scholarly journals The socio-spatial determinants of COVID-19 diffusion: the impact of globalisation, settlement characteristics and population

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Thomas Sigler ◽  
Sirat Mahmuda ◽  
Anthony Kimpton ◽  
Julia Loginova ◽  
Pia Wohland ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Total Population ◽  
Human Mobility ◽  
Household Size ◽  
Human Interaction ◽  
Population Characteristics ◽  
Disease Diffusion ◽  
Older Populations ◽  
The Impact ◽  
Over Time

Abstract Background COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of COVID-19 transmission, there remains an important gap in knowledge regarding the drivers of geographical diffusion between places, in particular at the global scale. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of reported COVID-19 diffusion over a six-week period in March and April 2020. Our exploratory analysis is based on reported COVID-19 data published by Johns Hopkins University which, despite its limitations, serves as the best repository of reported COVID-19 cases across nations. Results The quantile regression model suggests that globalisation, settlement, and population characteristics related to high human mobility and interaction predict reported disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total reported cases (per million) whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total reported cases (per million). Population density, and population characteristics such as total population, older populations, and household size are strong predictors in early weeks but have a muted impact over time on reported COVID-19 diffusion. In contrast, the impacts of interpersonal and trade globalisation are enhanced over time, indicating that human mobility may best explain sustained disease diffusion. Conclusions Model results confirm that globalisation, settlement and population characteristics, and variables tied to high human mobility lead to greater reported disease diffusion. These outcomes serve to inform suppression strategies, particularly as they are related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. It is likely that many of these processes are replicated at smaller geographical scales both within countries and within regions. Epidemiological strategies must therefore be tailored according to human mobility patterns, as well as countries’ settlement and population characteristics. We suggest that limiting human mobility to the greatest extent practical will best restrain COVID-19 diffusion, which in the absence of widespread vaccination may be one of the best lines of epidemiological defense.

scholarly journals The Socio-Spatial Determinants of COVID-19 Diffusion: The Impact of Globalisation, Settlement Characteristics and Population

2020 ◽  
Author(s):  
Thomas Sigler ◽  
Sirat Mahmuda ◽  
Anthony Kimpton ◽  
Julia Loginova ◽  
Pia Wohland-Jakhar ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Human Mobility ◽  
Developed Countries ◽  
Human Interaction ◽  
Population Characteristics ◽  
Mobility Patterns ◽  
Global Pandemic ◽  
Disease Diffusion ◽  
Older Populations ◽  
The Impact

Abstract Background: COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of the COVID-19 transmission, there remains a gap in knowledge regarding the drivers of geographical diffusion between places. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of COVID-19 diffusion over a six-week period in March and April 2020.Results: The quantile regression model suggest that globalisation and settlement population characteristics related to high human mobility predict disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total confirmed cases per million whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total confirmed cases per million. Conclusions: The analysis confirms that globalisation, settlement and population characteristics lead to greater disease diffusion, and primarily variables tied to high human mobility. These outcomes serve to inform policies around ‘flattening the curve’, particularly as they related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. Epidemiological strategies must be tailored to suit the range of human mobility patterns, as well as the variety of settlement and population characteristics.

scholarly journals The Socio-Spatial Determinants of COVID-19 Diffusion: The Impact of Globalisation, Settlement Characteristics and Population

2020 ◽  
Author(s):  
Thomas Sigler ◽  
Sirat Mahmuda ◽  
Anthony Kimpton ◽  
Julia Loginova ◽  
Pia Wohland-Jakhar ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Human Mobility ◽  
Developed Countries ◽  
Human Interaction ◽  
Population Characteristics ◽  
Mobility Patterns ◽  
Global Pandemic ◽  
Disease Diffusion ◽  
Older Populations ◽  
The Impact

Abstract Background: COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of the COVID-19 transmission, there remains a gap in knowledge regarding the drivers of geographical diffusion between places. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of COVID-19 diffusion over a six-week period in March and April 2020. Results: The quantile regression model suggest that globalisation and settlement population characteristics related to high human mobility predict disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total confirmed cases per million whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total confirmed cases per million. Conclusions: The analysis confirms that globalisation, settlement and population characteristics lead to greater disease diffusion, and primarily variables tied to high human mobility. These outcomes serve to inform policies around ‘flattening the curve’, particularly as they related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. Epidemiological strategies must be tailored to suit the range of human mobility patterns, as well as the variety of settlement and population characteristics.

scholarly journals A data-driven model of the COVID-19 spread among interconnected populations: epidemiological and mobility aspects following the lockdown in Italy

2021 ◽  
Author(s):  
Paolo Di Giamberardino ◽  
Daniela Iacoviello ◽  
Federico Papa ◽  
Carmela Sinisgalli
Keyword(s):  
Group Structure ◽  
Human Mobility ◽  
Epidemiological Data ◽  
Group Model ◽  
Disease Evolution ◽  
Analytical Stability ◽  
Containment Measures ◽  
Disease Diffusion ◽  
The Impact ◽  
Second Wave

AbstractAn epidemic multi-group model formed by interconnected SEIR-like structures is formulated and used for data fitting to gain insight into the COVID-19 dynamics and into the role of non-pharmaceutical control actions implemented to limit the infection spread since its outbreak in Italy. The single submodels provide a rather accurate description of the COVID-19 evolution in each subpopulation by an extended SEIR model including the class of asymptomatic infectives, which is recognized as a determinant for disease diffusion. The multi-group structure is specifically designed to investigate the effects of the inter-regional mobility restored at the end of the first strong lockdown in Italy (June 3, 2020). In its time-invariant version, the model is shown to enjoy some analytical stability properties which provide significant insights on the efficacy of the implemented control measurements. In order to highlight the impact of human mobility on the disease evolution in Italy between the first and second wave onset, the model is applied to fit real epidemiological data of three geographical macro-areas in the period March–October 2020, including the mass departure for summer holidays. The simulation results are in good agreement with the data, so that the model can represent a useful tool for predicting the effects of the combination of containment measures in triggering future pandemic scenarios. Particularly, the simulation shows that, although the unrestricted mobility alone appears to be insufficient to trigger the second wave, the human transfers were crucial to make uniform the spatial distribution of the infection throughout the country and, combined with the restart of the production, trade, and education activities, determined a time advance of the contagion increase since September 2020.

scholarly journals An Analysis on Mobile and Interactive Media Use by Young Children: The Good, the Bad and the Unknown

2019 ◽  
Vol 2 (2) ◽  
pp. p89
Author(s):  
Carlene Olivia Fider ◽  
Shey Quinton Olaoshebikan
Keyword(s):  
Young Children ◽  
Media Use ◽  
Interactive Media ◽  
Interactive Technology ◽  
Human Interaction ◽  
Mobile Media ◽  
Current Opinion ◽  
The Impact ◽  
Opinion Article ◽  
Over Time

The introduction of mobile media to children of very young ages continues to be a topic of discussion in many academic and professional circles. Over time, the suggested guidelines specific to children and interaction with mobile and interactive technology have changed, yet there are still some unknowns regarding the impact of replacing actual human interaction with interactive devices. While there are certainly benefits to having children exposed to these forms of technology, there are potential drawbacks. This current opinion article seeks provide a narrative regarding current work that is related to children and their engagement with interactive technology.

scholarly journals Quantifying the impacts of human mobility restriction on the spread of COVID-19: an empirical analysis from 344 cities of China

2020 ◽  
Author(s):  
Jing Tan ◽  
Yiquan Xiong ◽  
Shaoyang Zhao ◽  
Chunrong Liu ◽  
Shiyao Huang ◽  
...  
Keyword(s):  
Human Mobility ◽  
Large Population ◽  
Disease Spread ◽  
Population Mobility ◽  
Population Sizes ◽  
The Difference ◽  
Novel Coronavirus ◽  
The Impact ◽  
The City ◽  
Over Time

AbstractObjectiveSince the outbreak of novel coronavirus pneumonia (COVID-19), human mobility restriction measures have raised controversies, partly due to inconsistent findings. Empirical study is urgently needed to reliably assess the causal effects of mobility restriction.MethodsOur study applied the difference-in-difference (DID) model to assess declines of population mobility at the city level, and used the log-log regression model to examine the effects of population mobility declines on the disease spread measured by cumulative or new cases of COVID-19 over time, after adjusting for confounders.ResultsThe DID model showed that a continual expansion of the relative declines over time in 2020. After four weeks, population mobility declined by 54.81% (interquartile ranges, −65.50% to −43.56%). The accrued population mobility declines were associated with significant reduction of cumulative COVID-19 cases throughout six weeks (i.e., 1% decline of population mobility was associated with 0.72% (95%CI 0.50% to 0.93%) reduce of cumulative cases for one week, 1.42% two weeks, 1.69% three weeks, 1.72% four weeks,1.64% five weeks and 1.52% six weeks). The impact on weekly new cases seemed greater in the first four weeks, but faded thereafter. The effects on cumulative cases differed by cities of different population sizes, with greater effects seen in larger cities.ConclusionPersistent population mobility restrictions are well deserved. However, a change in the degree of mobility restriction may be warranted over time, particularly after several weeks of rigorous mobility restriction. Implementation of mobility restrictions in major cities with large population sizes may be even more important.

scholarly journals A longitudinal study of the impact of human mobility on the incidence of COVID-19 in India

2020 ◽  
Author(s):  
Sarbeswar Praharaj ◽  
Hoon Han
Keyword(s):  
Human Mobility ◽  
Estimating Equation ◽  
September 11 ◽  
Generalized Estimating Equation ◽  
Mobility Rate ◽  
Daily Mobility ◽  
Shopping Centres ◽  
Log Linear ◽  
The Impact ◽  
Over Time

AbstractHuman mobility plays a crucial role in determining how fast and where infectious diseases can spread. This study aims to investigate visit to which category of places among grocery, retail, parks, workplaces, residential, and transit stations is more associated with the incidence of COVID-19 in India. A longitudinal analysis of generalized estimating equation (GEE) with a Poisson log-linear model is employed to analyze the daily mobility rate and reported new cases of COVID-19 between March 14 and September 11, 2020. This study finds that mobility to places of grocery (food and vegetable markets, drug stores etc.) and retail (restaurants, cafes, shopping centres etc.) is significantly associated (at p<0.01) with the incidence of COVID-19. In contrast, visits to parks, transit stations and mobility within residential neighbourhoods are not statistically significant (p>0.05) in changing COVID-19 cases over time. These findings highlight that instead of blanket lockdown restrictions, authorities should adopt a place-based approach focusing on vulnerable hotspot locations to contain the COVID-19 and any future infectious disease.

scholarly journals Evolution of the Initially Recruited SHARE Panel Sample Over the First Six Waves

2020 ◽  
Vol 36 (3) ◽  
pp. 507-527
Author(s):  
Sabine Friedel ◽  
Tim Birkenbach
Keyword(s):  
Retention Rates ◽  
Panel Survey ◽  
Face To Face ◽  
National Surveys ◽  
Sample Composition ◽  
Older Populations ◽  
The Impact ◽  
Second Wave ◽  
Large Decline ◽  
Over Time

AbstractAttrition is a frequently observed phenomenon in panel studies. The loss of panel members over time can hamper the analysis of panel survey data. Based on data from the Survey of Health, Ageing and Retirement in Europe (SHARE), this study investigates changes in the composition of the initially recruited first-wave sample in a multi-national face-to-face panel survey of an older population over waves. By inspecting retention rates and R-indicators, we found that, despite declining retention rates, the composition of the initially recruited panel sample in Wave 1 remained stable after the second wave. Thus, after the second wave there is no further large decline in representativeness with regard to the first wave sample. Changes in the composition of the sample after the second wave over time were due mainly to mortality-related attrition. Non-mortality-related attrition had a slight effect on the changes in sample composition with regard to birth in survey country, area of residence, education, and social activities. Our study encourages researchers to investigate further the impact of mortality- and non-mortality-related attrition in multi-national surveys of older populations.

scholarly journals A Data-driven Model of the COVID-19 Spread among Interconnected Populations: Epidemiological and Mobility Aspects Following the Lock-down in Italy

2021 ◽  
Author(s):  
Paolo DI GIAMBERARDINO ◽  
Daniela Iacoviello ◽  
Federico Papa ◽  
Carmela Sinisgalli
Keyword(s):  
Group Structure ◽  
Human Mobility ◽  
Epidemiological Data ◽  
Group Model ◽  
Disease Evolution ◽  
Analytical Stability ◽  
Containment Measures ◽  
Disease Diffusion ◽  
The Impact ◽  
Second Wave

Abstract An epidemic multi-group model formed by interconnected SEIR-like structures is formulated and used for data fitting to gain insight into the COVID-19 dynamics and into the role of non-pharmaceutical control actions implemented to limit the infection spread since its outbreak in Italy. The single submodels provide a rather accurate description of the COVID-19 evolution in each subpopulation by an extended SEIR model including the class of asymptomatic infectives, which is recognized as a determinant for disease diffusion. The multi-group structure is specifically designed to investigate the effects of the interregional mobility restored at the end of the first strong lock-down in Italy (June 3, 2020). In its time-invariant version, the model is shown to enjoy some analytical stability properties which provide significant insights on the efficacy of the implemented control measurements. In order to highlight the impact of human mobility on the disease evolution in Italy between the first and second wave onset, the model is applied to fit real epidemiological data of three geographical macro-areas in the period March-October 2020, including the mass departure for summer holidays. The simulation results are in good agreement with the data, so that the model can represent a useful tool for predicting the effects of the combination of containment measures in triggering future pandemic scenarios. Particularly, the simulation shows that, although the unrestricted mobility alone appears to be insufficient to trigger the second wave, the human transfers were crucial to make uniform the spatial distribution of the infection throughout the country and, combined with the restart of (production, trade and education) activities, determined a time advance of the contagion increase (autumn 2020).

The Therapist’s Reaction to a Patient’s Suicide

Crisis ◽  
2011 ◽  
Vol 32 (2) ◽  
pp. 99-105 ◽  
Author(s):  
Friedrich Martin Wurst ◽  
Isabella Kunz ◽  
Gregory Skipper ◽  
Manfred Wolfersdorf ◽  
Karl H. Beine ◽  
...  
Keyword(s):  
Emotional Responses ◽  
Well Being ◽  
Emotional Reaction ◽  
Emotional Reactions ◽  
Analog Scale ◽  
General Hospitals ◽  
Item Questionnaire ◽  
Retrospective Nature ◽  
The Impact ◽  
Over Time

Background: A substantial proportion of therapists experience the loss of a patient to suicide at some point during their professional life. Aims: To assess (1) the impact of a patient’s suicide on therapists distress and well-being over time, (2) which factors contribute to the reaction, and (3) which subgroup might need special interventions in the aftermath of suicide. Methods: A 63-item questionnaire was sent to all 185 Psychiatric Clinics at General Hospitals in Germany. The emotional reaction of therapists to patient’s suicide was measured immediately, after 2 weeks, and after 6 months. Results: Three out of ten therapists suffer from severe distress after a patients’ suicide. The item “overall distress” immediately after the suicide predicts emotional reactions and changes in behavior. The emotional responses immediately after the suicide explained 43.5% of the variance of total distress in a regression analysis. Limitations: The retrospective nature of the study is its primary limitation. Conclusions: Our data suggest that identifying the severely distressed subgroup could be done using a visual analog scale for overall distress. As a consequence, more specific and intensified help could be provided to these professionals.

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