The Temporal Relationship Between Police Killings of Civilians and Criminal Homicide: A Refined Version of the Danger-Perception Theory

2001 ◽  
Vol 47 (2) ◽  
pp. 155-172 ◽  
Author(s):  
John M. MacDonald ◽  
Robert J. Kaminski ◽  
Geoffrey P. Alpert ◽  
Abraham N. Tennenbaum
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Temporal Relationship ◽  
National Level ◽  
Homicide Rate ◽  
Deadly Force ◽  
Future Research ◽  
Federal Bureau ◽  
Perception Theory ◽  
Refined Version

The connection between police use of deadly force and the criminal homicide rate has long been recognized in the literature. Their temporal relationship, however, has seldom been examined. The present study suggests that earlier research has underestimated the importance of the temporal relationship between the homicides that present the greatest level of public danger and police use of deadly force. This research suggests that police use of deadly force can best be understood through a “ratio-threat” version of the danger-perception theory. Through a time-series analysis of data from the Federal Bureau of Investigation's Supplementary Homicide Reports over a 21-year period, the ratio-threat hypothesis is confirmed. The results suggest that, on a national level, there exists a temporal connection between predatory crime and police use of deadly force. Implications for theory and future research are discussed.

scholarly journals The Impact of an Inactivated Hepatitis A Vaccine with One Dose in Brazil: A Retrospective Time-Series

Vaccines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 407
Author(s):  
Ana Luiza Bierrenbach ◽  
Yoonyoung Choi ◽  
Paula de Mendonça Batista ◽  
Fernando Brandão Serra ◽  
Cintia Irene Parellada ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Hepatitis A ◽  
National Level ◽  
Age Groups ◽  
High Rate ◽  
Series Analysis ◽  
Annual Reduction ◽  
The Impact ◽  
Vaccination Period

Background: In 2014, a recommended one-dose of inactivated hepatitis A vaccine was included in the Brazilian National Immunization Program targeting children 12–24 months. This decision addressed the low to intermediate endemicity status of hepatitis A across Brazil and the high rate of infection in children and adolescents between 5 and 19 years old. The aim of the study was to conduct a time-series analysis on hepatitis A incidence across age groups and to assess the hepatitis A distribution throughout Brazilian geographic regions. Methods: An interrupted time-series analysis was performed to assess hepatitis A incidence rates before (2010–2013) and after (2015–2018) hepatitis A vaccine program implementation. The time-series analysis was stratified by age groups while a secondary analysis examined geographic distribution of hepatitis A cases. Results: Overall incidence of hepatitis A decreased from 3.19/100.000 in the pre-vaccine period to 0.87/100.000 (p = 0.022) post-vaccine introduction. Incidence rate reduction was higher among children aged 1-4 years old, with an annual reduction of 67.6% in the post-vaccination period against a 7.7% annual reduction in the pre-vaccination period (p < 0.001). Between 2015 and 2018, the vaccination program prevented 14,468 hepatitis A cases. Conclusion: Our study highlighted the positive impact of a recommended one-dose inactivated hepatitis A vaccine for 1–4-years-old in controlling hepatitis A at national level.

scholarly journals An interrupted time series analysis of the lockdown policies in India: a national-level analysis of COVID-19 incidence

2021 ◽  
Author(s):  
Winter M Thayer ◽  
Md Zabir Hasan ◽  
Prithvi Sankhla ◽  
Shivam Gupta
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Incidence Rate ◽  
Case Reports ◽  
National Level ◽  
Model Development ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Additional Time ◽  
Series Analysis

Abstract India implemented a national mandatory lockdown policy (Lockdown 1.0) on 24 March 2020 in response to Coronavirus Disease 2019 (COVID-19). The policy was revised in three subsequent stages (Lockdown 2.0–4.0 between 15 April to 18 May 2020), and restrictions were lifted (Unlockdown 1.0) on 1 June 2020. This study evaluated the effect of lockdown policy on the COVID-19 incidence rate at the national level to inform policy response for this and future pandemics. We conducted an interrupted time series analysis with a segmented regression model using publicly available data on daily reported new COVID-19 cases between 2 March 2020 and 1 September 2020. National-level data from Google Community Mobility Reports during this timeframe were also used in model development and robustness checks. Results showed an 8% [95% confidence interval (CI) = 6–9%] reduction in the change in incidence rate per day after Lockdown 1.0 compared to prior to the Lockdown order, with an additional reduction of 3% (95% CI = 2–3%) after Lockdown 4.0, suggesting an 11% (95% CI = 9–12%) reduction in the change in COVID-19 incidence after Lockdown 4.0 compared to the period before Lockdown 1.0. Uptake of the lockdown policy is indicated by decreased mobility and attenuation of the increasing incidence of COVID-19. The increasing rate of incident case reports in India was attenuated after the lockdown policy was implemented compared to before, and this reduction was maintained after the restrictions were eased, suggesting that the policy helped to ‘flatten the curve’ and buy additional time for pandemic preparedness, response and recovery.

scholarly journals Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive

2019 ◽  
Vol 11 (10) ◽  
pp. 1235 ◽  
Author(s):  
Aaron M. Shew ◽  
Aniruddha Ghosh
Keyword(s):  
Time Series ◽  
Crop Production ◽  
Accuracy Assessment ◽  
Vegetation Indices ◽  
National Level ◽  
Google Earth ◽  
Dry Season ◽  
Future Research ◽  
Cropping Patterns

In many countries, in situ agricultural data is not available and cost-prohibitive to obtain. While remote sensing provides a unique opportunity to map agricultural areas and management characteristics, major efforts are needed to expand our understanding of cropping patterns and the potential for remotely monitoring crop production because this could support predictions of food shortages and improve resource allocation. In this study, we demonstrate a new method to map paddy rice using Google Earth Engine (GEE) and the Landsat archive in Bangladesh during the dry (boro) season. Using GEE and Landsat, dry-season rice areas were mapped at 30 m resolution for approximately 90,000 km2 annually between 2014 and 2018. The method first reconstructs spectral vegetation indices (VIs) for individual pixels using a harmonic time series (HTS) model to minimize the effect of any sensor inconsistencies and atmospheric noise, and then combines the time series indices with a rule-based algorithm to identify characteristics of rice phenology to classify rice pixels. To our knowledge, this is the first time an annual pixel-based time series model has been applied to Landsat at the national level in a multiyear analysis of rice. Findings suggest that the harmonic-time-series-based vegetation indices (HTS-VIs) model has the potential to map rice production across fragmented landscapes and heterogeneous production practices with comparable results to other estimates, but without local management or in situ information as inputs. The HTS-VIs model identified 4.285, 4.425, 4.645, 4.117, and 4.407 million rice-producing hectares for 2014, 2015, 2016, 2017, and 2018, respectively, which correlates well with national and district estimates from official sources at an average R-squared of 0.8. Moreover, accuracy assessment with independent validation locations resulted in an overall accuracy of 91% and a kappa coefficient of 0.83 for the boro/non-boro stable rice map from 2014 to 2018. We conclude with a discussion of potential improvements and future research pathways for this approach to spatiotemporal mapping of rice in heterogeneous landscapes.

scholarly journals Changing Climatic Factors Favor Dengue Transmission in Lahore, Pakistan

Environments ◽  
2019 ◽  
Vol 6 (6) ◽  
pp. 71
Author(s):  
Syed Ali Asad Naqvi ◽  
Bulbul Jan ◽  
Saima Shaikh ◽  
Syed Jamil Hasan Kazmi ◽  
Liaqat Ali Waseem ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Time Series Analysis ◽  
Temporal Relationship ◽  
Climatic Factors ◽  
Climatic Variability ◽  
Regression Analyses ◽  
Series Analysis ◽  
Dengue Transmission ◽  
Statistical Approaches

Dengue fever (DF) is a national health problem in Pakistan. It has become endemic in Lahore after its recent reemergence in 2016. This study investigates the impacts of climatic factors (temperature and rainfall) on DF transmission in the district of Lahore through statistical approaches. Initially, the climatic variability was explored using a time series analysis on climatic factors from 1970 to 2012. Furthermore, ordinary and multiple linear regression analyses were used to measure the simulating effect of climatic factors on dengue incidence from 2007 to 2012. The time series analysis revealed significant annual and monthly variability in climatic factors, which shaped a dengue-supporting environment. It also showed a positive temporal relationship between climatic factors and DF. Moreover, the regression analyses revealed a substantial monthly relationship between climatic factors and dengue incidence. The ordinary linear regression of rainfall versus dengue showed monthly R2 = 34.2%, whereas temperature versus dengue presented R2 = 38.0%. The multiple regression analysis showed a monthly significance of R2 = 44.6%. Consequently, our study shows a substantial synergism between dengue and climatic factors in Lahore. The present study could help in unveiling new ways for health prediction modeling of dengue and might be applicable in other subtropical and temperate climates.

scholarly journals Temporal relationship between occurrences of hand, foot and mouth disease, respiratory virus detection and febrile seizures in children in tropical Singapore: a time-series analysis

2018 ◽  
Vol 147 ◽  
Author(s):  
Y. Chen ◽  
C. Y. Chong ◽  
A. R. Cook ◽  
N. T. W. Sim ◽  
P. Horby ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Influenza A ◽  
Temporal Relationship ◽  
Respiratory Virus ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Influenza B ◽  
Series Analysis ◽  
Foot And Mouth

AbstractFebrile seizure (FS) in children is a common complication of infections with respiratory viruses and hand, foot and mouth disease (HFMD). We conducted a retrospective ecological time-series analysis to determine the temporal relationship between hospital attendances for FS and HFMD or respiratory virus infections. Epilepsy attendance was used as a control. Data from 2004 to 2012 FS and epilepsy hospital attendance, HFMD notifications to the Ministry of Health and from laboratory-confirmed viral respiratory infections among KK Women's and Children's Hospital inpatients were used. A multivariate linear regression analysis was conducted to evaluate the relationship between FS and the virus time series. Relative risks of FS by age were calculated using Bayesian statistical methods. Paediatric accident and emergency (A&E) attendances for FS were found to be associated with influenza A (extra 0.47 FS per influenza A case), B (extra 0.32 per influenza B case) and parainfluenza 3 (extra 0.35 per parainfluenza type 3 case). However, other viruses were not significantly associated with FS. None of the viruses were associated with epileptic seizure attendance. Influenza A, B and parainfluenza 3 viruses contributed to the burden of FS resulting in A&E attendance. Children at risk of FS should be advised to receive seasonal influenza vaccination.

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