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Munshi RM, Khayyat MM, Ben Slama S, Khayyat MM. A deep learning-based approach for predicting COVID-19 diagnosis. Heliyon 2024; 10:e28031. [PMID: 38596143 PMCID: PMC11002549 DOI: 10.1016/j.heliyon.2024.e28031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 03/06/2024] [Accepted: 03/11/2024] [Indexed: 04/11/2024] Open
Abstract
This paper focuses on forecasting the total count of confirmed COVID-19 cases in Saudi Arabia through a range of methodologies, including ARIMA, mathematical modeling, and deep learning network (DQN) techniques. Its primary aim is to anticipate the verified COVID-19 cases in Saudi Arabia, aiding in decision-making for life-saving interventions by enhancing awareness of COVID-19 infection. Mathematical modeling and ARIMA are employed for their efficacy in forecasting, while DQN approaches, particularly through comparative analysis, are utilized for prediction. This comparative analysis evaluates the predictive capacities of ARIMA, mathematical modeling, and DQN techniques, aiming to pinpoint the most reliable method for forecasting positive COVID-19 cases. The modeling encompasses COVID-19 cases in Saudi Arabia, the United Kingdom (UK), and Tunisia (TU) spanning from 2020 to 2021. Predicting the number of individuals likely to test positive for COVID-19 poses a challenge, requiring adherence to fundamental assumptions in mathematical and ARIMA projections. The proposed methodology was implemented on a local server. The DQN algorithm formulates a reward function to uphold target functional performance while balancing training and testing periods. The findings indicate that DQN technology surpasses conventional approaches in efficiency and accuracy for predictions.
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Affiliation(s)
- Raafat M. Munshi
- Department of Medical Laboratory Technology (MLT) Faculty of Applied Medical Sciences, King Abdulaziz University, Rabigh, Saudi Arabia
| | - Mashael M. Khayyat
- Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Sami Ben Slama
- Analysis and Processing of Electrical and Energy Systems Unit, Faculty of Sciences of Tunis El Manar, Tunis, 2092, Tunisia
- Faculty of Computing & Information Technology Information System Department, Jeddah, King Abdulaziz University, Saudi Arabia
| | - Manal Mahmoud Khayyat
- Department of Computer Science and Artificial Intelligence College of Computing, Umm Al-Qura University Makkah 24382, Saudi Arabia
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Alhhazmi A, Alferidi A, Almutawif YA, Makhdoom H, Albasri HM, Sami BS. Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases. Front Artif Intell 2024; 6:1327355. [PMID: 38375088 PMCID: PMC10875994 DOI: 10.3389/frai.2023.1327355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 12/27/2023] [Indexed: 02/21/2024] Open
Abstract
Healthcare is a topic of significant concern within the academic and business sectors. The COVID-19 pandemic has had a considerable effect on the health of people worldwide. The rapid increase in cases adversely affects a nation's economy, public health, and residents' social and personal well-being. Improving the precision of COVID-19 infection forecasts can aid in making informed decisions regarding interventions, given the pandemic's harmful impact on numerous aspects of human life, such as health and the economy. This study aims to predict the number of confirmed COVID-19 cases in Saudi Arabia using Bayesian optimization (BOA) and deep learning (DL) methods. Two methods were assessed for their efficacy in predicting the occurrence of positive cases of COVID-19. The research employed data from confirmed COVID-19 cases in Saudi Arabia (SA), the United Kingdom (UK), and Tunisia (TU) from 2020 to 2021. The findings from the BOA model indicate that accurately predicting the number of COVID-19 positive cases is difficult due to the BOA projections needing to align with the assumptions. Thus, a DL approach was utilized to enhance the precision of COVID-19 positive case prediction in South Africa. The DQN model performed better than the BOA model when assessing RMSE and MAPE values. The model operates on a local server infrastructure, where the trained policy is transmitted solely to DQN. DQN formulated a reward function to amplify the efficiency of the DQN algorithm. By examining the rate of change and duration of sleep in the test data, this function can enhance the DQN model's training. Based on simulation findings, it can decrease the DQN work cycle by roughly 28% and diminish data overhead by more than 50% on average.
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Affiliation(s)
- Areej Alhhazmi
- Medical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Al-Madinah Al-Munawarah, Saudi Arabia
| | - Ahmad Alferidi
- Department of Electrical Engineering, College of Engineering, Taibah University, Al-Madinah Al-Munawarah, Saudi Arabia
| | - Yahya A. Almutawif
- Medical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Al-Madinah Al-Munawarah, Saudi Arabia
| | - Hatim Makhdoom
- Medical Laboratories Technology Department, College of Applied Medical Sciences, Taibah University, Al-Madinah Al-Munawarah, Saudi Arabia
| | - Hibah M. Albasri
- Department of Biology, College of Science, Taibah University, Al-Madinah Al-Munawarah, Saudi Arabia
| | - Ben Slama Sami
- Computer Sciences Department, The Applied College, King Abdulaziz, Saudi Arabia University, Jeddah, Saudi Arabia
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Rahman MS, Paul KC, Rahman MM, Samuel J, Thill JC, Hossain MA, Ali GGMN. Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach. SUSTAINABLE CITIES AND SOCIETY 2023; 95:104570. [PMID: 37065624 PMCID: PMC10085879 DOI: 10.1016/j.scs.2023.104570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 04/01/2023] [Accepted: 04/01/2023] [Indexed: 06/19/2023]
Abstract
Cities become mission-critical zones during pandemics and it is vital to develop a better understanding of the factors that are associated with infection levels. The COVID-19 pandemic has impacted many cities severely; however, there is significant variance in its impact across cities. Pandemic infection levels are associated with inherent features of cities (e.g., population size, density, mobility patterns, socioeconomic condition, and health & environment), which need to be better understood. Intuitively, the infection levels are expected to be higher in big urban agglomerations, but the measurable influence of a specific urban feature is unclear. The present study examines 41 variables and their potential influence on the incidence of COVID-19 infection cases. The study uses a multi-method approach to study the influence of variables, classified as demographic, socioeconomic, mobility and connectivity, urban form and density, and health and environment dimensions. This study develops an index dubbed the pandemic vulnerability index at city level (PVI-CI) for classifying the pandemic vulnerability levels of cities, grouping them into five vulnerability classes, from very high to very low. Furthermore, clustering and outlier analysis provides insights on the spatial clustering of cities with high and low vulnerability scores. This study provides strategic insights into levels of influence of key variables upon the spread of infections, along with an objective ranking for the vulnerability of cities. Thus, it provides critical wisdom needed for urban healthcare policy and resource management. The calculation method for the pandemic vulnerability index and the associated analytical process present a blueprint for the development of similar indices for cities in other countries, leading to a better understanding and improved pandemic management for urban areas, and more resilient planning for future pandemics in cities across the world.
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Affiliation(s)
- Md Shahinoor Rahman
- Department of Earth and Environmental Sciences, New Jersey City University, Jersey City, NJ, 07305, USA
| | - Kamal Chandra Paul
- Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA
| | - Md Mokhlesur Rahman
- The William States Lee College of Engineering, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, Khulna, 9203, Bangladesh
| | - Jim Samuel
- E.J. Bloustein School of Planning & Public Policy, Rutgers University, NJ, 08901, USA
| | - Jean-Claude Thill
- Department of Geography and Earth Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA
- School of Data Science, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA
| | - Md Amjad Hossain
- Department of Accounting, Information Systems, and Finance, Emporia State University, Emporia, KS, 66801, USA
| | - G G Md Nawaz Ali
- Department of Computer Science and Information Systems, Bradley University, Peoria, IL, 61625, USA
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Alves A, da Costa NM, Morgado P, da Costa EM. Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies. Int J Health Geogr 2023; 22:8. [PMID: 37024965 PMCID: PMC10078027 DOI: 10.1186/s12942-023-00329-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 03/28/2023] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND COVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection. METHODS We present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination. RESULTS Regression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions. CONCLUSIONS This article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.
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Affiliation(s)
- André Alves
- Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276, Lisbon, Portugal.
| | - Nuno Marques da Costa
- Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276, Lisbon, Portugal
- Associate Laboratory TERRA, 1349-017, Lisbon, Portugal
| | - Paulo Morgado
- Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276, Lisbon, Portugal
- Associate Laboratory TERRA, 1349-017, Lisbon, Portugal
| | - Eduarda Marques da Costa
- Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276, Lisbon, Portugal
- Associate Laboratory TERRA, 1349-017, Lisbon, Portugal
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Kumar SS, Schreinemachers P, Pal AA, Manickam R, Nair RM, Srinivasan R, Harris J. The continued effects of COVID-19 on the lives and livelihoods of vegetable farmers in India. PLoS One 2023; 18:e0279026. [PMID: 36595541 PMCID: PMC9810155 DOI: 10.1371/journal.pone.0279026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 11/28/2022] [Indexed: 01/04/2023] Open
Abstract
India experienced a rapid rise in COVID-19 infections from March 2021. States imposed varying levels of lockdowns and curfews to curb the spread of the disease. These restrictions severely affected the functioning of food systems. The objective of this study was to analyze how COVID-19 continues to affect agricultural production, food security and household diets of vegetable farmers. A phone-based survey was conducted with 595 vegetable farmers in the states of Andhra Pradesh, Assam, Jharkhand, Karnataka and Odisha, 60% of whom had been interviewed a year earlier. Overall, 60% of farmers experienced decreased vegetable production; over 80% reported a reduction in consumption of at least one food group; and 45% reported some level of food insecurity between May 2020 and May 2021. Farmers who reported decreased staples production, difficulty accessing seeds/seedlings, or reduced their household spending were more likely to report decreased vegetable production. Vegetable consumption was positively associated with receipt of COVID-19 relief benefits, borrowing money, or having home gardens. Farmers who received public agricultural assistance, or had reduced expenses, were more likely to have lower vegetable consumption. Greater severity of food insecurity was associated with farmers belonging to underprivileged social groups, non-Hindus, or those who experienced decrease in livestock production, weather related disruptions or received COVID-19 assistance. This is one of few studies that have conducted a longitudinal assessment of the impacts across multiple waves of COVID-19. COVID-19 is seen to be one among several shocks experienced by farm households, and exacerbated existing issues within agriculture and food security. There is a need for public policy support to strengthen both production and consumption of vegetables.
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Affiliation(s)
- Sandhya S. Kumar
- World Vegetable Center, South and Central Asia, Patancheru, Telangana, India
| | | | - Arshad Ahmad Pal
- World Vegetable Center, South and Central Asia, Patancheru, Telangana, India
| | | | | | | | - Jody Harris
- World Vegetable Center, East and Southeast Asia, Bangkok, Thailand
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Guchhait S, Das S, Das N, Patra T. Mapping of space-time patterns of infectious disease using spatial statistical models: a case study of COVID-19 in India. Infect Dis (Lond) 2023; 55:27-43. [PMID: 36199164 DOI: 10.1080/23744235.2022.2129778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Mapping of infectious diseases like COVID-19 is the foremost importance for diseases control and prevention. This study attempts to identify the spatio-temporal pattern and evolution trend of COVID-19 at the district level in India using spatial statistical models. MATERIALS AND METHODS Active cases of eleven time-stamps (30 March-2 December, 2020) with an approximately 20-day interval are considered. The study reveals applications of spatial statistical tools, i.e. optimised hotspot and outlier analysis (which follow Gi* and Moran I statistics) and emerging hotspot with the base of space time cube, are effective for the spatio-temporal evolution of disease clusters. RESULTS The result shows the overall increasing trend of COVID-19 infection with a Mann-Kendall trend score of 2.95 (p = 0.0031). The spatial clusters of high infection (hotspots) and low infection (coldspots) change their location over time but are limited to the districts of the south-western states (Kerala, Karnataka, Andhra Pradesh, Maharashtra, Gujarat) and the north-eastern states (West Bengal, Jharkhand, Assam, Tripura, Manipur, etc.) respectively. CONCLUSIONS A total of eight types of patterns are identified, but the most concerning types are consecutive (7.24% of districts), intensifying (15.13% districts) and persistent (24.34% of districts) which will help health policy makers and the government to prioritize-based resource allocation and control measures.
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Affiliation(s)
- Santu Guchhait
- Department of Geography, Panskura Banamali College, Purba Medinipur, India
| | - Subhrangsu Das
- Department of Geography, Utkal University, Bhubaneswar, India
| | - Nirmalya Das
- Department of Geography, Panskura Banamali College, Purba Medinipur, India
| | - Tanmay Patra
- Department of Geography, Panskura Banamali College, Purba Medinipur, India
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Bhattacharyya R, Burman A, Singh K, Banerjee S, Maity S, Auddy A, Rout SK, Lahoti S, Panda R, Baladandayuthapani V. Role of multiresolution vulnerability indices in COVID-19 spread in India: a Bayesian model-based analysis. BMJ Open 2022; 12:e056292. [PMID: 36396323 PMCID: PMC9676421 DOI: 10.1136/bmjopen-2021-056292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 10/07/2022] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVES COVID-19 has differentially affected countries, with health infrastructure and other related vulnerability indicators playing a role in determining the extent of its spread. Vulnerability of a geographical region to COVID-19 has been a topic of interest, particularly in low-income and middle-income countries like India to assess its multifactorial impact on incidence, prevalence or mortality. This study aims to construct a statistical analysis pipeline to compute such vulnerability indices and investigate their association with metrics of the pandemic growth. DESIGN Using publicly reported observational socioeconomic, demographic, health-based and epidemiological data from Indian national surveys, we compute contextual COVID-19 Vulnerability Indices (cVIs) across multiple thematic resolutions for different geographical and spatial administrative regions. These cVIs are then used in Bayesian regression models to assess their impact on indicators of the spread of COVID-19. SETTING This study uses district-level indicators and case counts data for the state of Odisha, India. PRIMARY OUTCOME MEASURE We use instantaneous R (temporal average of estimated time-varying reproduction number for COVID-19) as the primary outcome variable in our models. RESULTS Our observational study, focussing on 30 districts of Odisha, identified housing and hygiene conditions, COVID-19 preparedness and epidemiological factors as important indicators associated with COVID-19 vulnerability. CONCLUSION Having succeeded in containing COVID-19 to a reasonable level during the first wave, the second wave of COVID-19 made greater inroads into the hinterlands and peripheral districts of Odisha, burdening the already deficient public health system in these areas, as identified by the cVIs. Improved understanding of the factors driving COVID-19 vulnerability will help policy makers prioritise resources and regions, leading to more effective mitigation strategies for the present and future.
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Affiliation(s)
- Rupam Bhattacharyya
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Anik Burman
- Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Sayantan Banerjee
- Operations Management and Quantitative Techniques Area, Indian Institute of Management Indore, Indore, Madhya Pradesh, India
| | - Subha Maity
- Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Arnab Auddy
- Department of Statistics, Columbia University, New York, New York, USA
| | - Sarit Kumar Rout
- Indian Institute of Public Health, Public Health Foundation of India, Bhubaneswar, Odisha, India
| | - Supriya Lahoti
- Public Health Foundation of India, New Delhi, Delhi, India
| | - Rajmohan Panda
- Public Health Foundation of India, New Delhi, Delhi, India
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Pandey B, Gu J, Ramaswami A. Characterizing COVID-19 waves in urban and rural districts of India. NPJ URBAN SUSTAINABILITY 2022; 2:26. [PMID: 37521776 PMCID: PMC9613454 DOI: 10.1038/s42949-022-00071-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 09/23/2022] [Indexed: 05/03/2023]
Abstract
Understanding spatial determinants, i.e., social, infrastructural, and environmental features of a place, which shape infectious disease is critically important for public health. We present an exploration of the spatial determinants of reported COVID-19 incidence across India's 641 urban and rural districts, comparing two waves (2020-2021). Three key results emerge using three COVID-19 incidence metrics: cumulative incidence proportion (aggregate risk), cumulative temporal incidence rate, and severity ratio. First, in the same district, characteristics of COVID-19 incidences are similar across waves, with the second wave over four times more severe than the first. Second, after controlling for state-level effects, urbanization (urban population share), living standards, and population age emerge as positive determinants of both risk and rates across waves. Third, keeping all else constant, lower shares of workers working from home correlate with greater infection risk during the second wave. While much attention has focused on intra-urban disease spread, our findings suggest that understanding spatial determinants across human settlements is also important for managing current and future pandemics.
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Affiliation(s)
- Bhartendu Pandey
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08540 USA
| | - Jianyu Gu
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08540 USA
- National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401 USA
| | - Anu Ramaswami
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08540 USA
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Suleimany M, Mokhtarzadeh S, Sharifi A. Community resilience to pandemics: An assessment framework developed based on the review of COVID-19 literature. INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION : IJDRR 2022; 80:103248. [PMID: 35991617 PMCID: PMC9375855 DOI: 10.1016/j.ijdrr.2022.103248] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 07/25/2022] [Accepted: 08/09/2022] [Indexed: 05/14/2023]
Abstract
The COVID-19 outbreak in 2019 and the challenges it posed to communities around the world, demonstrated the necessity of enhancing the resilience of communities to pandemics. In this regard, assessment frameworks can play an essential role and guide resilience-building efforts. However, the lack of a comprehensive assessment framework has led to a focus on sectoral evaluation. This study aims to propose an integrated framework for assessing the pandemic resilience of communities. For this purpose, we rely on a systematic review of literature indexed in major academic databases. We have thoroughly analyzed a total number of 115 related documents to extract relevant criteria. Findings show that many criteria and factors affect community resilience to pandemics. By inductive content coding in MAXQDA software, we have categorized these criteria into five dimensions of Institutional, Social, Economic, Infrastructural, and Demographic. Good leadership and management, insurance and governmental support, planning and preparation, expertise and labor, and available equipment and technologies are the most important institutional criteria. Communication and collective identity, mutual support, public safety and protection, public awareness, and social justice are the influential social criteria. Economic sustainability and resource availability are criteria of economic resilience. Sufficiency of services, public spaces, housing tenure, and transportation system are the main criteria related to the built environment and infrastructural dimension. Finally, demographic resilience includes physical health, psychological well-being, life quality, and hygiene. Based on these criteria, this study develops an integrated evaluation framework that researchers can implement along with conventional assessment and ranking methods to determine the level of community resilience to pandemics.
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Affiliation(s)
- Mahdi Suleimany
- Urban Planning and Management, University of Tehran, Tehran, Iran
| | - Safoora Mokhtarzadeh
- Department of Urbanism, Faculty of Architecture and Urbanism. Daneshpajoohan Pishro Institute, Isfahan, Iran
| | - Ayyoob Sharifi
- Hiroshima University, Graduate School of Humanities and Social Science, Japan
- Hiroshima University, Graduate School of Advances Science and Engineering, Japan
- Network for Education and Research on Peace and Sustainability (NERPS), Japan
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Pan J, Bardhan R. Evaluating the risk of accessing green spaces in COVID-19 pandemic: A model for public urban green spaces (PUGS) in London. URBAN FORESTRY & URBAN GREENING 2022; 74:127648. [PMID: 35721365 PMCID: PMC9195353 DOI: 10.1016/j.ufug.2022.127648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 06/10/2022] [Accepted: 06/11/2022] [Indexed: 05/07/2023]
Abstract
The pandemic caused by SARS-CoV-2 (COVID-19) at the beginning of 2020 has restricted the human population indoor with some allowance for recreation in green spaces for social interaction and daily exercise. Understanding and measuring the risk of COVID-19 infection during public urban green spaces (PUGS) visits is essential to reduce the spread of the virus and improve well-being. This study builds a data-fused risk assessment model to evaluate the risk of visiting the PUGS in London. Three parameters are used for risk evaluation: the number of new cases at the middle-layer super output area (MSOA) level, the accessibility of each public green space and the Indices of Multiple Deprivation at the lower-layer super output area (LSOA) level. The model assesses 1357 PUGS and identifies the risk in three levels, high, medium and low, according to the results of a two-step clustering analysis. The spatial variability of risk across the city is demonstrated in the evaluation. The evaluation of risk can provide a better metric to the decision-making at both the individual level, on deciding which green space to visit, and the borough level, on how to implement restricting measures on green space access.
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Affiliation(s)
- Jiayu Pan
- The Martin Centre for Architecture, Department of Architecture, University of Cambridge, Cambridge CB2 1PX, UK
| | - Ronita Bardhan
- The Martin Centre for Architecture, Department of Architecture, University of Cambridge, Cambridge CB2 1PX, UK
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Siljander M, Uusitalo R, Pellikka P, Isosomppi S, Vapalahti O. Spatiotemporal clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland. Spat Spatiotemporal Epidemiol 2022; 41:100493. [PMID: 35691637 PMCID: PMC8817446 DOI: 10.1016/j.sste.2022.100493] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 01/21/2022] [Accepted: 02/04/2022] [Indexed: 12/22/2022]
Abstract
This study aims to elucidate the variations in spatiotemporal patterns and sociodemographic determinants of SARS-CoV-2 infections in Helsinki, Finland. Global and local spatial autocorrelation were inspected with Moran's I and LISA statistics, and Getis-Ord Gi* statistics was used to identify the hot spot areas. Space-time statistics were used to detect clusters of high relative risk and regression models were implemented to explain sociodemographic determinants for the clusters. The findings revealed the presence of spatial autocorrelation and clustering of COVID-19 cases. High-high clusters and high relative risk areas emerged primarily in Helsinki's eastern neighborhoods, which are socioeconomically vulnerable, with a few exceptions revealing local outbreaks in other areas. The variation in COVID-19 rates was largely explained by median income and the number of foreign citizens in the population. Furthermore, the use of multiple spatiotemporal analysis methods are recommended to gain deeper insights into the complex spatiotemporal clustering patterns and sociodemographic determinants of the COVID-19 cases.
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Affiliation(s)
- Mika Siljander
- Earth Change Observation Laboratory, Department of Geosciences and Geography, P.O. Box 64, FI-00014 University of Helsinki, Helsinki, Finland; Department of Virology, Haartmaninkatu 3, P.O. Box 21, FI-00014 University of Helsinki, Helsinki, Finland.
| | - Ruut Uusitalo
- Earth Change Observation Laboratory, Department of Geosciences and Geography, P.O. Box 64, FI-00014 University of Helsinki, Helsinki, Finland; Department of Virology, Haartmaninkatu 3, P.O. Box 21, FI-00014 University of Helsinki, Helsinki, Finland; Department of Veterinary Biosciences, Agnes Sjöberginkatu 2, P.O. Box 66, FI-00014 University of Helsinki, Helsinki, Finland
| | - Petri Pellikka
- Earth Change Observation Laboratory, Department of Geosciences and Geography, P.O. Box 64, FI-00014 University of Helsinki, Helsinki, Finland; Helsinki Institute of Sustainability Science, University of Helsinki, Helsinki, Finland; Institute for Atmospheric and Earth System Research, University of Helsinki, Helsinki, Finland; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430000, China
| | - Sanna Isosomppi
- Epidemiological Operations Unit, P.O. Box 8650, 00099 City of Helsinki, Finland
| | - Olli Vapalahti
- Department of Virology, Haartmaninkatu 3, P.O. Box 21, FI-00014 University of Helsinki, Helsinki, Finland; Department of Veterinary Biosciences, Agnes Sjöberginkatu 2, P.O. Box 66, FI-00014 University of Helsinki, Helsinki, Finland; Virology and Immunology, Diagnostic Center, HUSLAB, Helsinki University Hospital, Helsinki, Finland
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Limon MR, Vallente JPC, Cajigal ARV, Aquino MU, Aragon JA, Acosta RL. Unmasking emerging issues in solid waste management: Knowledge and self-reported practices on the discarded disposable masks during the COVID-19 pandemic in the Philippines. ENVIRONMENTAL CHALLENGES (AMSTERDAM, NETHERLANDS) 2022; 6:100435. [PMID: 36632241 PMCID: PMC8743242 DOI: 10.1016/j.envc.2021.100435] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 11/28/2021] [Accepted: 12/30/2021] [Indexed: 04/19/2023]
Abstract
The COVID-19 global health crisis has resulted in the emergence of a new type of solid waste-inappropriately discarded disposable masks (DMs)-posing serious risks to the public health and to the environment. This study assessed the knowledge-("K") and self-reported practices-("P") of the general public in DM waste management. A researcher-developed instrument was utilized to gather data with a reliability coefficient index of 0.94. The survey was participated in by 13,116 online users. Pearson r and multiple linear regression were performed to test the relationship between the participants' demographic characteristics and their K and P. Results revealed that the participants obtained a weighted mean and standard deviation of 1.15±0.10, which shows that 11,597 or 88.41% are knowledgeable on solid waste disposal and management. The self-reported practices of the participants obtained weighted mean and standard deviation rating of 2.16±0.10, which is interpreted as "Always Practiced". This signifies that the participants adequately practiced the essentials in disposing DMs. Furthermore, there is a significant relationship between K and P with their demographic characteristics on disposing DMs like age, sex, level of education, annual income, and type of residence. The obtained Pearson r=-0.178 (p<.01) indicates that the level of knowledge of the participants is significantly related to the practices they apply in disposing used DMs. As a recommendation, campaigns and interventions on the proper disposal of DMs should be put forward and implemented, utilizing various social media resources and platforms that are conveniently accessible to the general public.
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Affiliation(s)
- Mark R Limon
- Technical-Vocational and Livelihood Education Department, College of Teacher Education, Mariano Marcos State University, Laoag City, Ilocos Norte 2900 Philippines
| | - John Paul C Vallente
- Secondary Education Department, College of Teacher Education, Mariano Marcos State University, Laoag City, Ilocos Norte 2900 Philippines
| | - Aris Reynold V Cajigal
- Secondary Education Department, College of Teacher Education, Mariano Marcos State University, Laoag City, Ilocos Norte 2900 Philippines
| | - Marlowe U Aquino
- Science and Technology Park, Mariano Marcos State University, Batac City, Ilocos Norte 2906 Philippines
| | - Jovenita A Aragon
- Early Childhood and Special Needs Education Department, Mariano Marcos State University, Laoag City, Ilocos Norte 2900 Philippines
| | - Rosabel L Acosta
- Secondary Education Department, College of Teacher Education, Mariano Marcos State University, Laoag City, Ilocos Norte 2900 Philippines
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Mallick SK, Pramanik M, Maity B, Das P, Sahana M. Plastic waste footprint in the context of COVID-19: Reduction challenges and policy recommendations towards sustainable development goals. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 796:148951. [PMID: 34271381 PMCID: PMC8487300 DOI: 10.1016/j.scitotenv.2021.148951] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 07/06/2021] [Accepted: 07/06/2021] [Indexed: 05/22/2023]
Abstract
The sudden surge in demand to use plastic products due to COVID-19 pandemic has increased plastic pollution. It has resulted into degradation of a broad range of habitats and ecosystems by destroying natural functions, water quality, and environmental sustainability. However, the government agencies, scientific communities, and the public, have started to give attention to this issue. So, in the present study, we used the correlation methods to check the relationship between COVID-19 affected population with the medical plastic waste (MPW) that has developed a conceptual model of the inter-linkages between the preventive measures of COVID-19 pandemic problems and the reduction challenges of plastic waste during and after pandemic scenarios. Emerging issues in the waste management during and after the COVID-19 are established by reviewing the literature, reports, policy briefs, and information from the website concerning COVID-19. Considering MPW management issues, we selected India as a case study to analyse the plastic waste footprint (PWF) due to COVID-19 pandemic. The correlation results showed COVID-19 affected population and MPW; COVID-19 affected population and PWF have a significant relationship (R2 = 0.60; Area under ROC curve 81.4%). It suggests an urgent need for plastic waste management initiatives. Moreover, substantial plastic products, human awareness, strict government regulations, and inclusive research can check plastic waste footprints in India and worldwide. Then discuss the specific pathways through which the immediate and long-term impacts operate and highlight the issues of hampering the sustainable development goals (SDGs) progress in India and beyond. Finally, call for coordinated assessment, support and appropriate short- and long-term mitigation and the policy measures of plastic waste problems during and after the COVID-19 pandemic.
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Affiliation(s)
- Suraj K Mallick
- Department of Geography, Vidyasagar University, Midnapore, West Bengal 721102, India.
| | - Malay Pramanik
- Department of Development and Sustainability, School of Environment, Resources and Development, Asian Institute of Technology (AIT), P O. Box 4, Klong Luang, Pathumthani 12120, Thailand; Centre for Geoinformatics, Jamsetji Tata School of Disaster Studies, Tata Institute of Social Sciences, Deonar, Mumbai 400088, India
| | - Biswajit Maity
- Department of Geography, Vidyasagar University, Midnapore, West Bengal 721102, India
| | - Pritiranjan Das
- Department of Geography, Vidyasagar University, Midnapore, West Bengal 721102, India
| | - Mehebub Sahana
- School of Environment, Education & Development, University of Manchester, United Kingdom
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Malakar S. Geospatial modelling of COVID-19 vulnerability using an integrated fuzzy MCDM approach: a case study of West Bengal, India. ACTA ACUST UNITED AC 2021; 8:3103-3116. [PMID: 34604502 PMCID: PMC8475317 DOI: 10.1007/s40808-021-01287-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 09/15/2021] [Indexed: 12/23/2022]
Abstract
COVID-19 is a worldwide transmitted pandemic that has brought a threatening challenge to Indian society and the economy. The disease has become a public health disaster, which has no effective medication. However, proper management and planning, which includes understanding the transmitting pattern, number of containment zones, vulnerable factors, and level of risk, may break the chain of transmission and reduce the number of cases. Hence, this study has attempted to model the COVID-19 vulnerability using an integrated fuzzy multi-criteria decision-making (MCDM) approach, namely fuzzy-analytical hierarchy process (AHP) and fuzzy-technique for order preference by similarity to ideal solution (TOPSIS) for West Bengal, India, through geographic information system (GIS). A total of 15 parameters were utilised to model the COVID-19 vulnerability, which was further categorised into three criteria: social vulnerability, epidemiological vulnerability, and physical vulnerability. The final vulnerability mapping has been done using these three criteria through the GIS platform. This study reveals that COVID-19 infection highly threatens about 20% of the total area of West Bengal, 23.42% moderately vulnerable, and 57.03% of the area comes under low vulnerability. The highly vulnerable region includes the Kolkata, South 24 Paraganas, and North 24 Paraganas, which are considered highly populated districts of West Bengal. Therefore government agencies should be more focused and plan accordingly to safeguard the community, especially the region with very high COVID-19 vulnerability, from further spreading the infection.
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Affiliation(s)
- Sukanta Malakar
- Centre for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302 India
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15
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Mandal J, Patel PP. Gauging the effects of the COVID-19 pandemic lockdowns on atmospheric pollution content in select countries. REMOTE SENSING APPLICATIONS : SOCIETY AND ENVIRONMENT 2021; 23:100551. [PMID: 36568402 PMCID: PMC9764693 DOI: 10.1016/j.rsase.2021.100551] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 05/18/2021] [Accepted: 05/21/2021] [Indexed: 12/27/2022]
Abstract
Image 1.
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Affiliation(s)
- Jayatra Mandal
- Department of Geography, Purash Kanpur Haridas Nandi Mahavidyalaya, Kanpur, Haora, 711410, West Bengal, India
| | - Priyank Pravin Patel
- Department of Geography, Presidency University, 86/1, College Street, Kolkata, 700073, West Bengal, India
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Khavarian-Garmsir AR, Sharifi A, Moradpour N. Are high-density districts more vulnerable to the COVID-19 pandemic? SUSTAINABLE CITIES AND SOCIETY 2021; 70:102911. [PMID: 36567891 PMCID: PMC9760197 DOI: 10.1016/j.scs.2021.102911] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 03/18/2021] [Accepted: 03/31/2021] [Indexed: 05/09/2023]
Abstract
The COVID-19 pandemic has brought questions about the desirability of compact urban development to the fore. There are some concerns that high density may be a risk factor that makes it challenging to contain the pandemic. This study aims to investigate the link between density and pandemic spread through a case study of Tehran that has been the epicenter of the pandemic in Iran. Based on data obtained from an online platform and analyzed using structural equation modeling, we found that density alone cannot be considered a risk factor for the spread of COVID-19. In fact, density alone did not explain the geographic distribution pattern of confirmed COVID-19 cases and deaths across the 22 municipal districts of Tehran. We, therefore, argue that efforts should be made to minimize concerns about living in dense urban environments. Indeed, residents of high-density districts can live safely when an outbreak occurs, provided they make some changes in lifestyle and follow public health instructions. Based on the findings, and considering other benefits of compact cities (e.g., climate change mitigation) planners and policy makers are encouraged to continue promoting compact urban forms. They can also use the results of this study in their efforts towards developing appropriate mechanisms and guidelines for effective management of future pandemics in cities.
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Affiliation(s)
- Amir Reza Khavarian-Garmsir
- Department of Geography and Urban Planning, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran
| | - Ayyoob Sharifi
- Hiroshima University, Graduate School of Humanities and Social Sciences, Japan
- Hiroshima University, Graduate School of Advanced Science and Engineering, Japan
- Network for Education and Research on Peace and Sustainability (NERPS), Japan
| | - Nabi Moradpour
- Department of Human Geography, Faculty of Geography, University of Tehran, Iran
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Kanga S, Meraj G, Sudhanshu, Farooq M, Nathawat MS, Singh SK. Analyzing the Risk to COVID-19 Infection using Remote Sensing and GIS. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2021; 41:801-813. [PMID: 33733497 PMCID: PMC8251091 DOI: 10.1111/risa.13724] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 11/24/2020] [Accepted: 02/25/2021] [Indexed: 09/21/2023]
Abstract
Globally, the COVID-19 pandemic has become a threat to humans and to the socioeconomic systems they have developed since the industrial revolution. Hence, governments and stakeholders call for strategies to help restore normalcy while dealing with this pandemic effectively. Since till now, the disease is yet to have a cure; therefore, only risk-based decision making can help governments achieve a sustainable solution in the long term. To help the decisionmakers explore viable actions, we propose a risk-based assessment framework for analyzing COVID-19 risk to areas, using integrated hazard and vulnerability components associated with this pandemic for effective risk mitigation. The study is carried on a region administrated by Jaipur municipal corporation (JMC), India. Based on the current understanding of this disease, we hypothesized different COVID-19 risk indices (C19Ri) of the wards of JMC such as proximity to hotspots, total population, population density, availability of clean water, and associated land use/land cover, are related with COVID-19 contagion and calculated them in a GIS-based multicriteria risk reduction method. The results showed disparateness in COVID-19 risk areas with a higher risk in north-eastern and south-eastern zone wards within the boundary of JMC. We proposed prioritizing wards under higher risk zones for intelligent decision making regarding COVID-19 risk reduction through appropriate management of resources-related policy consequences. This study aims to serve as a baseline study to be replicated in other parts of the country or world to eradicate the threat of COVID-19 effectively.
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Affiliation(s)
- Shruti Kanga
- Centre for Climate Change & Water Research (C3WR)Suresh Gyan Vihar UniversityJaipurRajasthan302017India
| | - Gowhar Meraj
- Centre for Climate Change & Water Research (C3WR)Suresh Gyan Vihar UniversityJaipurRajasthan302017India
- Department of Ecology, Environment and Remote SensingGovernment of Jammu and KashmirSrinagar190018India
| | - Sudhanshu
- Centre for Climate Change & Water Research (C3WR)Suresh Gyan Vihar UniversityJaipurRajasthan302017India
| | - Majid Farooq
- Centre for Climate Change & Water Research (C3WR)Suresh Gyan Vihar UniversityJaipurRajasthan302017India
- Department of Ecology, Environment and Remote SensingGovernment of Jammu and KashmirSrinagar190018India
| | - M. S. Nathawat
- Department of GeographyIndira Gandhi National Open University (IGNOU)New DelhiIndia
| | - Suraj Kumar Singh
- Centre for Sustainable DevelopmentSuresh Gyan Vihar UniversityJaipurRajasthan302017India
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