Extend documentation and fix typos and bugs in documentation

[mknaranja]

Changes and Information

Please briefly list the changes (main added features, changed items, or corrected bugs) made:

• Extensive additions with references to tutorials in getting started
• Updated figures (added to getting started and in general start)
• Some small typo corrections and changes to the documentation
• Extended MEmilio developer's list

If need be, add additional information and what the reviewer should look out for in particular:

• Two figures have been updated on my webspace (not explicitly visible in the changes)

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 97.38%. Comparing base (cd10687) to head (df24c24).
⚠️ Report is 7 commits behind head on main.

Additional details and impacted files

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  Coverage   97.38%   97.38%           
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  Files         188      188           
  Lines       16536    16619   +83     
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+ Hits        16103    16184   +81     
- Misses        433      435    +2     

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[kilianvolmer]

Thank you for updating the literature and adding links to the tutorials, that is a great addition!
However, I am not happy with the changes regarding flow model -> FlowModel and infection states -> InfectionStates. The latter is not changed in all locations and we now have a combination of InfectionStates, InfectionStates and InfectionStates. This should be unified.
FInally, I would like to remind you that we now build a preview for the documentation on every PR. You can access this one at https://memilio--1531.org.readthedocs.build/en/1531/index.html and check that all links are working.

[kilianvolmer]

Can we add a bit more context to this figure, for example mention sth. about differently distributed dwelling times?

[kilianvolmer]

Suggested change

The MEmilio library uses a modular organization of models where :doc:`compartmental or aggregated models<cpp/aggregated_models>` based on ODEs (ordinary differential equations) without and with Linear Chain Trick, IDEs (integro-differential equations), and SDEs (stochastic differential equations) share a maximum properties and interfaces (implemented in the *memilio* folder) to allow simple and straightforward model adaption with, e.g., demographic or spatial stratification (e.g. found in the *memilio/mobility* folder to create :doc:`metapopulation models<cpp/metapop>`) as shown in the following figure. :doc:`Agent-based models<cpp/individual_models>` are furthermore harmonized with most structures such as parameters, contact patterns, and non-pharmaceutical interventions (e.g. found in *memilio/epidemiology*).

The MEmilio library uses a modular organization of models where :doc:`compartmental or aggregated models<cpp/aggregated_models>` based on ODEs (ordinary differential equations) without and with Linear Chain Trick, IDEs (integro-differential equations), and SDEs (stochastic differential equations) share a maximum of properties and interfaces (implemented in the *memilio* folder) to allow simple and straightforward model adaption with, e.g., demographic or spatial stratification (e.g. found in the *memilio/mobility* folder to create :doc:`metapopulation models <metapop>`) as shown in the following figure. :doc:`Agent-based models <individual_models>` are furthermore harmonized with most structures such as parameters, contact patterns, and non-pharmaceutical interventions (e.g. found in *memilio/epidemiology*).

[kilianvolmer]

Suggested change

Below we guide you through several tutorials on using MEmilio's models through its Python interface. More experience users might directly start with the `Python exercises <https://github.com/SciCompMod/memilio-tutorials/tree/main/exercises>`_ which are derived versions from the tutorials or with `tutorial and exercises in C++ <https://github.com/SciCompMod/memilio-tutorials/tree/main/cpp-tutorials>`_. For more advanced aggregated models using the Linear Chain Trick or IDE-formulations, we currently only provided tutorials and exercises in C++. For the individual- or agent-based model (ABM), we currently only provide `ABM tutorials in C++ <https://github.com/SciCompMod/memilio-tutorials/tree/main/cpp-tutorials/abm>`_.

Below we guide you through several tutorials on using MEmilio's models through its Python interface. More experience users might directly start with the `Python exercises <https://github.com/SciCompMod/memilio-tutorials/tree/main/exercises>`_ which are derived versions from the tutorials or with `tutorial and exercises in C++ <https://github.com/SciCompMod/memilio-tutorials/tree/main/cpp-tutorials>`_. For more advanced aggregated models using the Linear Chain Trick or IDE-formulations, we currently only provide tutorials and exercises in C++. For the individual- or agent-based model (ABM), we currently only provide `ABM tutorials in C++ <https://github.com/SciCompMod/memilio-tutorials/tree/main/cpp-tutorials/abm>`_.

[kilianvolmer]

Suggested change

	In `Tutorial 01 <https://github.com/SciCompMod/memilio-tutorials/blob/main/tutorial01.py>`_, we show how set up and simulate a simple setting for our :doc:`ODE-SECIR model <models/osecir>`. The result of the tutorial is a figure of a well-known epidemic outcome.
	In `Tutorial 01 <https://github.com/SciCompMod/memilio-tutorials/blob/main/tutorial01.py>`_, we show how to set up and simulate a simple setting for our :doc:`ODE-SECIR model <models/osecir>`. The result of the tutorial is a figure of a well-known epidemic outcome.

[kilianvolmer]

Suggested change

	In `Tutorial 02 <https://github.com/SciCompMod/memilio-tutorials/blob/main/tutorial02.py>`_, we show how to obtain the numbers of newly symptomatic and hospitalized individuals for our :doc:`ODE-SECIR model <models/osecir>`. TThe result of the tutorial is shown in the following figure.
	In `Tutorial 02 <https://github.com/SciCompMod/memilio-tutorials/blob/main/tutorial02.py>`_, we show how to obtain the numbers of newly symptomatic and hospitalized individuals for our :doc:`ODE-SECIR model <cpp/models/osecir>`. The result of the tutorial is shown in the following figure.

[kilianvolmer]

Suggested change

	In `Tutorial 05 <https://github.com/SciCompMod/memilio-tutorials/blob/main/tutorial05.py>`_, we show how to distinguish individuals of three different age groups by their susceptibility with respect to severe and critical infections and simulate outcomes for our :doc:`ODE-SECIR model <models/osecir>`. The result of the tutorial is shown in the following figure.
	In `Tutorial 05 <https://github.com/SciCompMod/memilio-tutorials/blob/main/tutorial05.py>`_, we show how to distinguish individuals of three different age groups by their susceptibility with respect to severe and critical infections and simulate outcomes for our :doc:`ODE-SECIR model <cpp/models/osecir>`. The result of the tutorial is shown in the following figure.

[kilianvolmer]

Please also fix all other broken links.

[kilianvolmer]

Suggested change

Often interventions are targeted to specific types of locations such as schools, workplaces, or social gatherings. In order to most realistically model contact structures and NPIs across different locations, MEmilio uses simple and flexible lists of contact locations. In `Tutorial 10 <https://github.com/SciCompMod/memilio-tutorials/blob/main/tutorial10.py>`_, we explain with our :doc:`ODE-SECIR model <models/osecir>` contact structures can be stratified by locations and NPIs implemented in a location-specific way. The result of the tutorial is shown in the following figure.

Often interventions are targeted to specific types of locations such as schools, workplaces, or social gatherings. In order to most realistically model contact structures and NPIs across different locations, MEmilio uses simple and flexible lists of contact locations. In `Tutorial 10 <https://github.com/SciCompMod/memilio-tutorials/blob/main/tutorial10.py>`_, we explain with our :doc:`ODE-SECIR model <cpp/models/osecir>` how contact structures can be stratified by locations and NPIs implemented in a location-specific way. The result of the tutorial is shown in the following figure.

[kilianvolmer]

Suggested change

	While LCT models generalize assumptions from exponential to Gamma, IDE formulations such as presented `Wendler et al. (2026), DOI: 10.1016/j.amc.2025.129636 <https://doi.org/10.1016/j.amc.2025.129636>`_ allow the full flexibility to use any data-driven transition distribution.
	While LCT models generalize assumptions from exponential to Gamma, IDE formulations such as presented in `Wendler et al. (2026), DOI: 10.1016/j.amc.2025.129636 <https://doi.org/10.1016/j.amc.2025.129636>`_ allow the full flexibility to use any data-driven transition distribution.

[kilianvolmer]

Suggested change

	A major advantage of agent-based models (ABMs) is the possibility to model individuals with individual properties. In the MEmilio-ABM populations are set up straightforward with household structures. Testing and vaccination strategies can be targeted to individuals at particular locations or of selected age groups.
	A major advantage of agent-based models (ABMs) is the possibility to model individuals with individual properties. In the MEmilio-ABM, populations are set up straightforward with household structures. Testing and vaccination strategies can be targeted to individuals at particular locations or of selected age groups.

[kilianvolmer]

Should we move this text below Vincent?