Author: Antony Tang

On November 20th, Premier Ford has announced that he would bring “tough” new measures to places in Ontario. As many measures like social distancing are only effective if everyone complies, the topic of why someone would or would not comply with these measures became interesting to me. We have seen numerous protests against the lockdowns as well as other measures locally and in other places in the world. 

In the article by Brune and Wilson, the ‘Public Goods Game’ (PGG) was used to describe the COVID-19 measures situation. In a PGG, all players have a common goal which in this case is to return back to a life that is COVID free. But, it is possible to not contribute towards this goal and still enjoy the public good (the common goal). This goal is non excluding and should we reach it, those who did and did not contribute with enjoy the payoff equally.

Brune and Wilson break down the payoff as done in our class material for the social distancing measures that have been implemented. Brune and Wilson identify that the obvious benefit would be that it slows down the spread of COVID-19. However, the downside of this measure is the added stress social distancing can cause which also negatively impacts our health. In this PGG, Brune and Wilson identify that there is a minority of people who defect and choose to ignore these measures without regard for their own health and others. Brune and Wilson suggest that in order to decrease this number of people who choose to defect, the payoff for choosing this response should decrease. 

PGG says that cooperation decreases over time and I think that is an important aspect of PGG that makes it relatable the current COVID-19 situation. After almost a year, the amount of cooperation may go down and it may make controlling this virus more difficult than the first wave. 

Overall, I think Brune and Wilson were successful in identifying payoffs of social distancing however, I think it would have been more helpful if a payoff matrix was made like the ones we have seen in our course material with values assigned to the benefits and punishments in this ‘game’. I think having this matrix will help show why certain players in this game would choose a certain response and can even help governments decide how to increase the chances of players choosing to obey the restrictions. This can be done by decreasing the payoff as mentioned earlier by Brune and Wilson or through other means. Governments can try to introduce pure strategy nash equilibriums so that players would always choose to obey the restrictions.

Links:

https://academic.oup.com/emph/article/2020/1/181/5902452

In the midst of a global pandemic, we have seen many countries implement different measures to try to eradicate the virus. As we learned in lecture and as discussed in the article, viruses spread through social networks and can spread easily through strongly connected components. As a result, the article “A network-based explanation of why most COVID-19 infection curves are linear” by Turner, Klimek, and Hanel intrigued me because it was a network-based explanation for the spread of COVID-19 as well as a network-based explanation for prevention measures. 

The first measure that we have all heard is a lockdown as seen in early March in Ontario. Turner et al suggest that in order for a person to become infected with the virus they must satisfy two criteria as seen below:

1. There is a social interaction between an infected and a susceptible person and
2. This contact is intense enough to lead to disease transmission.

Assuming a realistic contact network with degree 5, there is an average of 7.2 critical contacts per person. With lockdown measures, this reduces the number of critical contacts of each person to 2.5 people. Essentially, given a social network where nodes are people and edges are intense contact as defined above, a lockdown removes many edges hence limiting the ease of spread throughout the network. 

Here we can see the results of the US, who did not implement lockdowns during the given timeframe and Austria, who did. We can conclude that bringing the number of critical contacts down to a household amount like 2.5 can dramatically change the course of the pandemic.

Now after the first wave, many places began to ease up on restrictions. Places like Ontario began to reopen businesses and lifted much of the lockdown as numbers began to drop. To prevent cases from flaring up again, another measure known as “Social Distancing” was implemented. 

As discussed by Turner et al, social distancing is another way of removing those edges between nodes hence creating a more sparse network. Even though more people were seeing each other, social distancing should limit the spread because those interactions would not satisfy criteria 2 as defined above. However, we know that humans are social beings and the longing for meaningful social interactions was immense after months of lockdown. Holidays, birthdays, and other events eventually caused a lot of those edges to appear again, creating more strongly connected components hence we have entered the second phase of this pandemic.

Link:

https://www.pnas.org/content/117/37/22684