Earth's magnetic field spatial-temporal changes in relation to the SARS-CoV-2 virus spreading

The results of a study on the possible connection between the spread of the SARS-CoV-2 virus and the Earth's magnetic field based on the analysis of a large array digital data for 95 countries of the world are presented. The dependence of the spatial SARS-CoV-2 virus spread on the magnitude of the BIGRF Earth's main magnetic field modular induction values was established. The maximum diseases number occurs in countries that are located in regions with reduced (25. 0-30. 0 μT) and increased (48. 0-55. 0 μT) values, with a higher correlation for the first case. The spatial dependence of the SARS-CoV-2 virus spreading on geomagnetic field dynamics over the past 70 years was revealed. The maximum diseases number refers to the areas with maximum changes in it, both in decrease direction (up to - 6500 nT) and increase (up to 2500 nT), with a more significant correlation for countries located in regions with increased geomagnetic field. © 2022 EAGE. All Rights Reserved.

COVID-19 and the Science of Where

Geographic location plays a crucial role in many aspects of research about the COVID-19 pandemic. Yet measurement of geographic location is necessarily imperfect, providing one of many sources of uncertainty in geospatial analysis. The ecological fallacy and the modifiable areal unit problem may lead to false inferences from such analysis. Spatial dependence and spatial heterogeneity are empirical properties of geospatial data that also impact inference and generalizability. Data provenance is a growing issue given the many ways in which data can be manipulated in preparation for analysis. The chapter ends with a discussion of critical spatial thinking as an umbrella term that encompasses all of these issues. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Spatial contagion during the first wave of the COVID‐19 pandemic: Some lessons from the case of Madrid, Spain

This paper analyses the magnitude of the spatial transmission of COVID‐19 through the municipalities of the region of Madrid during the first pandemic wave using a spatial contagion index. The study also provides additional insights into the main factors contributing to the spread of the virus in both time and space by estimating a novel conditional spatial contagion index. Our results reveal high values of spatial contagion before and during the national lockdown enacted on 15 March 2020, becoming medium/low since then. Furthermore, the study confirms the leading role of inter‐municipal mobility and population density in spatial contagion.Alternate :Este artículo analiza la magnitud de la transmisión espacial de COVID‐19 a través de los municipios de la región de Madrid durante la primera ola pandémica, para lo cual utiliza un índice de contagio espacial. El estudio también proporciona información adicional sobre los principales factores que contribuyen a la propagación del virus, tanto en el tiempo como en el espacio, mediante la estimación de un novedoso índice de contagio espacial condicional. Los resultados revelan altos valores de contagio espacial antes y durante el confinamiento nacional promulgado el 15 de marzo de 2020, pasando a ser medios o bajos desde entonces. Además, el estudio confirma el protagonismo de la movilidad intermunicipal y la densidad de población en el contagio espacial.Alternate :抄録本稿では、空間的感染指標を用いて、パンデミックの第一波におけるマドリッド地域の自治体におけるCOVID‐19の空間的伝播の規模を解析する。また、新しい条件付き空間感染指標を推定することにより、時間と空間の両方でウイルスの拡散に寄与する主要因子の解明の手掛かりを提供する。結果から、2020年3月15日に施行された全国的なロックダウン前とロックダウン中の空間的感染のレベルが高く、それ以降は中程度~低程度になっていることが明らかになった。本研究からさらに、都市間の移動性と人口密度が空間的感染の主導的役割となっていることを確認された。

Impact of the COVID-19 pandemic on air pollution from jet engines at airports in central eastern China.

Aircraft engine emissions (AEEs) generated during landing and takeoff (LTO) cycles are important air pollutant sources that directly impact the air quality at airports. Although the COVID-19 pandemic triggered an unprecedented collapse in the civil aviation industry, it also relieved some environmental pressure on airports. To quantify the impact of COVID-19 on AEEs, the amounts of three typical air pollutants (i.e., HC, CO, and NOx) from LTO cycles at airports in central eastern China were estimated before and after the pandemic. The study also explored the temporal variation and the spatial autocorrelation of both the emission quantity and the emission intensity, as well as their spatial associations with other socioeconomic factors. The results illustrated that the spatiotemporal distribution pattern of AEEs was significantly influenced by the policies implemented and the severity of COVID-19. The variations of AEEs at airports with similar characteristics and functional positions generally followed similar patterns. The results also showed that the studied air pollutants present positive spatial autocorrelation, and a positive spatial dependence was found between the AEEs and other external socioeconomic factors. Based on the findings, some possible policy directions for building a more sustainable and environment-friendly airport group in the post-pandemic era were proposed. This study provides practical guidance on continuous monitoring of the AEEs from LTO cycles and studying the impact of COVID-19 on the airport environment for other regions or countries.

Association Between Broadband Capacity and Social Vulnerability Factors in the United States: A County-Level Spatial Analysis.

This study evaluated relationships between county-level social vulnerability and broadband access using spatial clustering and regression approaches. County-level broadband availability (Federal Communications Commission [FCC] and Microsoft; 2019-2020), social vulnerability (COVID-19 Community Vulnerability Index [CCVI]; 2020), and primary care access (Area Health Resource File; 2019-2020) data sets were used. Two measures of broadband availability were considered: (1) Microsoft system-reported proportion of county population with broadband and (2) difference in FCC-reported and Microsoft-reported proportions of county population with broadband. Cluster maps were constructed using local Moran's I, and spatial Durbin models were estimated using primary care shortage designation and CCVI themes (socioeconomic status, minority status, housing/transportation/disability, epidemiological risk, health care system, high-risk environment, and population density). Among 3102 counties, county-level broadband coverage varied widely between Microsoft (0.39) and FCC (0.84), with greater coverage in the East and West, and larger discrepancies between FCC and Microsoft data in the South and Appalachia. In spatial regressions, a one-point increase in socioeconomic status vulnerability (0-least; 10-most vulnerable), was associated with a 2.0 percentage point (pp) reduction in broadband access (P < 0.001). Similar inverse relationships were observed with housing, epidemiological, and health care system variables. There were greater divergences between FCC and Microsoft measures with each one-point increase in socioeconomic status (1.4 pp), epidemiological risk (0.6 pp), and health care system (0.7 pp) vulnerability. More vulnerable counties had lower broadband and larger divergences between FCC and Microsoft data. Broadband is necessary for utilizing telehealth services; careful considerations in measuring broadband access can facilitate policies that improve equitable access to care.

Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model.

The global COVID-19 pandemic has taken a heavy toll on health, social, and economic costs since the end of 2019. Predicting the spread of a pandemic is essential to developing effective intervention policies. Since the beginning of this pandemic, many models have been developed to predict its pathways. However, the majority of these models assume homogeneous dynamics over the geographic space, while the pandemic exhibits substantial spatial heterogeneity. In addition, spatial interaction among territorial entities and variations in their magnitude impact the pandemic dynamics. In this study, we used a spatial extension of the SEIR-type epidemiological model to simulate and predict the 4-week number of COVID-19 cases in the Charlotte-Concord-Gastonia Metropolitan Statistical Area (MSA), USA. We incorporated a variety of covariates, including mobility, pharmaceutical, and non-pharmaceutical interventions, demographics, and weather data to improve the model's predictive performance. We predicted the number of COVID-19 cases for up to four weeks in the 10 counties of the studied MSA simultaneously over the time period 29 March 2020 to 13 March 2021, and compared the results with the reported number of cases using the root-mean-squared error (RMSE) metric. Our results highlight the importance of spatial heterogeneity and spatial interactions among locations in COVID-19 pandemic modeling.

Which food prices were more vulnerable to the COVID-19 blockade regulations in China? Comparison between perishable and storable vegetables

Purpose>This paper aims to investigate how differently the COVID-19 blockade regulations influence the prices of perishable and storable foods. The authors focus on the cases of the 2020 blockade at Hubei province and the 2021 blockade at Shijiazhuang city in China, and the authors examine how the blockade influenced the prices of Chinese cabbages (perishable) and potatoes (storable) within and around the blockade area.Design/methodology/approach>The paper employs the fixed effects model, the panel VAR (PVAR) model, and the spatial dynamic panel (SPD) model to estimate the impacts of the blockade on the food prices. It constructs the unique data set of 3-day average prices of Chinese cabbages and potatoes at main wholesale markets in China during the two urban blockade periods from January 1 to April 8 in 2020 and from January 1 to March 1 in 2021.Findings>The results from the SPD models indicate that the price of Chinese cabbages was more vulnerable and increased by 7.1–9.8% due to the two blockades while the price of potatoes increased by 1.2–6.1%. The blockades also significantly influenced the prices in the areas adjacent to the blockade area. The SPD results demonstrate that the impacts of the blockades would be overestimated if the spatial dependence is not controlled for in the fixed effects model and the PVAR model.Research limitations/implications>Because the research focuses on the cases in China, the results may lack generalizability. Further research for other countries is encouraged.Originality/value>This paper demonstrates the importance of considering food types and spatial dependence in examining the impact of the COVID-19 blockades on food prices.

Assessing the effect of containment measures on the spatio-temporal dynamic of COVID-19 in Italy.

This paper aims at investigating empirically whether and to what extent the containment measures adopted in Italy had an impact in reducing the diffusion of the COVID-19 disease across provinces. For this purpose, we extend the multivariate time-series model for infection counts proposed in Paul and Held (Stat Med 30(10):118-1136, 2011) by augmenting the model specification with B-spline regressors in order to account for complex nonlinear spatio-temporal dynamics in the propagation of the disease. The results of the model estimated on the time series of the number of infections for the Italian provinces show that the containment measures, despite being globally effective in reducing both the spread of contagion and its self-sustaining dynamics, have had nonlinear impacts across provinces. The impact has been relatively stronger in the northern local areas, where the disease occurred earlier and with a greater incidence. This evidence may be explained by the shared popular belief that the contagion was not a close-to-home problem but rather restricted to a few distant northern areas, which, in turn, might have led individuals to adhere less strictly to containment measures and lockdown rules.

A Spatiotemporal Analysis of the COVID-19 Pandemic in North Africa.

Spatial panel-data models are estimated to identify the factors of the prevalence of the coronavirus outbreak in North Africa. Using daily data on the number of cases collected between March 2020 and December 2021, three types of general models are investigated, and they include spatial spillovers between the neighboring countries of the region. In one model the spatial dependence is accounted for by adding a spatial lag of the dependent variable (SAR model). In an alternative specification, spatially correlated error terms are considered in the model (SEM), and in the third model a spatial lag dependent variable and spatially correlated errors are both added (SAC). To deal with unobservable individual heterogeneity, random and fixed individual effects specification are investigated in each of these models. The results of the maximum likelihood and generalized method of moments' estimations show that the lift of travel restrictions had an important impact on the spike in the numbers of COVID-19 cases in North Africa and that the effects of endogenous interactions between the countries are strongly significant. It is found that spatial spillovers and a change in the travel policy are the main factors that can explain the mechanism of spread the coronavirus pandemic in North Africa. However, more data on socio-demographic and behavioral variables and on vaccination rates are needed to better understand what caused the recent surge in the number of infections in the region.

A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy.

The main determinants of COVID-19 spread in Italy are investigated, in this work, by means of a D-vine copula based quantile regression. The outcome is the COVID-19 cumulative infection rate registered on October 30th 2020, with reference to the 107 Italian provinces, and it is regressed on some covariates of interest accounting for medical, environmental and demographic factors. To deal with the issue of spatial autocorrelation, the D-vine copula based quantile regression also embeds a spatial autoregressive component that controls for the extent of spatial dependence. The use of vine copula enhances model flexibility accounting for non-linear relationships and tail dependencies. Moreover, the model selection procedure leads to parsimonious models providing a rank of covariates based on their explanatory power with respect to the outcome.

Spatial Contagion during the First Wave of the COVID-19 Pandemic: Some Lessons from the Case of Madrid, Spain

This paper analyses the magnitude of the spatial transmission of COVID-19 through the municipalities of the region of Madrid during the first pandemic wave using a spatial contagion index. The study also provides additional insights into the main factors contributing to the spread of the virus in both time and space by estimating a novel conditional spatial contagion index. Our results reveal high values of spatial contagion before and during the national lockdown enacted on March 15, 2020, becoming medium/low since then. Furthermore, the study confirms the leading role of inter-municipal mobility and population density in spatial contagion.

A comparison of multiple neighborhood matrix specifications for spatio-temporal model fitting: a case study on COVID-19 data.

Establishing proper neighbor relations between a set of spatial units under analysis is essential when carrying out a spatial or spatio-temporal analysis. However, it is usual that researchers choose some of the most typical (and simple) neighborhood structures, such as the first-order contiguity matrix, without exploring other options. In this paper, we compare the performance of different neighborhood matrices in the context of modeling the weekly relative risk of COVID-19 over small areas located in or near Valencia, Spain. Specifically, we construct contiguity-based, distance-based, covariate-based (considering mobility flows and sociodemographic characteristics), and hybrid neighborhood matrices. We evaluate the goodness of fit, the overall predictive quality, the ability to detect high-risk spatio-temporal units, the capability to capture the spatio-temporal autocorrelation in the data, and the goodness of smoothing for a set of spatio-temporal models based on each of the neighborhood matrices. The results show that contiguity-based matrices, some of the distance-based matrices, and those based on sociodemographic characteristics perform better than the matrices based on k-nearest neighbors and those involving mobility flows. In addition, we test the linear combination of some of the constructed neighborhood matrices and the reweighting of these matrices after eliminating weak neighbor relations, without any model improvement.

Spatial variability of COVID-19 and its risk factors in Nigeria: A spatial regression method.

The novel and unprecedented Coronavirus disease (COVID-19) pandemic has negatively impacted most nations of the world within a short period. While its disproportionate social and spatial variability has been established, the reality in Nigeria is yet to be studied. In this paper, advanced spatial statistical techniques were engaged to study the burden of COVID-19 and its risk factors within the first quarter (March-May) of its incidence in Nigeria. The spatial autocorrelation (Moran's I) test reveals a significant but marginal cluster of COVID-19 occurrence in Nigeria (I = 0.11, p < 0.05). A model comparison between ordinary least square (OLS) and spatial error model (SER) was explored having checked for multicollinearity in the dataset. The OLS model explained about 64% (adjusted R2 = 0.64) of variation in COVID-19 cases, however with significantly clustered residuals. The SER model performed better with randomly distributed residuals. The significant predictors were population density, international airport, and literacy ratio. Furthermore, this study addressed the spatial planning implications of the ongoing disease outbreak while it advocates transdisciplinary approach to urban planning practices in Nigeria.

COVID-19 does not stop at open borders: Spatial contagion among local authority districts during England's first wave.

Infectious diseases generate spatial dependence or contagion not only between individuals but also between geographical units. New infections in one local district do not just depend on properties of the district, but also on the strength of social ties of its population with populations in other districts and their own degree of infectiousness. We show that SARS-CoV-2 infections during the first wave of the pandemic spread across district borders in England as a function of pre-crisis commute to work streams between districts. Crucially, the strength of this spatial contagion depends on the phase of the epidemic. In the first pre-lockdown phase, the spread of the virus across district borders is high. During the lockdown period, the cross-border spread of new infections slows down significantly. Spatial contagion increases again after the lockdown is eased but not statistically significantly so.

Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors.

This paper uses the exploratory spatial data analysis and the geodetector method to analyze the spatial and temporal differentiation characteristics and the influencing factors of the COVID-19 (corona virus disease 2019) epidemic spread in mainland China based on the cumulative confirmed cases, average temperature, and socio-economic data. The results show that: (1) the epidemic spread rapidly from January 24 to February 20, 2020, and the distribution of the epidemic areas tended to be stable over time. The epidemic spread rate in Hubei province, in its surrounding, and in some economically developed cities was higher, while that in western part of China and in remote areas of central and eastern China was lower. (2) The global and local spatial correlation characteristics of the epidemic distribution present a positive correlation. Specifically, the global spatial correlation characteristics experienced a change process from agglomeration to decentralization. The local spatial correlation characteristics were mainly composed of the'high-high' and 'low-low' clustering types, and the situation of the contiguous layout was very significant. (3) The population inflow from Wuhan and the strength of economic connection were the main factors affecting the epidemic spread, together with the population distribution, transport accessibility, average temperature, and medical facilities, which affected the epidemic spread to varying degrees. (4) The detection factors interacted mainly through the mutual enhancement and nonlinear enhancement, and their influence on the epidemic spread rate exceeded that of single factors. Besides, each detection factor has an interval range that is conducive to the epidemic spread.