Application-Driven Compression for Visualizing Large-Scale Time-Varying Volume Data

Water is a critical natural resource that sustains the productivity of many economic sectors, whether directly or indirectly. Climate change alongside rapid growth and development are a threat to water sustainability and regional productivity. In this paper, we develop an extension to the economic input-output model to assess the impact of water supply disruptions to regional economies. The model utilizes the inoperability variable, which measures the extent to which an infrastructure system or economic sector is unable to deliver its intended output. While the inoperability concept has been utilized in previous applications, this paper offers extensions that capture the time-varying nature of inoperability as the sectors recover from a disruptive event, such as drought. The model extension is capable of inserting inoperability adjustments within the drought timeline to capture time-varying likelihoods and severities, as well as the dependencies of various economic sectors on water. The model was applied to case studies of severe drought in two regions: (1) the state of Massachusetts (MA) and (2) the US National Capital Region (NCR). These regions were selected to contrast drought resilience between a mixed urban–rural region (MA) and a highly urban region (NCR). These regions also have comparable overall gross domestic products despite significant differences in the distribution and share of the economic sectors comprising each region. The results of the case studies indicate that in both regions, the utility and real estate sectors suffer the largest economic loss; nonetheless, results also identify region-specific sectors that incur significant losses. For the NCR, three sectors in the top 10 ranking of highest economic losses are government-related, whereas in the MA, four sectors in the top 10 are manufacturing sectors. Furthermore, the accommodation sector has also been included in the NCR case intuitively because of the high concentration of museums and famous landmarks. In contrast, the Wholesale Trade sector was among the sectors with the highest economic losses in the MA case study because of its large geographic size conducive for warehouses used as nodes for large-scale supply chain networks. Future modeling extensions could potentially include analysis of water demand and supply management strategies that can enhance regional resilience against droughts. Other regional case studies can also be pursued in future efforts to analyze various categories of drought severity beyond the case studies featured in this paper.

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Heterogeneity and effectiveness analysis of COVID-19 prevention and control in major cities in China through time-varying reproduction number estimation

Scientific Reports ◽

10.1038/s41598-020-79063-x ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Qing Cheng ◽

Zeyi Liu ◽

Guangquan Cheng ◽

Jincai Huang

Keyword(s):

Prevention And Control ◽

Large Scale ◽

Reproduction Number ◽

Correlation Method ◽

Control Measures ◽

Time Varying ◽

Short Period ◽

Significant Difference ◽

Prevention And Control Measures ◽

And Control

AbstractBeginning on December 31, 2019, the large-scale novel coronavirus disease 2019 (COVID-19) emerged in China. Tracking and analysing the heterogeneity and effectiveness of cities’ prevention and control of the COVID-19 epidemic is essential to design and adjust epidemic prevention and control measures. The number of newly confirmed cases in 25 of China’s most-affected cities for the COVID-19 epidemic from January 11 to February 10 was collected. The heterogeneity and effectiveness of these 25 cities’ prevention and control measures for COVID-19 were analysed by using an estimated time-varying reproduction number method and a serial correlation method. The results showed that the effective reproduction number (R) in 25 cities showed a downward trend overall, but there was a significant difference in the R change trends among cities, indicating that there was heterogeneity in the spread and control of COVID-19 in cities. Moreover, the COVID-19 control in 21 of 25 cities was effective, and the risk of infection decreased because their R had dropped below 1 by February 10, 2020. In contrast, the cities of Wuhan, Tianmen, Ezhou and Enshi still had difficulty effectively controlling the COVID-19 epidemic in a short period of time because their R was greater than 1.

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An alternative numerical method for estimating large-scale time-varying parameter seemingly unrelated regressions models

Econometrics and Statistics ◽

10.1016/j.ecosta.2020.11.003 ◽

2021 ◽

Author(s):

Stella Hadjiantoni ◽

Erricos John Kontoghiorghes

Keyword(s):

Numerical Method ◽

Large Scale ◽

Time Varying ◽

Seemingly Unrelated Regressions ◽

Time Varying Parameter

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Stochastic H∞ Robust Decentralized Tracking Control of Large-Scale Team Formation UAV Network System with Time-Varying Delay and Packet Dropout under Interconnected Couplings and Wiener Fluctuations

IEEE Access ◽

10.1109/access.2021.3065127 ◽

2021 ◽

pp. 1-1

Author(s):

Min-Yen Lee ◽

Bor-Sen Chen ◽

Cheng-Yu Tsai ◽

Chih-Lyang Hwang

Keyword(s):

Tracking Control ◽

Large Scale ◽

Team Formation ◽

Packet Dropout ◽

Network System ◽

Time Varying ◽

Time Varying Delay ◽

Varying Delay

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Hospitalization Rates Among Dialysis Patients During Hurricane Katrina

Prehospital and Disaster Medicine ◽

10.1017/s1049023x12000945 ◽

2012 ◽

Vol 27 (4) ◽

pp. 325-329 ◽

Cited By ~ 16

Author(s):

David Howard ◽

Rebecca Zhang ◽

Yijian Huang ◽

Nancy Kutner

Keyword(s):

Hurricane Katrina ◽

Large Scale ◽

Service Use ◽

Hospital Admissions ◽

Gulf Coast ◽

The United States ◽

Time Varying ◽

Dialysis Patients ◽

Hospitalization Rates ◽

The Impact

AbstractIntroductionDialysis centers struggled to maintain continuity of care for dialysis patients during and immediately following Hurricane Katrina's landfall on the US Gulf Coast in August 2005. However, the impact on patient health and service use is unclear.ProblemThe impact of Hurricane Katrina on hospitalization rates among dialysis patients was estimated.MethodsData from the United States Renal Data System were used to identify patients receiving dialysis from January 1, 2001 through August 29, 2005 at clinics that experienced service disruptions during Hurricane Katrina. A repeated events duration model was used with a time-varying Hurricane Katrina indicator to estimate trends in hospitalization rates. Trends were estimated separately by cause: surgical hospitalizations, medical, non-renal-related hospitalizations, and renal-related hospitalizations.ResultsThe rate ratio for all-cause hospitalization associated with the time-varying Hurricane Katrina indicator was 1.16 (95% CI, 1.05-1.29; P = .004). The ratios for cause-specific hospitalization were: surgery, 0.84 (95% CI, 0.68-1.04; P = .11); renal-related admissions, 2.53 (95% CI, 2.09-3.06); P < .001), and medical non-renal related, 1.04 (95% CI, 0.89-1.20; P = .63). The estimated number of excess renal-related hospital admissions attributable to Katrina was 140, representing approximately three percent of dialysis patients at the affected clinics.ConclusionsHospitalization rates among dialysis patients increased in the month following the Hurricane Katrina landfall, suggesting that providers and patients were not adequately prepared for large-scale disasters.Howard D, Zhang R, Huang Y, Kutner N. Hospitalization rates among dialysis patients during Hurricane Katrina. Prehosp Disaster Med. 2012;27(4):1-5.

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