(untitled) (Epidemiology, Biostatistics, and Public Health, 2024)
Background: Multiple interconnected key metrics are frequently available to track the pandemic progression. One of the difficulties health planners face is determining which provides the best description of the status of the health challenge. Methods: The aim of this study is to capture the information provided by multiple pandemic magnitudes in a single metric. Drawing on official Spanish data, we apply techniques of dimension reduction of time series to construct a synthetic pandemic indicator that, based on the multivariate information, captures the evolution of disease severity over time. Three metrics of the evolution of the COVID-19 pandemic are used to construct the composite severity indicator: the daily hospitalizations, ICU admissions and deaths attributable to the coronavirus. The time-varying relationship between the severity indicator and the number of positive cases is investigated. Results: A single indicator adequately explained the variability of the three time series during the analyzed period (May 2020–March 2022). The severity indicator was stable until mid-March 2021, then fell sharply until October 2021, before stabilizing again. The period of decline coincided with mass vaccination. By age group, the association between underlying severity and positive cases in those aged 80+ was almost 20 times higher than in those aged 20-49. Conclusion: Our methodology can be applied to other infectious diseases to monitor their severity evolution with a single metric. The synthetic indicator may be useful in assessing the impact of public health interventions on reducing disease severity.