scholarly journals Predictive Analyses of COVID‐19 Case Data to Estimate the Effectiveness of Nationwide Face Cover

2021 ◽  
Author(s):  
Hwanyong Kim ◽  
Jimmy Simpson ◽  
Byeonghwa Park
Keyword(s):  
Case Data ◽  
Predictive Analyses

Construction of a Damage Case Data Base

2015 ◽  
Vol 53 (5) ◽  
pp. 498-502
Author(s):  
M. Kobayakawa ◽  
M. Hisada
Keyword(s):  
Data Base ◽  
Case Data

scholarly journals Using Unstated Cases to Correct for COVID-19 Pandemic Outbreak and Its Impact on Easing the Intervention for Qatar

Biology ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 463
Author(s):  
Narjiss Sallahi ◽  
Heesoo Park ◽  
Fedwa El Mellouhi ◽  
Mustapha Rachdi ◽  
Idir Ouassou ◽  
...  
Keyword(s):  
Public Health ◽  
A Priori ◽  
Epidemiological Models ◽  
Sir Epidemic Model ◽  
Gulf Region ◽  
Epidemiological Modeling ◽  
Public Health Policies ◽  
Sir Epidemic ◽  
The Impact ◽  
Case Data

Epidemiological Modeling supports the evaluation of various disease management activities. The value of epidemiological models lies in their ability to study various scenarios and to provide governments with a priori knowledge of the consequence of disease incursions and the impact of preventive strategies. A prevalent method of modeling the spread of pandemics is to categorize individuals in the population as belonging to one of several distinct compartments, which represents their health status with regard to the pandemic. In this work, a modified SIR epidemic model is proposed and analyzed with respect to the identification of its parameters and initial values based on stated or recorded case data from public health sources to estimate the unreported cases and the effectiveness of public health policies such as social distancing in slowing the spread of the epidemic. The analysis aims to highlight the importance of unreported cases for correcting the underestimated basic reproduction number. In many epidemic outbreaks, the number of reported infections is likely much lower than the actual number of infections which can be calculated from the model’s parameters derived from reported case data. The analysis is applied to the COVID-19 pandemic for several countries in the Gulf region and Europe.

scholarly journals Understanding the Drivers of Mobility during the COVID-19 Pandemic in Florida, USA Using a Machine Learning Approach

2021 ◽  
Vol 10 (7) ◽  
pp. 440
Author(s):  
Guimin Zhu ◽  
Kathleen Stewart ◽  
Deb Niemeier ◽  
Junchuan Fan
Keyword(s):  
Race And Ethnicity ◽  
Learning Approach ◽  
Mobility Patterns ◽  
Random Forest Regression ◽  
Related Factors ◽  
Machine Learning Approach ◽  
The Mean ◽  
Case Data ◽  
Staying At Home ◽  
Very High

As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID-19 (SARS-CoV-2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, when COVID-19 case numbers were very high for three Florida counties, Miami-Dade, Broward, and Palm Beach counties. To investigate patterns, as well as drivers, related to changes in mobility across the tri-county region, a random forest regression model was built using sociodemographic, travel, and built environment factors, as well as COVID-19 positive case data. Mobility patterns declined in each county when new COVID-19 infections began to rise, beginning in mid-June 2020. While the mean number of bar and restaurant visits was lower overall due to closures, analysis showed that these visits remained a top factor that impacted mobility for all three counties, even with a rise in cases. Our modeling results suggest that there were mobility pattern differences between counties with respect to factors relating, for example, to race and ethnicity (different population groups factored differently in each county), as well as social distancing or travel-related factors (e.g., staying at home behaviors) over the two time periods prior to and after the spike of COVID-19 cases.

Death and Disposal Locations of Serial Homicides: The Effect on Recovery Timeframes

2021 ◽  
pp. 108876792110068
Author(s):  
Brendan Chapman ◽  
Cody Raymer ◽  
David A. Keatley
Keyword(s):  
Time Series ◽  
Exploratory Study ◽  
Serial Killers ◽  
Disposal Site ◽  
Time Since Death ◽  
Forensic Evidence ◽  
Hostile Environments ◽  
Case Data ◽  
Insight Into ◽  
Homicide Case

Many factors affect the solvability of homicides, including body disposal location and time between death and recovery. The aim of this exploratory study was to probe a number of spatiotemporal variables for trends across a subset of solved homicide case data from 54 North American serial killers, active between 1920 and 2016 (125 solved cases) to identify areas for further research. We investigated murder site and body disposal site as location variables with eight subcategories across eight discrete time series, seeking insight into how these factors may affect the early stages of an investigation and (therefore by inference) solvability. The findings showed that bodies recovered after 48 hours are more likely discovered outdoor while those discovered within 24 hours, within the victim’s residence. This has implications for the ability to recover forensic evidence when bodes are located after a prolonged time since death as well as in more hostile environments.

Violations of Assumptions in School-Based Single-Case Data

2013 ◽  
Vol 38 (4) ◽  
pp. 477-496 ◽  
Author(s):  
Benjamin George Solomon
Keyword(s):  
Single Case ◽  
School Based ◽  
Case Data

Calculation of the Worst-Case Data Patterns and Eye Diagram for Nonlinear High-Speed Links

2021 ◽  
pp. 1-5
Author(s):  
Xiuqin Chu ◽  
Na Li ◽  
Jun Wang ◽  
Yuhuan Luo ◽  
Feng Wu ◽  
...  
Keyword(s):  
High Speed ◽  
Worst Case ◽  
Eye Diagram ◽  
Case Data
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