# Speculations on 2021

Disclaimer: I am not an expert on covid, and I am interested in learning more about what the future might hold for us in 2021.

This year has been quite eventful (covid-19), but then again, this overextended staycation has also in some sense made it uneventful for many ( good example from a german ad). Covid-19 has been a real reality curveball. A global pandemic is undoubtedly a ticking time bomb, and given the historical rarity of such events, it is not something that you frequently consider. A pandemic is a sporadic event that messes up the economy and causes a global recession. I like speculating about the future, and it is interesting to ponder how 2021 will follow up on these exciting times.

## The three phases

We summarise the fight against the pandemic into three rough phases; the first two are:

1. The sudden reality blow and implementation of measures to deal with the spread.
2. The waiting game for the vaccine.

This partition is an oversimplification, but in these terms, we are entering the third phase, the distribution of the vaccine. I find this an exciting phase since the vaccine is probably the most sought after product on this planet. The manufacturers of the vaccine face some extreme challenges in scaling up production and the logistics of transporting the goods.

Countries need to deal with their vaccine strategies. I have mostly been following the news in Iceland and for the EU. The general trend is to minimize the number of casualties and hospitalizations and to keep the frontline of health care workers protected. There will still be quite some time until whole populations of countries will reach herd immunity since that requires at least 60% of the people to be vaccinated ( lower bound from this Lancet article). The global economy will suffer until we have vaccinated enough people to achieve close to international herd immunity. I do not expect this to happen in 2021, and it might even take a few years since the virus is so widespread and contagious.

There are at least two possible scenarios:

1. Everyone equally ramps up vaccination, and the whole world reaches herd immunity almost simultaneously.
2. Some countries will reach herd immunity before others.

I think it is safe to say that option one will most certainly not happen. So given that some countries will reach herd immunity well before others, what are the implications? The following are my speculations.

## Different vaccine strategies

Not all countries will implement the same vaccine strategies. When deciding on what method to pursue, we need to take into consideration how the vaccine works. The effects of the vaccine are twofold.

1. Local: Protection of a single individual.
2. Global: Herd immunity.

Most countries will not receive enough vaccine to reach herd immunity fast, so they will need to prioritize the first groups to get vaccinated. In Europe, these are almost exclusively older sensitive individuals and frontline health care workers. This prioritization is a smart approach. Protecting the more sensitive individuals will lower the number of hospitalizations in future waves, and protecting health care workers will make the health care system more robust against the increased spread.

I have not seen any alternatives to this general trend of strategies. Mirroring the local and global effects of the vaccine, we have two objectives along the way.

1. Protect sensitive and weak individuals.
2. Maximize effectiveness to achieve herd immunity.

I argue that these are somewhat opposing objectives. To understand why we will need to consider how a disease like covid-19 spread. The basic reproduction number $R_0$ or R nought is a number representing the rate of spread for a disease. This number is relatively simple to interpret. If the value is 2, then it means that each infected individual will on average infect two others. If the value is less than 1, the spread is declining, and if it is above 1, it is increasing.

For covid-19, $R_0$ has been evaluated to be in the range of $3.28-5.7$. Note that lockdown measures will affect $R_0$, also wearing masks and other sanitary standards. The value is thus dependent on the rules and regulations that people follow in general.

So $R_0$ is, in this sense, a dynamic value that we can control to mitigate the spread. But remember that it is defined as an average. To consider the bigger picture, we can consider individuals as nodes in a graph, and nodes are connected by a weighted edge if there is some positive likelihood of spread if individuals get infected. Let’s call the graph the spread graph. The weight of the edge between two nodes would imply the probability of transmission. Thus there would be high weights between very connected individuals, e.g., within families, and the weight would decrease fast with geographic distance.

This very complicated spread graph is what we are changing with all the measures we are taking against covid-19. The measures will either remove edges from the graph or decrease the weights dramatically. To maximize the vaccine’s effectiveness in terms of herd immunity, we would ideally find the most connected nodes in this graph and sort them by the number of outwards connections (weighted by the probability of transmission) and start vaccinating these highly connected individuals. These changes would mean that we change the graph by removing the maximum number of edges at a time. However, this scenario is impossible since there is no way for us to get a complete picture of this graph.

Now it might be clear why the two objectives of protecting sensitive individuals and maximizing progress towards herd immunity could be competing. I.e., if sensitive individuals have few outgoing edges in the spread graph, then vaccinating them will mostly focus on the first objective. It is also impossible to reach herd immunity if we do not have enough of the vaccine, so it is critical to protect sensitive individuals and health care workers.

When we have protected the sensitive individuals, we might start trying to find highly connected individuals, and vaccinating them, e.g., hairdressers, store clerks, fitness trainers, etc. But where do you draw the line of when we have protected the sensitive individuals? This process is a highly ethical matter.

## Incentives to help

Some countries will reach herd immunity faster and, in some cases, will have an abundance of the vaccine. They will be incentivized to help other countries to make a recovery for the global economy faster. The countries they choose to help will be the countries they are most dependent on concerning the economy. This selection will be a political debate in some countries since it will not be a clear choice on who should receive their extra doses.

Here I would like to see a foundation created (or, e.g., Melinda Gates Foundation) to take care of this (which might become a logistic nightmare). Ideally, the extra doses of vaccine should be redistributed as fast as possible, and no political debate should delay it. A centralized non-profit foundation could do wonders here to make the redistribution more effective. This global effort will require an abundance of funds.

## Quarantine zones

The countries that reach herd immunity fast will, in some sense, be the safest travel destinations. People traveling from these countries will face minimum restrictions in terms of being quarantined. People moving between countries could also hop to a quarantine zone country and stay there for a week to avoid being quarantined in a hotel room or something similar for a week. These quarantine zones could result in kickstart for global tourism and international passenger flight.

Smaller populations might reach herd immunity earlier. Having these herd immunity spots worldwide will create value for the global logistics chain and help us understand the vaccines' long-term effectiveness.

## Speculations

My words here are mere speculations; only time can tell how things will eventually pan out. I look forward to better times, and I believe in a bright future.

##### Guðmundur Einarsson
###### Research Scientist

I am interested in statistics, genetics and machine learning.

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