[Paper Review] Population-scale testing can suppress the spread of infectious disease

Jussi Taipale, Ioannis Kontoyiannis+1 more|arXiv (Cornell University)|Apr 14, 2021

Data-Driven Disease Surveillance8 references4 citations

TL;DR

This paper proposes population-scale, repeated, and concurrent testing combined with isolation of infected individuals as a rapid, robust intervention to suppress the spread of infectious diseases. It demonstrates analytically and through simulations that at a sufficiently high testing frequency, the effective reproduction number (Rt) can be driven below 1.0, leading to epidemic collapse, even without vaccines or pharmaceuticals.

ABSTRACT

Major advances in public health have resulted from disease prevention. However, prevention of a new infectious disease by vaccination or pharmaceuticals is made difficult by the slow process of vaccine and drug development. We propose an additional intervention that allows rapid control of emerging infectious diseases, and can also be used to eradicate diseases that rely almost exclusively on human-to-human transmission. The intervention is based on (1) testing every individual for the disease, (2) repeatedly, and (3) isolation of infected individuals. We show here that at a sufficient rate of testing, the reproduction number is reduced below 1.0 and the epidemic will rapidly collapse. The approach does not rely on strong or unrealistic assumptions about test accuracy, isolation compliance, population structure or epidemiological parameters, and its success can be monitored in real time by following the test positivity rate. In addition to the compliance rate and false negatives, the required rate of testing depends on the design of the testing regime, with concurrent testing outperforming random sampling. Provided that results are obtained rapidly, the test frequency required to suppress an epidemic is monotonic and near-linear with respect to R0, the infectious period, and the fraction of susceptible individuals. The testing regime is effective against both early phase and established epidemics, and additive to other interventions (e.g. contact tracing and social distancing). It is also robust to failure: any rate of testing reduces the number of infections, improving both public health and economic conditions. These conclusions are based on rigorous analysis and simulations of appropriate epidemiological models. A mass-produced, disposable test that could be used at home would be ideal, due to the optimal performance of concurrent tests that return immediate results.

Motivation & Objective

• To develop a scalable, non-pharmaceutical intervention for controlling emerging infectious diseases with minimal reliance on vaccine or drug development.
• To address the limitations of traditional public health measures like contact tracing and social distancing, especially in the early stages of an outbreak.
• To demonstrate that population-wide testing can suppress transmission even under realistic conditions of test inaccuracy and non-compliance.
• To provide a mathematically rigorous framework for determining the required testing frequency to achieve epidemic control.
• To show that testing is effective not only in early outbreaks but also in established epidemics, and is additive to other interventions.

Proposed method

• Proposes a population-scale testing strategy involving repeated, concurrent testing of all individuals in a population.
• Models the intervention using stochastic and deterministic epidemiological frameworks, including SIR-type compartmental models.
• Analyzes the effective reproduction number (Rt) as a function of testing frequency, infectious period, and population susceptibility.
• Introduces a real-time monitoring metric: the test positivity rate, which serves as a proxy for epidemic trends.
• Compares testing regimes: concurrent testing (all tested at once) outperforms random sampling in reducing Rt.
• Uses mathematical analysis and agent-based simulations to validate the robustness of the approach under varying parameters (e.g., false negatives, compliance).

Experimental results

Research questions

• RQ1 Can repeated, population-wide testing reduce the effective reproduction number (Rt) below 1.0, leading to epidemic suppression?
• RQ2 How does the required testing frequency scale with key epidemiological parameters such as R0, infectious period, and population susceptibility?
• RQ3 How does the performance of concurrent testing compare to random sampling in terms of epidemic control?
• RQ4 To what extent is the intervention robust to test inaccuracy (false negatives) and non-compliance with isolation?
• RQ5 Can the success of the testing strategy be monitored in real time using the test positivity rate?

Key findings

• At a sufficiently high rate of repeated, concurrent testing, the effective reproduction number (Rt) can be driven below 1.0, leading to rapid epidemic collapse.
• The required testing frequency scales nearly linearly with R0, the length of the infectious period, and the fraction of susceptible individuals.
• Concurrent testing significantly outperforms random sampling in reducing Rt, especially when results are available immediately.
• The test positivity rate serves as a reliable, real-time indicator of epidemic trends and can be used to monitor intervention effectiveness.
• Even suboptimal testing rates reduce the number of infections, improving both public health and economic outcomes.
• The approach is robust to realistic levels of false negatives and non-compliance, and remains effective across a wide range of epidemiological and structural conditions.