Time Course Analysis of Age-Gender Effect on Severity of COVID-19 Outbreak in Spain and Italy

Abstract

Several data science studies have analysed the gender effect on COVID19 with at least one of these limitations: 1) missing comparison across countries; 2) data not age-stratified; 3) analysis based on single date of epidemic period instead of time-course; 4) few variables analysed; 5) gender bias not adjusted by country’s population in that strata. Here, we address these limitations. A wide range of variables on the severity of COVID-19 in relation to gender and age, are analysed over an extended time course from March 2020 to when data are publicly available. Spanish and Italian data only are considered because they are the unique open access data to be comprehensive and harmonized according to a comparable format. Altogether our findings offer two key evidence-driven recommendations. First, since data collection is disharmonic across Europe, the creation of a European institute for standards in biomedical data collection could play a crucial role for fast open-source dissemination and analysis of harmonized data, which in turn could foster rapid and coordinated decision making in emergency periods. Second, since COVID-19 severity particularly impacts 60+ males, containing interventions might be more age/gender-adaptive and, to increase effectiveness and efficiency, focus to contrast contagion of these categories at risk.