Optimal allocation of limited test resources for the quantification of COVID-19 infections

Abstract

The systematic identification of infected individuals is critical for the containment of the COVID-19 pandemic. Currently, the spread of the disease is mostly quantified by the reported numbers of infections, hospitalisations, recoveries and deaths; these quantities inform epidemiology models that provide forecasts for the spread of the epidemic and guide policy making. The veracity of these forecasts depends on the discrepancy between the numbers of reported, and unreported yet infectious, individuals. We combine Bayesian experimental design with an epidemiology model and propose a methodology for the optimal allocation of limited testing resources in space and time, which maximises the information gain for such unreported infections. The proposed approach is applicable at the onset and spread of the epidemic and can forewarn of a possible recurrence of the disease after relaxation of interventions. We examine its application in Switzerland; the open source software is, however, readily adaptable to countries around the world. We find that following the proposed methodology can lead to vastly less uncertain predictions for the spread of the disease, thus improving estimates of the effective reproduction number and the future number of unreported infections. This information can provide timely and systematic guidance for the effective identification of infectious individuals and for decision-making regarding lockdown measures and the distribution of vaccines.

COVID-19 healthcare cost and length of hospital stay in Turkey: retrospective analysis from the first peak of the pandemic

“…World leaders were already warning of the sustainability of health care systems before the current pandemic hit [5,6]. The rapid and global spread of the pandemic has exacerbated existing problems and created new issues that will challenge decision-makers and negatively impact the health of the populace [7][8][9]. To mitigate the impact of this and future pandemics, decision-makers need to understand how their health systems were impacted.…”

How to make universal, voluntary testing for COVID-19 work? A behavioural economics perspective

“…Consequently, an increasing amount of work discusses the optimal allocation of (inevitably limited) testing resources (e.g. [11] ) and how COVID-19-testing strategies could usefully draw on the experience with former successful disease screening programs [31] .…”

Optimal control of the spatial allocation of COVID-19 vaccines: Italy as a case study

“…Interesting developments have recently arose during the ongoing SARS-CoV-2 pandemic [13,31,32]. The urgency of effective vaccination campaigns led to the development of modeling frameworks for the optimization of vaccine allocation, based on age or risk [3,4,12,13], space [33], dose timing [34,35], and the deployment of testing resources, using optimal control [36] or Bayesian experimental design [37], along with prioritization based on social contact networks [38].…”