Population Mobility Trends, Deprivation Index and the Spatio-Temporal Spread of Coronavirus Disease 2019 in Ireland

Madden, Jamie M.; More, Simon J.; Teljeur, Conor; Gleeson, Justin; Walsh, Cathal; McGrath, Guy

doi:10.3390/ijerph18126285

Cited by 13 publications

(12 citation statements)

References 36 publications

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“…Marcos César Ferreira also found that the population most vulnerable to COVID‐19 were those living in the districts with lower salaries and slum areas in the São Paulo municipality, Brazil 19 . Madden et al 22 found a similar result in Ireland; that is, the higher the deprivation index and the larger the population density, the higher the morbidity. However, the stay at home index (an indicator of population mobility) was negatively associated with morbidity.…”

Section: Discussionmentioning

confidence: 90%

Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022

Liang

Zi-tong

Guo

et al. 2022

Journal of Medical Virology

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The Omicron variant was first reported to the World Health Organization (WHO) from South Africa on November 24, 2021; this variant is spreading rapidly worldwide. No study has conducted a spatiotemporal analysis of the morbidity of Omicron infection at the country level; hence, to explore the spatial transmission of the Omicron variant among the 220 countries worldwide, we aimed to the analyze its spatial autocorrelation and to conduct a multiple linear regression to investigate the underlying factors associated with the pandemic. This study was an ecological study. Data on the number of confirmed cases were extracted from the WHO website. The spatiotemporal characteristic was described in a thematic map. The Global Moran Index (Moran's I) was used to detect the spatial autocorrelation, while the local indicators of spatial association (LISA) were used to analyze the local spatial correlation characteristics. The joinpoint regression model was used to explore the change in the trend of the Omicron incidence over time. The association between the morbidity of Omicron and influencing factors were analyzed using multiple linear regression. This study was an ecological study. Data on the number of confirmed cases were extracted from the WHO website. The spatiotemporal characteristic was described in a thematic map. The Global Moran Index (Moran's I) was used to detect the spatial autocorrelation, while the LISA were used to analyze the local spatial correlation characteristics. The joinpoint regression model was used to explore the change in the trend of the Omicron incidence over time. The association between the morbidity of Omicron and influencing factors were analyzed using multiple linear regression. The value of Moran's I was positive (Moran's I = 0.061, Z-score = 3.772, p = 0.007), indicating a spatial correlation of the morbidity of Omicron at the country level. From November 26, 2021 to February 26, 2022; the morbidity showed obvious spatial clustering. Hotspot clustering was observed mostly in Europe (locations in High-High category: 24). Coldspot clustering was observed mostly in Africa and Asia (locations in Low−Low category: 32). The result of joinpoint regression showed an increasing trend from December 21, 2021 to January 26, 2022. Results of the multiple linear regression analysis demonstrated that the morbidity of Omicron was strongly positively correlated with income support

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Section: Discussionmentioning

confidence: 90%

Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022

Liang

Zi-tong

Guo

et al. 2022

Journal of Medical Virology

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“…To minimize contact rates and avoid the transmission of COVID-19, it is essential to restrict movement as much as possible (Vannoni et al, 2020). The imposition of lockdown limitations resulted in a significant decrease in human mobility (Madden et al, 2021), Meanwhile, other experts have done comparable mobility studies to ascertain where pet cats travel while they are outdoors. Scientists have attempted to address this subject previously, either by tracking cats on foot or by collaring cats with radio transmitters, but Cat Tracker proved unique in its scope.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of Human Mobility of COVID-19 Epidemic in Kuala Lumpur using GIS

Qwasmi

Abdullah

Asmawi

2022

Am. J. Agric. Sci. Eng. Technol.

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During the third COVID-19 wave in Malaysia, the Ministry of Health suggested a complete lockdown (Movement Control Order-MCO). However, tracking people's movements would help curb the virus' spread. Movement data helps companies uncover human mobility patterns, analyse present and previous movement patterns, and anticipate future consequences. GIS can generate flow maps, themed maps, and flow charts. GIS can show people moving from one site to another and study the link between people moving and land usage. This work utilised GIS to predict and construct lockdown zones based on human mobility. Between November and December 2020, it was also used to tour the most popular places. A survey of 380 Malaysians was undertaken. At the same time, Mysejahtera apps provided data on people's movements. This research was done in Kuala Lumpur and the respondents' data was arranged into an excel document. Then, the GIS programme simulated human activity and plotted regions with a "likely increase in ratio/speed of infection." Between November and December 2020 (RMCO time), everyday movements of Kuala Lumpur people might be utilised to apply "restrictive measures for COVID-19 containment". Instead of a nationwide lockdown, these locations should be targeted for localised lockdowns. So, the government should concentrate on a smart lockdown strategy to prevent catastrophic economic devastation. This research also helped health authorities and governments minimise mobility by optimising the allocation of the most frequented areas concerning population density. This method helped control the pandemic more efficiently and scientifically.

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“…Nevertheless, an estimated third of the adult population had experienced some Covid-19 vaccine hesitancy, and 9% were opposed to the vaccine, with trends in resistance steadily increasing as the pandemic progressed [12,13]. As reported internationally, adults in Ireland who are vaccine hesitant are more likely to live-in urban settings and be in a lower income bracket [12], the same areas at increased risk for Covid-19 incidence and comorbidities [9].…”

Section: Introductionmentioning

confidence: 99%

“…SES-related factors such as diminished trust in governmental institutions, poorer health literacy, and logistical di culties simultaneously increase ones' likelihood of being vaccine hesitant [7,8] and ones' vulnerability to Covid-19 if unvaccinated. Experts have called for the prioritisation of densely populated deprived areas during vaccination rollout to capture people at increased risk of infection and hospitalisation [9]. However, ensuring equitable vaccination requires understanding and addressing community-speci c challenges associated with vaccination willingness and uptake [10].…”

Section: Introductionmentioning

confidence: 99%

Exploring key informants’ perceptions of Covid-19 vaccine hesitancy in a low-SES urban community in Ireland: emergence of a ‘5Cs’ model

Ingram

Roe

Downey

et al. 2022

Preprint

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Aim: The aim of this study was to explore key informants’ views on and experiences with Covid-19 vaccine hesitancy in a Dublin community of low socioeconomic status (SES) and to identify feasible, community-centred solutions for improving vaccination acceptance and uptake.Methods: Qualitative, semi-structured interviews were carried out at a local community-centre and a central hair salon. Twelve key informants from the target community were selected based on their professional experience with vulnerable population groups: the unemployed, adults in recovery from addiction, the elderly, and Irish Travellers. Inductive thematic framework analysis was conducted to identify emergent themes and sub-themes. Results: Drivers of vaccine hesitancy identified by key informants largely fell under the WHO ‘3Cs’ model of hesitancy: lack of confidence in the vaccine and its providers, complacency towards the health risks of Covid-19, and inconvenient access conditions. Covid-19 Communications emerged as a fourth ‘C’ whereby unclear and negative messages, confusing public health measures, and unmet expectations of the vaccine’s effectiveness exacerbated anti-authority sentiments and vaccine scepticism during the pandemic. Community-specific recommendations involved the provision of accurate and accessible information, collaborating with community-based organizations to build trust in the vaccine through relationship building and ongoing dialogue, and ensuring acceptable access conditions.Conclusions: A Confidence, Complacency, Convenience, Communications, Community-centred Solutions (‘5Cs’) model of vaccine hesitancy emerged through inductive analysis of key informant interviews in a low-SES urban community. The model and in-depth key informants’ perspectives can be used to compliment equitable vaccination efforts currently underway by Health Services Executive Ireland and non-governmental organizations.

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Population Mobility Trends, Deprivation Index and the Spatio-Temporal Spread of Coronavirus Disease 2019 in Ireland

Cited by 13 publications

References 36 publications

Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022

Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022

Impact of Human Mobility of COVID-19 Epidemic in Kuala Lumpur using GIS

Exploring key informants’ perceptions of Covid-19 vaccine hesitancy in a low-SES urban community in Ireland: emergence of a ‘5Cs’ model

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