Infection Rate Models for COVID-19 : Model Risk and Public Health News Sentiment Exposure Adjustments

During the COVID-19 pandemic, governments globally had to impose severe contact restriction measures and social mobility limitations in order to limit the exposure of the population to COVID-19. These public health policy decisions were informed by statistical models for infection rates in national populations. In this work we are interested in modelling the temporal evolution of national level infection counts for a variety of countries, in order to better understand the most reliable model structure for the COVID-19 epidemic growth curve. We will achieve this by exploring a variety of stochastic population growth models and comparing their calibration to the most widely used and reported growth model, the Gompertz population model, often referred to in the public health policy discourse during the COVID-19 pandemic. Model risk as we explore it in this work manifests in the inability to adequately capture the behaviour of the disease progression growth rate curve. Therefore, our concept of model risk is formed relative to the standard reference used by decision makers, the Gompertz model, and then we can characterize model risk mathematically as having two components: the dispersion of the observation distribution, and the structure of the intensity function over time for cumulative counts of new infections daily (i.e. the force of infection) attributed directly to the COVID-19 pandemic. We also explore how to incorporate in these population models the effect that governmental interventions have had on the number of infected cases. This is achieved through development of an exposure adjustment to the force of infection comprised of a purpose built sentiment index constructed from a variety of authoritative public health reporting. We find that exposure adjustments that incorporate sentiment are better able to calibrate to early stages of infection spread in all countries under study