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RAPID: Optimal allocation of COVID-19 testing based on context-specific outbreak control objectives

Abstract

The purpose of this project is to design a framework for objective-driven, context dependent disease surveillance strategies, designed to deal with sampling errors and biases. This framework will be applied to the allocation of COVID-19 testing based on multiple outbreak scenarios, employing the use of multiple models to improve decision-making for COVID-19 surveillance and control. This work will improve the response to the COVID-19 pandemic. Results will be presented to key federal agencies, so that they can be considered as part of the decision-making process for the COVID-19 outbreak. These methods will also be highly applicable to optimal vaccine allocation especially during the early stages of vaccine availability when supplies will be limited. In addition, it will provide a framework for future outbreak testing response. One postdoctoral researcher will be trained in the theory and methods of applied epidemiological research. With a growing, but still limited number of imperfect tests available, the way in which tests are allocated critically determines what we can learn and in turn, what inferences can be made with respect to managing the disease for individuals and populations. This poses an optimal allocation problem for limited resources. The context-dependent nature of allocating a limited number of tests introduces additional potential sources of error and bias, making the question of optimal testing allocation a unique challenge. Current modeling efforts focus necessarily on disease dynamics and the efficacy of intervention strategies, but few consider testing, contact tracing, and isolation strategies explicitly. Unlike shelter-in-place or social distancing mandates, the impact of test allocation on management strategy decision-making is density-dependent. While testing remains limited, it is critical to explicitly model test allocation and strategies that involve both monitoring and management. The key to successful surveillance is to actively design surveillance strategies for a specific objective. The project will use a principle of effective monitoring based on two steps. First, identify the objective of the monitoring? Second, tailor the sampling design to achieve that objective, in this case selecting groups of individuals in a nonrepresentative way and to separately estimate the probabilities that a randomly sampled individual would appear in these groups. Misclassification of disease state (e.g., false positives/negatives) due to the specificity and sensitivity of different tests, the performance of different test platforms and population-level incidence will also be addressed.

People

Katriona Shea

Professor of Biology and Alumni Professor in the Biological Sciences
Penn State University

Funding Source

Project Period

2020-2021