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On March 11th, 2020, the WHO officially characterized the the spread of the novel virus COVID-19 a pandemic. Ever since the first cases were reported from Wuhan, China on December of 2019, COVID-19 has brought sudden and unexpected changes to many of our lifestyles, which left many stuggling to adapt to such abrupt adjustments. 

COVID-19 has unarguably changed much of our lives. The below are some pictures on a math competition to Busan, Korea during January, when Korea first announced its 2 confirmed cases. None of us would ever know that this would be our last math competition, or last vacation, or last traditional market experience, for the year. 




Masks have been a part of my life for, unsurprisingly, quite some time, even before the start of the pandemic. Masks usage is actually quite widespread in many Asian cultures. Whenever the air quality falls to the 'bad' level, or I get the common cold, or when it gets cold, buying and wearing a mask for that day was regarded as quite normal. 



But on the other side of the globe, many people did not seem to understand the concept of voluntarily putting a piece of cloth around their respatory systems. At first, I found this perplexing; I, and everyone around me, had the belief that "mask = prevention" and wearing a mask would be effective. That was also why as the pandemic started to spread, the government had to regulate the supply of masks so that everyone could be guaranteed a minimum 2 masks per week, as demand rised expenontially.  

But the more I thought about it, I had always thought that masks were effective, but had never done anything to prove it on my own. Thus, I thought COVID would be a good case for me to study and visualize the spread of the disease. 


A while back, when I was in AP Calculus BC, we learned about logistic grwoth models, and did some practice sample questions were we modelled the spread of a disease in a population using a simple logistic growth curve. So I initially thought to use the same eqaution I did in Calculus for my research. 

However, through more research I quickly learned that there wore much more elaborate models I could use, and their equations readily available on the internet. Out of the ones I found, I decided to use the SIR(Susceptile -  Infectious - Recovered) diseas, which used the infection and rcovery rate of a disease to calculate the relationship between susceptible, infectious, and recovered people, thus producing one graph that showed number of people infected over time. 

Then I needed to obtain values for the infection and recovery rate for COVID-19. These could be obtained by using the r0 value of COVID-19, which was discovered to be around 2.4 in the early stages of the pandemic by a group of researchers in Wuhan, China. The recovery rate was 1/14, since it took on average 14 days to recover and recover rate = 1/(average days required to recover), the infection rate = r0 * (infection rate) = 0.16. 



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Now I needed to select a few countries to further analyze regarding the effectiveness of masks. Some of my seelction criteria were

1. Are(or were) they effectively using masks?

2. Do they have a significant population?

3. Is data available to be easily acquired regaring COVID-19 in the country?

4. When did they start implementing masks?

And the final 6 countries I used were: Italy, Spain, Czech Republic, Slovakia, Iran, and South Korea. These 6 countries had data that was readily available - an example of a country I planned to use but could not was Sweden, as they ddi not provide official statistics regarding recoveries in their own country. 

The time of implementation of facial coverage was also appropriate. Italy and Spain implemented face masks quite late into the pandemic, unlike their European counterparts, Czechia and Slovakia. Iran also were late to make such policies, unlike their Asian counterpart, South Korea. 

After some scavenging, I was able to obtain statistic regarding confirmed cases, recoveries, and deaths per day for each country. 


Now all was laeft as to compare the theoretical spread of COVID-19 in each country using the SIR model and the actual spread by creating two graphs, one being the SIR curve and another based on the information I collected. 

For the SIR curve, I wrote some Python code which would use Numpy and MatPlotLib libraries to create the curve based on population. Unfortunately, the code was made on my older computer which I do not have anymore, so I have included some pictures of the curves I made via Python. 

For the actual spread curves, for each country I organized the SIR statistics onto a CSV file then referenced it with Python to create graphs again using MatPlotLib. 

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All was left to analyze and compare the graphs of cuntries that had readily accepted facial coverage(Czechi, Slovakia, and Korea) and those who had not(Italy, Spain, Iran). 

The results indicated a clear correlation between masks and handling a pandemic. When comparing the actual and theoretical SIR models, if the actual curve is lower than the theoretical curve, this indicates that the country has experienced less COVID-19 cases than it should theoretically have, and thus constructed a successful defensive system. A higher actual curve indicates the opposite. Results show that there were 3 countries with lower actual curves, the Czech Republic, Slovakia, and the Republic of Korea - the same countries that had accepted face masks earlier on.

It was concluded that face masks have been a very effective tool against COVID-19. This indicates the future possibility of face masks becoming a widespread practice in future cases of a pandemic.

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