Public Health Policymaking using Insights from COVID-19 Modellingwith News Sentiment

Throughout the COVID-19 pandemic, severe contact restriction measures and social mobility limitations had to be put in effect by governments all over the world, to limit the exposure of the population to the novel coronavirus. These public health policy decisions were made by considering the output of statistical models for infection rates in national populations. To assist in the public effort, we conducted research where we modelled the temporal evolution of national-level infection counts for the United Kingdom (UK-Wales, England, Scotland), Germany, Italy, Spain, Japan, Australia and the United States for the period January 2020 to January 2021, in order to better understand the most reliable model structure for the COVID-19 epidemic growth curve. We achieved this by exploring a variety of stochastic population growth models and comparing their calibration, with and without an exposure adjustment, to the most widely used growth rate model, the Gompertz population model, often referred to in the public health policy discourse over the past year. In this work, we explore the statistical concept of model risk, which manifests in the inability to adequately capture the behavior of the disease progression growth rate curve. Model risk is mathematically characterized as having two components: The dispersion of the observation distribution and the structure of a function over time that describes the force of infection (the “intensity function”). Furthermore, we investigated how to include in these population models the effect that governmental interventions have had on the number of infected people. This was achieved through the development of an exposure adjustment to the force of infection comprised of a tailored sentiment index constructed from various authoritative public health news reporting, namely major global circulation newspapers, including The New York Times, The Guardian, The Telegraph, and Reuters global blog, as well as international, acknowledged health authorities, i.e. the European Centre for Disease Prevention and Control, the United Nations Economic Commission for Europe, the United States Centers for Disease Control and Prevention, and the World Health Organization. Our research revealed that the baseline Gompertz model is unable to adequately capture the pandemic evolution for all countries throughout the period of study, and, in addition, models that incorporated the proposed sentiment adjustment are better able to calibrate to the infection spread in all countries under study, particularly during the early stages of the pandemic.