Fixed a few typos

README.md

@@ -24,7 +24,7 @@ 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:
 
@@ -58,8 +58,7 @@ 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
@@ -78,9 +77,7 @@ 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).

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.  
+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.  

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 = """

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.