Graph Theory & Spread of Contagious Disease
(Article: https://www.nytimes.com/2019/09/11/health/nursing-homes-fungus.html)
This article discusses the impact of nursing homes on the spread of contagious disease, with an emphasis on drug-resistant germs such as Candida auris, a highly contagious and deadly fungus that has spread to about 800 people since it arrived in the U.S. four years ago. Drug-resistant infections are on the rise, and most efforts to combat them have focused on the overuse of antibiotics in humans and agriculture as well as infections acquired from short-term hospital stays. Now, public health officials are saying that nursing facilities and long-term hospitals are promoting the spread of the disease, especially those with patients on ventilators (machines designed to move breathable air into and out of the lungs). Since seriously-ill patients tend to have severely compromised immune systems and take multiple antibiotics, they cause the nursing homes and long-term care facilities they stay in to become “breeding grounds” for drug-resistant germs. Poor hygiene or staffing in these facilities add to the problem.
In studying the spread of infectious diseases, especially those of drug-resistant germs, graph theory should be applied in order to identify the major sources of these pathogens, patterns of disease spread, and potential measures that could be taken to prevent infections. For instance, mapping the incidence of drug-resistant infection and comparing it to the graph of hospitals and care facilities might reveal clustering around facilitates of the same geographic area or might identify the top “carriers” of infection between facilities. It could help researchers compare the extent to which visitors (family members), staff, and transport of patients contribute to the spread of infection, and help identify the types of interactions (meetings, acquaintances, household, etc.) that are most likely to cause infection. Perhaps, once the application of graph theory to epidemiology becomes more widespread, it could also be applied to non-contagious illnesses such as autoimmune or genetic disorders to model the incidence of these disorders and identify social or biological patterns that could lead to treatments, cures, or preventative measures.