Group Testing with a Dynamic Infection Spread

Arasli, Batuhan; Ulukuş, Şennur

doi:10.1109/isit50566.2022.9834486

Cited by 3 publications

(17 citation statements)

References 32 publications

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“…Ahn et al [17] and Arasli and Ulukus [18] considered correlations represented by arbitrary graphs drawn from a stochastic block model, albeit for adaptive group testing, unlike the nonadaptive approach in this paper. Their approaches generalized the i.i.d.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, Lin et al [13] also presented a hierarchical agglomerative algorithm for pooled testing in a social graph. We note that [13], [17], [18], [38] do not consider measurement noise.…”

Section: Related Workmentioning

confidence: 99%

“…Pooled or group testing has been suggested for improving testing efficiency [2]- [18]. Group testing involves mixing a subset of n individual samples into m < n pools.…”

Section: Introductionmentioning

confidence: 99%

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Group Testing with Side Information via Generalized Approximate Message Passing

Cao¹,

Goenka²,

Wong³

et al. 2022

Preprint

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Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In a group testing setup, we are given n samples, one per individual. Each individual is either infected or uninfected. These samples are arranged into m < n pooled samples, where each pool is obtained by mixing a subset of the n individual samples. Infected individuals are then identified using a group testing algorithm. In this paper, we incorporate side information (SI) collected from contact tracing (CT) into nonadaptive/single-stage group testing algorithms. We generate different types of possible CT SI data by incorporating different possible characteristics of the spread of disease. These data are fed into a group testing framework based on generalized approximate message passing (GAMP). Numerical results show that our GAMP-based algorithms provide improved accuracy. Compared to a loopy belief propagation algorithm, our proposed framework can increase the success probability by 0.25 for a group testing problem of n = 500 individuals with m = 100 pooled samples.

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Section: Related Workmentioning

confidence: 99%

“…Moreover, Lin et al [13] also presented a hierarchical agglomerative algorithm for pooled testing in a social graph. We note that [13], [17], [18], [38] do not consider measurement noise.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Group Testing with Side Information via Generalized Approximate Message Passing

Cao¹,

Goenka²,

Wong³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For a given group of individuals, the aim is to identify the infected set, as in the standard settings. In a recent line of works, the static nature of the majority of group testing works has been challenged, and dynamic models are proposed [34][35][36][37][38]. In these works, SIR [39] based dynamic infection spread models are studied.…”

Section: Introductionmentioning

confidence: 99%

“…Testing and identification are done over time while the infection status of the individuals also evolve. In this paper, we aim to challenge the standard, static definition of the group testing problem and approach it over a time dimension over a similar setting with our prior work [38]. By this way, we consider a setting where a group testing approach can be applied in systems that evolve over time, such as contagious diseases that spread within a population.…”

Section: Introductionmentioning

confidence: 99%

Dynamic SAFFRON: Disease Control Over Time via Group Testing

Arasli

Ulukuş

2022

Algorithms

Self Cite

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Group testing is an efficient algorithmic approach to the infection identification problem, based on mixing the test samples and testing the mixed samples instead of individually testing each sample. In this paper, we consider the dynamic infection spread model that is based on the discrete SIR model, which assumes the disease to be spread over time via infected and non-isolated individuals. In our system, the main objective is not to minimize the number of required tests to identify every infection, but instead, to utilize the available, given testing capacity T at each time instance to efficiently control the infection spread. We introduce and study a novel performance metric, which we coin as ϵ-disease control time. This metric can be used to measure how fast a given algorithm can control the spread of a disease. We characterize the performance of the dynamic individual testing algorithm and introduce a novel dynamic SAFFRON-based group testing algorithm. We present theoretical results and implement the proposed algorithms to compare their performances.

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Adaptive group testing strategy for infectious diseases using social contact graph partitions

Zhang¹,

Heath²

2023

Sci Rep

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Mass testing is essential for identifying infected individuals during an epidemic and allowing healthy individuals to return to normal social activities. However, testing capacity is often insufficient to meet global health needs, especially during newly emerging epidemics. Dorfman’s method, a classic group testing technique, helps reduce the number of tests required by pooling the samples of multiple individuals into a single sample for analysis. Dorfman’s method does not consider the time dynamics or limits on testing capacity involved in infection detection, and it assumes that individuals are infected independently, ignoring community correlations. To address these limitations, we present an adaptive group testing (AGT) strategy based on graph partitioning, which divides a physical contact network into subgraphs (groups of individuals) and assigns testing priorities based on the social contact characteristics of each subgraph. Our AGT aims to maximize the number of infected individuals detected and minimize the number of tests required. After each testing round (perhaps on a daily basis), the testing priority is increased for each neighboring group of known infected individuals. We also present an enhanced infectious disease transmission model that simulates the dynamic spread of a pathogen and evaluate our AGT strategy using the simulation results. When applied to 13 social contact networks, AGT demonstrates significant performance improvements compared to Dorfman’s method and its variations. Our AGT strategy requires fewer tests overall, reduces disease spread, and retains robustness under changes in group size, testing capacity, and other parameters. Testing plays a crucial role in containing and mitigating pandemics by identifying infected individuals and helping to prevent further transmission in families and communities. By identifying infected individuals and helping to prevent further transmission in families and communities, our AGT strategy can have significant implications for public health, providing guidance for policymakers trying to balance economic activity with the need to manage the spread of infection.

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Group Testing with a Dynamic Infection Spread

Cited by 3 publications

References 32 publications

Group Testing with Side Information via Generalized Approximate Message Passing

Group Testing with Side Information via Generalized Approximate Message Passing

Dynamic SAFFRON: Disease Control Over Time via Group Testing

Adaptive group testing strategy for infectious diseases using social contact graph partitions

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