A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020

An experienced racial-ethnic diversity dataset in the United States using human mobility data

Despite the importance of measuring racial-ethnic segregation and diversity in the United States, current measurements are largely based on the Census and, thus, only reflect segregation and diversity as understood through residential location. This leaves out the social contexts experienced throughout the course of the day during work, leisure, errands, and other activities. The National Experienced Racial-ethnic Diversity (NERD) dataset provides estimates of diversity for the entire United States at the census tract level based on the range of place and times when people have the opportunity to come into contact with one another. Using anonymized and opted-in mobile phone location data to determine co-locations of people and their demographic backgrounds, these measurements of diversity in potential social interactions are estimated at 38.2 m × 19.1 m scale and 15-minute timeframe for a representative year and aggregated to the Census tract level for purposes of data privacy. As well, we detail some of the characteristics and limitations of the data for potential use in national, comparative studies.

Spatiotemporal hierarchical Bayesian analysis to identify factors associated with COVID-19 in suburban areas in Colombia

Introduction

The pandemic had a profound impact on the provision of health services in Cúcuta, Colombia where the neighbourhood-level risk of Covid-19 has not been investigated. Identifying the sociodemographic and environmental risk factors of Covid-19 in large cities is key to better estimate its morbidity risk and support health strategies targeting specific suburban areas. This study aims to identify the risk factors associated with the risk of Covid-19 in Cúcuta considering inter -spatial and temporal variations of the disease in the city's neighbourhoods between 2020 and 2022.

Methods

Age-adjusted rate of Covid-19 were calculated in each Cúcuta neighbourhood and each quarter between 2020 and 2022. A hierarchical spatial Bayesian model was used to estimate the risk of Covid-19 adjusting for socioenvironmental factors per neighbourhood across the study period. Two spatiotemporal specifications were compared (a nonparametric temporal trend; with and without space-time interaction). The posterior mean of the spatial and spatiotemporal effects was used to map the Covid-19 risk.

Results

There were 65,949 Covid-19 cases in the study period with a varying standardized Covid-19 rate that peaked in October-December 2020 and April-June 2021. Both models identified an association of the poverty and stringency indexes, education level and PM10 with Covid-19 although the best fit model with a space-time interaction estimated a strong association with the number of high-traffic roads only. The highest risk of Covid-19 was found in neighbourhoods in west, central, and east Cúcuta.

Conclusions

The number of high-traffic roads is the most important risk factor of Covid-19 infection in Cucuta. This indicator of mobility and connectivity overrules other socioenvironmental factors when Bayesian models include a space-time interaction. Bayesian spatial models are important tools to identify significant determinants of Covid-19 and identifying at-risk neighbourhoods in large cities. Further research is needed to establish causal links between these factors and Covid-19.

Conditioning factors in the spreading of Covid-19 - Does geography matter?

There is evidence in literature that the spread of COVID-19 can be influenced by various geographic factors, including territorial features, climate, population density, socioeconomic conditions, and mobility. The objective of the paper is to provide an updated literature review on geographical studies analysing the factors which influenced COVID-19 spreading. This literature review took into account not only the geographical aspects but also the COVID-19-related outcomes (infections and deaths) allowing to discern the potential influencing role of the geographic factors per type of outcome. A total of 112 scientific articles were selected, reviewed and categorized according to subject area, aim, country/region of study, considered geographic and COVID-19 variables, spatial and temporal units of analysis, methodologies, and main findings. Our literature review showed that territorial features may have played a role in determining the uneven geography of COVID-19; for instance, a certain agreement was found regarding the direct relationship between urbanization degree and COVID-19 infections. For what concerns climatic factors, temperature was the variable that correlated the best with COVID-19 infections. Together with climatic factors, socio-demographic ones were extensively taken into account. Most of the analysed studies agreed that population density and human mobility had a significant and direct relationship with COVID-19 infections and deaths. The analysis of the different approaches used to investigate the role of geographic factors in the spreading of the COVID-19 pandemic revealed that the significance/representativeness of the outputs is influenced by the scale considered due to the great spatial variability of geographic aspects. In fact, a more robust and significant association between geographic factors and COVID-19 was found by studies conducted at subnational or local scale rather than at country scale.

Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022

During the SARS-CoV-2 pandemic, governments and public health authorities collected massive amounts of data on daily confirmed positive cases and incidence rates. These data sets provide relevant information to develop a scientific understanding of the pandemic's spatiotemporal dynamics. At the same time, there is a lack of comprehensive approaches to describe and classify patterns underlying the dynamics of COVID-19 incidence across regions over time. This seriously constrains the potential benefits for public health authorities to understand spatiotemporal patterns of disease incidence that would allow for better risk communication strategies and improved assessment of mitigation policies efficacy. Within this context, we propose an exploratory statistical tool that combines functional data analysis with unsupervised learning algorithms to extract meaningful information about the main spatiotemporal patterns underlying COVID-19 incidence on mainland Portugal. We focus on the timeframe spanning from August 2020 to March 2022, considering data at the municipality level. First, we describe the temporal evolution of confirmed daily COVID-19 cases by municipality as a function of time, and outline the main temporal patterns of variability using a functional principal component analysis. Then, municipalities are classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. Our findings reveal disparities in disease dynamics between northern and coastal municipalities versus those in the southern and hinterland. We also distinguish effects occurring during the 2020-2021 period from those in the 2021-2022 autumn-winter seasons. The results provide proof-of-concept that the proposed approach can be used to detect the main spatiotemporal patterns of disease incidence. The novel approach expands and enhances existing exploratory tools for spatiotemporal analysis of public health data.

Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium

Introduction

COVID-19 remains a major concern globally. Therefore, it is important to evaluate COVID-19's rapidly changing trends. The fractal dimension has been proposed as a viable method to characterize COVID-19 curves since epidemic data is often subject to considerable heterogeneity. In this study, we aim to investigate the association between various socio-demographic factors and the complexity of the COVID-19 curve as quantified through its fractal dimension.

Methods

We collected population indicators data (ethnic composition, socioeconomic status, number of inhabitants, population density, the older adult population proportion, vaccination rate, satisfaction, and trust in the government) at the level of the statistical sector in Belgium. We compared these data with fractal dimension indicators of COVID-19 incidence between 1 January - 31 December 2021 using canonical correlation analysis.

Results

Our results showed that these population indicators have a significant association with COVID-19 incidences, with the highest explanatory and predictive power coming from the number of inhabitants, population density, and ethnic composition.

Conclusion

It is important to monitor these population indicators during a pandemic, especially when dealing with targeted interventions for a specific population.

Impacts of COVID-19 pandemic on environment, society, and food security

Coronavirus disease (COVID)-19 is a viral and transferable disease caused by severe respiratory syndrome-coronavirus-2. It can spread through breathing droplets in human beings. It caused 5.32 million deaths around the world at the end of 2021. COVID-19 has caused several positive impacts as well, such as a reduction in air, water, and noise pollution. However, its negative impacts are by far critical such as increased death rate, increased release of microcontaminants (pesticides, biocides, pharmaceuticals, surfactants, polycyclic aromatic hydrocarbons (PAHs), flame retardants, and heavy metals), increased biomedical waste generation due to excessive use of safety equipment and its disposal, and municipal solid waste generation. Environmental pollution was significantly reduced due to lockdown during the COVID-19 period. Therefore, the quality of air and water improved. COVID-19 affected all sections of the population, particularly the most vulnerable members of society, and thus pushed more people into poverty. At the world level, it increased risks to food safety by increasing prices and lowering revenues, forcing households to reduce their food consumption in terms of quantity and quality. COVID-19 also upset various exercises e.g., horticulture, fisheries, domesticated animals, and agribusiness hence prohibiting the development of merchandise for poor-country ranchers. Most of the patients can self-recover from COVID-19 if they do not have any other diseases like high blood pressure, diabetes, and heart problems. Predictably, the appropriate execution of the proposed approaches (vaccination, wearing face masks, social distancing, sustainable industrialization) is helpful for worldwide environmental sustainability.

Housing situations and local COVID-19 infection dynamics using small-area data

Low socio-economic status is associated with higher SARS-CoV-2 incidences. In this paper we study whether this is a result of differences in (1) the frequency, (2) intensity, and/or (3) duration of local SARS-CoV-2 outbreaks depending on the local housing situations. So far, there is not clear evidence which of the three factors dominates. Using small-scale data from neighborhoods in the German city Essen and a flexible estimation approach which does not require prior knowledge about specific transmission characteristics of SARS-CoV-2, behavioral responses or other potential model parameters, we find evidence for the last of the three hypotheses. Outbreaks do not happen more often in less well-off areas or are more severe (in terms of the number of cases), but they last longer. This indicates that the socio-economic gradient in infection levels is at least in parts a result of a more sustained spread of infections in neighborhoods with worse housing conditions after local outbreaks and suggests that in case of an epidemic allocating scarce resources in containment measures to areas with poor housing conditions might have the greatest benefit.

Exploration of spatiotemporal heterogeneity and socio-demographic determinants on COVID-19 incidence rates in Sarawak, Malaysia

BACKGROUND

In Sarawak, 252 300 coronavirus disease 2019 (COVID-19) cases have been recorded with 1 619 fatalities in 2021, compared to only 1 117 cases in 2020. Since Sarawak is geographically separated from Peninsular Malaysia and half of its population resides in rural districts where medical resources are limited, the analysis of spatiotemporal heterogeneity of disease incidence rates and their relationship with socio-demographic factors are crucial in understanding the spread of the disease in Sarawak.

METHODS

The spatial dependence of district-wise incidence rates is investigated using spatial autocorrelation analysis with two orders of contiguity weights for various pandemic waves. Nine determinants are chosen from 14 covariates of socio-demographic factors via elastic net regression and recursive partitioning. The relationships between incidence rates and socio-demographic factors are examined using ordinary least squares, spatial lag and spatial error models, and geographically weighted regression.

RESULTS

In the first 8 months of 2021, COVID-19 severely affected Sarawak's central region, which was followed by the southern region in the next 2 months. In the third wave, based on second-order spatial weights, the incidence rate in a district is most strongly influenced by its neighboring districts' rate, although the variance of incidence rates is best explained by local regression coefficient estimates of socio-demographic factors in the first wave. It is discovered that the percentage of households with garbage collection facilities, population density and the proportion of male in the population are positively associated with the increase in COVID-19 incidence rates.

CONCLUSION

This research provides useful insights for the State Government and public health authorities to critically incorporate socio-demographic characteristics of local communities into evidence-based decision-making for altering disease monitoring and response plans. Policymakers can make well-informed judgments and implement targeted interventions by having an in-depth understanding of the spatial patterns and relationships between COVID-19 incidence rates and socio-demographic characteristics. This will effectively help in mitigating the spread of the disease.

The Role of Functional Urban Areas in the Spread of COVID-19 Omicron (Northern Spain)

This study focuses on the space-time patterns of the COVID-19 Omicron wave at a regional scale, using municipal data. We analyze the Basque Country and Cantabria, two adjacent regions in the north of Spain, which between them numbered 491,816 confirmed cases in their 358 municipalities from 15th November 2021 to 31st March 2022. The study seeks to determine the role of functional urban areas (FUAs) in the spread of the Omicron variant of the virus, using ESRI Technology (ArcGIS Pro) and applying intelligence location methods such as 3D-bins and emerging hot spots. Those methods help identify trends and types of problem area, such as hot spots, at municipal level. The results demonstrate that FUAs do not contain an over-concentration of COVID-19 cases, as their location coefficient is under 1.0 in relation to population. Nevertheless, FUAs do have an important role as drivers of spread in the upward curve of the Omicron wave. Significant hot spot patterns are found in 85.0% of FUA area, where 98.9% of FUA cases occur. The distribution of cases shows a spatially stationary linear correlation linked to demographically progressive areas (densely populated, young profile, and with more children per woman) which are well connected by highways and railroads. Based on this research, the proposed GIS methodology can be adapted to other case studies. Considering geo-prevention and WHO Health in All Policies approaches, the research findings reveal spatial patterns that can help policymakers in tackling the pandemic in future waves as society learns to live with the virus.

Space-time cluster detection techniques for infectious diseases: A systematic review

BACKGROUND

Public health organizations have increasingly harnessed geospatial technologies for disease surveillance, health services allocation, and targeting place-based health promotion initiatives.

METHODS

We conducted a systematic review around the theme of space-time clustering detection techniques for infectious diseases using PubMed, Web of Science, and Scopus. Two reviewers independently determined inclusion and exclusion.

RESULTS

Of 2,887 articles identified, 354 studies met inclusion criteria, the majority of which were application papers. Studies of airborne diseases were dominant, followed by vector-borne diseases. Most research used aggregated data instead of point data, and a significant proportion of articles used a repetition of a spatial clustering method, instead of using a "true" space-time detection approach, potentially leading to the detection of false positives. Noticeably, most articles did not make their data available, limiting replicability.

CONCLUSION

This review underlines recent trends in the application of space-time clustering methods to the field of infectious disease, with a rapid increase during the COVID-19 pandemic.

Air quality in the New Delhi metropolis under COVID-19 lockdown

Air pollution has been on continuous rise with increase in industrialization in metropolitan cities of the world. Several measures including strict climate laws and reduction in the number of vehicles were implemented by several nations. The COVID-19 pandemic provided a great opportunity to understand the daily human activities effect on air pollution. Majority nations restricted industrial activities and vehicular traffic to a large extent as a measure to restrict COVID-19 spread. In this paper, we analyzed the impact of such COVID19-induced lockdown on the air quality of the city of New Delhi, India. We analyzed the average concentration of common gaseous pollutants viz. sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO). These concentrations were obtained from the tropospheric column of Sentinel-5P (an earth observation satellite of European Space Agency) data. We observed that the city observed a significant drop in the level of atmospheric pollutant’s concentration for all the major pollutants as a result of strict lockdown measure. Such findings are also validated with pollutant data obtained from ground based monitoring stations. We observed that near-surface pollutant concentration dropped significantly by 50% for PM_2.5, 71.9% for NO2, and 88% for CO, after the lockdown period. Such studies would pave the path for implementing future air pollution control measures by environmentalists.

Inequalities in the evolution of the COVID-19 pandemic: an ecological study of inequalities in mortality in the first wave and the effects of the first national lockdown in England

OBJECTIVES

To examine how ecological inequalities in COVID-19 mortality rates evolved in England, and whether the first national lockdown impacted them. This analysis aimed to provide evidence for important lessons to inform public health planning to reduce inequalities in any future pandemics.

DESIGN

Longitudinal ecological study.

SETTING

307 lower-tier local authorities in England.

PRIMARY OUTCOME MEASURE

Age-standardised COVID-19 mortality rates by local authority, regressed on Index of Multiple Deprivation (IMD) and relevant epidemic dynamics.

RESULTS

Local authorities that started recording COVID-19 deaths earlier were more deprived, and more deprived authorities saw faster increases in their death rates. By 6 April 2020 (week 15, the earliest time that the 23 March lockdown could have begun affecting death rates) the cumulative death rate in local authorities in the two most deprived deciles of IMD was 54% higher than the rate in the two least deprived deciles. By 4 July 2020 (week 27), this gap had narrowed to 29%. Thus, inequalities in mortality rates by decile of deprivation persisted throughout the first wave, but reduced during the lockdown.

CONCLUSIONS

This study found significant differences in the dynamics of COVID-19 mortality at the local authority level, resulting in inequalities in cumulative mortality rates during the first wave of the pandemic. The first lockdown in England was fairly strict-and the study found that it particularly benefited those living in more deprived local authorities. Care should be taken to implement lockdowns early enough, in the right places-and at a sufficiently strict level-to maximally benefit all communities, and reduce inequalities.

Tempo-Spatial Modelling of the Spread of COVID-19 in Urban Spaces

The relationship between the social structure of urban spaces and the evolution of the COVID-19 pandemic is becoming increasingly evident. Analyzing the socio-spatial structure in relation to cases may be one of the keys to explaining the ways in which this contagious disease and its variants spread. The aim of this study is to propose a set of variables selected from the social context and the spatial structure and to evaluate the temporal spread of infections and their different degrees of intensity according to social areas. We define a model to represent the relationship between the socio-spatial structure of the urban space and the spatial distribution of pandemic cases. We draw on the theory of social area analysis and apply multivariate analysis techniques to check the results in the urban space of the city of Malaga (Spain). The proposed model should be considered capable of explaining the functioning of the relationships between societal structure, socio-spatial segregation, and the spread of the pandemic. In this paper, the study of the origins and consequences of COVID-19 from different scientific perspectives is considered a necessary approach to understanding this phenomenon. The personal and social consequences of the pandemic have been exceptional and have changed many aspects of social life in urban spaces, where it has also had a greater impact. We propose a geostatistical analysis model that can explain the functioning of the relationships between societal structure, socio-spatial segregation, and the temporal evolution of the pandemic. Rather than an aprioristic theory, this paper is a study by the authors to interpret the disparity in the spread of the pandemic as shown by the infection data.

Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning

The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Services (EMS) data, for the anticipated identification of outbreaks with very high granularity, up to single municipalities. The model, implemented for the region of Lombardy, Italy, showed robust performance, with an overall 80% accuracy in identifying the active spread of the disease. The further post-processing of the output was implemented to classify the territory into five risk classes, resulting in effectively anticipating the demand for interventions by EMS. This model shows state-of-art potentiality for future applications in the early detection of the burden of the impact of COVID-19, or other similar epidemics, on the healthcare system.

Spatial Syndromic Surveillance and COVID-19 in the U.S.: Local Cluster Mapping for Pandemic Preparedness

Maps have become the de facto primary mode of visualizing the COVID-19 pandemic, from identifying local disease and vaccination patterns to understanding global trends. In addition to their widespread utilization for public communication, there have been a variety of advances in spatial methods created for localized operational needs. While broader dissemination of this more granular work is not commonplace due to the protections under Health Insurance Portability and Accountability Act (HIPAA), its role has been foundational to pandemic response for health systems, hospitals, and government agencies. In contrast to the retrospective views provided by the aggregated geographies found in the public domain, or those often utilized for academic research, operational response requires near real-time mapping based on continuously flowing address level data. This paper describes the opportunities and challenges presented in emergent disease mapping using dynamic patient data in the response to COVID-19 for northeast Ohio for the period 2020 to 2022. More specifically it shows how a new clustering tool developed by geographers in the initial phases of the pandemic to handle operational mapping continues to evolve with shifting pandemic needs, including new variant surges, vaccine targeting, and most recently, testing data shortfalls. This paper also demonstrates how the geographic approach applied provides the framework needed for future pandemic preparedness.

Are spatial patterns of Covid-19 changing? Spatiotemporal analysis over four waves in the region of Cantabria, Spain

This research approaches the empirical study of the pandemic from a social science perspective. The main goal is to reveal spatiotemporal changes in Covid-19, at regional scale, using GIS technologies and the emerging three-dimensional bins method. We analyze a case study of the region of Cantabria (northern Spain) based on 29,288 geocoded positive Covid-19 cases in the four waves from the outset in March 2020 to June 2021. Our results suggest three main spatial processes: a reversal in the spatial trend, spreading first followed by contraction in the third and fourth waves; then the reduction of hot spots that represent problematic areas because of high presence of cases and growing trends; and finally, an increase in cold spots. All this generates relevant knowledge to help policy-makers from regional governments to design efficient containment and mitigation strategies. Our research is conducted from a geoprevention perspective, based on the application of targeted measures depending on spatial patterns of Covid-19 in real time. It represents an opportunity to reduce the socioeconomic impact of global containment measures in pandemic management.

The Spatial Association of Demographic and Population Health Characteristics with COVID-19 Prevalence Across Districts in India

In less-developed countries, the lack of granular data limits the researcher's ability to study the spatial interaction of different factors on the COVID-19 pandemic. This study designs a novel database to examine the spatial effects of demographic and population health factors on COVID-19 prevalence across 640 districts in India. The goal is to provide a robust understanding of how spatial associations and the interconnections between places influence disease spread. In addition to the linear Ordinary Least Square regression model, three spatial regression models-Spatial Lag Model, Spatial Error Model, and Geographically Weighted Regression are employed to study and compare the variables explanatory power in shaping geographic variations in the COVID-19 prevalence. We found that the local GWR model is more robust and effective at predicting spatial relationships. The findings indicate that among the demographic factors, a high share of the population living in slums is positively associated with a higher incidence of COVID-19 across districts. The spatial variations in COVID-19 deaths were explained by obesity and high blood sugar, indicating a strong association between pre-existing health conditions and COVID-19 fatalities. The study brings forth the critical factors that expose the poor and vulnerable populations to severe public health risks and highlight the application of geographical analysis vis-a-vis spatial regression models to help explain those associations.

OpenStreetMap Contribution to Local Data Ecosystems in COVID-19 Times: Experiences and Reflections from the Italian Case

Data and digital technologies have been at the core of the societal response to COVID-19 since the beginning of the pandemic. This work focuses on the specific contribution of the OpenStreetMap (OSM) project to address the early stage of the COVID-19 crisis (approximately from February to May 2020) in Italy. Several activities initiated by the Italian OSM community are described, including: mapping ‘red zones’ (the first municipalities affected by the emergency); updating OSM pharmacies based on the authoritative dataset from the Ministry of Health; adding information on delivery services of commercial activities during COVID-19 times; publishing web maps to offer COVID-19-specific information at the local level; and developing software tools to help collect new data. Those initiatives are analysed from a data ecosystem perspective, identifying the actors, data and data flows involved, and reflecting on the enablers and barriers for their success from a technical, organisational and legal point of view. The OSM project itself is then assessed in the wider European policy context, in particular against the objectives of the recent European strategy for data, highlighting opportunities and challenges for scaling successful approaches such as those to fight COVID-19 from the local to the national and European scales.

A geographical information system model to define COVID-19 problem areas with an analysis in the socio-economic context at the regional scale in the North of Spain

The work presented concerns the spatial behaviour of coronavirus disease 2019 (COVID-19) at the regional scale and the socio-economic context of problem areas over the 2020-2021 period. We propose a replicable geographical information systems (GIS) methodology based on geocodification and analysis of COVID-19 microdata registered by health authorities of the Government of Cantabria, Spain from the beginning of the pandemic register (29th February 2020) to 2nd December 2021. The spatial behaviour of the virus was studied using ArcGIS Pro and a 1x1 km vector grid as the homogeneous reference layer. The GIS analysis of 45,392 geocoded cases revealed a clear process of spatial contraction of the virus after the spread in 2020 with 432 km2 of problem areas reduced to 126.72 km2 in 2021. The socio-economic framework showed complex relationships between COVID-19 cases and the explanatory variables related to household characteristics, socio-economic conditions and demographic structure. Local bivariate analysis showed fuzzier results in persistent hotspots in urban and peri-urban areas. Questions about ‘where, when and how’ contribute to learning from experience as we must draw inspiration from, and explore connections to, those confronting the issues related to the current pandemic.

COVID-19 Variant Surveillance and Social Determinants in Central Massachusetts: Development Study (Preprint)

Background

Public health scientists have used spatial tools such as web-based Geographical Information System (GIS) applications to monitor and forecast the progression of the COVID-19 pandemic and track the impact of their interventions. The ability to track SARS-CoV-2 variants and incorporate the social determinants of health with street-level granularity can facilitate the identification of local outbreaks, highlight variant-specific geospatial epidemiology, and inform effective interventions. We developed a novel dashboard, the University of Massachusetts’ Graphical user interface for Geographic Information (MAGGI) variant tracking system that combines GIS, health-associated sociodemographic data, and viral genomic data to visualize the spatiotemporal incidence of SARS-CoV-2 variants with street-level resolution while safeguarding protected health information. The specificity and richness of the dashboard enhance the local understanding of variant introductions and transmissions so that appropriate public health strategies can be devised and evaluated.

Objective

We developed a web-based dashboard that simultaneously visualizes the geographic distribution of SARS-CoV-2 variants in Central Massachusetts, the social determinants of health, and vaccination data to support public health efforts to locally mitigate the impact of the COVID-19 pandemic.

Methods

MAGGI uses a server-client model–based system, enabling users to access data and visualizations via an encrypted web browser, thus securing patient health information. We integrated data from electronic medical records, SARS-CoV-2 genomic analysis, and public health resources. We developed the following functionalities into MAGGI: spatial and temporal selection capability by zip codes of interest, the detection of variant clusters, and a tool to display variant distribution by the social determinants of health. MAGGI was built on the Environmental Systems Research Institute ecosystem and is readily adaptable to monitor other infectious diseases and their variants in real-time.

Results

We created a geo-referenced database and added sociodemographic and viral genomic data to the ArcGIS dashboard that interactively displays Central Massachusetts’ spatiotemporal variants distribution. Genomic epidemiologists and public health officials use MAGGI to show the occurrence of SARS-CoV-2 genomic variants at high geographic resolution and refine the display by selecting a combination of data features such as variant subtype, subject zip codes, or date of COVID-19–positive sample collection. Furthermore, they use it to scale time and space to visualize association patterns between socioeconomics, social vulnerability based on the Centers for Disease Control and Prevention’s social vulnerability index, and vaccination rates. We launched the system at the University of Massachusetts Chan Medical School to support internal research projects starting in March 2021.

Conclusions

We developed a COVID-19 variant surveillance dashboard to advance our geospatial technologies to study SARS-CoV-2 variants transmission dynamics. This real-time, GIS-based tool exemplifies how spatial informatics can support public health officials, genomics epidemiologists, infectious disease specialists, and other researchers to track and study the spread patterns of SARS-CoV-2 variants in our communities.

Prospective spatial-temporal clusters of COVID-19 in local communities: case study of Kansas City, Missouri, United States

Kansas City, Missouri, became one of the major United States hotspots for COVID-19 due to an increase in the rate of positive COVID-19 test results. Despite the large numbers of positive cases in Kansas City, MO, the spatial-temporal analysis of data has been less investigated. However, it is critical to detect emerging clusters of COVID-19 and enforce control and preventive policies within those clusters. We conducted a prospective Poisson spatial-temporal analysis of Kansas City, MO data to detect significant space-time clusters of COVID-19 positive cases at the zip code level in Kansas City, MO. The analysis focused on daily infected cases in four equal periods of 3 months. We detected temporal patterns of emerging and re-emerging space-time clusters between March 2020 and February 2021. Three statistically significant clusters emerged in the first period, mainly concentrated in downtown. It increased to seven clusters in the second period, spreading across a broader region in downtown and north of Kansas City. In the third period, nine clusters covered large areas of north and downtown Kansas City, MO. Ten clusters were present in the last period, further extending the infection along the State Line Road. The statistical results were communicated with local health officials and provided the necessary guidance for decision-making and allocating resources (e.g., vaccines and testing sites). As more data become available, statistical clustering can be used as a COVID-19 surveillance tool to measure the effects of vaccination.

Geographical network model for COVID-19 spread among dynamic epidemic regions

Pandemic due to SARS-CoV-2 (COVID-19) has affected to world in several aspects: high number of confirmed cases, high number of deaths, low economic growth, among others. Understanding of spatio-temporal dynamics of the virus is helpful and necessary for decision making, for instance to decide where, whether and how, non-pharmaceutical intervention policies are to be applied. This point has not been properly addressed in literature since typical strategies do not consider marked differences on the epidemic spread across country or large territory. Those strategies assume similarities and apply similar interventions instead. This work is focused on posing a methodology where spatio-temporal epidemic dynamics is captured by means of dividing a territory in time-varying epidemic regions, according to geographical closeness and infection level. In addition, a novel Lagrangian-SEIR-based model is posed for describing the dynamic within and between those regions. The capabilities of this methodology for identifying local outbreaks and reproducing the epidemic curve are discussed for the case of COVID-19 epidemic in Jalisco state (Mexico). The contagions from July 31, 2020 to March 31, 2021 are analyzed, with monthly adjustments, and the estimates obtained at the level of the epidemic regions present satisfactory results since Relative Root Mean Squared Error RRMSE is below 15% in most of regions, and at the level of the whole state outstanding with RRMSE below 5%.

The spatial dissemination of COVID-19 and associated socio-economic consequences

The ongoing coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc worldwide with millions of lives claimed, human travel restricted and economic development halted. Leveraging city-level mobility and case data, our analysis shows that the spatial dissemination of COVID-19 can be well explained by a local diffusion process in the mobility network rather than a global diffusion process, indicating the effectiveness of the implemented disease prevention and control measures. Based on the constructed case prediction model, it is estimated that there could be distinct social consequences if the COVID-19 outbreak happened in different areas. During the epidemic control period, human mobility experienced substantial reductions and the mobility network underwent remarkable local and global structural changes toward containing the spread of COVID-19. Our work has important implications for the mitigation of disease and the evaluation of the socio-economic consequences of COVID-19 on society.

Spatiotemporal patterns of the COVID-19 epidemic in Mexico at the municipality level

In recent history, Coronavirus Disease 2019 (COVID-19) is one of the worst infectious disease outbreaks affecting humanity. The World Health Organization has defined the outbreak of COVID-19 as a pandemic, and the massive growth of the number of infected cases in a short time has caused enormous pressure on medical systems. Mexico surpassed 3.7 million confirmed infections and 285,000 deaths on October 23, 2021. We analysed the spatio-temporal patterns of the COVID-19 epidemic in Mexico using the georeferenced confirmed cases aggregated at the municipality level. We computed weekly Moran’s I index to assess spatial autocorrelation over time and identify clusters of the disease using the “flexibly shaped spatial scan” approach. Finally, we compared Euclidean, cost, resistance distances and gravitational model to select the best-suited approach to predict inter-municipality contagion. We found that COVID-19 pandemic in Mexico is characterised by clusters evolving in space and time as parallel epidemics. The gravitational distance was the best model to predict newly infected municipalities though the predictive power was relatively low and varied over time. This study helps us understand the spread of the epidemic over the Mexican territory and gives insights to model and predict the epidemic behaviour.

A Review of Influencing Factors on Spatial Spread of COVID-19 Based on Geographical Perspective

The COVID-19 outbreak is a manifestation of the contradiction between man and land. Geography plays an important role in epidemic prevention and control with its cross-sectional characteristics and spatial perspective. Based on a systematic review of previous studies, this paper summarizes the research progress on factors influencing the spatial spread of COVID-19 from the research content and method and proposes the main development direction of geography in epidemic prevention and control research in the future. Overall, current studies have explored the factors influencing the epidemic spread on different scales, including global, national, regional and urban. Research methods are mainly composed of quantitative analysis. In addition to the traditional regression analysis and correlation analysis, the spatial lag model, the spatial error model, the geographically weighted regression model and the geographic detector have been widely used. The impact of natural environment and economic and social factors on the epidemic spread is mainly reflected in temperature, humidity, wind speed, air pollutants, population movement, economic development level and medical and health facilities. In the future, new technologies, new methods and new means should be used to reveal the driving mechanism of the epidemic spread in a specific geographical space, which is refined, multi-scale and systematic, with emphasis on exploring the factors influencing the epidemic spread from the perspective of spatial and behavioral interaction, and establish a spatial database platform that combines the information of residents' cases, the natural environment and economic society. This is of great significance to further play the role of geography in epidemic prevention and control.