From 60955971fd1c1680f544d5d8fe025399b2dfd0be Mon Sep 17 00:00:00 2001 From: Pietro Monticone <38562595+pitmonticone@users.noreply.github.com> Date: Fri, 22 Jul 2022 16:05:22 +0200 Subject: [PATCH] Fixed a few typos. --- README.md | 8 ++++---- seirmo/apps/descriptions/seir_model.md | 4 ++-- seirmo/apps/seir_simulation_app.py | 2 +- seirmo/plots/_figures.py | 8 ++++---- 4 files changed, 11 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 86f555c..0713c77 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,7 @@ The deterministic model supposes that the population is large and well-mixed, an | κ | Inverse of the average latent period | 1/t | | γ | Inverse of the average duration of infectiousness | 1/t | -β > 0 controls the rate of tranmission, κ > 0 the rate at which exposed individuals become infectious, and γ > 0 the rate at which individuals recover. The model also requires initial conditions for each compartment: S(0), E(0), I(0), and R(0), which represent the initial number of people in each category. +β > 0 controls the rate of transmission, κ > 0 the rate at which exposed individuals become infectious, and γ > 0 the rate at which individuals recover. The model also requires initial conditions for each compartment: S(0), E(0), I(0), and R(0), which represent the initial number of people in each category. The deterministic model solves this set of ODEs: @@ -48,7 +48,7 @@ The stochastic model also supposes that the population is homogeneous, but it su ![SEIR stochastic model reactions](./images/SEIR_stochastic_reactions.png) -The model is solved using the Gillespie algorithm (see documentation here: https://en.wikipedia.org/wiki/Gillespie_algorithm). The timesteps are sampled randomly. At each timestep, only one reaction takes place, and which reaction takes place is determined randomly following their propensities. For example, for a given time t, if the reaction occuring is a susceptible individual becoming exposed, then the following changes occur in the densities: +The model is solved using the Gillespie algorithm (see documentation here: https://en.wikipedia.org/wiki/Gillespie_algorithm). The timesteps are sampled randomly. At each timestep, only one reaction takes place, and which reaction takes place is determined randomly following their propensities. For example, for a given time t, if the reaction occurring is a susceptible individual becoming exposed, then the following changes occur in the densities: @@ -61,7 +61,7 @@ where _(t+1)_ is the next timestep. ## Installation procedure -One way to install the module is to download the repositiory to your machine of choice and type the following commands in the terminal. +One way to install the module is to download the repository to your machine of choice and type the following commands in the terminal. ```bash git clone https://github.com/SABS-R3-Epidemiology/seirmo.git cd ../path/to/the/file @@ -79,7 +79,7 @@ pip install -e . Some documentation on the program's classes and methods can be found here: https://seirmo.readthedocs.io/en/latest/ ### References -List of ressources that can be useful for the project: +List of resources that can be useful for the project: * Gillespie D, 1977. Exact stochastic simulation of coupled chemical reactions (https://doi.org/10.1021/j100540a008) * Erban R, Chapman J and Maini P, 2007. A practical guide to stochastic simulations of reaction-diffusion processes (https://arxiv.org/abs/0704.1908) * Bauer F, 2008. Compartmental models in epidemiology (https://link.springer.com/chapter/10.1007/978-3-540-78911-6_2). diff --git a/seirmo/apps/descriptions/seir_model.md b/seirmo/apps/descriptions/seir_model.md index 1230987..bf5b19e 100644 --- a/seirmo/apps/descriptions/seir_model.md +++ b/seirmo/apps/descriptions/seir_model.md @@ -2,6 +2,6 @@ The SEIR model is a model of Ordinary Differential Equations (ODEs). It assigns The model is characterised by few constants, which include the *reproduction number*, the *incubation period* and the *infection period*. The *reproduction number* measures the number of infected cases originating from primary infections, the *incubation period* defines the average period of time for exposed individuals to become infectious, and the *infection period* is the average period of time for infected patients to recover from the disease. -The system of ODEs is solved to retrieve the number of inviduals in each S, E, I and R group. The incidence number is then inferred from the solution. To solve the system of ODEs, the initial value of each group is required. Different initial values will give different solutions. +The system of ODEs is solved to retrieve the number of individuals in each S, E, I and R group. The incidence number is then inferred from the solution. To solve the system of ODEs, the initial value of each group is required. Different initial values will give different solutions. -You are welcome to explore the effect of initial sizes of the S, E, I and R groups, as well as the different transition periods with the paramter sliders below. \ No newline at end of file +You are welcome to explore the effect of initial sizes of the S, E, I and R groups, as well as the different transition periods with the parameter sliders below. \ No newline at end of file diff --git a/seirmo/apps/seir_simulation_app.py b/seirmo/apps/seir_simulation_app.py index fb8bc1a..6037ca1 100644 --- a/seirmo/apps/seir_simulation_app.py +++ b/seirmo/apps/seir_simulation_app.py @@ -102,7 +102,7 @@ individuals, to be nonzero. You are welcome to explore the effect of initial sizes of the S, E, I and R groups, as well as -the different transition periods with the paramter sliders below. +the different transition periods with the parameter sliders below. """ reference = """ diff --git a/seirmo/plots/_figures.py b/seirmo/plots/_figures.py index 05769de..4c502ce 100644 --- a/seirmo/plots/_figures.py +++ b/seirmo/plots/_figures.py @@ -40,7 +40,7 @@ def add_data( Defaults to 'Time'. inc_key Key label of the DataFrame which specifies the - incididence number. Defaults to 'Incidence Number'. + incidence number. Defaults to 'Incidence Number'. """ # Plot a bar chart for the data @@ -74,7 +74,7 @@ def add_simulation(self, data, time_key='Time', Defaults to 'Time'. inc_key Key label of the DataFrame which specifies - the incididence number. Defaults to 'Incidence Number'. + the incidence number. Defaults to 'Incidence Number'. """ @@ -309,7 +309,7 @@ def add_data(self, data, time_key='Time', Defaults to 'Time'. inc_key Key label of the DataFrame which specifies - the incididence number. Defaults to 'Incidence Number'. + the incidence number. Defaults to 'Incidence Number'. """ @@ -338,7 +338,7 @@ def add_simulation( Defaults to 'Time'. inc_key Key label of the DataFrame which specifies - the incididence number. Defaults to 'Incidence Number'. + the incidence number. Defaults to 'Incidence Number'. compartment_keys The list of key labels of the DataFrame which specify the compartments.