The impact of Test Positivity on Surveillance with Asymptomatic Carriers

Abstract

Objectives: Recent studies show that Test Positivity Rate (TPR) gain a better correlation than incidence with the number of hospitalized patients in COVID-19 pandemic. Nevertheless, epidemiologist remain sceptical concerning the widespread use of this metric for surveillance, and indicators based on known cases like incidence are still preferred de- spite the large number of asymptomatic carriers which remain unknown. Our aim is to compare TPR and incidence, to determine which of the two has the best characteristics to predict the trend of hospitalized patients in COVID-19 pandemic. Methods: We perform a retrospective study considering 60 cases, using global and local data from Italy in different waves of the pandemic, in order to detect peaks in TPR time series, and peaks in incidence, finding which of the two has the best ability to anticipate peaks in patients admitted in hospitals. Results: On average the best TPR based approach anticipates incidence of about 4.6 days (95% CI 2.8, 6.4), more precisely the average distance between TPR peaks and hospitalized peaks is 17.6 days (95% CI 15.0, 20.4) with respect to 13.0 days (95% CI 10.4, 15.8) obtained for incidence. Moreover, the average difference between TPR and incidence increases to more than 6 days in the Delta outbreak during Summer 2021, where presumably the percentage of asymptomatic carriers was larger. Conclusions: We conclude that TPR should be used as primary indicator to enable early intervention and for planning hospitals admissions in infectious diseases with asymptomatic carriers.

Forecasting by Analogy: A Parallel between the Trend of Confirmed COVID-19 Deaths in the Winters of 2022/2023 and 2023/2024 in Italy

“…We know very well that this approach is subject to many limitations and runs the risk of failure. Nonetheless, we have decided to use neither the traditional COVID-19 spread indicators of the classical SIR/SEIR models, nor alternative but simpler regression models [8], because recent studies have shown that more basic and intuitive metrics can establish a better correlation, than traditional indicators, with both the number of hospitalized patients and deaths during a COVID-19 epidemic [9,10]. Even if epidemiologists may remain skeptical about the use of those basic metrics for carrying out a real time surveillance, we are confident that our approach can still be useful to predict deaths over a rather long period of time, essentially because it is not influenced by the large number of asymptomatic carriers which remain still unknown and have often contributed to the failure of traditional epidemiological methods used to modeling the COVID-19 diffusion.…”