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An Exploratory Study on Murders in the Chaos of COVID-19: An Analysis of Changes in Murder Rates and Patterns in Trinidad and Tobago
The study assessed the changes in murder counts and patterns under COVID-19 conditions in The Republic of Trinidad and Tobago. Initial research indicates that crime rates and patterns have changed under the COVID-19 pandemic possibly because of government implemented restrictions. The specific impac...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9684839/ http://dx.doi.org/10.1007/s43576-022-00075-w |
Sumario: | The study assessed the changes in murder counts and patterns under COVID-19 conditions in The Republic of Trinidad and Tobago. Initial research indicates that crime rates and patterns have changed under the COVID-19 pandemic possibly because of government implemented restrictions. The specific impact of these responses on murder has not been examined. To fill this gap in the literature the study utilized several interrupted time-series analyses to assess the change(s) in murder trends and their possible relationship to restrictions in movement associated with COVID-19. Overall, murder decreased while under COVID-19 conditions. However, the change was not consistent across different classifications of murder, with some increasing while others continued a downward trend that started before COVID-19 restrictions. The findings suggest that the change in murder trends under COVID-19 was not based on a linear relationship with restrictions in movement. Rather the results suggest that the data contains nonlinear components, which are associated with an initial but inconsistent change in murder counts among different classifications of murder. Which was followed by a phase of diminishing return on the effect of restriction on movement on the murder count. Further, in support of the findings the forecasting of murder was found to be better achieved by an algorithm which supports continuous non-linear functions (Artificial Neural Networks) when compared to a linear approach (Bayesian Structural Time-Series). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s43576-022-00075-w. |
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