Why the branching process cannot model how epidemic spreads in the real world
Why the branching process cannot model how epidemic spreads in the real world
Link: https://phys.org/news/2018-11-tracking-epidemic-requires-modelsbut-wrong.html
In the lecture, we discussed the branching process to model how the epidemic spreads: once we have a patient zero and a basic reproductive number R, the disease will spread to R people from one patient per step. The researchers from Northwestern University believes that “patient zero is more likely to infect her friends and family than a stranger who lives across town.”. Thus, they want to find out the basic reproductive number in different settings. For example, what is the number when the disease spreads in workplaces and how is it different from the case where the diseases spread between family members. To do so, they use “the data from a 2009 outbreak of swine flu in Italy and the Netherlands.” The result of such study can be used to predict how the disease in the future spreads.
However, to the researchers’ surprise, it is much more dynamic than they thought, i.e the assumption in the branching process, that the basic reproductive number is a constant for a single disease is wrong. It depends on many more factors. For example, “a middle-schooler from a big family is going to infect many more people in his household than a retiree who lives alone.” It means that the reproductive number can be different for each person. Also, “epidemics tend to tear through high-traffic places like schools and offices at the start, then slow down as they reach more isolated areas.”
Through these findings, the researchers build another model that can accurately predict how the disease spread for the data they used. However, this model does not work for countries that have different social structures than Italy and the Netherlands.
This study is an extension of the branching process we studied in the lecture. In fact, it points out that the model is too simplistic to model the real world. In order to do so, we need a more complex model that takes other factors(not just the disease itself) into account.