Blog Post #3: Epidemics in the Time of COVID-19
This year has been one of hardship and uncertainty with the COVID-19 pandemic taking over the world. In March, our lives turned upside down when we were sent home from school and back home with everyone quarantining from the virus. Now, we have adapted to these trying times, but we can reflect on the journey we’ve been on and think of it in the perspective of INFO 2040. In the latter half of the semester, we learned about epidemics and the role they play with individuals impacting large groups; COVID-19 is a perfect example of this concept. While “epidemic” and “pandemic” sound slightly different, it is worth noting that a pandemic is just an epidemic on a larger scale. I will be citing COVID-19 facts from NPR to apply to the concept of epidemics.
Before we jump into INFO 2040, we can note that the number of COVID-19 cases has exponentially grown from the time we started quarantining in March to now. There are over millions of active cases in the United States with about 300,000 people dead as a result and that number will only grow. Every state except Vermont and Hawaii is in a red zone. Though we have been discussing social networks for the majority of the semester, we can now take a turn to thinking of networks on the level of diseases. It is essential to view how diseases play into networks to stop the spread and create plans to defeat the flu.
In an epidemic, the first person to spread the disease is referred to as “Patient Zero;” in this case, this could be the first coronavirus case found in the Hubei province in China back in last November. They then meet however many people represented by variable k and those first edges between Patient Zero and other nodes are the First Wave. This group then multiplies exposure to the disease as Patient Zero did to create the Second Wave, the Second Wave will come into contact with others to create the Third Wave, and so forth. This large network shows how the virus came to be uncontrolled and too many people were in contact with the disease to slow it down. There then comes the question of if the epidemic will continue forever or will it eventually die out; I believe we have been in this state of uncertainty for the majority of the year but have traveled closer to the finish line.
Expanding on the epidemics module, the branching process model explains that each node has a number of neighbors (k) in the next wave and passes on the virus with a probability of p. There is a basic reproductive number R=pk; if R<1, then the disease is not replenishing and R>1, then it is more than replenishing itself. When R<1, it can die out in a number of steps but the disease will grow indefinitely with a probability higher than 0. We have been in state in which the basic reproductive number is more than 1 the whole year; for that reason, COVID-19 has persisted indefinitely and we have been required to wear masks to do anything we can to stop the spread. Bringing the mathematics and statistics into real life, we can reduce p and bring R to less than 1 by practicing better sanitary measures. To reduce k, another variable in the equation, we can stop large gatherings and quarantine. Cornell implemented rules for COVID-19 in which we could not be in gatherings larger than 10 and we performed the latter for a great deal of 2020.
Lastly, we take a look at the SIR model in the concept of COVID-19 (Susceptible (S), Infectious (I), Removed (R)). In the SIR model, nodes in the state of I will infect each neighbor in the state of S with a probability of p. After affecting those in the state of S, R will be removed from the network. This visual is best explained by how someone is diagnosed with COVID-19, infects others around them, quarantines, and then has the coronavirus antibodies which removes them from the network model. This chain reaction goes forward through the model turning over more cases of I to R and S to I to R. The concept of an epidemic from INFO 2040 smoothly transitions on paper to real life; we see how cases of coronavirus increase exponentially and why we are where we are today.