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

Cheng, Sibo; Cc, Pain; Guo, Yan; Arcucci, Rossella

doi:10.48550/arxiv.2103.00485

Cited by 3 publications

(2 citation statements)

References 47 publications

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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

show abstract

“…Another important work in this framework is the international collaboration [14], in which it clearly presents that the regional characters of some parameters are important. More recently, let us mention [15] in which a model with vaccination and using an Ensemble Kalman Filter is implemented for Saudi Arabia, and [16], which discusses vaccination strategies by using data assimilation techniques. Reference [17] tracks the effective reproduction number with the Kalman Filter, and provides estimates for the effectiveness of non-pharmaceutical interventions, while [18] uses Bayesian sequential data assimilation for forecasting the evolution of COVID-19 in several Mexican localities.…”

Section: Introductionmentioning

confidence: 99%

Analysis of COVID-19 Spread in Tokyo through an Agent-Based Model with Data Assimilation

Sun

Richard

Miyoshi

et al. 2022

JCM

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In this paper, we introduce an agent-based model together with a particle filter approach to study the spread of COVID-19. Investigations are mainly performed on the metropolis of Tokyo, but other prefectures of Japan are also briefly surveyed. A novel method for evaluating the effective reproduction number is one of the main outcomes of our approach. Other unknown parameters are also evaluated. Uncertain quantities, such as, for example, the probability that an infected agent develops symptoms, are tested and discussed, and the stability of our computations is examined. Detailed explanations are provided for the model and for the assimilation process.

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Observation data compression for variational assimilation of dynamical systems

Cheng

Lucor

Argaud³

2021

Preprint

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Accurate estimation of error covariances (both background and observation) is crucial for efficient observation compression approaches in data assimilation of large-scale dynamical problems. We propose a new combination of a covariance tuning algorithm with existing PCA-type data compression approaches, either observation-or information-based, with the aim of reducing the computational cost of real-time updating at each assimilation step. Relying on a local assumption of flow-independent error covariances, dynamical assimilation residuals are used to adjust the covariance in each assimilation window. The estimated covariances then contribute to better specify the principal components of either the observation dynamics or the state-observation sensitivity. The proposed approaches are first validated on a shallow water twin experiment with correlated and non-homogeneous observation error. Proper selection of flow-independent assimilation windows, together with sampling density for background error estimation, and sensitivity of the approaches to the observations error covariance knowledge, are also discussed and illustrated with various numerical tests and results. The method is then applied to a more challenging industrial hydrological model with real-world data and non-linear transformation operator provided by an operational precipitation-flow simulation software.

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

Cited by 3 publications

References 47 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

Analysis of COVID-19 Spread in Tokyo through an Agent-Based Model with Data Assimilation

Observation data compression for variational assimilation of dynamical systems

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