Real-time Updating of Dynamic Social Networks for COVID-19 Vaccination Strategies

Cheng, Sibo; Arcucci, Rossella; Pain, Christopher C.; Guo, Yike

doi:10.1101/2021.03.11.21253356

Cited by 9 publications

(4 citation statements)

References 69 publications

(96 reference statements)

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“…Other studies assume that more information is acquired over time and propose algorithms to estimate parameters (see Thompson et al 6 and the articles reviewed therein) or to adapt interventions (see Probert et al 7 and Cheng et al, 8 for instance) in real time. Yet, in these studies as well, information acquisition is seen as a passive process and is not stemming from active strategic decisions.…”

Section: Passive Uncertainty Management In Public Healthmentioning

confidence: 99%

Optimal Epidemic Control under Uncertainty: Tradeoffs between Information Collection and Other Actions

Flaig¹,

Houy

2023

Med Decis Making

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Background Recent epidemics and measures taken to control them—through vaccination or other actions—have highlighted the role and importance of uncertainty in public health. There is generally a tradeoff between information collection and other uses of resources. Whether this tradeoff is solved explicitly or implicitly, the concept of value of information is central to inform policy makers in an uncertain environment. Method We use a deterministic SIR (susceptible, infectious, recovered) disease emergence and transmission model with vaccination that can be administered as 1 or 2 doses. The disease parameters and vaccine characteristics are uncertain. We study the tradeoffs between information acquisition and 2 other measures: bringing vaccination forward and acquiring more vaccine doses. To do this, we quantify the expected value of perfect information (EVPI) under different constraints faced by public health authorities (i.e., the time of the vaccination campaign implementation and the number of vaccine doses available). Results We discuss the appropriateness of different responses under uncertainty. We show that, in some cases, vaccinating later or with less vaccine doses but more information about the epidemic, and the efficacy of control strategies may bring better results than vaccinating earlier or with more doses and less information, respectively. Conclusion In the present methodological article, we show in an abstract setting how clearly defining and treating the tradeoff between information acquisition and the relaxation of constraints can improve public health decision making. Highlights Uncertainties can seriously hinder epidemic control, but resolving them is costly. Thus, there are tradeoffs between information collection and alternative uses of resources. We use a generic SIR model with vaccination and a value-of-information framework to explore these tradeoffs. We show in which cases vaccinating later with more information about the epidemic and the efficacy of control measures may be better—or not—than vaccinating earlier with less information. We show in which cases vaccinating with fewer vaccine doses and more information about the epidemic and the efficacy of control measures may be better—or not—than vaccinating with more doses and less information.

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Section: Passive Uncertainty Management In Public Healthmentioning

confidence: 99%

Optimal Epidemic Control under Uncertainty: Tradeoffs between Information Collection and Other Actions

Flaig¹,

Houy

2023

Med Decis Making

View full text Add to dashboard Cite

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“…It includes fresh data in real time. It is suggested in [45] to merge several sources of temporal information using a sequential network-updating strategy based on data assimilation techniques.…”

Section: Related Workmentioning

confidence: 99%

A Novel Scheme for Classification of Epilepsy Using Machine Learning and a Fuzzy Inference System Based on Wearable-Sensor Health Parameters

2022

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The tremendous growth of health-related digital information has transformed machine learning algorithms, allowing them to deliver more relevant information while remotely monitoring patients in modern telemedicine. However, patients with epilepsy are likely to die or have post-traumatic difficulties. As a result, early disease detection could be essential for a person’s survival. Hence, early diagnosis of epilepsy based on health parameters is needed. This paper presents a classification of epilepsy disease based on wearable-sensor health parameters that use a hybrid approach with ensemble machine learning and a fuzzy logic inference system. The ensemble machine learning classifiers are used to predict epilepsy events using ensemble bagging and ensemble boosting regression. The experimental results show that compared to the ensemble bagging classifiers and other state-of-the-art methods, the ensemble boosting classifier with the fuzzy inference system outperformed with a 97% accuracy rate.

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“…Other studies assume that more information is acquired over time and propose algorithms to estimate parameters (see [6] and the articles reviewed therein) or to adapt interventions (see [7, 8], for instance) in real-time. Yet in these studies just as in studies assuming fixed information, information acquisition is seen as a passive process, and is not stemming from active strategic decisions.…”

Section: Introductionmentioning

confidence: 99%

Optimal epidemic control under uncertainty: trade-offs between information collection and other actions

Flaig¹,

Houy

2022

Preprint

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Background: Recent epidemics and measures taken to control them -- through vaccination or other actions -- have highlighted the role and importance of uncertainty in public health. There is generally a trade-off between information collection and other uses of resources. Explicitly or more implicitly, the concept of expected value of perfect information (EVPI) is central in order to inform policy makers in an uncertain environment. Method: We use a simple SIR disease emergence and transmission model with vaccination that can be administered as one or two doses. The disease parameters and vaccine characteristics are uncertain. We study the trade-offs between information acquisition and two other measures: bringing vaccination forward, and acquiring more vaccine doses. To do this, we quantify the EVPI under different constraints faced by public health authorities, i.e. the time of the vaccination campaign implementation and the number of vaccine doses available. Results: We discuss the appropriateness of different responses under uncertainty. We show that in some cases, vaccinating later or with less vaccine doses but more information may bring better results than vaccinating earlier or with more doses and less information respectively. Conclusion: In the present methodological paper, we show in an abstract setting how clearly defining and treating the trade-off between information acquisition and the relaxation of constraints can improve public health decision making.

show abstract

Real-time Updating of Dynamic Social Networks for COVID-19 Vaccination Strategies

Cited by 9 publications

References 69 publications

Optimal Epidemic Control under Uncertainty: Tradeoffs between Information Collection and Other Actions

Optimal Epidemic Control under Uncertainty: Tradeoffs between Information Collection and Other Actions

A Novel Scheme for Classification of Epilepsy Using Machine Learning and a Fuzzy Inference System Based on Wearable-Sensor Health Parameters

Optimal epidemic control under uncertainty: trade-offs between information collection and other actions

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