/*

* Copyright (C) 2020-2026 MEmilio

*

* Authors: Julia Bicker, René Schmieding

*

* Contact: Martin J. Kuehn <[email protected]>

*

* Licensed under the Apache License, Version 2.0 (the "License");

* you may not use this file except in compliance with the License.

* You may obtain a copy of the License at

*

* http://www.apache.org/licenses/LICENSE-2.0

*

* Unless required by applicable law or agreed to in writing, software

* distributed under the License is distributed on an "AS IS" BASIS,

* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

* See the License for the specific language governing permissions and

* limitations under the License.

*/

#include "d_abm/quad_well.h"

#include "d_abm/simulation.h"

#include "d_abm/parameters.h"

#include "memilio/utils/random_number_generator.h"

#include "memilio/data/analyze_result.h"

#include "memilio/epidemiology/adoption_rate.h"

#include <vector>

enum class InfectionState

{

S,

E,

C,

I,

R,

D,

Count

};

int main()

{

//Example how to run a simulation of the diffusive ABM using the quadwell potential

using Model = mio::dabm::Model<QuadWell<InfectionState>>;

std::vector<Model::Agent> agents(1000);

//Random variables for initialization of agents' position and infection state

auto& pos_rng = mio::UniformDistribution<double>::get_instance();

auto& sta_rng = mio::DiscreteDistribution<size_t>::get_instance();

//Infection state distribution

std::vector<double> pop_dist{0.98, 0.01, 0.005, 0.005, 0., 0.};

for (auto& a : agents) {

//Agents are equally distributed in [-2,2]x[-2,2] at the beginning

a.position =

Eigen::Vector2d{pos_rng(mio::thread_local_rng(), -2., 2.), pos_rng(mio::thread_local_rng(), -2., 2.)};

a.status = static_cast<InfectionState>(sta_rng(mio::thread_local_rng(), pop_dist));

}

//Set adoption rates

std::vector<mio::AdoptionRate<ScalarType, InfectionState>> adoption_rates;

for (size_t region = 0; region < 4; ++region) {

adoption_rates.push_back({InfectionState::S,

InfectionState::E,

mio::regions::Region(region),

0.1,

{{InfectionState::C, 1}, {InfectionState::I, 0.5}}});

adoption_rates.push_back({InfectionState::E, InfectionState::C, mio::regions::Region(region), 1.0 / 5., {}});

adoption_rates.push_back({InfectionState::C, InfectionState::R, mio::regions::Region(region), 0.2 / 3., {}});

adoption_rates.push_back({InfectionState::C, InfectionState::I, mio::regions::Region(region), 0.8 / 3., {}});

adoption_rates.push_back({InfectionState::I, InfectionState::R, mio::regions::Region(region), 0.99 / 5., {}});

adoption_rates.push_back({InfectionState::I, InfectionState::D, mio::regions::Region(region), 0.01 / 5., {}});

}

//Set interaction radius and noise term of the diffusion process

double interaction_radius = 0.5;

double noise = 0.4;

double dt = 0.1;

double tmax = 30.;

Model model(agents, adoption_rates, interaction_radius, noise, {InfectionState::D});

auto sim = mio::dabm::Simulation(model, 0.0, dt);

sim.advance(tmax);

auto interpolated_results = mio::interpolate_simulation_result(sim.get_result());

interpolated_results.print_table({"S", "E", "C", "I", "R", "D "});

}