From 0dd02199341c93f4b756750b42206094262040ab Mon Sep 17 00:00:00 2001 From: Bruno Goncalves Date: Fri, 12 Apr 2024 16:17:08 -0400 Subject: [PATCH] update README --- README.md | 22 ++++++++++++++++------ images/SIR_results.png | Bin 86854 -> 14190 bytes 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 65ef5a9..7bca19d 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,6 @@ +
+
+ # epidemik Compartmental Epidemic Models in Python @@ -33,8 +36,9 @@ Here's a brief high-level overview of the tech stack the `epidemik` package uses - The model is implemented as a directed multigraph using [networkx](https://networkx.org/) - Ordinary Differential Equations are numerically integrated using [scipy](https://scipy.org/) -- Random numbers are generated by[numpy](https://numpy.org/) -- Model structure visualizations rely on [matplotlib](https://matplotlib.org/) +- Random numbers are generated by [numpy](https://numpy.org/) +- Model structure visualizations rely on [matplotlib](https://matplotlib.org/) +- Progress bars generated by [tqdm](https://tqdm.github.io/)
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@@ -44,13 +48,17 @@ Here's a brief high-level overview of the tech stack the `epidemik` package uses ## Usage[![](https://raw.githubusercontent.com/DataForScience/epidemik/main/images/pin.svg)](#usage) -`epidemik` provides three main modules, `EpiModel`, `NetworkEpiModel` and `MetaEpiModel`, usually imported directly from the `epidemik` package using +`epidemik` provides three main modules, `EpiModel`, `NetworkEpiModel` and `MetaEpiModel`, usually imported directly from the `epidemik` package using the module name ```python from epidemik import EpiModel ``` -To instanciate a new compartmental model we just need to create a `EpiModel` object and add the relevant transitions: +- __EpiModel__ -e Simple compartmental model in a homogeneously mixed population. +- __NetworkEpiModel__ - Compartmental model on a network where nodes interact only along edges connecting them. +- __MetaEpiModel__ - Meta population model where populations interact with one another along the edges of a network. Each sub-population has it's own internal __EpiModel__ instance. + +To instantiate a new compartmental model we just need to create a `EpiModel` object and add the relevant transitions: ```python beta = 0.2 @@ -75,7 +83,7 @@ resulting in a simple description of hte model structure. R0=2.00 -or a graphical representation by calling `draw_model`: +or a graphical representation by calling `draw_model()`: ```python SIR.draw_model() @@ -89,7 +97,9 @@ The models value of the Basic Reproductive Number (R~0~) can be determined using SIR.R0() ``` +There are two ways to explore the dynamics of the model, each with it's corresponding method. +To integrate numerically the Ordinary Differential Equations that describe the model dynamics, we can call the `integrate()` method. The first argument is the number of time steps to integrate over and the remaining arguments are the initial populations of each compartment. ```python N = 10_000 @@ -145,7 +155,7 @@ Thank you so much for your interest in growing our community! ## License[![](https://raw.githubusercontent.com/DataForScience/epidemik/main/images/pin.svg)](#license) -`epidemik` is free and open-source software licensed under the [MIT License](https://choosealicense.com/licenses/mit/) [2024] - Bruno Gonçalves. Please have a look at the [LICENSE.md](LICENSE) for more details. +`epidemik` is free and open-source software licensed under the [MIT License](https://choosealicense.com/licenses/mit/) [2024] - Bruno Gonçalves, Data For Science, Inc. Please have a look at the [LICENSE.md](LICENSE) for more details.
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