High Density, Low Epidemic Rate – An Ant’s Approach to Survival
Link: https://www.sciencedaily.com/releases/2018/11/181123135020.htm
In class, we took a look at a way to model the spread of diseases based on (1) contact between an infected individual and a non-infected individual and (2) the probability of transmitting the infection across that contact. We furthered our model by taking a look at the three states of life in an epidemic (Susceptible, Infectious and Removed), and deducing that effective ways of limiting the spread of diseases can come from reducing the probability of transmission (which we denoted p) or reducing the number of contacts an infectious individual has (which we denoted k). Practically speaking, we saw that decreasing p was more feasible for a community, since it could be achieved by, for example, increasing sanitation. However, decreasing k seemed a lot more far-fetched, since it would involve effectively putting an infected individual in quarantine. From this article, we see that ants have their own disease defense mechanism that manages to both decrease p and decrease k while not leaving individuals extremely isolated.
The basis of an ant colony’s disease defense mechanism is rooted in the structure of the ant colony itself. Ant colonies have the queen ant, which is the only ant able to reproduce, as well as worker ants. The younger worker ants, referred to as nurses, take care of the young ants at the center of the colony. Older worker ants, referred to as foragers, leave the nest to find food for the rest of the colony. When a disease is not present, these groups often intermingle. However, this experiment showed that when some foragers were exposed to high amounts of fungal spores (as is to be expected since foragers are the only ants that leave the colony and can be exposed to various diseases), the structure of the colony changes. All ants remain in their own clusters, with foragers only interacting with foragers and nurses only interacting with nurses. Doing this reduces the k value for the infected foragers, since they interact with a smaller number of ants in the colony. At the same time, the experiment had forager ants that were exposed to lower amounts of fungal spores, and were able to develop an immunity to the disease. Due to the presence of immune foragers in the infected forager cluster, we can consider some ants already “removed” from the network, meaning they will survive the infection. This in turn effectively decreases the p value of the disease (since it isn’t as likely to spread to forager ants as to all ants in the colony), and guarantees that some foragers remain once the infection runs its course.
From the response of an ant colony above, we see that the more important members of the colony (the nurses and the queen) are never susceptible to the infection since they never leave the colony and are isolated from infected groups of foragers. At the same time, foragers are always susceptible to disease, and can become infectious; nevertheless, some foragers can develop immunity beforehand and become removed from the network, and some will still survive after the disease runs its course. Eventually, we see that the ant colony structure will return to the way it was. All in all, we see how our SIR model is an effective way of modeling disease spread, as we see in ant colonies, and we also see an interesting way that ants manage to prevent epidemics from spreading in their highly-dense colony, an environment which seems ideal for diseases to spread due to high chance of contact.