Epidemiological trends in COVID-19 pandemic: prospective critical appraisal of observations from six countries in Europe and the USA

Velicu, Maria Alexandra; Furlanetti, Luciano; Jung, Josephine; Ashkan, Keyoumars

INTRODUCTION: Europe was the epicentre of the COVID-19 pandemic in March 2020, with the highest number of cases and deaths between March and April. In May, the infection numbers registered a fall followed by a second new rise, not proportionally reflected by an increase in the number of deaths. We aimed to investigate the relationship between disease prevalence and infection fatality rate (IFR), and the number of intensive care unit (ICU) and hospital admissions over time, to develop a predictive model, as well as appraising the potential contributing factors underpinning this complex relationship. METHODS: A prospective epidemiological study using data from six countries collected between 10 March and 4 September 2020. Data on the number of daily hospital and ICU admissions with COVID-19 were gathered, and the IFR and the prevalence were calculated. Trends over time were analysed. A linear regression model was used to determine the association between the fatality rates and the number of admissions. FINDINGS: The prediction model confirmed the linear association between the fatality rates and the numbers of ICU and hospital admissions. The exception was during the peak of the COVID-19 pandemic when the model underestimated the fatalities indicating that a substantial number of deaths occurred outside of the hospitals. The fatality rates decreased in all countries from May until September regardless of the trends in prevalence, differences in healthcare systems or strategic variations in handling the pandemic. INTERPRETATION: The observed gradual reduction in COVID-19 fatality rates over time despite varying disease prevalence and public health measures across multiple countries warrants search for a biological explanation. While our understanding of this novel virus grows, hospital and ICU admission rates remain effective predictors of patient outcomes which can be used as early warning signs for escalation of public health measures.