Predictive Capacity of COVID-19 Test Positivity Rate

Abstract

COVID-19 infections can spread silently, due to the simultaneous presence of significant numbers of both critical and asymptomatic to mild cases. While, for the former reliable data are available (in the form of number of hospitalization and/or beds in intensive care units), this is not the case of the latter. Hence, analytical tools designed to generate reliable forecast and future scenarios, should be implemented to help decision-makers to plan ahead (e.g., medical structures and equipment). Previous work of one of the authors shows that an alternative formulation of the Test Positivity Rate (TPR), i.e., the proportion of the number of persons tested positive in a given day, exhibits a strong correlation with the number of patients admitted in hospitals and intensive care units. In this paper, we investigate the lagged correlation structure between the newly defined TPR and the hospitalized people time series, exploiting a rigorous statistical model, the Seasonal Auto Regressive Moving Average (SARIMA). The rigorous analytical framework chosen, i.e., the stochastic processes theory, allowed for a reliable forecasting about 12 days ahead of those quantities. The proposed approach would also allow decision-makers to forecast the number of beds in hospitals and intensive care units needed 12 days ahead. The obtained results show that a standardized TPR index is a valuable metric to monitor the growth of the COVID-19 epidemic. The index can be computed on daily basis and it is probably one of the best forecasting tools available today for predicting hospital and intensive care units overload, being an optimal compromise between simplicity of calculation and accuracy.

Keywords: COVID-19; health system management; predictive capacity; test positivity rate.

Figure 1

The Test Positivity Rate (TPR) index (orange dotted line) predictive capacity.

Figure 2

The TPR index (orange dotted lines) and hospitalized patients time series of Toscana, Veneto, Piemonte, and Alto Adige.

Figure 2

The TPR index (orange dotted lines) and hospitalized patients time series of Toscana, Veneto, Piemonte, and Alto Adige.

Figure 3

Forecasting hospitalized patients growth in 5 different scenarios for regions: Toscana, Alto Adige, Piemonte, and Veneto (also including a fast lowering example). The orange dotted lines represent the TPR index.

Figure 4

Developing point-of-care (instant) screening tests for COVID-19: data collection, sensors technology, TPR calculation, and information flows.