Analyzing Contact Tracing's Impact on COVID-19 Spread

Inspiration

COVID-19 highlighted the need for better disease control methods. Despite quarantining and mandatory masking, roughly 50% of American adults report that they contracted COVID. This led me to believe that there must be better ways to deal with epidemics/pandemics.

What it does

This research used a Susceptible-Exposed-Infected-Recovered-Susceptible (SEIRS) epidemiological model created in Python to investigate the effectiveness of varying levels of contact tracing in curbing COVID-19 transmission. Simulations were conducted over a 180-day period with a simulated population of 10,000 individuals. Variables were created to hold the number of individuals in each stage, and other factors like transmission rate and incubation period were changed to fit the characteristics of COVID-19. Values were also added to simulate no, low, medium, and high levels of contact tracing. The simulation revealed that higher levels of contact tracing resulted in fewer individuals in each stage of infection (except for initially susceptible individuals). For instance, when comparing scenarios with no contact tracing to those with high contact tracing, high contact tracing resulted in a lower average of individuals in each group. However, the number of initially susceptible individuals was lower with no contact tracing compared to levels of high contact tracing. There was also no clear trend when comparing the amount of initially susceptible individuals in all levels of contact tracing. Despite this, the overall trend indicated that higher contact tracing levels were more effective in curbing disease spread than none/low levels of contact tracing. This highlights the role of contact tracing in controlling COVID-19 transmission -- moving forward, integrating contact tracing measures into pandemic response efforts is crucial to mitigate the spread of infectious diseases and safeguard public health on a global scale.

How we built it

I built the simulation using Python in PyCharm, incorporating epidemiological parameters specific to COVID-19. The model includes variables such as transmission rate, incubation period, and infectious period, with scenarios for no, low, medium, and high contact tracing levels.

Challenges we ran into

I faced challenges in accurately parameterizing the model with real-world data and ensuring that the simulation realistically represented disease dynamics. Debugging the code to handle different contact tracing levels was also a significant hurdle.

Accomplishments that we're proud of

I successfully developed a functional simulation that demonstrates the impact of contact tracing on disease spread. My model provides clear insights into how different strategies can influence outbreak control.

What we learned

I was able to explore biomedical computation/bioinformatics, and will likely explore the field in the future. I also learned about disease dynamics and epidemiology, and the important of using precise data to make sure the simulation was accurate. Finally, I improved my coding skills in Python. We learned how to apply computational modeling techniques to epidemiology, deepen our understanding of disease dynamics, and the importance of precise data in simulation accuracy. We also improved our coding skills in Python.

What's next for Analyzing Contact Tracing's Impact on COVID-19 Spread

I plan to advance the model by incorporating birth and death, and also other measures like social distancing and wearing masks, to make the model as accurate as possible.

Built With

• pycharm
• python