunction sample_graph)
223209
{
224210
mio::Graph<mio::SimulationNode<Simulation>, mio::MigrationEdge> sim_graph;
225211

226-
//sample global parameters
227-
auto& shared_params_model = m_graph.nodes()[0].property;
228-
draw_sample_infection(shared_params_model);
229-
auto& shared_contacts = shared_params_model.parameters.template get<mio::ContactPatterns>();
230-
shared_contacts.draw_sample_dampings();
231-
auto& shared_dynamic_npis = shared_params_model.parameters.template get<DynamicNPIsInfected>();
232-
shared_dynamic_npis.draw_sample();
233-
234-
for (auto& params_node : m_graph.nodes()) {
235-
auto& node_model = params_node.property;
236-
237-
//sample local parameters
238-
draw_sample_demographics(params_node.property);
239-
240-
//copy global parameters
241-
//save demographic parameters so they aren't overwritten
242-
auto local_icu_capacity = node_model.parameters.template get<ICUCapacity>();
243-
auto local_tnt_capacity = node_model.parameters.template get<TestAndTraceCapacity>();
244-
auto local_holidays = node_model.parameters.template get<ContactPatterns>().get_school_holidays();
245-
node_model.parameters = shared_params_model.parameters;
246-
node_model.parameters.template get<ICUCapacity>() = local_icu_capacity;
247-
node_model.parameters.template get<TestAndTraceCapacity>() = local_tnt_capacity;
248-
node_model.parameters.template get<ContactPatterns>().get_school_holidays() = local_holidays;
249-
250-
node_model.parameters.template get<ContactPatterns>().make_matrix();
251-
node_model.apply_constraints();
252-
253-
sim_graph.add_node(params_node.id, node_model, m_t0, m_dt_integration);
212+
auto sampled_graph = sample_graph(m_graph);
213+
for (auto&& node : sampled_graph.nodes()) {
214+
sim_graph.add_node(node.id, node.property, m_t0, m_dt_integration);
254215
}
255-
256-
for (auto& edge : m_graph.edges()) {
257-
auto edge_params = edge.property;
258-
apply_dampings(edge_params.get_coefficients(), shared_contacts.get_dampings(), [&edge_params](auto& v) {
259-
return make_migration_damping_vector(edge_params.get_coefficients().get_shape(), v);
260-
});
261-
edge_params.set_dynamic_npis_infected(shared_dynamic_npis);
262-
sim_graph.add_edge(edge.start_node_idx, edge.end_node_idx, edge_params);
216+
for (auto&& edge : sampled_graph.edges()) {
217+
sim_graph.add_edge(edge.start_node_idx, edge.end_node_idx, edge.property);
263218
}
264219

265220
return make_migration_sim(m_t0, m_dt_graph_sim, std::move(sim_graph));

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