Detecting spatio-temporal hotspots of scarlet fever in Taiwan with spatio-temporal Gi* statistic

Tang, Jiahong; Tseng, Tzu-Jung; Chan, Ta‐Chien

doi:10.1371/journal.pone.0215434

Cited by 15 publications

(5 citation statements)

References 35 publications

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“…A subsequent paper reporting on spatial analyses and spatiotemporal analyses (e.g. Cusimano et al , 2010; Ceyhan et al , 2013; Špatenková and Virrantaus, 2013; Guldåker and Hallin, 2014; Marco et al , 2017; Liu et al , 2018; Low et al , 2018; Bringula and Balahadia, 2019; Chhetri et al , 2018; Yoo et al , 2018; Elia et al , 2019; Tang et al , 2019; Xia et al , 2019) that we are also conducting on the same VFRS incident dataset will be submitted hereafter.…”

Section: Discussionmentioning

confidence: 99%

Shifting patterns of emergency incidents during the COVID-19 pandemic in the City of Vaughan, Canada

Solis

Wimaladasa

Asgary

et al. 2021

IJES

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PurposeThe COVID-19 pandemic has changed many facets of urban life and operations, including emergency incidents. This study examines how COVID-19 has brought about changes in, and shifting patterns of, emergency incidents in the City of Vaughan, Ontario, Canada. This study aims to derive insights that could potentially inform planning and decision-making of fire and rescue service operations as further stages of the pandemic unfold.Design/methodology/approachStandard temporal analysis methods are applied to investigate the changes in the number and nature of emergency incidents, as recorded sequentially in the city's fire and rescue service incident report database, through various phases or waves of the pandemic and the associated public health measures that have been introduced.FindingsThe study analyses show a decrease in the number of emergency calls compared to previous reference years. Vehicle-related incidents show the highest decline, and changes in daily and hourly pattens are consistent with public health measures in place during each stage of the pandemic. The study concludes that the COVID-19 pandemic has had a significant impact on demand for emergency services provided by the fire department.Originality/valueThe authors believe this is the first study applying temporal analysis on a city's emergency incident response data spanning various phases/waves of the COVID-19 pandemic. The analysis may be replicated for other municipal fire services, which can generate further insights that may apply to specific local conditions and states of the pandemic.

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Section: Discussionmentioning

confidence: 99%

Shifting patterns of emergency incidents during the COVID-19 pandemic in the City of Vaughan, Canada

Solis

Wimaladasa

Asgary

et al. 2021

IJES

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“…Particularly, the Z value of the hot spot analysis G i * statistic was used to identify the cold and hot spots in the distribution of cancer cases among residents in Lujiang County. For example, the value of Z(G i *) > 1.96 indicates a high-value spatial cluster or hot spots, while Z(G i *) < −1.96 indicates a low-value spatial cluster or cold spot [ 18 , 19 ].…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Distribution and Evolution of Digestive Tract Cancer Cases in Lujiang County, China since 2012

Lin

Zhang

et al. 2022

IJERPH

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This study aims to analyze the spatiotemporal distribution and evolution of digestive tract cancer (DTC) in Lujiang County, China by using the geographic information system technology. Results of this study are expected to provide a scientific basis for effective prevention and control of DTC. The data on DTC cases in Lujiang County, China, were downloaded from the Data Center of the Center for Disease Control and Prevention in Hefei, Anhui Province, China, while the demographic data were sourced from the demographic department in China. Systematic statistical analyses, including the spatial empirical Bayes smoothing, spatial autocorrelation, hotspot statistics, and Kulldorff’s retrospective space-time scan, were used to identify the spatial and spatiotemporal clusters of DTC. GM(1,1) and standard deviation ellipses were then applied to predict the future evolution of the spatial pattern of the DTC cases in Lujiang County. The results showed that DTC in Lujiang County had obvious spatiotemporal clustering. The spatial distribution of DTC cases increases gradually from east to west in the county in a stepwise pattern. The peak of DTC cases occurred in 2012–2013, and the high-case spatial clusters were located mainly in the northwest of Lujiang County. At the 99% confidence interval, two spatiotemporal clusters were identified. From 2012 to 2017, the cases of DTC in Lujiang County gradually shifted to the high-incidence area in the northwest, and the spatial distribution range experienced a process of “dispersion-clustering”. The cases of DTC in Lujiang County will continue to move to the northwest from 2018 to 2025, and the predicted spatial clustering tends to be more obvious.

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“…1.96 indicates a high-value spatial cluster or hot spots, while Z(G i *) -1.96 indicates a low-value spatial cluster or cold spot [18,19] .…”

Section: Hot Spot Analysismentioning

confidence: 99%

Spatiotemporal Distribution and Evolution of Digestive Tract Cancer Cases in Hefei, China Since 2012

Lin

Zhang

et al. 2021

Preprint

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Background: Cancer has become the major killer of deaths among Chinese urban and rural residents, and about 50% of new cases are from liver cancer, gastric cancer, and esophageal cancer. Digestive tract cancer(DTC) has received widespread attention due to its poor treatment effect and high mortality rate, as well as severe physical, psychological, mental, and economic trauma to patients and their families.Methods: The data on DTC cases in Lujiang County in Hefei, China, were downloaded from the Data Center of the Center for Disease Control and Prevention in Hefei, Anhui Province, China, while the demographic data were sourced from the demographic department in China. Systematic statistical analyses, including the spatial empirical Bayes smoothing, spatial autocorrelation, hotspot statistics, and Kulldorff's retrospective space-time scan, were used to identify the spatial and spatiotemporal clusters of DTC. GM(1,1) and standard deviation ellipses were then applied to predict the future evolution of the spatial pattern of the DTC cases in Lujiang County. Results: DTC in Lujiang County had obvious spatiotemporal clustering. The spatial distribution of DTC cases increases gradually from east to west in the county in a stepwise pattern. The peak of DTC cases occurred in 2012-2013, and the high-case spatial clusters were located mainly in the northwest of Lujiang County. At the 1% significance level, two spatiotemporal clusters were identified. From 2012 to 2017, the cases of DTC in Lujiang County gradually shifted to the high-incidence area in the northwest, and the spatial distribution range experienced a process of “dispersion-clustering”. The cases of DTC in Lujiang County will continue to move to the northwest from 2018 to 2025, and the predicted spatial clustering tends to be more obvious. Conclusions: High-incidence time of DTC was 2012-2013, and the number of cases increased from east to west in Lujiang County. High clusters were mostly located in the northwestern area and agglomerated from southeast to northwestern especially after 2014.

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Detecting spatio-temporal hotspots of scarlet fever in Taiwan with spatio-temporal Gi* statistic

Cited by 15 publications

References 35 publications

Shifting patterns of emergency incidents during the COVID-19 pandemic in the City of Vaughan, Canada

Shifting patterns of emergency incidents during the COVID-19 pandemic in the City of Vaughan, Canada

Spatiotemporal Distribution and Evolution of Digestive Tract Cancer Cases in Lujiang County, China since 2012

Spatiotemporal Distribution and Evolution of Digestive Tract Cancer Cases in Hefei, China Since 2012

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