The socio-economic determinants of COVID-19: A spatial analysis of German county level data

Modelling changing patterns in the COVID‐19 geographical distribution: Madrid’s case

We analyse the transmission factors shaping the spatial distribution of COVID‐19 infections during the distinct phases of the pandemic’s first wave in Madrid, Spain, by fitting a spatial regression model capturing neighbourhood effects between municipalities. Our findings highlight that factors such as population, mobility, and tourism were instrumental in the days before the national lockdown. As a result, already in the early part of the lockdown phase, a geographical pattern emerged in the spread of the disease, along with the positive (negative) impact of age (wealth) on virus transmission. Thereafter, spatial links between municipalities weakened, as the influences of mobility and tourism were eroded by mass quarantine. However, in the de‐escalation phase, mobility reappeared, reinforcing the geographical pattern, an issue that policymakers must pay heed to. Indeed, a counterfactual analysis shows that the number of infections without the lockdown would have been around 170% higher.

Impacts of COVID-19 on urban rail transit ridership using the Synthetic Control Method

The outbreak of COVID-19 in 2020 has had drastic impacts on urban economies and activities, with transit systems around the world witnessing an unprecedented decline in ridership. This paper attempts to estimate the effect of COVID-19 on the daily ridership of urban rail transit (URT) using the Synthetic Control Method (SCM). Six variables are selected as the predictors, among which four variables unaffected by the pandemic are employed. A total of 22 cities from Asia, Europe, and the US with varying timelines of the pandemic outbreak are selected in this study. The effect of COVID-19 on the URT ridership in 11 cities in Asia is investigated using the difference between their observed ridership reduction and the potential ridership generated by the other 11 cities. Additionally, the effect of the system closure in Wuhan on ridership recovery is analyzed. A series of placebo tests are rolled out to confirm the significance of these analyses. Two traditional methods (causal impact analysis and straightforward analysis) are employed to illustrate the usefulness of the SCM. Most Chinese cities experienced about a 90% reduction in ridership with some variation among different cities. Seoul and Singapore experienced a minor decrease compared to Chinese cities. The results suggest that URT ridership reductions are associated with the severity and duration of restrictions and lockdowns. Full system closure can have severe impacts on the speed of ridership recovery following resumption of service, as demonstrated in the case of Wuhan with about 22% slower recovery. The results of this study can provide support for policymakers to monitor the URT ridership during the recovery period and understand the likely effects of system closure if considered in future emergency events.

Density or Connectivity: What Are the Main Causes of the Spatial Proliferation of COVID-19 in Korea?

COVID-19 has sparked a debate on the vulnerability of densely populated cities. Some studies argue that high-density urban centers are more vulnerable to infectious diseases due to a higher chance of infection in crowded urban environments. Other studies, however, argue that connectivity rather than population density plays a more significant role in the spread of COVID-19. While several studies have examined the role of urban density and connectivity in Europe and the U.S., few studies have been conducted in Asian countries. This study aims to investigate the role of urban spatial structure on COVID-19 by comparing different measures of urban density and connectivity during the first eight months of the outbreak in Korea. Two measures of density were derived from the Korean census, and four measures of connectivity were computed using social network analysis of the Origin-Destination data from the 2020 Korea Transport Database. We fitted both OLS and negative binomial models to the number of confirmed COVID-19 patients and its infection rates at the county level, collected individually from regional government websites in Korea. Results show that both density and connectivity play an important role in the proliferation of the COVID-19 outbreak in Korea. However, we found that the connectivity measure, particularly a measure of network centrality, was a better indicator of COVID-19 proliferation than the density measures. Our findings imply that policies that take into account different types of connectivity between cities might be necessary to contain the outbreak in the early phase.

Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States

Since December 2019, the world has witnessed the stringent effect of an unprecedented global pandemic, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of January 29,2021, there have been 100,819,363 confirmed cases and 2,176,159 deaths reported. Among the countries affected severely by COVID-19, the United States tops the list. Research has been conducted to discuss the causal associations between explanatory factors and COVID-19 transmission in the contiguous United States. However, most of these studies focus more on spatial associations of the estimated parameters, yet exploring the time-varying dimension in spatial econometric modeling appears to be utmost essential. This research adopts various relevant approaches to explore the potential effects of driving factors on COVID-19 counts in the contiguous United States. A total of three global spatial regression models and two local spatial regression models, the latter including geographically weighted regression (GWR) and multiscale GWR (MGWR), are performed at the county scale to take into account the scale effects. For COVID-19 cases, ethnicity, crime, and income factors are found to be the strongest covariates and explain most of the variance of the modeling estimation. For COVID-19 deaths, migration (domestic and international) and income factors play a critical role in explaining spatial differences of COVID-19 deaths across counties. Such associations also exhibit temporal variations from March to July, as supported by better performance of MGWR than GWR. Both global and local associations among the parameters vary highly over space and change across time. Therefore, time dimension should be paid more attention to in the spatial epidemiological analysis. Among the two local spatial regression models, MGWR performs more accurately, as it has slightly higher Adj. R2 values (for cases, R2 = 0.961; for deaths, R2 = 0.962), compared to GWR's Adj. R2 values (for cases, R2 = 0.954; for deaths, R2 = 0.954). To inform policy-makers at the nation and state levels, understanding the place-based characteristics of the explanatory forces and related spatial patterns of the driving factors is of paramount importance. Since it is not the first time humans are facing public health emergency, the findings of the present research on COVID-19 therefore can be used as a reference for policy designing and effective decision making.

Geographically varying relationships of COVID-19 mortality with different factors in India

COVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance ([Formula: see text]) with smaller Akaike Information Criterion (AICc [Formula: see text]) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran's [Formula: see text] and [Formula: see text]) in the residuals. It is found that more than 86% of local [Formula: see text] values are larger than 0.60 and almost 68% of [Formula: see text] values are within the range 0.80-0.97. Moreover, some interesting local variations in the relationships are also found.

Socioeconomic, demographic and healthcare determinants of the COVID-19 pandemic: an ecological study of Spain

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has posed a major challenge to health, economic and political systems around the world. Understanding the socioeconomic, demographic and health determinants affecting the pandemic is of interest to stakeholders. The purpose of this ecological study is to analyse the effect of the different socioeconomic, demographic and healthcare determinants on the mortality rate and estimated cumulative incidence of COVID-19 first wave in the Spanish regions.

METHODS

From the available data of the 17 Spanish regions (Autonomous Communities), we have carried out an ecological study through multivariate linear regression using ordinary least squares. To do this, we conducted an analysis using two distinct dependent variables: the logarithm of mortality rate per 1,000,000 inhabitants and the estimated cumulative incidence. The study has 12 explanatory variables.

RESULTS

After applying the backward stepwise multivariate analysis, we obtained a model with nine significant variables at different levels for mortality rate and a model with seven significant variables for estimated cumulative incidence. Among them, six variables are statistically significant and of the same sign in both models: "Nursing homes beds", "Proportion of care homes over 100 beds", "Log GDP per capita", "Aeroplane passengers", "Proportion of urban people", and the dummy variable "Island region".

CONCLUSIONS

The different socioeconomic, demographic and healthcare determinants of each region have a significant effect on the mortality rate and estimated cumulative incidence of COVID-19 in territories where the measures initially adopted to control the pandemic have been identical.

[Space-time spread of COVID-19 deaths in ArgentinaDistribuição espaço-temporal de mortes por COVID-19 na Argentina]

OBJECTIVES

Describe the space-time spread of COVID-19 deaths and analyze its socio-spatial inequalities in Argentina.

METHODS

COVID-19 deaths in Argentina as of October 17, 2020 were analyzed using data onday, month, and year, and place of residence. The space-time permutation scan method was used to detect the presence of space-time clusters. Poverty levels, population densities, and percentage of older adults in the population were compared for areas in high-mortality clusters and low-mortality clusters.

RESULTS

Five high-mortality clusters were detected between March 21 and August 27 in the Greater Buenos Aires conurbation and the northeast of the province of Buenos Aires. Low-mortality clusters were located on the periphery of the urban area from mid-September to mid-October and in central and northwestern Argentina between late April and late August. High-mortality clusters were located in areas with higher population densities and higher percentages of older adults in population, comparedto low-mortality clusters.

CONCLUSIONS

No high-mortality clusters were detected between September and mid-October. Norhave we detected a spatial spread of deaths to areas of low socioeconomic status at the national level. Our results support the first phase of the mortality spread model, affecting the largest urban area in Argentina.