Model-based evaluation of alternative reactive class closure strategies against COVID-19

AbstractThere are contrasting results concerning the effect of reactive school closure on SARS-CoV-2 transmission. To shed light on this controversy, we developed a data-driven computational model of SARS-CoV-2 transmission. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 17.3% (95%CI: 8.0–26.8%), due to the low probability of timely identification of infections in the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Depending on the contribution of schools to transmission, this strategy can greatly reduce COVID-19 burden even when school contribution to transmission and immunity in the population is low. Moving forward, the adoption of antigen-based screenings in schools could be instrumental to limit COVID-19 burden while vaccines continue to be rolled out.

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Model-based evaluation of alternative reactive class closure strategies against COVID-19

10.1101/2021.04.18.21255683 ◽

2021 ◽

Author(s):

Quanhui Liu ◽

Juanjuan Zhang ◽

Cheng Peng ◽

Maria Litvinova ◽

Shudong Huang ◽

...

Keyword(s):

Syndromic Surveillance ◽

Population Level ◽

Screening Strategy ◽

School Closure ◽

Young Population ◽

Case Identification ◽

Low Probability ◽

Antigen Tests ◽

Shed Light ◽

Roll Out

There are contrasting results concerning the effect of reactive school closure on SARS-CoV-2 transmission. To shed light on this controversy, here we develop a data-driven computational model of SARS-CoV-2 transmission to investigate mechanistically the effect on COVID-19 outbreaks of school closure strategies based on syndromic surveillance and antigen screening of students. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 13.1% (95%CI: 8.6%-20.2 %), due to the low probability of timely symptomatic case identification among the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Should population-level social distancing measures unrelated to schools enable maintaining the reproduction number (R) at 1.3 or lower, an antigen-based screening strategy is estimated to fully prevent COVID-19 outbreaks in the general population. Depending on the contribution of schools to transmission, this strategy can either prevent COVID-19 outbreaks for R up to 1.9 or to at least greatly reduce outbreak size in very conservative scenarios about school contribution to transmission. Moving forward, the adoption of antigen-based screenings in schools could be instrumental to limit COVID-19 burden while vaccines continue to roll out through 2021, especially in light of possible continued emergence of SARS-CoV-2 variants.

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A computational model-based approach for atlas construction of aortic Doppler velocity profiles for segmentation purposes

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2017.09.003 ◽

2018 ◽

Vol 40 ◽

pp. 23-32 ◽

Cited By ~ 1

Author(s):

Vedrana Baličević ◽

Hrvoje Kalinić ◽

Sven Lončarić ◽

Maja Čikeš ◽

Bart Bijnens

Keyword(s):

Computational Model ◽

Velocity Profiles ◽

Doppler Velocity ◽

Model Based ◽

Atlas Construction

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The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽

10.3390/s21062085 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2085

Author(s):

Xue-Bo Jin ◽

Ruben Jonhson Robert RobertJeremiah ◽

Ting-Li Su ◽

Yu-Ting Bai ◽

Jian-Lei Kong

Keyword(s):

Kalman Filter ◽

State Estimation ◽

Future Development ◽

Data Driven ◽

Estimation Methods ◽

Multiple Model ◽

Development Trends ◽

Research Results ◽

Model Driven ◽

Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

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Multi-Inputs and Multi-Outputs equivalent model based on data driven controller for a robotic system

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.2661 ◽

2020 ◽

Vol 53 (2) ◽

pp. 9784-9789

Author(s):

Josué Gómez ◽

Chidentree Treesatayapun ◽

América Morales

Keyword(s):

Robotic System ◽

Data Driven ◽

Equivalent Model ◽

Model Based

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Data-driven Model-based Detection of Malicious Insiders via Physical Access Logs

ACM Transactions on Modeling and Computer Simulation ◽

10.1145/3309540 ◽

2019 ◽

Vol 29 (4) ◽

pp. 1-25 ◽

Cited By ~ 2

Author(s):

Carmen Cheh ◽

Uttam Thakore ◽

Ahmed Fawaz ◽

Binbin Chen ◽

William G. Temple ◽

...

Keyword(s):

Data Driven ◽

Model Based ◽

Malicious Insiders ◽

Access Logs ◽

Physical Access

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Adaptive network traffic control with an integrated model-based and data-driven approach and a decentralised solution method

Transportation Research Part C Emerging Technologies ◽

10.1016/j.trc.2021.103154 ◽

2021 ◽

Vol 128 ◽

pp. 103154

Author(s):

Z.C. Su ◽

Andy H.F. Chow ◽

R.X. Zhong

Keyword(s):

Network Traffic ◽

Traffic Control ◽

Solution Method ◽

Integrated Model ◽

Data Driven ◽

Adaptive Network ◽

Model Based ◽

Data Driven Approach

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A DATA-DRIVEN APPROACH TO USER-EXPERIENCE-FOCUSED MODEL-BASED ROADMAPPING FOR NEW PRODUCT PLANNING

Proceedings of the Design Society ◽

10.1017/pds.2021.7 ◽

2021 ◽

Vol 1 ◽

pp. 61-70

Author(s):

Ilia Iuskevich ◽

Andreas-Makoto Hein ◽

Kahina Amokrane-Ferka ◽

Abdelkrim Doufene ◽

Marija Jankovic

Keyword(s):

New Product Development ◽

User Experience ◽

Planning Process ◽

New Product ◽

Data Driven ◽

User Needs ◽

User Testing ◽

Model Based ◽

Data Driven Approach ◽

To Receive

AbstractUser experience (UX) focused business needs to survive and plan its new product development (NPD) activities in a highly turbulent environment. The latter is a function of volatile UX and technology trends, competition, unpredictable events, and user needs uncertainty. To address this problem, the concept of design roadmapping has been proposed in the literature. It was argued that tools built on the idea of design roadmapping have to be very flexible and data-driven (i.e., be able to receive feedback from users in an iterative manner). At the same time, a model-based approach to roadmapping has emerged, promising to achieve such flexibility. In this work, we propose to incorporate design roadmapping to model-based roadmapping and integrate it with various user testing approaches into a single tool to support a flexible data-driven NPD planning process.

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