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 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 Mobility Patterns in Different Age Groups in Japan during the COVID-19 Pandemic: a Small Area Time Series Analysis through March 2021

2021 ◽  
Author(s):  
Shuhei Nomura ◽  
Yuta Tanoue ◽  
Daisuke Yoneoka ◽  
Stuart Gilmour ◽  
Takayuki Kawashima ◽  
...  
Keyword(s):  
Care Delivery ◽  
Human Mobility ◽  
Age Groups ◽  
Virus Transmission ◽  
Initial Response ◽  
Health Care Delivery System ◽  
Population Characteristics ◽  
Mobility Patterns ◽  
Mobility Data ◽  
The Status

AbstractIn the COVID-19 era, movement restrictions are crucial to slow virus transmission and have been implemented in most parts of the world, including Japan. To find new insights on human mobility and movement restrictions encouraged (but not forced) by the emergency declaration in Japan, we analyzed mobility data at 35 major stations and downtown areas in Japan—each defined as an area overlaid by several 125-meter grids—from September 1, 2019 to March 19, 2021. Data on the total number of unique individuals per hour passing through each area were obtained from Yahoo Japan Corporation (i.e., more than 13,500 data points for each area). We examined the temporal trend in the ratio of the rolling seven-day daily average of the total population to a baseline on January 16, 2020, by ten-year age groups in five time frames. We demonstrated that the degree and trend of mobility decline after the declaration of a state of emergency varies across age groups and even at the subregional level. We demonstrated that monitoring dynamic geographic and temporal mobility information stratified by detailed population characteristics can help guide not only exit strategies from an ongoing emergency declaration, but also initial response strategies before the next possible resurgence. Combining such detailed data with data on vaccination coverage and COVID-19 incidence (including the status of the health care delivery system) can help governments and local authorities develop community-specific mobility restriction policies. This could include strengthening incentives to stay home and raising awareness of cognitive errors that weaken people's resolve to refrain from nonessential movement.

The Impact of Stationarity, Regularity, and Context on the Predictability of Individual Human Mobility

2021 ◽  
Vol 7 (4) ◽  
pp. 1-24
Author(s):  
Douglas Do Couto Teixeira ◽  
Aline Carneiro Viana ◽  
Jussara M. Almeida ◽  
Mrio S. Alvim
Keyword(s):  
State Of The Art ◽  
Human Mobility ◽  
Contextual Information ◽  
Prediction Method ◽  
Mobility Prediction ◽  
Mobility Patterns ◽  
Distinct Cell ◽  
Inherent Nature ◽  
The One ◽  
The Impact

Predicting mobility-related behavior is an important yet challenging task. On the one hand, factors such as one’s routine or preferences for a few favorite locations may help in predicting their mobility. On the other hand, several contextual factors, such as variations in individual preferences, weather, traffic, or even a person’s social contacts, can affect mobility patterns and make its modeling significantly more challenging. A fundamental approach to study mobility-related behavior is to assess how predictable such behavior is, deriving theoretical limits on the accuracy that a prediction model can achieve given a specific dataset. This approach focuses on the inherent nature and fundamental patterns of human behavior captured in that dataset, filtering out factors that depend on the specificities of the prediction method adopted. However, the current state-of-the-art method to estimate predictability in human mobility suffers from two major limitations: low interpretability and hardness to incorporate external factors that are known to help mobility prediction (i.e., contextual information). In this article, we revisit this state-of-the-art method, aiming at tackling these limitations. Specifically, we conduct a thorough analysis of how this widely used method works by looking into two different metrics that are easier to understand and, at the same time, capture reasonably well the effects of the original technique. We evaluate these metrics in the context of two different mobility prediction tasks, notably, next cell and next distinct cell prediction, which have different degrees of difficulty. Additionally, we propose alternative strategies to incorporate different types of contextual information into the existing technique. Our evaluation of these strategies offer quantitative measures of the impact of adding context to the predictability estimate, revealing the challenges associated with doing so in practical scenarios.

“Air pollution arrested not imprisoned”: The impact of laws invoked on Covid-19 city lockdowns

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Emmanuel Mensah Aboagye ◽  
◽  
Nana Osei Owusu ◽  
Keyword(s):  
Air Pollution ◽  
Environmental Problem ◽  
World Health Organisation ◽  
Developed Countries ◽  
World Health ◽  
Economic Activities ◽  
Global Pandemic ◽  
Depth Study ◽  
The Government ◽  
The Impact

Air pollution continues to be an environmental problem that poses a lot of health risks to the young and aged. Developed countries have invested heavily to curb this environmental problem, causing severe threats to human lives, yet the results do not look convincing. In developing countries, the situation is difficult than they can imagine, resulting in governments borrowing to fight what looks like a lost battle [1-3]. The in-depth study of this environmental menace - air pollution, suggests that the government enacts stringent measures to help fight this battle. This is because air pollution has natural (volcanic eruption) and anthropogenic (human activities) causes. In December 2019, the deadly Coronavirus (Covid-19) outbreak was soon declared as a global pandemic by the World Health Organisation (WHO) [4]. Majority of countries have had their share of the impact of this outbreak. Many countries resorted to city lockdown to strictly control the movement of people and economic activities as recommended by WHO.

scholarly journals Impact on Air Quality of the COVID-19 Lockdown in the Urban Area of Palermo (Italy)

2020 ◽  
Vol 17 (20) ◽  
pp. 7375 ◽  
Author(s):  
Marcello Vultaggio ◽  
Daniela Varrica ◽  
Maria Grazia Alaimo
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Area ◽  
Human Mobility ◽  
Infected People ◽  
National Crisis ◽  
Global Pandemic ◽  
Pm 10 ◽  
The Impact

At the end of 2019, the first cases of coronavirus disease (COVID-19) were reported in Wuhan, China. Thereafter, the number of infected people increased rapidly, and the outbreak turned into a national crisis, with infected individuals all over the country. The COVID-19 global pandemic produced extreme changes in human behavior that affected air quality. Human mobility and production activities decreased significantly, and many regions recorded significant reductions in air pollution. The goal of our investigation was to evaluate the impact of the COVID-19 lockdown on the concentrations of the main air pollutants in the urban area of Palermo (Italy). In this study, the trends in the average concentrations of CO, NO2, O3, and PM10 in the air from 1 January 2020 to 31 July 2020 were compared with the corresponding average values detected at the same monitoring stations in Palermo during the previous five years (2015–2019). During the lockdown period (10 March–30 April), we observed a decrease in the concentrations of CO, NO2, and particulate matter (PM)10, calculated to be about 51%, 50%, and 45%, respectively. This confirms that air pollution in an urban area is predominantly linked to vehicular traffic.

scholarly journals Lockdown related travel behavior undermines the containment of SARS-CoV-2

2020 ◽  
Author(s):  
Nishant Kishore ◽  
Rebecca Kahn ◽  
Pamela P. Martinez ◽  
Pablo M. De Salazar ◽  
Ayesha S. Mahmud ◽  
...  
Keyword(s):  
Rural Areas ◽  
Travel Behavior ◽  
Human Mobility ◽  
Unintended Consequences ◽  
Disease Spread ◽  
Mobility Patterns ◽  
Long Distance ◽  
Mobility Data ◽  
Travel Restrictions ◽  
The Impact

ABSTRACTIn response to the SARS-CoV-2 pandemic, unprecedented policies of travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns - defined here as restrictions on both local movement or long distance travel - will determine how effective these kinds of interventions are. Here, we measure the impact of the announcement and implementation of lockdowns on human mobility patterns by analyzing aggregated mobility data from mobile phones. We find that following the announcement of lockdowns, both local and long distance movement increased. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. We find that travel surges following announcements of lockdowns can increase seeding of the epidemic in rural areas, undermining the goal of the lockdown of preventing disease spread. Appropriate messaging surrounding the announcement of lockdowns and measures to decrease unnecessary travel are important for preventing these unintended consequences of lockdowns.

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 The impact of dengue illness on social distancing and caregiving behavior

2021 ◽  
Vol 15 (7) ◽  
pp. e0009614
Author(s):  
Kathryn L. Schaber ◽  
Amy C. Morrison ◽  
William H. Elson ◽  
Helvio Astete-Vega ◽  
Jhonny J. Córdova-López ◽  
...  
Keyword(s):  
Human Mobility ◽  
Social Contact ◽  
Febrile Illness ◽  
Well Being ◽  
Denv Infection ◽  
Transmission Dynamics ◽  
Mobility Patterns ◽  
Social Contacts ◽  
Infectious Individual ◽  
The Impact

Background Human mobility among residential locations can drive dengue virus (DENV) transmission dynamics. Recently, it was shown that individuals with symptomatic DENV infection exhibit significant changes in their mobility patterns, spending more time at home during illness. This change in mobility is predicted to increase the risk of acquiring infection for those living with or visiting the ill individual. It has yet to be considered, however, whether social contacts are also changing their mobility, either by socially distancing themselves from the infectious individual or increasing contact to help care for them. Social, or physical, distancing and caregiving could have diverse yet important impacts on DENV transmission dynamics; therefore, it is necessary to better understand the nature and frequency of these behaviors including their effect on mobility. Methodology and principal findings Through community-based febrile illness surveillance and RT-PCR infection confirmation, 67 DENV positive (DENV+) residents were identified in the city of Iquitos, Peru. Using retrospective interviews, data were collected on visitors and home-based care received during the illness. While 15% of participants lost visitors during their illness, 22% gained visitors; overall, 32% of all individuals (particularly females) received visitors while symptomatic. Caregiving was common (90%), particularly caring by housemates (91%) and caring for children (98%). Twenty-eight percent of caregivers changed their behavior enough to have their work (and, likely, mobility patterns) affected. This was significantly more likely when caring for individuals with low “health-related quality of well-being” during illness (Fisher’s Exact, p = 0.01). Conclusions/Significance Our study demonstrates that social contacts of individuals with dengue modify their patterns of visitation and caregiving. The observed mobility changes could impact a susceptible individual’s exposure to virus or a presymptomatic/clinically inapparent individual’s contribution to onward transmission. Accounting for changes in social contact mobility is imperative in order to get a more accurate understanding of DENV transmission.

scholarly journals Change of human mobility during COVID-19: A United States case study

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259031
Author(s):  
Justin Elarde ◽  
Joon-Seok Kim ◽  
Hamdi Kavak ◽  
Andreas Züfle ◽  
Taylor Anderson
Keyword(s):  
Human Mobility ◽  
Principal Component ◽  
Population Characteristics ◽  
Mobility Patterns ◽  
Public Places ◽  
The Us ◽  
Us Counties ◽  
And Behavior ◽  
Global Spatial Autocorrelation ◽  
Latent Components

With the onset of COVID-19 and the resulting shelter in place guidelines combined with remote working practices, human mobility in 2020 has been dramatically impacted. Existing studies typically examine whether mobility in specific localities increases or decreases at specific points in time and relate these changes to certain pandemic and policy events. However, a more comprehensive analysis of mobility change over time is needed. In this paper, we study mobility change in the US through a five-step process using mobility footprint data. (Step 1) Propose the Delta Time Spent in Public Places (ΔTSPP) as a measure to quantify daily changes in mobility for each US county from 2019-2020. (Step 2) Conduct Principal Component Analysis (PCA) to reduce the ΔTSPP time series of each county to lower-dimensional latent components of change in mobility. (Step 3) Conduct clustering analysis to find counties that exhibit similar latent components. (Step 4) Investigate local and global spatial autocorrelation for each component. (Step 5) Conduct correlation analysis to investigate how various population characteristics and behavior correlate with mobility patterns. Results show that by describing each county as a linear combination of the three latent components, we can explain 59% of the variation in mobility trends across all US counties. Specifically, change in mobility in 2020 for US counties can be explained as a combination of three latent components: 1) long-term reduction in mobility, 2) no change in mobility, and 3) short-term reduction in mobility. Furthermore, we find that US counties that are geographically close are more likely to exhibit a similar change in mobility. Finally, we observe significant correlations between the three latent components of mobility change and various population characteristics, including political leaning, population, COVID-19 cases and deaths, and unemployment. We find that our analysis provides a comprehensive understanding of mobility change in response to the COVID-19 pandemic.

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