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Updated: May 3, 2026

Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy

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Published on: April 3, 2018

An optimization framework for large-scale screening under limited testing capacity with application to COVID-19.

Jiayi Lin¹, Hrayer Aprahamian², George Golovko³

¹Department of Industrial and Systems Engineering, Texas A &M University, College Station, 77843, TX, USA. jiayilin@tamu.edu.

Health Care Management Science

|April 24, 2024

Summary

Targeted mass screening optimizes limited testing resources by identifying high-risk groups. This proactive approach significantly reduces misclassifications in disease detection, improving public health strategies.

Keywords:

COVID-19 Group testing Heterogeneous populations Operations management Operations research Targeted screening

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Area of Science:

Operations Research
Public Health
Epidemiology

Background:

Mass screening is crucial for disease control but challenged by limited testing capacity.
Effective screening requires targeting susceptible or high-need populations.
Existing methods may not optimally allocate resources under constraints.

Purpose of the Study:

To develop and analyze targeted mass screening strategies for heterogeneous populations.
To optimize the selection of sub-populations for screening under limited capacity.
To compare individual and Dorfman group testing schemes for efficiency and accuracy.

Main Methods:

Formulation of optimization models for individual and Dorfman group testing.
Analysis of model properties to develop efficient solution techniques.
Case study application using COVID-19 geographic data in the United States.

Main Results:

Proactive targeted screening schemes significantly reduce misclassifications compared to standard practices.
Optimization models provide insights into optimal test allocation and design.
The study demonstrates the effectiveness of data-driven recommendations for policy-makers.

Conclusions:

Targeted mass screening, informed by population risk data, is superior to traditional methods under capacity constraints.
Efficient and accurate screening protocols can be developed through optimization.
The findings offer practical guidance for managing public health resources during disease outbreaks.