Spread of Disease
Source: https://www.sciencedaily.com/releases/2018/06/180614212651.htm
This article discusses how large-scale disease outbreaks are studied through networks and how mathematical models have a practical application in public health. The model that is explained is the Susceptible-Infected-Susceptible (SIS) model, which takes human behavior and communication into account when looking at spread of disease. For example, people will adopt measures to prevent themselves from getting sick and reduce risk of transmission. This behavior results from communication over various network forums. Therefore, disease outbreak changes as communication occurs and people alter their behavior. In order to evaluate the proposed model, researchers applied the network to the spread of severe acute respiratory syndrome (SARS), in which public health measures are the only way to stop the outbreak. Their results indicate that rapid behavioral response will curb SARS outbreaks – this includes public health measures (washing hands, wearing protective masks) and regulations of public institutions, which is represented in a network as an individual node with many edges.
While the article uses the SIS model and in class we learned about the SIR model, the concepts of both are similar. The only difference between the two models of epidemic spread is that in the SIS model, an individual returns to the susceptible group upon recovery because there is possibility of reinfection. However, in the SIR model it is understood that an individual cannot be reinfected by the same disease after recovery. In class, we learned about the three parameters of the SIR model – network, probability of transmission, and incubation. While this article did not explicitly discuss each step, its methods took into account the probability of transmission of SARS and the incubation period was kept constant. The network aspect was discussed and it has a major role in disease outbreak. A node that has many edges has a greater probability of transmission, however, it can also have a reduced probability of transmission based on communication with other nodes. The article discusses how communication can limit spread of disease, which is interesting when you think about how disease can spread. Therefore, this article enhances my understanding of epidemics from what we learned in lecture while calculating the spread of a specific disease under certain conditions, altering the network.