Predictive Capacity of COVID-19 Test Positivity Rate.

Livio Fenga,Mauro Gaspari

doi:10.3390/s21072435

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.

Highlights

One of the aspects that makes the COVID-19 pandemic difficult to control, is 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 hospitalizations and/or beds in Intensive Care Units (ICUs)), this is not the case of the latter [1,2,3]
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
Sampled at a daily frequency—are those necessary to compute the TPR (the number of new persons tested positive for COVID-19; the number of tests done considering both molecular (PCR) tests and antigen tests, and the number of healed persons), and those related to the number of hospitalizations and beds in intensive care units occupied by patients tested positive for COVID-19

Summary

Introduction

One of the aspects that makes the COVID-19 pandemic difficult to control, is 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 hospitalizations and/or beds in Intensive Care Units (ICUs)), this is not the case of the latter [1,2,3]. Those who contracted the virus are unaware of such a condition and, enter the status of spreaders or, in the worse case, superspreaders Such a phenomenon, commonly referred to as under-ascertainment, is the primary reason for a disease to spread uncontrolled. All the efforts so far done to model and predict such a disease might hardly support the idea that a uniformly “better”

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