Analyzing the Risk to COVID-19 Infection using Remote Sensing and GIS

Applications of geographical information system and spatial analysis in Indian health research: a systematic review

BACKGROUND

Health is a fundamental right intricately linked to geographical factors, as evidenced in the health geography literature. The application of geographical information systems (GISs) plays a pivotal role in mapping diseases and evaluating healthcare accessibility and is acknowledged by the World Health Organisation as a solution for enhancing health system resilience and achieving universal health coverage. This study focuses on the geographical perspectives to provide insights into the evolving role of GIS in addressing challenges and shaping healthcare strategies in health research within India. The primary objective was to analyse the utilisation of GISs in health studies specifically on three thematic areas: disease surveillance, health risk assessment, and healthcare access.

METHODS

The databases were searched using sixteen keywords for peer-reviewed articles published during 2000-2022. A systematic literature survey was conducted, drawing from peer-reviewed literature available in the PubMed, Web of Science and Scopus databases. Out of 162 search results, 58 articles were selected for review after three rounds of eliminations based on the predefined specific criteria. A systematic literature review was conducted following PRISMA guidelines addressing two research questions.

RESULTS

This review reveals the extensive use of GIS in health studies since its inception, with particular prominence as a decision-making tool in the aftermath of the COVID-19 outbreak. The analysis of scientific articles focused on disease surveillance, risk assessment, and healthcare access in the Indian context demonstrates the effectiveness of GIS in managing and planning healthcare resources and services. GIS has proven to be an indispensable instrument for understanding spatial patterns in disease and optimising health interventions.

CONCLUSION

This systematic review underscores the critical role of GIS in health research, particularly in the Indian context. The versatile applications of GIS in disease surveillance, risk assessment, and healthcare access highlight its effectiveness as a tool for managing and planning healthcare strategies, contributing to a more resilient and accessible healthcare system in India and beyond.

Delineating dengue risk zones in Jaipur: An interdisciplinary approach to inform public health strategies

Dengue fever (DF) is a pervasive public health concern in tropical climates, with densely populated regions, such as India, disproportionately affected. Addressing this issue requires a multifaceted understanding of the environmental and sociocultural factors that contribute to the risk of dengue infection. This study aimed to identify high-risk zones for DF in Jaipur, Rajasthan, India, by integrating physical, demographic, and epidemiological data in a comprehensive risk analysis framework. We investigated environmental variables, such as soil type and plant cover, to characterize the potential habitats of Aedes aegypti, the primary dengue vector. Concurrently, demographic metrics were evaluated to assess the population's susceptibility to dengue outbreaks. High-risk areas were systematically identified through a comparative analysis that integrated population density and incidence rates per ward. The results revealed a significant correlation between high population density and an increased risk of dengue, predominantly facilitated by vertical transmission. Spatially, these high-risk zones are concentrated in the northern and southern sectors of Jaipur, with the northern and southwestern wards exhibiting the most acute risk profiles. This study underscores the importance of targeted public health interventions and vaccination campaigns in vulnerable areas. It further lays the groundwork for future research to evaluate the effectiveness of such interventions, thereby contributing to the development of robust evidence-based strategies for dengue risk mitigation.

Mapping Vulnerability to Potential Crisis Events in Surabaya City: A GIS-Based Approach

Background

This study aims to develop a vulnerability map for Surabaya using GIS-based Multi-Criteria Decision Analysis (MCDA) to assess the city's vulnerability to COVID-19.

Methods

Six key factors influencing vulnerability were identified and their relative importance determined through the Analytic Hierarchy Process (AHP) pairwise comparison matrix. GIS was utilized to classify Surabaya's vulnerability into five levels: very low, low, medium, high, and very high.

Results

The resulting vulnerability map provides essential insights for decision-makers, healthcare professionals, and disaster management teams. It enables strategic resource allocation, targeted interventions, and formulation of comprehensive response strategies tailored to specific needs of vulnerable districts.

Conclusions

Through these measures, Surabaya can enhance its resilience and preparedness, ensuring the well-being of its residents in the face of potential emergency outbreaks.

COVID-19 Pandemic Risk Assessment: Systematic Review

Background

The COVID-19 pandemic presents the possibility of future large-scale infectious disease outbreaks. In response, we conducted a systematic review of COVID-19 pandemic risk assessment to provide insights into countries' pandemic surveillance and preparedness for potential pandemic events in the post-COVID-19 era.

Objective

We aim to systematically identify relevant articles and synthesize pandemic risk assessment findings to facilitate government officials and public health experts in crisis planning.

Methods

This study followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and included over 620,000 records from the World Health Organization COVID-19 Research Database. Articles related to pandemic risk assessment were identified based on a set of inclusion and exclusion criteria. Relevant articles were characterized based on study location, variable types, data-visualization techniques, research objectives, and methodologies. Findings were presented using tables and charts.

Results

Sixty-two articles satisfying both the inclusion and exclusion criteria were identified. Among the articles, 32.3% focused on local areas, while another 32.3% had a global coverage. Epidemic data were the most commonly used variables (74.2% of articles), with over half of them (51.6%) employing two or more variable types. The research objectives covered various aspects of the COVID-19 pandemic, with risk exposure assessment and identification of risk factors being the most common theme (35.5%). No dominant research methodology for risk assessment emerged from these articles.

Conclusion

Our synthesized findings support proactive planning and development of prevention and control measures in anticipation of future public health threats.

Asymmetric nexus between pandemic uncertainty and public health spendings: Evidence from quantile estimation

The COVID-19 pandemic has brought significant challenges to healthcare systems worldwide, prompting governments to allocate substantial resources toward public health spendings (PHS). However, the uncertainties surrounding the pandemic have raised questions about the effectiveness and sustainability of such expenditures. This research analyzes the nonlinear link between pandemic uncertainty (PNU) and PHS in countries with highest PNU (USA, India, France, Germany, UK, Saudi Arabia, South Korea, Indonesia, Japan, and China). Previous studies have employed panel data methodologies to establish consistent findings regarding the relationship between pandemics and health spendings, regardless of the fact that several countries have not autonomously recognized this connection. In contrast, this current research adopts a distinctive tool called "quantile-on-quantile," which enables the examination of time series dependency within each economy, providing both international and country-specific perspectives on the relationship between the variables. The estimations indicate that PNU leads to an increase in PHS in the vast majority of economies chosen by us, focusing on definite segments of the data distribution. Moreover, the data demonstrates that there are differences in the asymmetry between the variables across various nations. This underscores the need for policymakers to take careful deliberation when formulating policies related to health spendings and addressing the challenges posed by pandemic uncertainty.

Mapping COVID-19's potential infection risk based on land use characteristics: A case study of commercial activities in two Egyptian cities

The contagious COVID-19 has recently emerged and evolved into a world-threatening pandemic outbreak. After pursuing rigorous prophylactic measures two years ago, most activities globally reopened despite the emergence of lethal genetic strains. In this context, assessing and mapping activity characteristics-based hot spot regions facilitating infectious transmission is essential. Hence, our research question is: How can the potential hotspots of COVID-19 risk be defined intra-cities based on the spatial planning of commercial activity in particular? In our research, Zayed and October cities, Egypt, characterized by various commercial activities, were selected as testbeds. First, we analyzed each activity's spatial and morphological characteristics and potential infection risk based on the Centre for Disease Control and Prevention (CDCP) criteria and the Kriging Interpolation method. Then, using Google Mobility, previous reports, and semi-structured interviews, points of interest and population flow were defined and combined with the last step as interrelated horizontal layers for determining hotspots. A validation study compared the generated activity risk map, spatial COVID-19 cases, and land use distribution using logistic regression (LR) and Pearson coefficients (rxy). Through visual analytics, our findings indicate the central areas of both cities, including incompatible and concentrated commercial activities, have high-risk peaks (LR = 0.903, rxy = 0.78) despite the medium urban density of districts, indicating that urban density alone is insufficient for public health risk reduction. Health perspective-based spatial configuration of activities is advised as a risk assessment tool along with urban density for appropriate decision-making in shaping pandemic-resilient cities.

Risk assessment of infectious disease epidemic based on fuzzy Bayesian network

The prevention and control of infectious disease epidemic (IDE) is an important task for every country and region. Risk assessment is significant for the prevention and control of IDE. Fuzzy Bayesian networks (FBN) can capture complex causality and uncertainty. The study developed a novel FBN model, integrating grounded theory, interpretive structural model, and expert weight determination algorithm for the risk assessment of IDE. The algorithm is proposed by the authors for expert weighting in fuzzy environment. The proposed FBN model comprehensively takes into account the risk factors and the interaction among them, and quantifies the uncertainty of IDE risk assessment, so as to make the assessment results more reliable. Taking the epidemic situation of COVID-19 in Wuhan as a case, the application of the proposed model is illustrated. And sensitivity analysis is performed to identify the important risk factors of IDE. Moreover, the effectiveness of the model is checked by the three-criterion-based quantitative validation method including variation connection, consistent effect, and cumulative limitation. Results show that the probability of the outbreak of COVID-19 in Wuhan is as high as 82.26%, which is well-matched with the actual situation. "Information transfer mechanism," "coordination and cooperation among various personnel," "population flow," and "ability of quarantine" are key risk factors. The constructed model meets the above three criteria. The application potential and effectiveness of the developed FBN model are demonstrated. The study provides decision support for preventing and controlling IDE.

Interactions of urbanisation, climate variability, and infectious disease dynamics: insights from the Coimbatore district of Tamil Nadu

Climate change and shifts in land use/land cover (LULC) are critical factors affecting the environmental, societal, and health landscapes, notably influencing the spread of infectious diseases. This study delves into the intricate relationships between climate change, LULC alterations, and the prevalence of vector-borne and waterborne diseases in Coimbatore district, Tamil Nadu, India, between 1985 and 2015. The research utilised Landsat-4, Landsat-5, and Landsat-8 data to generate LULC maps, applying the maximum likelihood algorithm to highlight significant transitions over the years. This study revealed that built-up areas have increased by 67%, primarily at the expense of agricultural land, which was reduced by 51%. Temperature and rainfall data were obtained from APHRODITE Water Resources, and with a statistical analysis of the time series data revealed an annual average temperature increase of 1.8 °C and a minor but statistically significant rainfall increase during the study period. Disease data was obtained from multiple national health programmes, revealing an increasing trend in dengue and diarrhoeal diseases over the study period. In particular, dengue cases surged, correlating strongly with the increase in built-up areas and temperature. This research is instrumental for policy decisions in public health, urban planning, and climate change mitigation. Amidst limited research on the interconnections among infectious diseases, climate change, and LULC changes in India, our study serves as a significant precursor for future management strategies in Coimbatore and analogous regions.

Review of an intelligent indoor environment monitoring and management system for COVID-19 risk mitigation

The coronavirus disease (COVID-19) outbreak has turned the world upside down bringing about a massive impact on society due to enforced measures such as the curtailment of personal travel and limitations on economic activities. The global pandemic resulted in numerous people spending their time at home, working, and learning from home hence exposing them to air contaminants of outdoor and indoor origins. COVID-19 is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which spreads by airborne transmission. The viruses found indoors are linked to the building's ventilation system quality. The ventilation flow in an indoor environment controls the movement and advection of any aerosols, pollutants, and Carbon Dioxide (CO2) created by indoor sources/occupants; the quantity of CO2 can be measured by sensors. Indoor CO2 monitoring is a technique used to track a person's COVID-19 risk, but high or low CO2 levels do not necessarily mean that the COVID-19 virus is present in the air. CO2 monitors, in short, can help inform an individual whether they are breathing in clean air. In terms of COVID-19 risk mitigation strategies, intelligent indoor monitoring systems use various sensors that are available in the marketplace. This work presents a review of scientific articles that influence intelligent monitoring development and indoor environmental quality management system. The paper underlines that the non-dispersive infrared (NDIR) sensor and ESP8266 microcontroller support the development of low-cost indoor air monitoring at learning facilities.

The role of remote sensing during a global disaster: COVID-19 pandemic as case study

Remotely sensed imagery is used as a tool to aid decision makers and scientists in a variety of fields. A recent world event in which satellite imagery was extensively relied on by a variety of stakeholders was the COVID-19 pandemic. In this article we aim to give an overview of the types of information offered through remote sensing (RS) to help address different issues related to the pandemic. We also discuss about the stakeholders that benefited from the data, and the value added by its availability. The content is presented under four sub-sections; namely (1) the use of RS in real-time decision-making and strategic planning during the pandemic; how RS revealed the (2) environmental changes and (3) social and economic impacts caused by the pandemic. And (4) how RS informed our understanding of the epidemiology of SARS-CoV-2, the pathogen responsible for the pandemic. High resolution optical imagery offered updated on-the-ground data for e.g., humanitarian aid organizations, and informed operational decision making of shipping companies. Change in the intensity of air and water pollution after reduced anthropogenic activities around the world were captured by remote sensing - supplying concrete evidence that can help inform improved environmental policy. Several economic indicators were measured from satellite imagery, showing the spatiotemporal component of economic impacts caused by the global pandemic. Finally, satellite based meteorological data supported epidemiological studies of environmental disease determinants. The varied use of remote sensing during the COVID-19 pandemic affirms the value of this technology to society, especially in times of large-scale disasters.

Perspectives from remote sensing to investigate the COVID-19 pandemic: A future-oriented approach

As scientific technology and space science progress, remote sensing has emerged as an innovative solution to ease the challenges of the COVID-19 pandemic. To examine the research characteristics and growth trends in using remote sensing for monitoring and managing the COVID-19 research, a bibliometric analysis was conducted on the scientific documents appearing in the Scopus database. A total of 1,509 documents on this study topic were indexed between 2020 and 2022, covering 165 countries, 577 journals, 5239 institutions, and 8,616 authors. The studies related to remote sensing and COVID-19 have a significant increase of 30% with 464 articles. The United States (429 articles, 28.42% of the global output), China (295 articles, 19.54% of the global output), and the United Kingdom (174 articles, 11.53%) appeared as the top three most contributions to the literature related to remote sensing and COVID-19 research. Sustainability, Science of the Total Environment, and International Journal of Environmental Research and Public Health were the three most productive journals in this research field. The utmost predominant themes were COVID-19, remote sensing, spatial analysis, coronavirus, lockdown, and air pollution. The expansion of these topics appears to be associated with cross-sectional research on remote sensing, evidence-based tools, satellite mapping, and geographic information systems (GIS). Global pandemic risks will be monitored and managed much more effectively in the coming years with the use of remote sensing technology.

Integrated remote sensing and field-based approach to assess the temporal evolution and future projection of meanders: A case study on River Manu in North-Eastern India

A common phenomenon associated with alluvial rivers is their meander evolution, eventually forming cutoffs. Point bar deposits and ox-bow lakes are the products of lateral bend migration and meander cutoff. The present study focuses on identifying the meanders of River Manu and their cutoffs. Moreover, this study compares the temporal evolution and predicts the progress of selected meanders of River Manu. In the present research, the Survey of India topographical map, satellite imagery, and geographic information system (GIS) technique were used to examine the evolution of the Manu River meander. Subsequently, a field visit was done to the selected cutoffs and meanders of River Manu to ascertain the present status and collect data. It has been observed that many cutoffs have undergone temporal changes, and their sizes have decreased. Some have become dried or converted to agricultural fields. The width of River Manu has decreased in all the selected bends from 1932 to 2017. The sinuosity index has changed from 2.04 (1932) to 1.90 (2017), and the length of the river has decreased by 7 km in 85 years (1932–2017). The decrease in length is evident from lowering the number of meanders. Uniformity coefficient and coefficient of curvature of the bank soil samples were calculated, indicating that the soil is poorly graded and falls under the cohesionless category. Based on cross-section analysis, sediment discharge, grain-size analysis of the bank material, channel planform change, and radius of curvature, it can be stated that almost all the selected bends have the probability of future cutoff. The highest probabilities were observed in bend 3 (Jalai) and bend 4 (Chhontail). This work is aimed to provide planners with decisions regarding the construction of roads and bridges in areas that show the huge dynamicity of river meandering.

Assessing the Groundwater Reserves of the Udaipur District, Aravalli Range, India, Using Geospatial Techniques

Population increase has placed ever-increasing demands on the available groundwater (GW) resources, particularly for intensive agricultural activities. In India, groundwater is the backbone of agriculture and drinking purposes. In the present study, an assessment of groundwater reserves was carried out in the Udaipur district, Aravalli range, India. It was observed that the principal aquifer for the availability of groundwater in the studied area is quartzite, phyllite, gneisses, schist, and dolomitic marble, which occur in unconfined to semi-confined zones. Furthermore, all primary chemical ingredients were found within the permissible limit, including granum. We also found that the average annual rainfall days in a year in the study area was 30 from 1957 to 2020, and it has been found that there are chances to receive surplus rainfall once in every five deficit rainfall years. Using integrated remote sensing, GIS, and a field-based spatial modeling approach, it was found that the dynamic GW reserves of the area are 637.42 mcm/annum, and the total groundwater draft is 639.67 mcm/annum. The deficit GW reserves are 2.25 mcm/annum from an average rainfall of 627 mm, hence the stage of groundwater development is 100.67% and categorized as over-exploited. However, as per the relationship between reserves and rainfall events, surplus reserves are available when rainfall exceeds 700 mm. We conclude that enough static GW reserves are available in the studied area to sustain the requirements of the drought period. For the long-term sustainability of groundwater use, controlling groundwater abstraction by optimizing its use, managing it properly through techniques such as sprinkler and drip irrigation, and achieving more crop-per-drop schemes, will go a long way to conserving this essential reserve, and create maximum groundwater recharge structures.

Conceptualizing Walking and Walkability in the Smart City through a Model Composite w2 Smart City Utility Index

This paper explores walking and walkability in the smart city and makes a case for their centrality in the debate on the resilience and sustainability of smart cities, as outlined in the United Nations’ (UN) Sustainable Development Goals (SDGs). It is argued that, while the human/inhabitant-centric paradigm of urban development consolidates, and research on walking, walkability, and pedestrian satisfaction flourishes, the inroads of ICT render it necessary to reflect on these issues in the conceptually- and geographically-delimited space of the smart city. More importantly, it becomes imperative to make respective findings useful and usable for policymakers. To this end, by approaching walking and walkability through the lens of utility, the objective of this paper is to develop a conceptual framework in which the relevance of walking and walkability, hereafter referred to as w2, as a distinct subject of research in the smart cities debate is validated. This framework is then employed to construct a model of a composite w2 smart city utility index. With the focus on the development of the conceptual framework, in which the w2 utility index is embedded, this paper constitutes the first conceptual step of the composite index development process. The value added of this paper is three-fold: First, the relevance of walking and walkability as a distinct subject of research in the realm of smart cities research is established. Second, a mismatch between end-users’ satisfaction derived from walking and their perception of walkability and the objective factors influencing walking and walkability is identified and conceptualized by referencing the concept of utility. Third, a model smart city w2 utility index is proposed as a diagnostic and prognostic tool that, in the subsequent stages of research and implementation, will prove useful for decisionmakers and other stakeholders involved in the process of managing smart cities.

Spatial Analysis of COVID-19 Vaccination: A Scoping Review

Spatial analysis of COVID-19 vaccination research is increasing in recent literature due to the availability of COVID-19 vaccination data that usually contain location components. However, to our knowledge, no previous study has provided a comprehensive review of this research area. Therefore, in this scoping review, we examined the breadth of spatial and spatiotemporal vaccination studies to summarize previous findings, highlight research gaps, and provide guidelines for future research. We performed this review according to the five-stage methodological framework developed by Arksey and O'Malley. We screened all articles published in PubMed/MEDLINE, Scopus, and Web of Science databases, as of 21 September 2021, that had employed at least one form of spatial analysis of COVID-19 vaccination. In total, 36 articles met the inclusion criteria and were organized into four main themes: disease surveillance (n = 35); risk analysis (n = 14); health access (n = 16); and community health profiling (n = 2). Our findings suggested that most studies utilized preliminary spatial analysis techniques, such as disease mapping, which might not lead to robust inferences. Moreover, few studies addressed data quality, modifiable areal unit problems, and spatial dependence, highlighting the need for more sophisticated spatial and spatiotemporal analysis techniques.

GIS-based COVID-19 vulnerability mapping in the West Bank, Palestine

From the beginning of the COVID-19 pandemic, the world stands idly by in the face of the virus spreading. The prediction of highly vulnerable population and the implementation of proper actions are very important steps to break the infection chain of any virus. This will, in turn, reduce the economic and social impact of this virus outbreak. In this study, the COVID-19 vulnerability map for the West Bank, Palestine was developed. Analytic Hierarchy Process (AHP) was used to develop the COVID-19 vulnerability map. The Geographic Information system (GIS) in combination with multi-criteria decision analysis (MCDA) was adopted to estimate the COVID-19 vulnerability index (CVI) based on some selected potential criteria including population, population density, elderly population, accommodation and food service activities, school students, chronic diseases, hospital beds, health insurance, and pharmacy. The results of this study show that Nablus, Jerusalem, and Hebron governorates are under very high vulnerability. Tulkarm, Ramallah & Al-Bireh and Jenin governorates are high vulnerable to COVID-19. Additionally, 82 % of the West Bank population are under high to very high COVID-19 vulnerability classes. Moreover, 14% and 4 % are medium and low to very low vulnerable, respectively. The obtained results are of high value to help decision-makers to take proper actions as early as possible mainly in the highly COVID-19 vulnerable governorates to control the risk associated with the potential outbreak of the virus and accordingly to protect social life and to sustain economic conditions.

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.

Assessing regional risk of COVID-19 infection from Wuhan via high-speed rail

This paper demonstrates that transportation networks may be used to assess and predict the regional risk of COVID-19 infection from the outbreak. We use China's high-speed rail (HSR) network at the scale of prefecture level to assess, based on a probabilistic risk model, the risk of COVID-19 infection from Wuhan to the country's 31 province-level regions at the early stage of domestic spread. We find that the high-risk regions are mainly distributed along the southern half of Beijing-Hong Kong HSR line, where a large number of infection cases have been confirmed at the early stage. Furthermore, the two components of the infection risk, namely, the probability (proxied by the region's correlation with Wuhan through HSR) and the impact (proxied by the region's population with mobility), can play different roles in the risk ranking for different regions. For public health administrators, these findings may be used for better decision making, including the preparation of emergency plans and supplies, and the allocation of limited resources, before the extensive spread of the epidemic. Moreover, the administrators should adopt different intervention measures for different regions, so as to better mitigate the epidemic spread according to their own risk scenarios with respect to the probability of occurring and, once occurred, the impact.

Global Systemic Risk and Resilience for Novel Coronavirus and COVID-19

This Special Issue is dedicated to issues and challenges related to pandemic risk and resilience, with a focus on policy and operations of global systems in the COVID-19 pandemic. The cascading effects of emerging and reemerging infectious diseases to the global economy are a critical interest. Measures to confront the ongoing pandemic are an urgent need. Data analysis at regional and global scales is helping to prioritize response and resilience across locations of high risks. The risk sciences are available for addressing human health and infection risks; the evaluation of risk management strategies and tradeoffs; risk perception as it relates to information processing and receiving risk communication; and tracking system resilience as it relates to various imposed measures.