1341 smm model with more indices

cpp/examples/d_abm.cpp

@@ -1,5 +1,5 @@
 /*
-* Copyright (C) 2020-2024 German Aerospace Center (DLR-SC)
+* Copyright (C) 2020-2025 MEmilio
 *
 * Authors: Julia Bicker, René Schmieding
 *

cpp/examples/graph_abm.cpp

@@ -1,5 +1,5 @@
 /*
-* Copyright (C) 2020-2024 MEmilio
+* Copyright (C) 2020-2025 MEmilio
 *
 * Authors: Julia Bicker
 *

cpp/examples/smm.cpp

@@ -1,7 +1,7 @@
 /*
-* Copyright (C) 2020-2024 German Aerospace Center (DLR-SC)
+* Copyright (C) 2020-2025 MEmilio
 *
-* Authors: Julia Bicker, René Schmieding
+* Authors: Julia Bicker, René Schmieding, Kilian Volmer
 *
 * Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
 *
@@ -18,6 +18,7 @@
 * limitations under the License.
 */
 
+#include "memilio/epidemiology/age_group.h"
 #include "smm/simulation.h"
 #include "smm/model.h"
 #include "smm/parameters.h"
@@ -33,61 +34,80 @@ enum class InfectionState
     R,
     D,
     Count
-
 };
 
+using Age     = mio::AgeGroup;
+using Species = mio::AgeGroup;
+
 int main()
 {
 
     //Example how to run the stochastic metapopulation models with four regions
-    const size_t num_regions = 4;
-    using Model              = mio::smm::Model<ScalarType, num_regions, InfectionState>;
+    const size_t num_regions    = 4;
+    const size_t num_age_groups = 1;
+    const size_t num_groups     = 1;
+    using Model                 = mio::smm::Model<ScalarType, num_regions, InfectionState, num_age_groups, num_groups>;
 
     ScalarType numE = 12, numC = 4, numI = 12, numR = 0, numD = 0;
 
     Model model;
     //Population are distributed uniformly to the four regions
     for (size_t r = 0; r < num_regions; ++r) {
-        model.populations[{mio::regions::Region(r), InfectionState::S}] =
+        model.populations[{mio::regions::Region(r), InfectionState::S, Age(0), Species(0)}] =
             (1000 - numE - numC - numI - numR - numD) / num_regions;
-        model.populations[{mio::regions::Region(r), InfectionState::E}] = numE / num_regions;
-        model.populations[{mio::regions::Region(r), InfectionState::C}] = numC / num_regions;
-        model.populations[{mio::regions::Region(r), InfectionState::I}] = numI / num_regions;
-        model.populations[{mio::regions::Region(r), InfectionState::R}] = numR / num_regions;
-        model.populations[{mio::regions::Region(r), InfectionState::D}] = numD / num_regions;
+        model.populations[{mio::regions::Region(r), InfectionState::E, Age(0), Species(0)}] = numE / num_regions;
+        model.populations[{mio::regions::Region(r), InfectionState::C, Age(0), Species(0)}] = numC / num_regions;
+        model.populations[{mio::regions::Region(r), InfectionState::I, Age(0), Species(0)}] = numI / num_regions;
+        model.populations[{mio::regions::Region(r), InfectionState::R, Age(0), Species(0)}] = numR / num_regions;
+        model.populations[{mio::regions::Region(r), InfectionState::D, Age(0), Species(0)}] = numD / num_regions;
     }
 
     //Set infection state adoption and spatial transition rates
-    std::vector<mio::AdoptionRate<ScalarType, InfectionState>> adoption_rates;
-    std::vector<mio::smm::TransitionRate<ScalarType, InfectionState>> transition_rates;
+    std::vector<mio::AdoptionRate<ScalarType, InfectionState, Age, Species>> adoption_rates;
+    std::vector<mio::smm::TransitionRate<ScalarType, InfectionState, Age, Species>> transition_rates;
     for (size_t r = 0; r < num_regions; ++r) {
         adoption_rates.push_back({InfectionState::S,
                                   InfectionState::E,
                                   mio::regions::Region(r),
                                   0.1,
-                                  {{InfectionState::C, 1}, {InfectionState::I, 0.5}}});
-        adoption_rates.push_back({InfectionState::E, InfectionState::C, mio::regions::Region(r), 1.0 / 5., {}});
-        adoption_rates.push_back({InfectionState::C, InfectionState::R, mio::regions::Region(r), 0.2 / 3., {}});
-        adoption_rates.push_back({InfectionState::C, InfectionState::I, mio::regions::Region(r), 0.8 / 3., {}});
-        adoption_rates.push_back({InfectionState::I, InfectionState::R, mio::regions::Region(r), 0.99 / 5., {}});
-        adoption_rates.push_back({InfectionState::I, InfectionState::D, mio::regions::Region(r), 0.01 / 5., {}});
+                                  {{InfectionState::C, 1, mio::regions::Region(3), {Age(0), Species(0)}},
+                                   {InfectionState::I, 0.5, mio::regions::Region(1), {Age(0), Species(0)}}},
+                                  {Age(0), Species(0)}});
+        adoption_rates.push_back(
+            {InfectionState::C, InfectionState::R, mio::regions::Region(r), 0.2 / 3., {}, {Age(0), Species(0)}});
+        adoption_rates.push_back(
+            {InfectionState::E, InfectionState::C, mio::regions::Region(r), 1.0 / 5., {}, {Age(0), Species(0)}});
+        adoption_rates.push_back(
+            {InfectionState::C, InfectionState::I, mio::regions::Region(r), 0.8 / 3., {}, {Age(0), Species(0)}});
+        adoption_rates.push_back(
+            {InfectionState::I, InfectionState::R, mio::regions::Region(r), 0.99 / 5., {}, {Age(0), Species(0)}});
+        adoption_rates.push_back(
+            {InfectionState::I, InfectionState::D, mio::regions::Region(r), 0.01 / 5., {}, {Age(0), Species(0)}});
     }
 
     //Agents in infection state D do not transition
     for (size_t s = 0; s < static_cast<size_t>(InfectionState::D); ++s) {
         for (size_t i = 0; i < num_regions; ++i) {
             for (size_t j = 0; j < num_regions; ++j)
                 if (i != j) {
-                    transition_rates.push_back(
-                        {InfectionState(s), mio::regions::Region(i), mio::regions::Region(j), 0.01});
-                    transition_rates.push_back(
-                        {InfectionState(s), mio::regions::Region(j), mio::regions::Region(i), 0.01});
+                    transition_rates.push_back({InfectionState(s),
+                                                mio::regions::Region(i),
+                                                mio::regions::Region(j),
+                                                0.01,
+                                                {Age(0), Species(0)},
+                                                {Age(0), Species(0)}});
+                    transition_rates.push_back({InfectionState(s),
+                                                mio::regions::Region(j),
+                                                mio::regions::Region(i),
+                                                0.01,
+                                                {Age(0), Species(0)},
+                                                {Age(0), Species(0)}});
                 }
         }
     }
 
-    model.parameters.get<mio::smm::AdoptionRates<ScalarType, InfectionState>>()   = adoption_rates;
-    model.parameters.get<mio::smm::TransitionRates<ScalarType, InfectionState>>() = transition_rates;
+    model.parameters.get<mio::smm::AdoptionRates<ScalarType, InfectionState, Age, Species>>()   = adoption_rates;
+    model.parameters.get<mio::smm::TransitionRates<ScalarType, InfectionState, Age, Species>>() = transition_rates;
 
     ScalarType dt   = 0.1;
     ScalarType tmax = 30.0;

cpp/memilio/epidemiology/adoption_rate.h

@@ -1,7 +1,7 @@
 /* 
 * Copyright (C) 2020-2025 MEmilio
 *
-* Authors: René Schmieding, Julia Bicker
+* Authors: René Schmieding, Julia Bicker, Kilian Volmer
 *
 * Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
 *
@@ -23,35 +23,45 @@
 #include "memilio/utils/index.h"
 #include "memilio/config.h"
 #include "memilio/geography/regions.h"
+#include <limits>
+#include <tuple>
+#include <optional>
 
 namespace mio
 {
 
-/**
- * @brief Struct defining an influence for a second-order adoption.
- * The population having "status" is multiplied with "factor."
- * @tparam Status An infection state enum.
- */
-template <typename FP, class Status>
-struct Influence {
-    Status status;
-    FP factor;
-};
-
 /**
  * @brief Struct defining a possible status adoption in a Model based on Poisson Processes.
  * The AdoptionRate is considered to be of second-order if there are any "influences".
  * In the d_abm and smm simulations, "from" is implicitly an influence, scaled by "factor". This is multiplied by
  * the sum over all "influences", which scale their "status" with the respective "factor".
  * @tparam Status An infection state enum.
+ * @tparam Groups Additional grouping indices.
  */
-template <typename FP, class Status>
+template <typename FP, class Status, class... Groups>
 struct AdoptionRate {
+
+    /**
+ * @brief Struct defining an influence for a second-order adoption.
+ * The population having "status" is multiplied with "factor."
+ * @tparam status An infection state enum.
+ * @tparam factor Scaling factor for the influence.
+ * @tparam 
+ * @tparam Groups Additional grouping indices.
+ */
+    struct Influence {
+        Status status;
+        FP factor;
+        std::optional<mio::regions::Region> region = std::nullopt;
+        std::tuple<Groups...> group_indices{};
+    };
+
     Status from; // i
     Status to; // j
     mio::regions::Region region; // k
     FP factor; // gammahat_{ij}^k
-    std::vector<Influence<FP, Status>> influences; // influences[tau] = ( Psi_{i,j,tau} , gamma_{i,j,tau} )
+    std::vector<Influence> influences; // influences[tau] = ( Psi_{i,j,tau} , gamma_{i,j,tau} )
+    std::tuple<Groups...> group_indices{};
 };
 
 } // namespace mio

cpp/models/d_abm/model.cpp

@@ -1,5 +1,5 @@
 /*
-* Copyright (C) 2020-2025 German Aerospace Center (DLR-SC)
+* Copyright (C) 2020-2025 MEmilio
 *
 * Authors: René Schmieding
 *

cpp/models/d_abm/model.h

@@ -1,5 +1,5 @@
 /*
-* Copyright (C) 2020-2025 German Aerospace Center (DLR-SC)
+* Copyright (C) 2020-2025 MEmilio
 *
 * Authors: René Schmieding, Julia Bicker
 *

cpp/models/d_abm/parameters.h

@@ -1,5 +1,5 @@
 /*
-* Copyright (C) 2020-2024 German Aerospace Center (DLR-SC)
+* Copyright (C) 2020-2025 MEmilio
 *
 * Authors: René Schmieding, Julia Bicker
 *

cpp/models/d_abm/quad_well.h

@@ -1,5 +1,5 @@
 /* 
-* Copyright (C) 2020-2025 German Aerospace Center (DLR-SC)
+* Copyright (C) 2020-2025 MEmilio
 *
 * Authors: René Schmieding, Julia Bicker
 *

cpp/models/d_abm/simulation.cpp

@@ -1,5 +1,5 @@
 /*
-* Copyright (C) 2020-2024 MEmilio
+* Copyright (C) 2020-2025 MEmilio
 *
 * Authors: René Schmieding, Julia Bicker
 *

cpp/models/d_abm/simulation.h

@@ -1,5 +1,5 @@
 /*
-* Copyright (C) 2020-2025 German Aerospace Center (DLR-SC)
+* Copyright (C) 2020-2025 MEmilio
 *
 * Authors: René Schmieding, Julia Bicker
 *

cpp/models/d_abm/single_well.h

@@ -1,5 +1,5 @@
 /* 
-* Copyright (C) 2020-2025 German Aerospace Center (DLR-SC)
+* Copyright (C) 2020-2025 MEmilio
 *
 * Authors: Julia Bicker, René Schmieding
 *

cpp/models/graph_abm/graph_abm_mobility.cpp

@@ -1,5 +1,5 @@
 /*
-* Copyright (C) 2020-2024 MEmilio
+* Copyright (C) 2020-2025 MEmilio
 *
 * Authors: Julia Bicker
 *

cpp/models/graph_abm/graph_abm_mobility.h

@@ -1,5 +1,5 @@
 /*
-* Copyright (C) 2020-2024 MEmilio
+* Copyright (C) 2020-2025 MEmilio
 *
 * Authors: Julia Bicker
 *

cpp/models/graph_abm/graph_abmodel.h

@@ -1,5 +1,5 @@
 /*
-* Copyright (C) 2020-2024 MEmilio
+* Copyright (C) 2020-2025 MEmilio
 *
 * Authors: Julia Bicker
 *

cpp/models/hybrid/temporal_hybrid_model.cpp

@@ -1,5 +1,5 @@
 /* 
-* Copyright (C) 2020-2025 German Aerospace Center (DLR-SC)
+* Copyright (C) 2020-2025 MEmilio
 *
 * Authors: Julia Bicker, René Schmieding
 *

cpp/models/hybrid/temporal_hybrid_model.h

@@ -1,5 +1,5 @@
 /* 
-* Copyright (C) 2020-2025 German Aerospace Center (DLR-SC)
+* Copyright (C) 2020-2025 MEmilio
 *
 * Authors: Julia Bicker, René Schmieding
 *

cpp/models/smm/model.cpp

@@ -1,5 +1,5 @@
 /*
-* Copyright (C) 2020-2025 German Aerospace Center (DLR-SC)
+* Copyright (C) 2020-2025 MEmilio
 *
 * Authors: René Schmieding
 *