Statistical methods for characterising the severity of an emerging pathogen: case studies of the COVID-19 pandemic

Modelling of an ongoing epidemic of a new pathogen poses several particular challenges, especially when the cases are often symptomless or easy to mistake for a common disease such as a cold or flu. Mathematical models of infectious diseases rely on the input of data, and at the beginning of the COVID-19 pandemic very limited datasets were available to the researchers. This caused the estimates to have large biases, including large uncertainty intervals or the need to assume the severity of the virus across different countries was the same. This assumption was necessary at the time but made the results highly questionable e.g. when data from a high-income country were used to model the epidemic in a low- or middle-income country.

An additional problem posed by the data was the delay with which they were collected. Surveillance of an ongoing epidemic requires daily monitoring of the numbers of new cases or deaths, but this was often impossible due to the reporting delays, as a lot of hospital staff was overwhelmed with the number of patients and unable to promptly update the data online. This led to systematic underreporting of the true state of the epidemic.

The main topic of my PhD projects is closing some of those data gaps and decreasing the bias in the data. In the first project, we provided estimates of common epidemiological distributions, such as onset-to-death time, for the COVID-19 epidemic in Brazil based on hospitalisation data. Our estimates gave evidence of spatial heterogeneity in the fitted distributions. In the second project, we proposed a new approach to correcting the delays in data reporting using latent Gaussian processes (GPs) and presented the applicability of the method to the COVID-19 mortality data in Brazil.