This code describes and models the current coronavirus (COVID-19) pandemic in the state of Idaho. With this, we hope to provide information on how well Idaho is dealing with COVID-19.
It models the pandemic in Idaho as a kinetics function of time in days, with equations that model the uninfected, infected, recovered, and fatalities in Idaho.
This code requires access to a command line terminal and to have a github account to access the p2-kinetics repository, for the purpose of collaboration. For this program to run, python must be installed on the local machine. To download Python, go to terminal, type in the command line "apt-get install python". This will start downloading Python onto the local machine. Your computer will ask a few permission questions and you will have to let it install Python onto the local machine.
The code also requires that numpy, matplotlib.pyplot, sys, and solve_ivp to be imported. All the previously mentioned functions and numpy are part of bash and will run when the code is initiated.
To run the code on your computer, pull the p2-kinetics repository from github by cloning it, or fork the repository and pull the code from your own personal repository.
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Go to github.com/mse150s20/p2-kinetics
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Click on "Clone or download" tab and click on the copy button next to the github address.
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It is recommended to clone with SSH.
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In your own terminal enter: "$ git clone git@github.com:mse150s20/p2-kinetics.git"
The directory and all it's contents shoudl now be clond to your machine.
To use this code you must be in the directory that contains the code, p2-kinetics. Once in the "p2-kinetics" directory type "$ python plot-infections.py" + the path to the file to the .csv file containing the data to be evaluated.
$ cd p2-kinetics/
$ python plot-infections.py Data/idaho_infections.csv
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$ python Real_Data.py Data/idaho_infections.csv
A plot will appear if everything is working correctly.
A graph that displays a model of the data and the data that has already been collected.
A graph that displays the collected data.
A graph that displays the collected data in a logarithmic format.
A graph that displays the model of the data.
###Images The Images Directory is the place where all the graphs are saved to so they are in a centeralized location.
The idaho_infections.csv file contains the reported numbers of the total reported infected people, deaths, and recoveries in Idaho. The file starts on the first day of reported cases in the state. It also includes the number of new cases of each designation. This csv file works in conjunction with kinetics.py as it will show the comparison of the reported data alongside with the projected data.
The data directory contains text files and .csv files from group research sources. Our working model pulls data from this directory when generating our model. idaho_infections.csv is one example of the files contained in this directory. The idaho_infections.csv file contains the reported numbers of the total reported infections, fatalities, and recoveries in Idaho. The file starts on the first day of reported cases in the state. It also includes the number of new cases of each designation. This csv file works in conjunction with Model_VS_Real_Data.py as it will show the comparison of the reported data alongside the projected data of the model.
The General_Playing directory contains several working models to be manipulated in order to determine the best fit for the k1 and k2 values of the final predictive model. These models cover varying infection rates, recovery rates, and comparisons to the current to-date data from our sources.
This graph, when pulling from Data/idaho_infections.csv, plots actual numbers of infections and fatalities in the state of Idaho from day 0, as the first person was infected, to most current update of covid-19 statistics.
Lists all contributors to the project with contact info.
Project 2 descriptions: Modeling kinetics: rate of change between things!
Healthy -> Infected -> Recovered
The rates of change between Healthy->Infected and Infected->Recovered will show us how quickly COVID-19 spreads and how quickly people can recover from COVID-19.
Healthy: This is how many healthy, uninfected people there are in Idaho.
Infected: This is the number of COVID-19 positive people there are in Idaho.
Recovered: This is the number of people who have recovered from a COVID-19 infection.
The equation for finding how many new infections per day is (rInfect*Healthy*Infected)/(Healthy+Infected+Recovered). Applying this tells us that initially when almost everyone is Healthy and very few are infected, the growth of infected is very slow. When Healthy and Infected are equal to each other is when the growth of infected is at its peak.
rInfect: This is a constant that is multiplied by the number of Healthy people. The product of Healthy times rInfect gives us the number of new Infections per day.
rRecover: This is a constant that is multiplied by the number of Infected people. The product of Infected time rRecover gives us the number of new Recovered people per day. The rate at which people recover is completely dependent on how many infected people there are thus the equation for new Recovered is (rRecovered**Infected). Applying this information tells us as the number of Infected people goes up, the number of new Recovered people will also go up. The peak of Recoveries will be when there the greatest number of Infected people.
Our data shows a spike in the beginning when we were not prepared or taking the steps to prevent the virus spreading. Since the social distancing, it has flattened a little so we should continue to distance as much as possible until we have more information or more recovered people so we have a better chance of overcoming this. #This is where we talk about what this data means for us. What are the next steps we need to take? Should we continue social distancing? Comment on how well the state is handling it. Etc...
There are a lot of variables to consider when deciding on the future of virus precautions. The idaho_infections.csv data with our higher k1 variable to more closely represent the standard curve indicates the virus is not spreading as quickly as predicted. Something else to consider is due to the likelihood that a vaccine will not be ready within the year, gradual re-introduction of people into society to start building immunity may be the best option.
The most apparent effect of decreasing k1 was delaying the "ramping up" of the population.
Within the General_PLaying Folder, there is a file named "playing.py". By using vim to access the file, the variables k1 and k2 can be changed. Then, the playing.py file can be graphed by using the python command. The results of this graph will display the day in which there are the maximum number of cases. The color bar on the side can be used to indicate what conditions of k1 and k2 will produce the days of maximum cases.
A higher k1 is required to get the infection to ramp up in a similar timeframe to the real infections (i.e. smaller k1 = later beginning of mass infection). However, a k2 is needed to control the k1; it can't be too big or too small as to squash the infected graph, or to let it grow exponentially large. Finer tuning needed.
As it stands the way that the graphs are generated it is not likely we will be able to make an accurate model. The data is still too young, and has too many outside factors; the current trend of the infections also does not match up with the form that the model graph takes.
- Idaho Division of Public Health - https://public.tableau.com/profile/idaho.division.of.public.health#
- This source contains data on the statewide cases in Idaho. It contains information on recovered, hospitalized, ICU admittances, and cases among health care workers. It also contains data on COVID-19 demographics and county data.
- New York Times GitHub Repo - https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv
- Idaho Statesman - https://www.idahostatesman.com/news/coronavirus/article242127516.html
- This article by the Idaho Stateman provides a recent chart of new virus cases vs. confirmed cases in Idaho, as of May 13th, 2020, as well as providing demographics of the majority groups that are being affected, and in what counties. This provides insight into how population denisty and relative exposure to the virus in a given profession can influence infection/death rates.
- This article by KTVB 7 provides a visual representation of the spread of the virus in Idaho with an interactive map and timelines. Numbers of confirmed cases, deaths, and recoveries are displayed by county on a map as well as on a timeline, showing the curves for each situation to more easily visualize how many people are recovering/dying once infected.
- Centers for Disease Control and Prevention - https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/summary.html#:~:text=On%20March%2011%2C%20the,of%20novel%20influenza%20viruses.
- The CDC provides one of the most up to date information regarding the COVID-19 situation in the United States. It provides recommendations for what individuals and businesses should do as well as information on the risk and severity of COVID-19