The spread of COVID-19 in London: Network effects and optimal lockdowns.

We generalise a stochastic version of the workhorse SIR (Susceptible-Infectious-Removed) epidemiological model to account for spatial dynamics generated by network interactions. Using the London metropolitan area as a salient case study, we show that commuter network externalities account for about 42% of the propagation of COVID-19. We find that the UK lockdown measure reduced total propagation by 44%, with more than one third of the effect coming from the reduction in network externalities. Counterfactual analyses suggest that: (i) the lockdown was somehow late, but further delay would have had more extreme consequences; (ii) a targeted lockdown of a small number of highly connected geographic regions would have been equally effective, arguably with significantly lower economic costs; (iii) targeted lockdowns based on threshold number of cases are not effective, since they fail to account for network externalities.

A methodological approach for modeling the spread of disease using geographical discrete-event spatial models

The study of infectious disease models has become increasingly important during the COVID-19 pandemic. The forecasting of disease spread using mathematical models has become a common practice by public health authorities, assisting in creating policies to combat the spread of the virus. Common approaches to the modeling of infectious diseases include compartmental differential equations and cellular automata, both of which do not describe the spatial dynamics of disease spread over unique geographical regions. We introduce a new methodology for modeling disease spread within a pandemic using geographical models. We demonstrate how geography-based Cell-Discrete-Event Systems Specification (DEVS) and the Cadmium JavaScript Object Notation (JSON) library can be used to develop geographical cellular models. We exemplify the use of these methodologies by developing different versions of a compartmental model that considers geographical-level transmission dynamics (e.g. movement restriction or population disobedience to public health guidelines), the effect of asymptomatic population, and vaccination stages with a varying immunity rate. Our approach provides an easily adaptable framework that allows rapid prototyping and modifications. In addition, it offers deterministic predictions for any number of regions simulated simultaneously and can be easily adapted to unique geographical areas. While the baseline model has been calibrated using real data from Ontario, we can update and/or add different infection profiles as soon as new information about the spread of the disease become available. © The Author(s) 2023.

Spatial Distribution of COVID-19 Hospitalizations and Associated Risk Factors in Health Insurance Data Using Bayesian Spatial Modelling.

The onset of COVID-19 across the world has elevated interest in geographic information systems (GIS) for pandemic management. In Germany, however, most spatial analyses remain at the relatively coarse level of counties. In this study, we explored the spatial distribution of COVID-19 hospitalizations in health insurance data of the AOK Nordost health insurance. Additionally, we explored sociodemographic and pre-existing medical conditions associated with hospitalizations for COVID-19. Our results clearly show strong spatial dynamics of COVID-19 hospitalizations. The main risk factors for hospitalization were male sex, being unemployed, foreign citizenship, and living in a nursing home. The main pre-existing diseases associated with hospitalization were certain infectious and parasitic diseases, diseases of the blood and blood-forming organs, endocrine, nutritional and metabolic diseases, diseases of the nervous system, diseases of the circulatory system, diseases of the respiratory system, diseases of the genitourinary and symptoms, and signs and findings not classified elsewhere.

The Value of Pooling in Last-Mile Delivery

Last-mile delivery services have become ubiquitous in the recent past. Delivery services for food (eg., DoorDash, Grubhub, Uber Eats) and groceries (eg., Instacart, Cornershop) earned a combined revenue of $25B in 2020, and are expected to exceed $72B in revenues by 2025. The COVID-19 pandemic accelerated the growth of such services by making their value proposition even more attractive. The lower risk of contact coupled with the convenience of ordering from the comfort of their homes led to widespread customer adoption. Even so, most last-mile delivery services are not profitable. The high cost of delivery is cited as the major cause of losses. Thus, analyzing the factors influencing delivery costs is crucial for understanding the long-term viability of these services. The pooling of orders is a critical source of efficiency in last-mile delivery. We propose a queuing-based spatial model for the delivery process to analyze the value created by pooling. We demonstrate how the trade-off between delivery times and the cost of delivery, mediated by the extent of pooling, dictates which services will be economically viable. Our simulation study of a typical grocery delivery service in Los Angeles, California suggests that delivery times of less than 1 hour are unprofitable for most regions in the US. We find that driver wages account for 90% of the delivery cost. We also discuss the potential impact of technological innovations such as automated delivery and labor regulations on the profitability of last-mile delivery services. © 2022 IEEE.

Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh

This research aimed to investigate the spatial autocorrelation and heterogeneity throughout Bangladesh's 64 districts. Moran I and Geary C are used to measure spatial autocorrelation. Different conventional models, such as Poisson-Gamma and Poisson-Lognormal, and spatial models, such as Conditional Autoregressive (CAR) Model, Convolution Model, and modified CAR Model, have been employed to detect the spatial heterogeneity. Bayesian hierarchical methods via Gibbs sampling are used to implement these models. The best model is selected using the Deviance Information Criterion. Results revealed Dhaka has the highest relative risk due to the city's high population density and growth rate. This study identifies which district has the highest relative risk and which districts adjacent to that district also have a high risk, which allows for the appropriate actions to be taken by the government agencies and communities to mitigate the risk effect. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

LABOUR MARKET OUTCOMES OF ROMANIAN MIGRANTS UNDER THE COVID-19 PANDEMIC AND DIGITALISATION CHALLENGES: A SPATIAL BOOTSTRAP APPROACH

Purpose: The research conducted in this chapter approaches a topical subject and examines the labour market advancement of Romanian migrants within several receiving economies across the European Union, as well as the impact of international migration on the Romanian economy and labour market, also considering the present context of the Covid-19 pandemic and digitalisation challenges. Method: The methodology embeds spatial bootstrap analysis (spatial lag and error models) applied to a newly compiled dataset for Romania during 2000–2020. Findings: Main findings of the current research update and complement the specialised literature with new data on Romanian migration by identifying unknown potential reasons that generated the departure of the Romanian labour force abroad and several credentials of the return migration intentions and strategies. Originality and significance of findings: The results mainly entail some of the essential effects generated by the Covid-19 pandemic regarding the interplay between the labour market and international migration, with a keen focus on Romania. © 2022 by Marilen Gabriel Pirtea, Graţiela Georgiana Noja, Mirela Cristea and Irina-Maria Grecu.

Community-level characteristics of COVID-19 vaccine hesitancy in England: A nationwide cross-sectional study.

One year after the start of the COVID-19 vaccination programme in England, more than 43 million people older than 12 years old had received at least a first dose. Nevertheless, geographical differences persist, and vaccine hesitancy is still a major public health concern; understanding its determinants is crucial to managing the COVID-19 pandemic and preparing for future ones. In this cross-sectional population-based study we used cumulative data on the first dose of vaccine received by 01-01-2022 at Middle Super Output Area level in England. We used Bayesian hierarchical spatial models and investigated if the geographical differences in vaccination uptake can be explained by a range of community-level characteristics covering socio-demographics, political view, COVID-19 health risk awareness and targeting of high risk groups and accessibility. Deprivation is the covariate most strongly associated with vaccine uptake (Odds Ratio 0.55, 95%CI 0.54-0.57; most versus least deprived areas). The most ethnically diverse areas have a 38% (95%CI 36-40%) lower odds of vaccine uptake compared with those least diverse. Areas with the highest proportion of population between 12 and 24 years old had lower odds of vaccination (0.87, 95%CI 0.85-0.89). Finally increase in vaccine accessibility is associated with COVID-19 vaccine coverage (OR 1.07, 95%CI 1.03-1.12). Our results suggest that one year after the start of the vaccination programme, there is still evidence of inequalities in uptake, affecting particularly minorities and marginalised groups. Strategies including prioritising active outreach across communities and removing practical barriers and factors that make vaccines less accessible are needed to level up the differences.

Quantifying the impact of air pollution on Covid-19 hospitalisation and death rates in Scotland.

Better understanding the risk factors that exacerbate Covid-19 symptoms and lead to worse health outcomes is vitally important in the public health fight against the virus. One such risk factor that is currently under investigation is air pollution concentrations, with some studies finding statistically significant effects while other studies have found no consistent associations. The aim of this paper is to add to this global evidence base on the potential association between air pollution concentrations and Covid-19 hospitalisations and deaths, by presenting the first study on this topic at the small-area scale in Scotland, United Kingdom. Our study is one of the most comprehensive to date in terms of its temporal coverage, as it includes all hospitalisations and deaths in Scotland between 1st March 2020 and 31st July 2021. We quantify the effects of air pollution on Covid-19 outcomes using a small-area spatial ecological study design, with inference using Bayesian hierarchical models that allow for the residual spatial correlation present in the data. A key advantage of our study is its extensive sensitivity analyses, which examines the robustness of the results to our modelling assumptions. We find clear evidence that PM2.5 concentrations are associated with hospital admissions, with a 1 µgm-3 increase in concentrations being associated with between a 7.4% and a 9.3% increase in hospitalisations. In addition, we find some evidence that PM2.5 concentrations are associated with deaths, with a 1 µgm-3 increase in concentrations being associated with between a 2.9% and a 10.3% increase in deaths.

Effects of Urban Vibrancy on an Urban Eco-Environment: Case Study on Wuhan City.

In the context of rapid urbanisation and an emerging need for a healthy urban environment, revitalising urban spaces and its effects on the urban eco-environment in Chinese cities have attracted widespread attention. This study assessed urban vibrancy from the dimensions of density, accessibility, liveability, diversity, and human activity, with various indicators using an adjusted spatial TOPSIS (technique for order preference by similarity to an ideal solution) method. The study also explored the effects of urban vibrancy on the urban eco-environment by interpreting PM 2.5 and land surface temperature using "big" and "dynamic" data, such as those from mobile and social network data. Thereafter, spatial modelling was performed to investigate the influence of urban vibrancy on air pollution and temperature with inverted and extracted remote sensing data. This process identified spatial heterogeneity and spatial autocorrelation. The majority of the dimensions, such as density, accessibility, liveability, and diversity, are negatively correlated with PM 2.5, thereby indicating that the advancement of urban vibrancy in these dimensions potentially improves air quality. Conversely, improved accessibility increases the surface temperature in most of the districts, and large-scale infrastructure construction generally contributes to the increase. Diversity and human activity appear to have a cooling effect. In the future, applying spatial heterogeneity is advised to assess urban vibrancy and its effect on the urban eco-environment, to provide valuable references for spatial urban planning, improve public health and human wellbeing, and ensure sustainable urban development.

Spatial Models and Masks in Indoor Analysis for the Spread of COVID-19

Face masks have been shown to slow or stop the spread of airborne COVID-19 droplets and aerosols. There is an apparent lack of research examining the effect of different types of masks used at the same time, and their impact on the spread of viral particles in a spatial sense. We introduce a rapid prototype model to overcome the issues in the available research using the Cell-DEVS formalism. We also build scenarios for the model to examine the effectiveness of all types of masks and respirators recommended by the World Health Organization on the spread of viral particles in an indoor environment. © 2021 IEEE.

Spatial Variability of the ‘Airbnb Effect’: A Spatially Explicit Analysis of Airbnb’s Impact on Housing Prices in Sydney

Over the last decade, the emergence and significant growth of home-sharing platforms, such as Airbnb, has coincided with rising housing unaffordability in many global cities. It is in this context that we look to empirically assess the impact of Airbnb on housing prices in Sydney—one of the least affordable cities in the world. Employing a hedonic property valuation model, our results indicate that Airbnb’s overall effect is positive. A 1% increase in Airbnb density is associated with approximately a 2% increase in property sales price. However, recognizing that Airbnb’s effect is geographically uneven and given the fragmented nature of Sydney’s housing market, we also employ a GWR to account for the spatial variation in Airbnb activity. The findings confirm that Airbnb’s influence on housing prices is varied across the city. Sydney’s northern beaches and parts of western Sydney experience a statistically significant value uplift attributable to Airbnb activity. However, traditional tourist locations focused around Sydney’s CBD and the eastern suburbs experience insignificant or negative property price impacts. The results highlight the need for policymakers to consider local Airbnb and housing market contexts when deciding the appropriate level and design of Airbnb regulation.

Association of Population Density and Distance to the City with the Risks of COVID-19: A Bayesian Spatial Analysis

The outbreak of Coronavirus disease-2019 (Covid-19) poses a severe threat around the world. Although several studies of modelling Covid-19 cases have been done, there appears to have been limited research into modelling Covid-19 using Bayesian hierarchical spatial models. This study aims to examine the most suitable Bayesian spatial CAR Leroux models in modelling the number of confirmed Covid-19 cases without and with covariates namely distance to the capital city and population density. Data on the number of confirmed positive cases of Covid-19 (March 20, 2020 - August 30, 2021) in 15 sub-districts in Makassar City, the number of populations, population density, and distance to the city are used. The best model selection is based on several criteria, namely Deviance Information Criteria (DIC), Watanabe Akaike Information Criteria (WAIC), residuals from Moran's I Modification (MMI), and the 95% credible interval does not contain zero. The results showed that the best model in modelling Covid-19 is spatial CAR Leroux with hyperprior Inverse-Gamma (0.5, 0.05) model with the incorporation of distance to the capital city. It is found that there was a negative correlation between the distance to the capital city and Covid-19 risk, but the association between population density and the relative risk of Covid-19 was not statistically significant. Ujung Pandang district and Sangkarrang Island have the highest and the lowest relative risk respectively. © 2021 Institute of Physics Publishing. All rights reserved.