Coronavirus (COVID-19) Outbreaks, Vaccination, Politics and Society: The Continuing Challenge

This books comprises of 24 chapters by experts from developed and developing countries. The book cover Argentina, Australia, Bangladesh, Brazil, Canada, Fiji, France, India, Indonesia, Italy, Japan, Malaysia, Mexico, Papua New Guinea, South Africa, Taiwan, Thailand, the UK and England, USA, West Africa, and Zambia. © TheEditor(s) (ifapplicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021, 2022.

County-level variation in synthetic opioid and heroin overdose incidents in Pennsylvania during the COVID-19 pandemic

Objective: Evaluate differences in synthetic opioid overdose rates, including semi-synthetic heroin, by individual demographic (N = 14,665) and county-level (N = 67) characteristics in Pennsylvania between 2018 and 2020, and before and after the onset of the COVID-19 pandemic. Method: We used the 2018–2021 Pennsylvania Opioid Overdose Information Network data, which include information on overdose incidents in Pennsylvania that require emergency response. We performed multilevel negative binomial regressions to measure individual and county-level differences in overdose rates, controlling for differences by year. Results: At the state level, there were no significant changes in overdose incident rates by year, but 11 counties experienced significant rate increases 2018-19 to 2020-21. Black individuals demonstrated a nine times higher incident rate than White individuals. Additionally, people living in counties with higher educational attainment, higher poverty, and higher population density had lower overdose incident rates than their counterparts. Conclusions: The synthetic opioid overdose rate has accelerated since the onset of the COVID-19 pandemic, but this trend varies across counties. Particular attention should be paid to the counties with significant increases since the onset of the COVID-19 pandemic in 2020, and more local and regional analyses are required to understand community needs in the face of the ongoing opioid epidemic. © 2023 Elsevier Ltd

Therapeutic landscapes during the COVID-19 pandemic: increased and intensified interactions with nature

The COVID-19 pandemic has had a drastic impact on the course of everyday life for much of the world's population and many people have experienced an unprecedented increase in anxiety and depression while their access to a range of coping mechanisms has been reduced. For those privileged enough to have nearby and safe access to natural environments, green and blue spaces have become an important enabler of everyday wellbeing. In this paper we explore the role of everyday interactions with nature for the maintenance of wellbeing, during the first and second ‘wave' of infections in the Netherlands. Based on qualitative interviews with 30 participants in spring/summer and autumn of 2020, we detail how relationships with nature in the local surroundings and in the home qualitatively and effectively changed in response to COVID-19 induced confinement, resulting in the becoming-therapeutic of everyday micro-geographies. Amongst our participants, the conditions of semi-lockdown gave rise to increased interactions with nature, both in their outdoor surroundings and in the home. These increased interactions also led to intensified emotional and sensory experiences with nature and a greater sense of familiarity with their surroundings, which strengthened place-attachments and contributed to an increased sense of wellbeing.

Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year.

The present work aims to give an overview on the international scientific papers related to the territorial spreading of SARS-CoV-2, with a specific focus upon applied quantitative geography and territorial analysis, to define a general structure for epidemiological geography research. The target publications were based on GIS spatial analysis, both in the sense of topological analysis and descriptive statistics or lato sensu geographical approaches. The first basic purpose was to organize and enhance the vast knowledge developments generated hitherto by the first pandemic that was studied "on-the-fly" all over the world. The consequent target was to investigate to what extent researchers in geography were able to draw scientifically consistent conclusions about the pandemic evolution, as well as whether wider generalizations could be reasonably claimed. This implied an analysis and a comparison of their findings. Finally, we tested what geographic approaches can say about the pandemic and whether a reliable spatial analysis routine for mapping infectious diseases could be extrapolated. We selected papers proposed for publication during 2020 and 209 articles complied with our parameters of query. The articles were divided in seven categories to enhance existing commonalities. In some cases, converging conclusions were extracted, and generalizations were derived. In other cases, contrasting or inconsistent findings were found, and possible explanations were provided. From the results of our survey, we extrapolated a routine for the production of epidemiological geography analyses, we highlighted the different steps of investigation that were attained, and we underlined the most critical nodes of the methodology. Our findings may help to point out what are the most critical conceptual challenges of epidemiological mapping, and where it might improve to engender informed conclusions and aware outcomes.

Exploration of inter-jurisdictional TB programming and mobility in a Canadian First Nation community.

BACKGROUND: Colonially imposed jurisdictional boundaries that have little meaning to Indigenous peoples in Canada may confound tuberculosis (TB) prevention and care activities. This study explores how inter-jurisdictional mobility and the current accommodation of mobility through policies and programming sustain a regional TB epidemic in northwestern Saskatchewan, and northeastern Alberta. METHODS: A qualitative instrumental case study was performed using a community based participatory approach. Semi-structured interviews were conducted with First Nations peoples from a high-incidence community in Canada including community-based healthcare workers. These interview data are presented in the context of a multi-level document analysis of TB program guidelines. RESULTS: The location of the community, and related lack of access to employment, services and care, necessitates mobility across jurisdictional boundaries. There are currently no formal federal or provincial guidelines in place to accommodate highly mobile patients and clients within and across provincial TB prevention and care programs. As a result, locally developed community-based protocols, and related ad-hoc strategies ensure continuity of care. CONCLUSION: Indigenous peoples living in remote communities face unique push/pull factors that motivate mobility. When these motivations exist in communities with increased risk of contagion by communicable infectious diseases such as TB, public health risks extend into increasingly large areas with competing jurisdictional authority. Such mobility poses several threats to TB elimination. We have identified a gap in TB services to systematically accommodate mobility, with specific implications for Indigenous peoples and reconciliation. We recommend clearly defined communication paths and inter-jurisdictional coordination to ensure maintenance of care for mobile populations.

Temporal evolution and spatial pattern of the COVID-19 epidemic in the United States

It is the cornerstone of precise and scientific prevention and control to understand the temporal evolution and spatial pattern of the COVID-19 epidemic. Based on the county-level COVID-19 case of the United States from January 22, 2020 to October 8, 2021, we explored and analyzed the epidemic by using time series analysis, spatial autocorrelation analysis and gravity center trajectory analysis. The results show that: (1) the epidemic in the United States experienced four stages of low incidence, growth, peak and rebound with June 15, September 30 and October 1, 2020 as the cut-off points. (2) The global Moran index experienced a process of 'increase-decrease-increase-stability', with the maximum value exceeding 0.6, indicating that the epidemic has obvious spatial aggregation;the epidemic is dominated by high-high clusters (over 150 counties) and low-low clusters (over 500 counties), presenting a pattern of 'three cores and multiple islands' and 'north-south belt'. (3) In 60% of states, the trajectory of the epidemic center of gravity is near-linear type. The epidemic hotspots in these states were relatively stable over time. In more than half of the states, the curve of the moving distance of the epidemic center of gravity is exponential. These states experienced a very rapid epidemic. This study is expected to provide a reference for evaluating the effectiveness of epidemic prevention measures and determining targeted epidemic prevention measures, as well as accumulate experience for future research on the spread of different infectious diseases in different regions. © 2022 IEEE.

Spatio-Temporal Variations of the COVID-19 Epidemic in Mexico

Mexico is one of the countries worst affected by the Coronavirus Disease 2019 (COVID-19). Analyzing the spatiotemporal spread processes of the COVID-19 epidemic in Mexico is of great significance in terms of preventing its further transmission. This study obtained COVID-19 cases and deaths at the municipality level in Mexico from February 28, 2020, to February 27, 2022, and adopted Hoover index, spatial autocorrelation analysis, and epidemic center calculation to reveal the spatio-temporal pattern of the pandemic nationwide. The results showed that the COVID-19 outbreak in Mexico experienced an initial low-level transmission and four concentrated outbreaks. In terms of spatial transmission pattern, COVID-19 cases showed clear spatial clustering characteristics (Moran's I: 0.48), and large cities with more social interactions (such as Mexico City, Guadalajara, etc.) were most affected. In terms of the directional characteristics of the COVID-19 impact, the epidemiological center constantly shifted in the northeast-southwest direction due to the changing severity of the epidemic in the northwestern coast and the central part of Mexico during the initial outbreak phase. Accordingly, the centers of the three subsequent outbreaks moved to the southeast, northwest, and southeast. The COVID-19 epidemic spread very rapidly in Mexico, especially in the second phase. In the four concentrated outbreaks, the time for the distribution of cases to form a relatively stable spatial pattern was 99 days, 15 days, 95 days, and 42 days, respectively. But the difference of transmission rate at the state level is significant. The state with earlier outbreaks, such as Mexico City, spreads faster. This study revealed the characteristics and laws of the spread of infectious diseases at the national scale, and provided a reference for the prevention and control of the COVID-19 epidemic and future emerging infectious diseases. © 2022 IEEE.

From State capitals to traditional communities. The interiorization stages of Covid-19 in the Brazilian Amazon

Understanding the transmission dynamics of Covid-19 is essential for actions to be taken to contain other outbreaks. In the Amazon region, the interiorization process reflects the territorial occupation pattern. This work aims to investigate the process of interiorization of the epidemic caused by the SARS COV-2 virus in the Amazon region, using epidemiological weeks as the temporal unit. A multiscale approach is adopted, which allows us to indicate four phases of interiorization, from the arrival by air in the capitals of the northern region, to record of cases in traditional communities in the Lower Amazon Health Region. The proposed phases are discussed according to the perception of community leaders regarding the pandemic and health actions carried out by the State. The results show that it is essential to consider the specificities of the Amazon environment when formulating effective health strategies, which did not occur in the case of the referred pandemic.

Bridging research and dissemination in the CoViD-19 era: a WebGIS dashboard for the Autonomous Province of Trento (Italy)

The paper presents an ongoing project devoted to the study, the analysis and the representation of epidemiological data related to CoViD-19 spread in the territory of the Province of Trento (Italy), both for scientific and communication purposes. In this broader context, the construction of a digital cartography tool as a WebGIS to allow local communities understanding of epidemiological spread is presented. Data have been supplied by the local Provincial Health Authority;statistic have been processed in order to develop municipality scale vector polygonal coropleth and point maps in order to show affected, health and death rate distribution. A timeline allows the representation of changes and dynamics from Spring 2020 to the current date. The database provides "on-the-fly" data to the production scripts of maps and time charts. These scripts querying the database produce a geographic file in the geojson standard interchange format. This file is read by the javascript scripts based on the leaflet libraries for the production of the final maps. In a similar process, scripts based on the chart.js library produce the graph of the data temporal variation, automatically reading dates and interval time of analysis. A custom procedure was developed to allow the periodic update of the dataset. New information is added to the database by uploading an external spreadsheet. The study presents the methodology to develop and assess the WebGIS for managing, visualize and analyse Coronavirus diffusion. Future implementation of the WebGIS will expand the used data and allow the comparison with social and environmental factors.

Career aspirations and trajectories of geographies of health and health care graduates: A cross‐sectional study

Indeed, health geography scholars are in high demand for their capacity to think spatially and to acknowledge the broader determinants of health;the emphases on health, well-being, environments, and place mean that health geographers can deviate and pivot their acquired knowledge and skills to various career options and settings. Keywords: health geography;Canadian Association of Geography (CAG);Canada;géographie de la santé;Association canadienne des géographes (ACG) EN health geography Canadian Association of Geography (CAG) Canada FR géographie de la santé Association canadienne des géographes (ACG) 512 523 12 09/12/22 20220901 NES 220901 Introduction As a sub-discipline of human geography, health geography evolved from its initial construct of "medical geography", which focused on ecological perspectives of disease and in doing so, employed a heavily positivist approach. Given the diverse and evolving substantive, methodological, and theoretical approaches within health geography, this study explores the career aspirations and experiences of health geography graduates in relation to employment within this sub-discipline and aims to help inform the future direction of health geography. Other (25%) related fields that were identified include Indigenous health, immigrant health, climate change and health, non-communicable disease, injury, epidemiology, education, and determinants of health. [Extracted from the article] Copyright of Canadian Geographer is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

EXPERIENCES OF PLACE-BOUND SOCIALITY AMIDST THE COVID-19 PANDEMIC: A QUALITATIVE ANALYSIS OF ENVIRONMENT-RELATED COPING STRATEGIES

In the context of the COVID-19 pandemic, places of public encounter were effectively inhibited by lockdown regulations. In addition to several quantitative studies of the impact of the ongoing pandemic on society, little is known about the use of one’s spatial environment on individual coping strategies mitigating physical isolation. Through an explorative qualitative study we derived a typology of coping strategies that helped participants to balance responsible action and the urgent need for social contact.Our approach aligns with well-known theory in the field of place (Cresswell 2020) and place-bound sociality (cf. Schatzki 2002) in the context of phenomenology (Sloan & Bowe 2014, Rehorick 1991, Seamon 1979). Sixteen participants were selected reflecting diverse conceptualisation of community and representing socioeconomic and gender diversity in both urban and rural areas of the German state of Bavaria. Semi-structured interviews were con-ducted in the beginning of the second wave of COVID-19 restrictions from the end of November 2020 to early December, to reflect expectations and early routines associated with the isolation. In addition to social and individual, a variety of environment-related coping strategies can be observed. We (1) interpret those coping strategies, (2) discuss the essential function of places for the coordination and negotiation of social activities, and (3) relate the importance of public spaces to weak social ties (Granovetter1973) emphasising their outstanding value for individual wellbeing. © 2022, Erdkunde. All rights reserved.

Global regularity and local variability of the space-temporal patterns of COVID 19 in Aragón (Spain)

Data from confirmed COVID-19 cases in Aragón (Spain), aggregated in 123 Basic Health Areas over 50 consecutive weeks, were used to identify, measure and characterise the spatio-temporal patterns of the pandemic. This was done using spatial and temporal autocorrelation measures, obtained from the data through the application of spatial statistics procedures (global and local Moran's I). The spatial and temporal incidence of COVID-19 in Aragón was neither homogeneous nor random, showing a certain overall regularity and notable local variability. This model can be explained by a process of spatial diffusion modified by long-distance contagions and restricted by measures implemented to control the pandemic. The information obtained is of great utility for public health decision-making relating to the organisation of healthcare resources and future measures to prevent and control the pandemic. © 2022 Asociacion de Geografos Espanoles. All rights reserved.

Therapeutic landscapes during the COVID-19 pandemic: increased and intensified interactions with nature

The COVID-19 pandemic has had a drastic impact on the course of everyday life for much of the world’s population and many people have experienced an unprecedented increase in anxiety and depression while their access to a range of coping mechanisms has been reduced. For those privileged enough to have nearby and safe access to natural environments, green and blue spaces have become an important enabler of everyday wellbeing. In this paper we explore the role of everyday interactions with nature for the maintenance of wellbeing, during the first and second ‘wave’ of infections in the Netherlands. Based on qualitative interviews with 30 participants in spring/summer and autumn of 2020, we detail how relationships with nature in the local surroundings and in the home qualitatively and effectively changed in response to COVID-19 induced confinement, resulting in the becoming-therapeutic of everyday micro-geographies. Amongst our participants, the conditions of semi-lockdown gave rise to increased interactions with nature, both in their outdoor surroundings and in the home. These increased interactions also led to intensified emotional and sensory experiences with nature and a greater sense of familiarity with their surroundings, which strengthened place-attachments and contributed to an increased sense of wellbeing. [ FROM AUTHOR] Copyright of Social & Cultural Geography is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Geographies of the COVID-19 pandemic in Spain: The return of the rural environment

The COVID-19 pandemic was the worst public, economic and social health crisis in Spain since the Civil War. This virus caused thousands of deaths and hundreds of thousands of infections, with Catalonia and Madrid as the most affected territories. A first exploratory analysis shows, with the scarce reliable data available so far, that this pandemic has had a special impact on urban areas with higher population density and pollution levels, while rural areas, despite having a much higher at-risk population and a much more precarious healthcare system, have proven to be much more resilient to coronavirus expansion. All this opens the door to revaluing the importance of the rural environment as an analytical category and as a space for opportunities and life in the face of present and future pandemics and not just problems and crises. © 2022, Universitat Autonoma de Barcelona. All rights reserved.

The conspiracy of Covid-19 and 5G: Spatial analysis fallacies in the age of data democratization.

In a context of mistrust in public health institutions and practices, anti-COVID/vaccination protests and the storming of Congress have illustrated that conspiracy theories are real and immanent threat to health and wellbeing, democracy, and public understanding of science. One manifestation of this is the suggested correlation of COVID-19 with 5G mobile technology. Throughout 2020, this alleged correlation was promoted and distributed widely on social media, often in the form of maps overlaying the distribution of COVID-19 cases with the instillation of 5G towers. These conspiracy theories are not fringe phenomena, and they form part of a growing repertoire for conspiracist activist groups with capacities for organised violence. In this paper, we outline how spatial data have been co-opted, and spatial correlations asserted by conspiracy theorists. We consider the basis of their claims of causal association with reference to three key areas of geographical explanation: (1) how social properties are constituted and how they exert complex causal forces, (2) the pitfalls of correlation with spatial and ecological data, and (3) the challenges of specifying and interpreting causal effects with spatial data. For each, we consider the unique theoretical and technical challenges involved in specifying meaningful correlation, and how their discarding facilitates conspiracist attribution. In doing so, we offer a basis both to interrogate conspiracists' uses and interpretation of data from elementary principles and offer some cautionary notes on the potential for their future misuse in an age of data democratization. Finally, this paper contributes to work on the basis of conspiracy theories in general, by asserting how - absent an appreciation of these key methodological principles - spatial health data may be especially prone to co-option by conspiracist groups.

EPIsembleVis: A geo-visual analysis and comparison of the prediction ensembles of multiple COVID-19 models.

We present EPIsembleVis, a web-based comparative visual analysis tool for evaluating the consistency of multiple COVID-19 prediction models. Our approach analyzes a collection of COVID-19 predictions from different epidemiological models as an ensemble and utilizes two metrics to quantify model performance. These metrics include (a) prediction uncertainty (represented as the dispersion of predictions in each ensemble) and (b) prediction error (calculated by comparing individual model predictions with the recorded data). Through an interactive visual interface, our approach provides a data-driven workflow for (a) selecting and constructing the COVID-19 model prediction ensemble based on the spatiotemporal overlap of available predictions of multiple epidemiological models, (b) quantifying the model performance using both the uncertainty of each model prediction ensemble, and the error of each ensemble member that represents individual model predictions, and (c) visualizing the spatiotemporal variability in the projection performance of individual models using a suite of novel ensemble visualization techniques, such as the data availability map, a spatiotemporal textured-tile calendar, multivariate rose chart, and time-series leaflet glyph. We demonstrate the capability of our ensemble visual interface through a case study that investigates the performance of weekly COVID-19 predictions, which are provided through the COVID-19 Forecast Hub UMass-Amherst Influenza Forecasting Center of Excellence [47] for the United States and United States Territories. The EPIsembleVis tool is implemented using open-source web technologies and adaptive system design, rendering it interoperable with Elasticsearch and Kibana for automatically ingesting COVID-19 predictions from online repositories, and it is generalizable for analyzing worldwide projections from more epidemiological models.

COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms.

The reduction of population concentration in some urban land uses is one way to prevent and reduce the spread of COVID-19 disease. Therefore, the objective of this study is to prepare the risk mapping of COVID-19 in Tehran, Iran, using machine learning algorithms according to socio-economic criteria of land use. Initially, a spatial database was created using 2282 locations of patients with COVID-19 from 2 February 2020 to 21 March 2020 and eight socio-economic land uses affecting the disease-public transport stations, supermarkets, banks, automated teller machines (ATMs), bakeries, pharmacies, fuel stations, and hospitals. The modeling was performed using three machine learning algorithms that included random forest (RF), adaptive neuro-fuzzy inference system (ANFIS), and logistic regression (LR). Feature selection was performed using the OneR method, and the correlation between land uses was obtained using the Pearson coefficient. We deployed 70% and 30% of COVID-19 patient locations for modeling and validation, respectively. The results of the receiver operating characteristic (ROC) curve and the area under the curve (AUC) showed that the RF algorithm, which had a value of 0.803, had the highest modeling accuracy, which was followed by the ANFIS algorithm with a value of 0.758 and the LR algorithm with a value of 0.747. The results showed that the central and the eastern regions of Tehran are more at risk. Public transportation stations and pharmacies were the most correlated with the location of COVID-19 patients in Tehran, according to the results of the OneR technique, RF, and LR algorithms. The results of the Pearson correlation showed that pharmacies and banks are the most incompatible in distribution, and the density of these land uses in Tehran has caused the prevalence of COVID-19.

Multiplicity Eludes Peer Review: The Case of COVID-19 Research.

Multiplicity arises when data analysis involves multiple simultaneous inferences, increasing the chance of spurious findings. It is a widespread problem frequently ignored by researchers. In this paper, we perform an exploratory analysis of the Web of Science database for COVID-19 observational studies. We examined 100 top-cited COVID-19 peer-reviewed articles based on p-values, including up to 7100 simultaneous tests, with 50% including >34 tests, and 20% > 100 tests. We found that the larger the number of tests performed, the larger the number of significant results (r = 0.87, p < 10-6). The number of p-values in the abstracts was not related to the number of p-values in the papers. However, the highly significant results (p < 0.001) in the abstracts were strongly correlated (r = 0.61, p < 10-6) with the number of p < 0.001 significances in the papers. Furthermore, the abstracts included a higher proportion of significant results (0.91 vs. 0.50), and 80% reported only significant results. Only one reviewed paper addressed multiplicity-induced type I error inflation, pointing to potentially spurious results bypassing the peer-review process. We conclude the need to pay special attention to the increased chance of false discoveries in observational studies, including non-replicated striking discoveries with a potentially large social impact. We propose some easy-to-implement measures to assess and limit the effects of multiplicity.

Increased risk of COVID-19 from walking dogs? Most likely, a spurious finding.

We analyse the paper "The spread of SARS-CoV-2 in Spain: Hygiene habits, sociodemographic profile, mobility patterns and comorbidities" authored by Rodríguez-Barranco et al. (2021), published in Environmental Research, vol.192, January 2021. The study was carried out under challenging conditions and provides original data of great value for exploratory purposes. Nevertheless, we found that the authors have not considered the potential effect of the multiple hypothesis testing carried out until obtaining the final model on the increased occurrence of false discoveries by mere chance. After adjusting the results provided in the paper for the effects of multiple testing, we conclude that only one of the five factors cited as statistically significant and relevant in the article, living with someone who has suffered from COVID-19, remained significantly related to the relative prevalence of COVID-19. Therefore, the preeminent role given in the analysed work to walking the dog as one of the main transmission routes of COVID-19 probably does not correspond to an actual effect. Instead, until replicated by other studies, it should be considered a spurious discovery.

Did Covid-19 hit harder in peripheral areas? The case of Italian municipalities.

The first wave of Covid-19 pandemic had a geographically heterogeneous impact even within the most severely hit regions. Exploiting a triple-differences methodology, we find that in Italy Covid-19 hit relatively harder in peripheral areas: the excess mortality in peripheral areas was almost double that of central ones in March 2020 (1.2 additional deaths every 1000 inhabitants). We leverage a rich dataset on Italian municipalities to explore mechanisms behind this gradient. We first show that socio-demographic and economic features at municipal level are highly collinear, making it hard to identify single-variable causal relationships. Using Principal Components Analysis we model excess mortality and show that areas with higher excess mortality have lower income, lower education, larger households, lower trade and higher industrial employments, and older population. Our findings highlight a strong centre-periphery gradient in the harshness of Covid-19, which we believe is also highly relevant from a policy-making standpoint.