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Abdrabo KI, Mabrouk M, Han H, Saber M, Kantoush SA, Sumi T. Mapping COVID-19's potential infection risk based on land use characteristics: A case study of commercial activities in two Egyptian cities. Heliyon 2024; 10:e24702. [PMID: 38312664 PMCID: PMC10834811 DOI: 10.1016/j.heliyon.2024.e24702] [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: 01/24/2023] [Revised: 01/07/2024] [Accepted: 01/12/2024] [Indexed: 02/06/2024] Open
Abstract
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.
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Affiliation(s)
- Karim I. Abdrabo
- Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, Japan
- Faculty of Urban and Regional Planning, Cairo University, Giza, Egypt
| | - Mahmoud Mabrouk
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China
- Faculty of Urban and Regional Planning, Cairo University, Giza, Egypt
| | - Haoying Han
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China
- Faculty of Innovation and Design, City University of Macau, Macau
| | - Mohamed Saber
- Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, Japan
| | - Sameh A. Kantoush
- Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, Japan
| | - Tetsuya Sumi
- Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, Japan
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Ghatee MA, Kanannejad Z, Nikaein K, Fallah N, Sabz G. Geo-climatic risk factors for chronic rhinosinusitis in southwest Iran. PLoS One 2023; 18:e0288101. [PMID: 37406025 DOI: 10.1371/journal.pone.0288101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 06/20/2023] [Indexed: 07/07/2023] Open
Abstract
Chronic rhinosinusitis (CRS) is a prevalent and disabling paranasal sinus disease associated with some environmental factors. In this study, we evaluated the effect of geo-climatic factors on CRS in a region of southwest Iran. The study mapped the residency addresses of 232 patients with CRS who lived in Kohgiluyeh and Boyer-Ahmad province and had undergone sinus surgery from 2014 to 2019. The effects of Mean Annual Humidity (MAH), Mean Annual Rainfall (MAR), Mean Annual Temperature (MAT), maximum MAT (maxMAT), minimum MAT (minMAT), Mean Annual Evaporation (MAE), wind, elevation, slope, and land cover were assessed on the occurrence of CRS using Geographical Information System (GIS). Statistical analysis was performed using univariate and multivariate binary logistic regression. Patients came from 55 points including villages, towns, and cities. In univariate analysis, climatic factors including MAT (OR = 0.537), minMAT (OR = 0.764), maxMAT (OR = 0.63), MAR (OR = 0.994), and MAH (OR = 0.626) were significantly related to CRS occurrence. Elevation (OR = 0.999), slope (OR = 0.9), and urban setting (OR = 24.667) were the significant determinants among geographical factors when analyzed independently. The multivariate analysis found maxMAT (OR = 0.5), MAR (OR = 0.994), elevation (OR = 0.998), and urban (OR = 16.8) as significant factors affecting CRS occurrence. The urban setting is the most critical factor affecting CRS disease. Cold and dry areas and low attitude are the other risk factors for CRS in Kohgiluyeh and Boyer-Ahmad province, southwest Iran.
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Affiliation(s)
- Mohammad Amin Ghatee
- Cellular and Molecular Research Center, Yasuj University of Medical Sciences, Yasuj, Iran
- Department of Parasitology, School of Medicine, Yasuj University of Medical Sciences, Yasuj, Iran
| | - Zahra Kanannejad
- Allergy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Koorosh Nikaein
- Student Research Committee, Yasuj University of Medical Sciences, Yasuj, Iran
| | - Niloufar Fallah
- Student Research Committee, Yasuj University of Medical Sciences, Yasuj, Iran
| | - Gholamabbas Sabz
- Cellular and Molecular Research Center, Yasuj University of Medical Sciences, Yasuj, Iran
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Jayallan B, Ngah NF, Hussain NI, Nik Jaafar NR, Aizuddin AN, Yong MH, Md Din N, Bastion MLC. Impact of Postponement of Appointments on Vision and Psychological Well-Being Among Outpatients Attending Ophthalmology Clinics: A Malaysian Perspective. Cureus 2023; 15:e38423. [PMID: 37273393 PMCID: PMC10233503 DOI: 10.7759/cureus.38423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/26/2023] [Indexed: 06/06/2023] Open
Abstract
INTRODUCTION During the COVID-19 pandemic, non-frontline medical disciplines, including ophthalmology, were advised to minimize their services to channel crucial healthcare resources to manage the surge in COVID-19 cases. The ophthalmology department postponed all non-urgent appointments and elective surgical procedures. However, little is known about the visual and mental health impact of these changes in ophthalmology services. Therefore, our study aimed to explore the impact of postponement in ophthalmology outpatient clinic appointments towards visual acuity (VA) changes and the psychological well-being of patients during the COVID-19 pandemic in Malaysia. METHODOLOGY This cross-sectional study, utilizing a convenience sampling method, recruited patients attending ophthalmology outpatient clinic services from July 2020 to June 2021 to participate in the study. The Snellen chart was used to measure the VA, and the Kessler psychological distress scale (K-10) was used to measure psychological distress levels among patients with (study) and without (controls) postponement of the appointment. Results: A total of 485 patients were included in the data analysis; 267 study and 218 controls. There is a statistically significant difference in categorical change of VA (p < 0.001) and categorical K-10 score (p = 0.048) among the study and control groups. Nonetheless, a decline in VA alone does not show a statistically significant association with an increased probability of experiencing psychological distress (p=0.149). CONCLUSION Postponement of ophthalmology appointments negatively affected the VA and the psychological well-being of patients. Appropriate assessment of patients before postponing their appointment is crucial to mitigate the worsening of VA and psychological distress.
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Affiliation(s)
- Bannu Jayallan
- Department of Ophthalmology, Hospital Canselor Tunku Mukhriz Universiti Kebangsaan Malaysia, Kuala Lumpur, MYS
| | - Nor Fariza Ngah
- Department of Ophthalmology, Hospital Shah Alam, Selangor, MYS
| | | | - Nik Ruzyanei Nik Jaafar
- Department of Psychiatry, Hospital Canselor Tunku Mukhriz Universiti Kebangsaan Malaysia, Kuala Lumpur, MYS
| | - Azimatun Noor Aizuddin
- Department of Research and Statistics, Hospital Canselor Tunku Mukhriz Universiti Kebangsaan Malaysia, Kuala Lumpur, MYS
| | - Meng Hsien Yong
- Department of Ophthalmology, Hospital Canselor Tunku Mukhriz Universiti Kebangsaan Malaysia, Kuala Lumpur, MYS
| | - Norshamsiah Md Din
- Department of Ophthalmology, Hospital Canselor Tunku Mukhriz Universiti Kebangsaan Malaysia, Kuala Lumpur, MYS
| | - Mae-Lynn Catherine Bastion
- Department of Ophthalmology, Hospital Canselor Tunku Mukhriz Universiti Kebangsaan Malaysia, Kuala Lumpur, MYS
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Atek S, Bianchini F, De Vito C, Cardinale V, Novelli S, Pesaresi C, Eugeni M, Mecella M, Rescio A, Petronzio L, Vincenzi A, Pistillo P, Giusto G, Pasquali G, Alvaro D, Villari P, Mancini M, Gaudenzi P. A predictive decision support system for coronavirus disease 2019 response management and medical logistic planning. Digit Health 2023; 9:20552076231185475. [PMID: 37545633 PMCID: PMC10399258 DOI: 10.1177/20552076231185475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 06/14/2023] [Indexed: 08/08/2023] Open
Abstract
Objective Coronavirus disease 2019 demonstrated the inconsistencies in adequately responding to biological threats on a global scale due to a lack of powerful tools for assessing various factors in the formation of the epidemic situation and its forecasting. Decision support systems have a role in overcoming the challenges in health monitoring systems in light of current or future epidemic outbreaks. This paper focuses on some applied examples of logistic planning, a key service of the Earth Cognitive System for Coronavirus Disease 2019 project, here presented, evidencing the added value of artificial intelligence algorithms towards predictive hypotheses in tackling health emergencies. Methods Earth Cognitive System for Coronavirus Disease 2019 is a decision support system designed to support healthcare institutions in monitoring, management and forecasting activities through artificial intelligence, social media analytics, geospatial analysis and satellite imaging. The monitoring, management and prediction of medical equipment logistic needs rely on machine learning to predict the regional risk classification colour codes, the emergency rooms attendances, and the forecast of regional medical supplies, synergically enhancing geospatial and temporal dimensions. Results The overall performance of the regional risk colour code classifier yielded a high value of the macro-average F1-score (0.82) and an accuracy of 85%. The prediction of the emergency rooms attendances for the Lazio region yielded a very low root mean square error (<11 patients) and a high positive correlation with the actual values for the major hospitals of the Lazio region which admit about 90% of the region's patients. The prediction of the medicinal purchases for the regions of Lazio and Piemonte has yielded a low root mean squared percentage error of 16%. Conclusions Accurate forecasting of the evolution of new cases and drug utilisation enables the resulting excess demand throughout the supply chain to be managed more effectively. Forecasting during a pandemic becomes essential for effective government decision-making, managing supply chain resources, and for informing tough policy decisions.
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Affiliation(s)
- Sofiane Atek
- Department of Aerospace and Mechanical Engineering, Sapienza University of Rome, Rome, Italy
| | | | - Corrado De Vito
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy
| | - Vincenzo Cardinale
- Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Umberto I Policlinico of Rome, Rome, Italy
| | - Simone Novelli
- Department of Aerospace and Mechanical Engineering, Sapienza University of Rome, Rome, Italy
| | - Cristiano Pesaresi
- Department of Letters and Modern Cultures, Sapienza University of Rome, Rome, Italy
| | - Marco Eugeni
- Department of Aerospace and Mechanical Engineering, Sapienza University of Rome, Rome, Italy
| | - Massimo Mecella
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | | | | | | | | | | | | | - Domenico Alvaro
- Sapienza Information-Based Technology InnovaTion Center for Health (STITCH), Sapienza University of Rome, Rome, Italy
| | - Paolo Villari
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy
| | - Marco Mancini
- Department of Letters and Modern Cultures, Sapienza University of Rome, Rome, Italy
| | - Paolo Gaudenzi
- Department of Aerospace and Mechanical Engineering, Sapienza University of Rome, Rome, Italy
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Jiménez-Carvelo AM, Li P, Erasmus SW, Wang H, van Ruth SM. Spatial-Temporal Event Analysis as a Prospective Approach for Signalling Emerging Food Fraud-Related Anomalies in Supply Chains. Foods 2022; 12:foods12010061. [PMID: 36613277 PMCID: PMC9818448 DOI: 10.3390/foods12010061] [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: 11/22/2022] [Revised: 12/18/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
One of the pillars on which food traceability systems are based is the unique identification and recording of products and batches along the supply chain. Patterns of these identification codes in time and place may provide useful information on emerging food frauds. The scanning of codes on food packaging by users results in interesting spatial-temporal datasets. The analysis of these data using artificial intelligence could advance current food fraud detection approaches. Spatial-temporal patterns of the scanned codes could reveal emerging anomalies in supply chains as a result of food fraud in the chain. These patterns have not been studied yet, but in other areas, such as biology, medicine, credit card fraud, etc., parallel approaches have been developed, and are discussed in this paper. This paper projects these approaches for transfer and implementation in food supply chains in view of future applications for early warning of emerging food frauds.
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Affiliation(s)
- Ana M. Jiménez-Carvelo
- Food Quality and Design, Wageningen University and Research, P.O. Box 17, 6700 AA Wageningen, The Netherlands
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, C/Fuentenueva, s/n, E-18071 Granada, Spain
| | - Pengfei Li
- Food Quality and Design, Wageningen University and Research, P.O. Box 17, 6700 AA Wageningen, The Netherlands
| | - Sara W. Erasmus
- Food Quality and Design, Wageningen University and Research, P.O. Box 17, 6700 AA Wageningen, The Netherlands
| | - Hui Wang
- School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast BT9 5BN, UK
| | - Saskia M. van Ruth
- Food Quality and Design, Wageningen University and Research, P.O. Box 17, 6700 AA Wageningen, The Netherlands
- Institute for Global Food Security, School of Biological Sciences, Queen’s University, 19 Chlorine Gardens, Belfast BT9 5DL, UK
- UCD School of Agriculture and Food Science, University College Dublin, 4 Dublin, Ireland
- Correspondence:
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Curtis AJ, Ajayakumar J, Curtis J, Brown S. Spatial Syndromic Surveillance and COVID-19 in the U.S.: Local Cluster Mapping for Pandemic Preparedness. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:8931. [PMID: 35897298 PMCID: PMC9330043 DOI: 10.3390/ijerph19158931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/14/2022] [Accepted: 07/16/2022] [Indexed: 02/04/2023]
Abstract
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.
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Affiliation(s)
- Andrew J. Curtis
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; (A.J.C.); (J.A.)
| | - Jayakrishnan Ajayakumar
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; (A.J.C.); (J.A.)
| | - Jacqueline Curtis
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; (A.J.C.); (J.A.)
| | - Sam Brown
- University Hospitals, Cleveland, OH 44106, USA;
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