Fanny Bergström: Computational methods for real-time surveillance of infectious diseases: nowcasting and risk classification

Abstract

The real-time analysis of infectious disease surveillance data is one of the essential components in shaping the response during infectious disease outbreaks such as major food-borne outbreaks or the COVID-19 pandemic. Public health agencies and governments typically monitor disease dynamics using time-series of reported cases or fatalities to assess the effectiveness of preventive measures and plan further actions. Here I will present my first two PhD projects, both relatining to COVID-19 monitoring. The first project was on bayesian nowcasting of Swedish fatalities with COVID-19 where we by the inclusion of lead indicatiors improved the predicive performance of the nowcasts. The second project is on an early detection algorithm for identifying areas of rapid increase of disease burden such as hospitalizations or deaths used by the World Health Organisation where we extend the statistical methodological details to improve the assessment.