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Fizzle Testing: An Equation Utilizing Random Surveillance to Help Reduce COVID-19 Risks by Christopher Cullenbine; Joseph Rohrer; Erin Almand; J. Steel; Matthew Davis; Christopher Carson; Steven Hasstedt; John Sitko; Douglas Wickert is a Mathematics article available to read on EtoBox.

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A closed-form equation, the Fizzle Equation, was derived from a mathematical model predicting Severe Acute Respiratory Virus-2 dynamics, optimized for a 4000-student university cohort. This equation sought to determine the frequency and percentage of random surveillance testing required to prevent an outbreak, enabling an institution to develop scientifically sound public health policies to bring the effective reproduction number of the virus below one, halting virus progression. Model permutations evaluated the potential spread of the virus based on the level of random surveillance testing, increased viral infectivity and implementing additional safety measures. The model outcomes included: required level of surveillance testing, the number of infected individuals, and the number of quarantined individuals. Using the derived equations, this study illustrates expected infection load and how testing policy can prevent outbreaks in an institution. Furthermore, this process is iterative, making it possible to develop responsive policies scaling the amount of surveillance testing based on prior testing results, further conserving resources.

Who reads Fizzle Testing: An Equation Utilizing Random Surveillance to Help Reduce COVID-19 Risks?

Author

Christopher Cullenbine; Joseph Rohrer; Erin Almand; J. Steel; Matthew Davis; Christopher Carson; Steven Hasstedt; John Sitko; Douglas Wickert

Publisher

MDPI AG

Published

2021

Language

EN

Field

Mathematics (Physical Sciences)