scholarly journals Improving the estimation of the COVID-19 effective reproduction number using nowcasting

2021 ◽  
pp. 096228022110089
Author(s):  
Joaquin Salas
Keyword(s):  
Reproduction Number ◽  
The Self ◽  
Data Driven ◽  
Statistical Behavior ◽  
Final Count ◽  
Data Driven Approach

As the interactions between people increases, the impending menace of COVID-19 outbreaks materializes, and there is an inclination to apply lockdowns. In this context, it is essential to have easy-to-use indicators for people to employ as a reference. The effective reproduction number of confirmed positives, Rt, fulfills such a role. This document proposes a data-driven approach to nowcast Rt based on previous observations’ statistical behavior. As more information arrives, the method naturally becomes more precise about the final count of confirmed positives. Our method’s strength is that it is based on the self-reported onset of symptoms, in contrast to other methods that use the daily report’s count to infer this quantity. We show that our approach may be the foundation for determining useful epidemy tracking indicators.

scholarly journals Data-driven modeling and forecasting of COVID-19 outbreak for public policy making

2020 ◽  
Author(s):  
Agus Hasan ◽  
Endah Putri ◽  
Hadi Susanto ◽  
Nuning Nuraini
Keyword(s):  
Public Policy ◽  
Disease Transmission ◽  
Reproduction Number ◽  
Case Fatality ◽  
Data Driven ◽  
Infectious Individual ◽  
Transmission Index ◽  
Modeling And Forecasting ◽  
Data Driven Approach ◽  
Data Driven Modeling

This paper presents a data-driven approach for COVID-19 outbreak modeling and forecasting, which can be used by public policy and decision makers to control the outbreak through Non-Pharmaceutical Interventions (NPI). First, we apply an extended Kalman filter (EKF) to a discrete-time stochastic augmented compartmental model to estimate the time-varying effective reproduction number Rt. We use daily confirmed cases, active cases, recovered cases, deceased cases, Case-Fatality-Rate (CFR), and infectious time as inputs for the model. Furthermore, we define a Transmission Index (TI) as a ratio between the instantaneous and the maximum value of the effective reproduction number. The value of TI shows the disease transmission in a contact between a susceptible and an infectious individual due to current measures such as physical distancing and lock-down relative to a normal condition. Based on the value of TI, we forecast different scenarios to see the effect of relaxing and tightening public measures. Case studies in three countries are provided to show the practicability of our approach.

Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

2012 ◽  
Author(s):  
Michael Ghil ◽  
Mickael D. Chekroun ◽  
Dmitri Kondrashov ◽  
Michael K. Tippett ◽  
Andrew Robertson ◽  
...  
Keyword(s):  
Dynamic Models ◽  
Data Driven ◽  
Stochastic Dynamic ◽  
Extended Range ◽  
Data Driven Approach ◽  
Low Dimensional

A Mostly Data-Driven Approach to Inverse Text Normalization

2017 ◽  
Author(s):  
Ernest Pusateri ◽  
Bharat Ram Ambati ◽  
Elizabeth Brooks ◽  
Ondrej Platek ◽  
Donald McAllaster ◽  
...  
Keyword(s):  
Data Driven ◽  
Data Driven Approach ◽  
Text Normalization

scholarly journals Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1571 ◽  
Author(s):  
Jhonatan Camacho Navarro ◽  
Magda Ruiz ◽  
Rodolfo Villamizar ◽  
Luis Mujica ◽  
Jabid Quiroga
Keyword(s):  
Correlation Analysis ◽  
Damage Assessment ◽  
Structural Damage ◽  
Cross Correlation ◽  
Data Driven ◽  
Cross Correlation Analysis ◽  
Data Driven Approach

scholarly journals Computation and data driven discovery of topological phononic materials

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jiangxu Li ◽  
Jiaxi Liu ◽  
Stanley A. Baronett ◽  
Mingfeng Liu ◽  
Lei Wang ◽  
...  
Keyword(s):  
High Throughput Screening ◽  
Electronic System ◽  
Nodal Line ◽  
Data Driven ◽  
Single Type ◽  
Type I ◽  
Structure Property ◽  
Topological Quantum ◽  
Data Driven Approach ◽  
Spin Electronic

AbstractThe discovery of topological quantum states marks a new chapter in both condensed matter physics and materials sciences. By analogy to spin electronic system, topological concepts have been extended into phonons, boosting the birth of topological phononics (TPs). Here, we present a high-throughput screening and data-driven approach to compute and evaluate TPs among over 10,000 real materials. We have discovered 5014 TP materials and grouped them into two main classes of Weyl and nodal-line (ring) TPs. We have clarified the physical mechanism for the occurrence of single Weyl, high degenerate Weyl, individual nodal-line (ring), nodal-link, nodal-chain, and nodal-net TPs in various materials and their mutual correlations. Among the phononic systems, we have predicted the hourglass nodal net TPs in TeO3, as well as the clean and single type-I Weyl TPs between the acoustic and optical branches in half-Heusler LiCaAs. In addition, we found that different types of TPs can coexist in many materials (such as ScZn). Their potential applications and experimental detections have been discussed. This work substantially increases the amount of TP materials, which enables an in-depth investigation of their structure-property relations and opens new avenues for future device design related to TPs.

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