Paper Recommendation 1: How Hidden Markov Models Unmasked the True Scale of COVID19
This is the first in what will hopefully be an intermittent series of appreciation posts of other people's papers. Here it goes: One of the big statistical problems during the COVID pandemic was that the official case counts were never the whole story. The number reported each day depended not only on how many people were actually infected, but also on how many were tested, how quickly laboratories processed samples, and how public health systems recorded cases. In other words, the observed data were only a partial and noisy picture of the real epidemic. This is exactly the kind of problem state-space models, or SSMs, are designed to handle. In the 2020 work of Fernández-Fontelo, Moriña, Cabaña, Arratia, and Puig, the main idea was to separate the epidemic into two layers: a hidden layer representing the true number of infections, and an observed layer representing the reported ...