1306 Added extensive documentation (#1314)

Added extensive documentation of the MEmilio framework.

Co-authored-by: Martin Kühn <Martin.Kuehn@dlr.de>
Co-authored-by: Kilian Volmer <13285635+kilianvolmer@users.noreply.github.com>
Co-authored-by: Sascha Korf <51127093+xsaschako@users.noreply.github.com>
Co-authored-by: reneSchm <49305466+reneSchm@users.noreply.github.com>
Co-authored-by: jubicker <113909589+jubicker@users.noreply.github.com>
Co-authored-by: Carlotta Gerstein <carlotta-gerstein@web.de>
Co-authored-by: Anna Wendler <106674756+annawendler@users.noreply.github.com>
Co-authored-by: Daniel Richter <danielrichter01@gmx.de>
Co-authored-by: MaxBetz <104758467+MaxBetzDLR@users.noreply.github.com>
Co-authored-by: David Kerkmann <44698825+DavidKerkmann@users.noreply.github.com>

Author: 11 people

.github/pull_request_template.md

@@ -22,7 +22,8 @@ Please check our [git workflow](https://memilio.readthedocs.io/en/latest/develop
 - [ ] New code adheres to [coding guidelines](https://memilio.readthedocs.io/en/latest/development.html#coding-guidelines)
 - [ ] No large data files have been added (files should in sum not exceed 100 KB, avoid PDFs, Word docs, etc.)
 - [ ] Tests are added for new functionality and a local test run was successful (with and without OpenMP)
-- [ ] Appropriate **documentation** for new functionality has been added (Doxygen in the code and explanations in the online documentation)
+- [ ] Appropriate **documentation within the code** (Doxygen) for new functionality has been added in the code
+- [ ] Appropriate **external documentation** (ReadTheDocs) for new functionality has been added to the online documentation
 - [ ] Proper attention to licenses, especially no new third-party software with conflicting license has been added
 - [ ] (For ABM development) Checked [benchmark results](https://memilio.readthedocs.io/en/latest/development.html#agent-based-model-development) and ran and posted a local test above from before and after development to ensure performance is monitored.
 

README.md

@@ -9,17 +9,17 @@ MEmilio implements various models for infectious disease dynamics, from simple c
 
 If you use MEmilio, please cite our work
 
-- Bicker J, Kerkmann D, Korf S, Plötzke L, Schmieding R, Wendler A, Zunker H et al. (2025)  *MEmilio - a High Performance Modular Epidemics Simulation Software*. Available at `https://github.com/SciCompMod/memilio` and `https://elib.dlr.de/213614/`.
+- Bicker J, Kerkmann D, Korf S, Plötzke L, Schmieding R, Wendler A, Zunker H et al. (2025)  *MEmilio - a High Performance Modular Epidemics Simulation Software*. Available at https://github.com/SciCompMod/memilio and https://elib.dlr.de/213614/ .
 
 and, in particular, for
 
-- Ordinary differential equation-based (ODE) and Graph-ODE models: Zunker H, Schmieding R, Kerkmann D, Schengen A, Diexer S, et al. (2024). *Novel travel time aware metapopulation models and multi-layer waning immunity for late-phase epidemic and endemic scenarios*. *PLOS Computational Biology* 20(12): e1012630. `https://doi.org/10.1371/journal.pcbi.1012630`
-- Integro-differential equation-based (IDE) models: Wendler AC, Plötzke L, Tritzschak H, Kühn MJ. (2024). *A nonstandard numerical scheme for a novel SECIR integro differential equation-based model with nonexponentially distributed stay times*. Submitted for publication. `https://arxiv.org/abs/2408.12228`
-- Agent-based models (ABMs): Kerkmann D, Korf S, Nguyen K, Abele D, Schengen A, et al. (2025). *Agent-based modeling for realistic reproduction of human mobility and contact behavior to evaluate test and isolation strategies in epidemic infectious disease spread*. *Computers in Biology and Medicine* 193: 110269. `DOI:10.1016/j.compbiomed.2025.110269 <https://doi.org/10.1016/j.compbiomed.2025.110269>`_
-- Hybrid agent-metapopulation-based models: Bicker J, Schmieding R, Meyer-Hermann M, Kühn MJ. (2025). *Hybrid metapopulation agent-based epidemiological models for efficient insight on the individual scale: A contribution to green computing*. *Infectious Disease Modelling* 10(2): 571-590. `https://doi.org/10.1016/j.idm.2024.12.015`
-- Graph Neural Networks: Schmidt A, Zunker H, Heinlein A, Kühn MJ. (2024). *Towards Graph Neural Network Surrogates Leveraging Mechanistic Expert Knowledge for Pandemic Response*. arXiv. `https://arxiv.org/abs/2411.06500`
-- ODE-based models with Linear Chain Trick: Plötzke L, Wendler A, Schmieding R, Kühn MJ. (2024). *Revisiting the Linear Chain Trick in epidemiological models: Implications of underlying assumptions for numerical solutions*. Submitted for publication. `https://doi.org/10.48550/arXiv.2412.09140`
-- Behavior-based ODE models: Zunker H, Dönges P, Lenz P, Contreras S, Kühn MJ. (2025). *Risk-mediated dynamic regulation of effective contacts de-synchronizes outbreaks in metapopulation epidemic models*. Chaos, Solitons & Fractals. `https://doi.org/10.1016/j.chaos.2025.116782`
+- Ordinary differential equation-based (ODE) and Graph-ODE models: Zunker H, Schmieding R, Kerkmann D, Schengen A, Diexer S, et al. (2024). *Novel travel time aware metapopulation models and multi-layer waning immunity for late-phase epidemic and endemic scenarios*. *PLOS Computational Biology* 20(12): e1012630. https://doi.org/10.1371/journal.pcbi.1012630
+- Integro-differential equation-based (IDE) models: Wendler A, Plötzke L, Tritzschak H, Kühn MJ. (2026). *A nonstandard numerical scheme for a novel SECIR integro differential equation-based model with nonexponentially distributed stay times*. *Applied Mathematics and Computation* 509: 129636. https://doi.org/10.1016/j.amc.2025.129636
+- Agent-based models (ABMs): Kerkmann D, Korf S, Nguyen K, Abele D, Schengen A, et al. (2025). *Agent-based modeling for realistic reproduction of human mobility and contact behavior to evaluate test and isolation strategies in epidemic infectious disease spread*. *Computers in Biology and Medicine* 193: 110269. https://doi.org/10.1016/j.compbiomed.2025.110269
+- Hybrid agent-metapopulation-based models: Bicker J, Schmieding R, Meyer-Hermann M, Kühn MJ. (2025). *Hybrid metapopulation agent-based epidemiological models for efficient insight on the individual scale: A contribution to green computing*. *Infectious Disease Modelling* 10(2): 571-590. https://doi.org/10.1016/j.idm.2024.12.015
+- Graph Neural Networks: Schmidt A, Zunker H, Heinlein A, Kühn MJ. (2025). *Graph Neural Network Surrogates to leverage Mechanistic Expert Knowledge towards Reliable and Immediate Pandemic Response*. Submitted for publication. https://doi.org/10.48550/arXiv.2411.06500
+- ODE-based models with Linear Chain Trick: Plötzke L, Wendler A, Schmieding R, Kühn MJ. (2024). *Revisiting the Linear Chain Trick in epidemiological models: Implications of underlying assumptions for numerical solutions*. Submitted for publication. https://doi.org/10.48550/arXiv.2412.09140
+- Behavior-based ODE models: Zunker H, Dönges P, Lenz P, Contreras S, Kühn MJ. (2025). *Risk-mediated dynamic regulation of effective contacts de-synchronizes outbreaks in metapopulation epidemic models*. Chaos, Solitons & Fractals. https://doi.org/10.1016/j.chaos.2025.116782
 
 
 **Getting started**
@@ -28,6 +28,12 @@ and, in particular, for
 The documentation for MEmilio can be found at 
 https://memilio.readthedocs.io/en/latest/index.html
 
+**Publication simulations**
+
+Simulations used for publications, along with their specific plotting and processing scripts, 
+are available in a separate repository:
+https://github.com/SciCompMod/memilio-simulations
+
 **Development**
 
 The coding guidelines and git workflow description can be found in the documentation at 

cpp/examples/smm.cpp

@@ -19,6 +19,7 @@
 */
 
 #include "smm/simulation.h"
+#include "smm/model.h"
 #include "smm/parameters.h"
 #include "memilio/data/analyze_result.h"
 #include "memilio/epidemiology/adoption_rate.h"

docs/source/_static/custom.css

@@ -25,11 +25,13 @@ dl.class > dt:first-of-type {
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   width: -webkit-fill-available;
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+
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@@ -39,3 +41,48 @@ dl.class > dt:first-of-type {
 .wy-table-responsive table th {
   white-space: normal;
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+
+/* Team page styles */
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+  border-radius: 8px;
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