COVID-19: Connecting the Dots Between Spreaders and the Vulnerable

In the last post I introduced the idea of connections and how they can affect the spread of COVID-19. In this post I will go a little further down the COVID data rabbit hole to where the abstract is real and relationships can be fatal.

You are probably all familiar with the old saying, "It's not what you know, but who you know". For those who don't, it means if you want to move up the ladder, your knowledge and skills are less important than your network of personal contacts (Wiktionary). In this time of pandemic, there is a downside to connections if you test positive. The virus will know who you know and can use that ladder to move on to the next host.

No matter how reclusive you are, we are all connected to other people: friends, relatives, your spouse and kids, work mates, bar buddies, pickup basketball teams. And, of course, the people we know have their own set of acquaintances, and so on down the line. Before delving deeper into the intricacies of "social networks", as they are called, it is important to know about the background conditions that exist which determine where the most vulnerable live. It is these people, at risk of severe complications from COVID-19, who need to guard against connections to those already infected.

The CDC has been alerting us all, over and over, that certain individuals are more likely to have negative outcomes from the virus. In Austin, Texas, researchers have compiled a set of measures that can be used to identify those populations and locate them on a map (Houston Map). The measures they used included a number of economic, environmental, and health care factors that can influence vulnerability..

The idea was that if you know where these areas are, it can help in the allocation of testing and health care efforts. The data for the study came from national and local databases that tied records of the following statistics to census tract areas:

• access to hospitals and medical care
• underlying medical conditions
• exposure to pollutants
• areas prone to disasters and flooding
• other lifestyle choices like smoking and drinking

But how does the virus find these people? That is where the concept of networks can help to uncover the "invisible threads" tying us all together.

Networks are defined as a set of nodes connected to each other by links or "edges". Networks can be used to describe many phenomena, including computer connectivity, electrical systems, biological interactions, and financial transactions (Network Theory). Social Networks describe connections between people and between people and other entities they may interact with. In epidemiology, the study of disease within a population, Social Networks can be used to visualize the spread of disease and possible interventions to control it. This type of Social Network is referred to as a Contact Network.

The data required to construct a Contact Network is produced by Contact Tracing. In an outbreak, many investigators are needed to interview people who are infected, tracing back along the individual's set of contacts to determine who they may have come in contact with and when. This list of contacts might contain people who will become infected through the contact or who infected the individual. Tracing is very time consuming and depends on the cooperation and recollection of the patients. It therefore has the most affect on disease control when the rate of infection is low (SNA For Tracing). Automated tracing through cell phone proximity logging can speed up the process of identifying contacts, as long as security concerns are addressed. In the end, though, manual interviews are still needed to provide health and quarantine support (Practical Application).

By adding a geographical value to network nodes, researchers at Penn State were able to locate individuals associated with nodes on a map while maintaining the links between them (Where You Go).

The networks they mapped above are referred to as components. The nodes in each network represent groups of at risk street youths whose residences are shown linked to each other. The networks are overlain on a density heat map of locations where at risk behaviors, like drug use, were performed by individuals in the network. The map shows a high level of overlap between the various networks relative to the risk sites, indicating a more cohesive interaction between networks. It seems possible that in a disease investigation, similar mappings of contacts along with areas of vulnerable populations might provide clues to transmission sources within those communities.

In the next post, I will look more closely at typical Contact Network analyses and how they help uncover gathering places that accelerate disease transmission.