  Recent infectious disease outbreaks, such as the COVID-19 pandemic and the
Zika epidemic in Brazil, have demonstrated both the importance and difficulty
of accurately forecasting novel infectious diseases. When new diseases first
emerge, we have little knowledge of the transmission process, the level and
duration of immunity to reinfection, or other parameters required to build
realistic epidemiological models. Time series forecasts and machine learning,
while less reliant on assumptions about the disease, require large amounts of
data that are also not available in early stages of an outbreak. In this study,
we examine how knowledge of related diseases can help make predictions of new
diseases in data-scarce environments using transfer learning. We implement both
an empirical and a theoretical approach. Using empirical data from Brazil, we
compare how well different machine learning models transfer knowledge between
two different disease pairs: (i) dengue and Zika, and (ii) influenza and
COVID-19. In the theoretical analysis, we generate data using different
transmission and recovery rates with an SIR compartmental model, and then
compare the effectiveness of different transfer learning methods. We find that
transfer learning offers the potential to improve predictions, even beyond a
model based on data from the target disease, though the appropriate source
disease must be chosen carefully. While imperfect, these models offer an
additional input for decision makers during pandemic response.
