Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots

Lorch, Lars; Kremer, Heiner; Trouleau, William; Tsirtsis, Stratis; Szanto, Aron; Schölkopf, Bernhard; Gomez-Rodriguez, Manuel

doi:10.1145/3530774

Cited by 16 publications

(36 citation statements)

References 49 publications

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“…-Event/clock-based simulation: The simulators of temporal events often belong to one of two categories: event-based or clock-based. In an event-based simulation [8,10,17,18,21], queues of planned events are executed by their order of occurrences. In a clock-based simulation [9,16,19,20,22], a series of periodic changes to simulator modules occur at specified time intervals.…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…Each time, one of the {Φ 1 , Φ 2 , …, Φ k } patterns with their corresponding probabilities { π 1 , π 2 , …, π k } is chosen, and its members are generated from the corresponding pattern. Unlike [ 10 ], this strategy prioritizes creating families over individuals. The advantages of this approach compared to those that create individuals first and then assign them to families are discussed below.…”

Section: Design and Implementationmentioning

confidence: 99%

“…Therefore, our work’s main contributions compared to existing simulators in different aspects such as population generation, simulation, policy enforcement, and runtime details are discussed in the following paragraph. We tried to compare with a diverse set of highly-respected simulators in terms of the goal, i.e., the problem they intend to solve, and the simulation methods in different pieces of literature, e.g., epidemiology [ 8 , 9 ], artificial intelligence [ 10 ], social network [ 11 ], economics [ 12 ], etc. Some of them are retrieved by running queries on the Google Scholar database to find the recent highly-cited simulations.…”

Section: Introductionmentioning

confidence: 99%

“…Many cities with almost the same population structure can be sampled from this random variable to derive confidence bounds on the simulation’s obtained results. Lots of existing simulators [ 10 – 12 , 19 , 20 ] lie under the umbrella of probabilistic nature.…”

Section: Introductionmentioning

confidence: 99%

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Pyfectious: An individual-level simulator to discover optimal containment policies for epidemic diseases

et al. 2023

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Simulating the spread of infectious diseases in human communities is critical for predicting the trajectory of an epidemic and verifying various policies to control the devastating impacts of the outbreak. Many existing simulators are based on compartment models that divide people into a few subsets and simulate the dynamics among those subsets using hypothesized differential equations. However, these models lack the requisite granularity to study the effect of intelligent policies that influence every individual in a particular way. In this work, we introduce a simulator software capable of modeling a population structure and controlling the disease’s propagation at an individualistic level. In order to estimate the confidence of the conclusions drawn from the simulator, we employ a comprehensive probabilistic approach where the entire population is constructed as a hierarchical random variable. This approach makes the inferred conclusions more robust against sampling artifacts and gives confidence bounds for decisions based on the simulation results. To showcase potential applications, the simulator parameters are set based on the formal statistics of the COVID-19 pandemic, and the outcome of a wide range of control measures is investigated. Furthermore, the simulator is used as the environment of a reinforcement learning problem to find the optimal policies to control the pandemic. The obtained experimental results indicate the simulator’s adaptability and capacity in making sound predictions and a successful policy derivation example based on real-world data. As an exemplary application, our results show that the proposed policy discovery method can lead to control measures that produce significantly fewer infected individuals in the population and protect the health system against saturation.

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Section: Plos Computational Biologymentioning

confidence: 99%

Section: Design and Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pyfectious: An individual-level simulator to discover optimal containment policies for epidemic diseases

et al. 2023

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“…An agent-based model using a smartphone-based contact network was proposed and further, a differential equation-based model was developed [23] , [26] . They evaluated several possible scenarios and highlighted the importance of smartphone-contact tracing on controlling the COVID-19 outbreak.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Effectiveness of Isolation and Contact-Tracing Interventions for Early Transmission Dynamics of COVID-19 in South Korea

2021

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Recent COVID-19 outbreaks pose serious public health challenges all around the world. South Korea had experienced the early outbreak of the COVID-19 pandemic and implemented early effective interventions. The 2020 COVID-19 outbreak in South Korea showed spatial hot spots and super-spreading events. As a result of these super-spreading events, three huge outbreaks of the COVID-19 have occurred in Korea from February to December 2020. To capture the intrinsic nature of heterogeneity, an agent-based model has been developed focusing on early transmission dynamics of COVID-19 in South Korea. Based on the social empirical contact information of early confirmed cases of COVID-19, we have constructed a scale-free network. Our agent-based model has incorporated essential individual variability such as different contact numbers and infectivity levels. In the absence of vaccines or treatment, contact tracing, case-isolation, quarantine are the most critical interventions to prevent larger outbreaks. First, we investigate the impacts of critical factors on various epidemic outputs such as incidence and cumulative incidence. These critical factors include contact numbers, transmission rates, infectivity of presymptomatic or asymptomatic cases, and contact-tracing with quarantine intervention. Furthermore, the effectiveness of case isolation and contact-tracing (followed by quarantine) is evaluated under various scenarios. Our results indicate that case isolation combined with contact-tracing quarantine is much more effective under a moderate level of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathcal R_{0}$ \end{document} (smaller transmission rates or contact numbers) and presymptomatic cases. However, the efficacy of interventions reduces significantly for a higher level of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mathcal R_{0}$ \end{document} (larger transmission rates or contact numbers) with a high level of infectivity (in presymptomatic cases). This highlights the key role of efficient contact-tracing and case-isolation to mitigate larger outbreaks or super-spreading events.

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Source identification via contact tracing in the presence of asymptomatic patients

Ódor,

Vuckovic,

Ndoye

et al. 2023

Appl Netw Sci

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Inferring the source of a diffusion in a large network of agents is a difficult but feasible task, if a few agents act as sensors revealing the time at which they got hit by the diffusion. One of the main limitations of current source identification algorithms is that they assume full knowledge of the contact network, which is rarely the case, especially for epidemics, where the source is called patient zero. Inspired by recent implementations of contact tracing algorithms, we propose a new framework, which we call Source Identification via Contact Tracing Framework (SICTF). In the SICTF, the source identification task starts at the time of the first hospitalization, and initially we have no knowledge about the contact network other than the identity of the first hospitalized agent. We may then explore the network by contact queries, and obtain symptom onset times by test queries in an adaptive way, i.e., both contact and test queries can depend on the outcome of previous queries. We also assume that some of the agents may be asymptomatic, and therefore cannot reveal their symptom onset time. Our goal is to find patient zero with as few contact and test queries as possible. We implement two local search algorithms for the SICTF: the LS algorithm, which has recently been proposed by Waniek et al. in a similar framework, is more data-efficient, but can fail to find the true source if many asymptomatic agents are present, whereas the LS+ algorithm is more robust to asymptomatic agents. By simulations we show that both LS and LS+ outperform previously proposed adaptive and non-adaptive source identification algorithms adapted to the SICTF, even though these baseline algorithms have full access to the contact network. Extending the theory of random exponential trees, we analytically approximate the source identification probability of the LS/ LS+ algorithms, and we show that our analytic results match the simulations. Finally, we benchmark our algorithms on the Data-driven COVID-19 Simulator (DCS) developed by Lorch et al., which is the first time source identification algorithms are tested on such a complex dataset.

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Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots

Cited by 16 publications

References 49 publications

Pyfectious: An individual-level simulator to discover optimal containment policies for epidemic diseases

Pyfectious: An individual-level simulator to discover optimal containment policies for epidemic diseases

Assessing the Effectiveness of Isolation and Contact-Tracing Interventions for Early Transmission Dynamics of COVID-19 in South Korea

Source identification via contact tracing in the presence of asymptomatic patients

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