ABSTRACT
The main purpose of this applied research paper is to optimize the probabilities of the false negative (FN) error, β, and the false positive (FP) error, α in a pandemic healthcare setting. The overall objective is to estimate the number of those patients falsely declared uninfected, and those falsely declared infected, aiming to recalibrate the overall count of cases aligned with the world’s mobilization and vaccination efforts. Incomplete FN results can have devastating impacts on current efforts to contain the SARS-CoV-2 (COVID-19) outbreak as infected patients are mistakenly given the go-ahead to return to normal life, likely infecting others. The whole world experienced in 2020 that the number of deaths were undercounted due to existence of false negatives or asymptomatic carriers. But there did not exist universally unbiased scientific methods other than controversial comparisons with the past seasonal death records. This game-theoretic research effort fills a void to replace guesswork and judgment-calls by employing data-scientific health informatics. This article reasons by citing real-data examples why von Neumann’s mixed-strategy game-theoretic feasible solutions to predict the FN cases are noteworthy to prevent more fatalities by facilitating timely pandemic mobilization. FP counts are however not so critical other than causing panic and waste of resources. In the wake of vaccination relief efforts, this research topic is still valid and invaluable for the future unprecedented pandemics such as a hypothetical COVID-35. A fringe benefit of the article is to transform hypothesis testing from subjective to objective for scientific, medical and engineering decisions. Evolutionary game theory may be incorporated for evolving and mutating pandemic variants e.g. OMICRON and DELTA for further research tips. Copyright © 2022 by the authors.