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Drikvandi M, Goudarzi M, Molavinia S, Baboli Z, Goudarzi G. The impact of COVID-19 pandemic lockdowns on air quality index: a systematic review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 2024; 34:1687-1700. [PMID: 37454284 DOI: 10.1080/09603123.2023.2234841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023]
Abstract
During the outbreak of the novel coronavirus disease 2019 (COVID-19), many countries implemented lockdown policies to control its transmission. These restrictions provided an opportunity to rest and recover the environment. This systematic review (SR) aimed to evaluate the impact of COVID-19 lockdowns on the Air Quality Index (AQI) in countries worldwide. ScienceDirect and PubMed were searched using relevant keywords to identify studies published until March 2020. Overall, 20 studies were included in the SR based on the eligibility criteria. The results show that COVID-19-related lockdown policies positively affect AQI by restricting air-polluting activities, such as transportation, industry, and construction. However, it is important to note that these policies are ineffective in controlling sources of natural air pollution and local dust. The findings of this study emphasize the need for policymakers to approve legislation limiting the sources of air pollutants.
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Affiliation(s)
- Mehrsa Drikvandi
- Department of Environmental Health Engineering, School of Public Health, Ahvaz Jundishapur University of Medical Science, Ahvaz, Iran
| | - Mahdis Goudarzi
- Department of Environmental Health Engineering, School of Public Health, Ahvaz Jundishapur University of Medical Science, Ahvaz, Iran
| | - Shahrzad Molavinia
- Department of Toxicology, Faculty of Pharmacy, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
- Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Zeynab Baboli
- Department of Environmental Health Engineering, Behbahan Faculty of Medical Sciences, Behbahan, Iran
| | - Gholamreza Goudarzi
- Department of Environmental Health Engineering, School of Public Health, Ahvaz Jundishapur University of Medical Science, Ahvaz, Iran
- Air Pollution and Respiratory Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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2
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Wang Y, Gong G, Shi X, Huang Y, Deng X. Investigation of the effects of temperature and relative humidity on the propagation of COVID-19 in different climatic zones. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023:10.1007/s11356-023-28237-x. [PMID: 37341939 DOI: 10.1007/s11356-023-28237-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 06/09/2023] [Indexed: 06/22/2023]
Abstract
This study aims to evaluate the effects of temperature and relative humidity on the propagation of COVID-19 for indoor heating, ventilation, and air conditioning design and policy development in different climate zones. We proposed a cumulative lag model with two specific parameters of specific average temperature and specific relative humidity to evaluate the impact of temperature and relative humidity on COVID-19 transmission by calculating the relative risk of cumulative effect and the relative risk of lag effect. We considered the temperature and relative humidity corresponding to the relative risk of cumulative effect or the relative risk of lag effect equal to 1 as the thresholds of outbreak. In this paper, we took the overall relative risk of cumulative effect equal to 1 as the thresholds. Data on daily new confirmed cases of COVID-19 since January 1, 2021, to December 31, 2021, for three sites in each of four climate zones similar to cold, mild, hot summer and cold winter, and hot summer and warm winter were selected for this study. Temperature and relative humidity had a lagged effect on COVID-19 transmission, with peaking the relative risk of lag effect at a lag of 3-7 days for most regions. All regions had different parameters areas with the relative risk of cumulative effect greater than 1. The overall relative risk of cumulative effect was greater than 1 in all regions when specific relative humidity was higher than 0.4, and when specific average temperature was higher than 0.42. In areas similar to hot summer and cold winter, temperature and the overall relative risk of cumulative effect were highly monotonically positively correlated. In areas similar to hot summer and warm winter, there was a monotonically positive correlation between relative humidity and the overall relative risk of cumulative effect. This study provides targeted recommendations for indoor air and heating, ventilation, and air conditioning system control strategies and outbreak prevention strategies to reduce the risk of COVID-19 transmission. In addition, countries should combine vaccination and non-pharmaceutical control measures, and strict containment policies are beneficial to control another pandemic of COVID-19 and similar viruses.
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Affiliation(s)
- Yuxin Wang
- College of Civil Engineering of Hunan University (HNU), Changsha, 410082, People's Republic of China
| | - Guangcai Gong
- College of Civil Engineering of Hunan University (HNU), Changsha, 410082, People's Republic of China.
| | - Xing Shi
- College of Civil Engineering of Hunan University (HNU), Changsha, 410082, People's Republic of China
| | - Yuting Huang
- College of Civil Engineering of Hunan University (HNU), Changsha, 410082, People's Republic of China
| | - Xiaorui Deng
- College of Civil Engineering of Hunan University (HNU), Changsha, 410082, People's Republic of China
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Khodabakhsh P, Asadnia A, Moghaddam AS, Khademi M, Shakiba M, Maher A, Salehian E. Prediction of in-hospital mortality rate in COVID-19 patients with diabetes mellitus using machine learning methods. J Diabetes Metab Disord 2023; 22:1-14. [PMID: 37363202 PMCID: PMC10182753 DOI: 10.1007/s40200-023-01228-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 04/24/2023] [Indexed: 06/28/2023]
Abstract
Background Since its emergence in December 2019, until June 2022, coronavirus 2019 (COVID-19) has impacted populations all around the globe with it having been contracted by ~ 535 M people and leaving ~ 6.31 M dead. This makes identifying and predicating COVID-19 an important healthcare priority. Method and Material The dataset used in this study was obtained from Shahid Beheshti University of Medical Sciences in Tehran, and includes the information of 29,817 COVID-19 patients who were hospitalized between October 8, 2019 and March 8, 2021. As diabetes has been shown to be a significant factor for poor outcome, we have focused on COVID-19 patients with diabetes, leaving us with 2824 records. Results The data has been analyzed using a decision tree algorithm and several association rules were mined. Said decision tree was also used in order to predict the release status of patients. We have used accuracy (87.07%), sensitivity (88%), and specificity (80%) as assessment metrics for our model. Conclusion Initially, this study provided information about the percentages of admitted Covid-19 patients with various underlying disease. It was observed that diabetic patients were the largest population at risk. As such, based on the rules derived from our dataset, we found that age category (51-80), CPR and ICU residency play a pivotal role in the discharge status of diabetic inpatients.
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Affiliation(s)
- Pooneh Khodabakhsh
- Department of IT and Computer Engineering, Azad Islamic University South Tehran Branch, Tehran, Iran
| | - Ali Asadnia
- Institute of Social Sciences, Department of Business Analytics, Marmara University, Istanbul, Turkey
| | | | - Maryam Khademi
- Department of Applied Mathematics, Azad Islamic University South Tehran Branch, Tehran, Iran
| | - Majid Shakiba
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Maher
- Department of Health Policy, Economics and Management, School of Management and Medical Education, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Elham Salehian
- Department of Information Technology, Medical Science of Shahid, Beheshti University, Tehran, Iran
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4
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Begou P, Kassomenos P. The ecosyndemic framework of the global environmental change and the COVID-19 pandemic. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159327. [PMID: 36220476 PMCID: PMC9547397 DOI: 10.1016/j.scitotenv.2022.159327] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/03/2022] [Accepted: 10/05/2022] [Indexed: 06/16/2023]
Abstract
The ecosyndemic theory combines the concept of 'synergy' with 'epidemic' and the term "eco" implies the role of the environmental changes. Each of the conditions enhances the negative impacts of the other in an additive way making our society more vulnerable and heightening individual risk factors. In this study, we analyze the mutually reinforcing links between the environment and health from the complexity angle of the ecosyndemic theory and propose the characterization of the COVID-19 pandemic as ecosyndemic. We use the term 'ecosyndemic' because the global environmental change contributes to local-scale, regional-scale and global-scale alterations of the Earth's systems. These changes have their root causes in the way that people interact with the physical, chemical, and biotic factors of the environment. These interactions disturb nature and the consequences have feedbacks in every living organism.
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Affiliation(s)
- Paraskevi Begou
- Laboratory of Meteorology and Climatology, Department of Physics, University of Ioannina, GR-45110 Ioannina, Greece.
| | - Pavlos Kassomenos
- Laboratory of Meteorology and Climatology, Department of Physics, University of Ioannina, GR-45110 Ioannina, Greece
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Zhai G, Qi J, Zhou W, Wang J. The non-linear and interactive effects of meteorological factors on the transmission of COVID-19: A panel smooth transition regression model for cities across the globe. INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION : IJDRR 2023; 84:103478. [PMID: 36505181 PMCID: PMC9721135 DOI: 10.1016/j.ijdrr.2022.103478] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 10/14/2022] [Accepted: 12/01/2022] [Indexed: 05/11/2023]
Abstract
The ongoing pandemic created by COVID-19 has co-existed with humans for some time now, thus resulting in unprecedented disease burden. Previous studies have demonstrated the non-linear and single effects of meteorological factors on viral transmission and have a question of how to exclude the influence of unrelated confounding factors on the relationship. However, the interactions involved in such relationships remain unclear under complex weather conditions. Here, we used a panel smooth transition regression (PSTR) model to investigate the non-linear interactive impact of meteorological factors on daily new cases of COVID-19 based on a panel dataset of 58 global cities observed between Jul 1, 2020 and Jan 13, 2022. This new approach offers a possibility of assessing interactive effects of meteorological factors on daily new cases and uses fixed effects to control other unrelated confounding factors in a panel of cities. Our findings revealed that an optimal temperature range (0°C-20 °C) for the spread of COVID-19. The effect of RH (relative humidity) and DTR (diurnal temperature range) on infection became less positive (coefficient: 0.0427 to -0.0142; p < 0.05) and negative (coefficient: -0.0496 to -0.0248; p < 0.05) with increasing average temperature(T). The highest risk of infection occurred when the temperature was -10 °C and RH was >80% or when the temperature was 10 °C and DTR was 1 °C. Our findings highlight useful implications for policymakers and the general public.
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Affiliation(s)
- Guangyu Zhai
- School of Economics and Management, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Jintao Qi
- School of Economics and Management, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Wenjuan Zhou
- Gansu Provincial Hospital, Lanzhou, 730000, China
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6
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Salcido A, Castro T. Influence of meteorological patterns on the 2020 COVID-19 pandemic in the Mexico City region. ENVIRONMENTAL ADVANCES 2022; 7:100157. [PMID: 34957431 PMCID: PMC8688192 DOI: 10.1016/j.envadv.2021.100157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 12/11/2021] [Accepted: 12/17/2021] [Indexed: 06/14/2023]
Abstract
Meteorology is a critical factor affecting respiratory infectious diseases such as MERS, SARS, and influenza, but its effect on the spread of the COVID-19 disease remains controversial. Nevertheless, since the infected people cough-jets produce plumes of droplets and aerosols that can travel for several meters in the atmosphere, the possible influence of wind circulation and atmospheric turbulence on the infectious plume's fate cannot be ignored. This paper applied cluster analysis for identifying the near surface wind circulation patterns and associated temperature and humidity distributions in the Mexico City Metropolitan Area (MCMA), then their influence on the spread of the COVID-19 disease during the 2020 pandemic was discussed. Meteorology data and daily numbers of confirmed COVID-19 infections were obtained from public sources. An intense infection activity occurred from October to December 2020, and notable spreading of the disease toward the southwest and south MCMA was observed. In the same period, temperature and humidity conditions that could favor the virus stability and replication were detected in the same sectors, besides 60% of the wind observations revealed considerable northerly components. These findings suggested the existence of correlations between both phenomena. For assessing the possible relationship, the Pearson coefficients between the daily confirmed infections and the temperature and inward flux were estimated, and values from -0.32 to -0.55 and 0.62 to 0.70 were obtained. Correlation was negligible for relative humidity. Multilinear regression for the daily infections in response to the meteorological variables produced coefficients of determination from 0.3839 to 0.6138. Because of its implications for public health, this topic deserves a more in-depth investigation.
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Affiliation(s)
- Alejandro Salcido
- Departamento de Física, Universidad Autónoma Metropolitana-Iztapalapa, San Rafael Atlixco 186, Ciudad de México 09340, Mexico
| | - Telma Castro
- Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México. Circuito exterior, Ciudad Universitaria, 04510, Coyoacán, Ciudad de México, Mexico
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7
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Milošević D, Middel A, Savić S, Dunjić J, Lau K, Stojsavljević R. Mask wearing behavior in hot urban spaces of Novi Sad during the COVID-19 pandemic. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 815:152782. [PMID: 34990675 PMCID: PMC8720675 DOI: 10.1016/j.scitotenv.2021.152782] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 12/25/2021] [Accepted: 12/26/2021] [Indexed: 05/17/2023]
Abstract
Urban overheating (due to climate change and urbanization) and COVID-19 are two converging crises that must be addressed in tandem. Fine-scale, place-based, people-centric biometeorological and behavioral data are needed to implement context-specific preventative measures such as mask-wearing. This study collected local biometeorological measurements in diverse urban spaces (square, urban park, river quay) in Novi Sad, Serbia on hot sunny summer days (27-30 August 2020) during the COVID-19 pandemic. Observations were supplemented by an online survey asking questions about thermal sensation, comfort, and concurrent protective behavior of the local population. Biometeorological measurements show that the main square in the city center was the most thermally uncomfortable area. According to the survey, it was also perceived as the least safe space to not contract the virus. The urban park was perceived as the most thermally comfortable area in the morning and during midday. It was also considered the safest urban space for outdoor activities. In the evening, the river quay was the most thermally comfortable area in the city. Intra-urban differences in Physiologically Equivalent Temperatures were highest during midday, while differences in air temperatures were highest in the evening. More than 70% of the respondents did not wear face masks when it was hot because of breathing issues and feeling warmer than without mask. Most people wearing a mask felt "slightly warm" in the morning and evening, while the majority of respondents felt "hot" during midday. Only 3% of the respondents felt comfortable while wearing a mask, while 97% experienced some degree of discomfort (from slight discomfort to very uncomfortable). Our study shows that fine scale temporal and spatial urban biometeorological data and population surveys should be included in decision-making processes during the pandemic to develop climate-sensitive health services that are place-based, people-centric, and facilitate planning towards green, resilient, and inclusive cities.
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Affiliation(s)
- Dragan Milošević
- Climatology and Hydrology Research Centre, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 3, 21000 Novi Sad, Serbia.
| | - Ariane Middel
- School of Arts, Media and Engineering, School of Computing and Augmented Intelligence, Arizona State University, 950 S. Forest Mall, Stauffer B258, Tempe, AZ 85281, USA.
| | - Stevan Savić
- Climatology and Hydrology Research Centre, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 3, 21000 Novi Sad, Serbia.
| | - Jelena Dunjić
- Department of Geography, Tourism and Hotel Management, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 3, 21000 Novi Sad, Serbia.
| | - Kevin Lau
- Institute of Future Cities, The Chinese University of Hong Kong, Hong Kong.
| | - Rastislav Stojsavljević
- Department of Geography, Tourism and Hotel Management, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 3, 21000 Novi Sad, Serbia.
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Yassin MF, Aldashti HA. Stochastic analysis of the relationship between atmospheric variables and coronavirus disease (COVID-19) in a hot, arid climate. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2022; 18:500-516. [PMID: 34156152 PMCID: PMC8427079 DOI: 10.1002/ieam.4481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 02/02/2021] [Accepted: 06/18/2021] [Indexed: 06/13/2023]
Abstract
The rapid outbreak of the coronavirus disease (COVID-19) has affected millions of people all over the world and killed hundreds of thousands. Atmospheric conditions can play a fundamental role in the transmission of a virus. The relationship between several atmospheric variables and the transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are therefore investigated in this study, in which the State of Kuwait, which has a hot, arid climate, is considered during free movement (without restriction), partial lockdown (partial restrictions), and full lockdown (full restriction). The relationship between the infection rate, growth rate, and doubling time for SARS-CoV-2 and atmospheric variables are also investigated in this study. Daily data describing the number of COVID-19 cases and atmospheric variables, such as temperature, relative humidity, wind speed, visibility, and solar radiation, were collected for the period February 24 to May 30, 2020. Stochastic models were employed to analyze how atmospheric variables can affect the transmission of SARS-CoV-2. The normal and lognormal probability and cumulative density functions (PDF and CDF) were applied to analyze the relationship between atmospheric variables and COVID-19 cases. The Spearman's rank correlation test and multiple regression model were used to investigate the correlation of the studied variables with the transmission of SARS-CoV-2 and to confirm the findings obtained from the stochastic models. The results indicate that relative humidity had a significant negative correlation with the number of COVID-19 cases, whereas positive correlations were observed for cases of infection and temperature, wind speed, and visibility. The infection rate for SARS-CoV-2 is directly proportional to the air temperature, wind speed, and visibility, whereas inversely related to the humidity. The lowest growth rate and longest doubling time of the COVID-19 infection occurred during the full lockdown period. The results in this study may help the World Health Organization (WHO) make specific recommendations about the outbreak of COVID-19 for decision-makers around the world. Integr Environ Assess Manag 2022;18:500-516. © 2021 SETAC.
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Affiliation(s)
- Mohamed F. Yassin
- Environmental Pollution and Climate ProgramKuwait Institute for Research and Science, SafatKuwait
| | - Hassan A. Aldashti
- Department of MeteorologyDirectorate General of Civil Aviation, SafatKuwait
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Chang Z, Zhan Z, Zhao Z, You Z, Liu Y, Yan Z, Fu Y, Liang W, Zhao L. Application of artificial intelligence in COVID-19 medical area: a systematic review. J Thorac Dis 2022; 13:7034-7053. [PMID: 35070385 PMCID: PMC8743418 DOI: 10.21037/jtd-21-747] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 09/02/2021] [Indexed: 01/08/2023]
Abstract
Background Coronavirus disease 2019 (COVID-19) has caused a large-scale global epidemic, impacting international politics and the economy. At present, there is no particularly effective medicine and treatment plan. Therefore, it is urgent and significant to find new technologies to diagnose early, isolate early, and treat early. Multimodal data drove artificial intelligence (AI) can potentially be the option. During the COVID-19 Pandemic, AI provided cutting-edge applications in disease, medicine, treatment, and target recognition. This paper reviewed the literature on the intersection of AI and medicine to analyze and compare different AI model applications in the COVID-19 Pandemic, evaluate their effectiveness, show their advantages and differences, and introduce the main models and their characteristics. Methods We searched PubMed, arXiv, medRxiv, and Google Scholar through February 2020 to identify studies on AI applications in the medical areas for the COVID-19 Pandemic. Results We summarize the main AI applications in six areas: (I) epidemiology, (II) diagnosis, (III) progression, (IV) treatment, (V) psychological health impact, and (VI) data security. The ongoing development in AI has significantly improved prediction, contact tracing, screening, diagnosis, treatment, medication, and vaccine development for the COVID-19 Pandemic and reducing human intervention in medical practice. Discussion This paper provides strong advice for using AI-based auxiliary tools for related applications of human diseases. We also discuss the clinicians’ role in the further development of AI. They and AI researchers can integrate AI technology with current clinical processes and information systems into applications. In the future, AI personnel and medical workers will further cooperate closely.
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Affiliation(s)
- Zhoulin Chang
- College of Mechanical and Electrical Engineering, Guangdong University of Science and Technology, Dongguan, China
| | - Zhiqing Zhan
- The Third Clinical College, Guangzhou Medical University, Guangzhou, China
| | - Zifan Zhao
- Nanshan College, Guangzhou Medical University, Guangzhou, China
| | - Zhixuan You
- Nanshan College, Guangzhou Medical University, Guangzhou, China
| | - Yang Liu
- School of Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Zhihong Yan
- Kuangji Medical Technology (Guangdong Hengqin) Co., Ltd., Zhuhai, China
| | - Yong Fu
- Kuangji Medical Technology (Guangdong Hengqin) Co., Ltd., Zhuhai, China
| | - Wenhua Liang
- Department of Thoracic Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Lei Zhao
- Department of Physiology, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China
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10
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Spada A, Tucci FA, Ummarino A, Ciavarella PP, Calà N, Troiano V, Caputo M, Ianzano R, Corbo S, de Biase M, Fascia N, Forte C, Gambacorta G, Maccione G, Prencipe G, Tomaiuolo M, Tucci A. Structural equation modeling to shed light on the controversial role of climate on the spread of SARS-CoV-2. Sci Rep 2021; 11:8358. [PMID: 33863938 PMCID: PMC8052355 DOI: 10.1038/s41598-021-87113-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 03/23/2021] [Indexed: 12/23/2022] Open
Abstract
Climate seems to influence the spread of SARS-CoV-2, but the findings of the studies performed so far are conflicting. To overcome these issues, we performed a global scale study considering 134,871 virologic-climatic-demographic data (209 countries, first 16 weeks of the pandemic). To analyze the relation among COVID-19, population density, and climate, a theoretical path diagram was hypothesized and tested using structural equation modeling (SEM), a powerful statistical technique for the evaluation of causal assumptions. The results of the analysis showed that both climate and population density significantly influence the spread of COVID-19 (p < 0.001 and p < 0.01, respectively). Overall, climate outweighs population density (path coefficients: climate vs. incidence = 0.18, climate vs. prevalence = 0.11, population density vs. incidence = 0.04, population density vs. prevalence = 0.05). Among the climatic factors, irradiation plays the most relevant role, with a factor-loading of - 0.77, followed by temperature (- 0.56), humidity (0.52), precipitation (0.44), and pressure (0.073); for all p < 0.001. In conclusion, this study demonstrates that climatic factors significantly influence the spread of SARS-CoV-2. However, demographic factors, together with other determinants, can affect the transmission, and their influence may overcome the protective effect of climate, where favourable.
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Affiliation(s)
- Alessia Spada
- Statistics and Mathematics Area, Department of Economics, University of Foggia, Foggia, Italy
| | - Francesco Antonio Tucci
- Agorà Biomedical Sciences, Etromapmax Pole, Lesina (FG), Italy
- Department of Pathology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Aldo Ummarino
- Agorà Biomedical Sciences, Etromapmax Pole, Lesina (FG), Italy.
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, 4, 20090, Pieve Emanuele (MI), Italy.
| | | | - Nicholas Calà
- Agorà Biomedical Sciences, Etromapmax Pole, Lesina (FG), Italy
| | | | - Michele Caputo
- Agorà Biomedical Sciences, Etromapmax Pole, Lesina (FG), Italy
| | | | - Silvia Corbo
- Agorà Biomedical Sciences, Etromapmax Pole, Lesina (FG), Italy
| | - Marco de Biase
- Agorà Biomedical Sciences, Etromapmax Pole, Lesina (FG), Italy
| | - Nicola Fascia
- Agorà Biomedical Sciences, Etromapmax Pole, Lesina (FG), Italy
| | - Chiara Forte
- Agorà Biomedical Sciences, Etromapmax Pole, Lesina (FG), Italy
| | | | | | | | | | - Antonio Tucci
- Agorà Biomedical Sciences, Etromapmax Pole, Lesina (FG), Italy
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11
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Imdad K, Sahana M, Rana MJ, Haque I, Patel PP, Pramanik M. A district-level susceptibility and vulnerability assessment of the COVID-19 pandemic's footprint in India. Spat Spatiotemporal Epidemiol 2020; 36:100390. [PMID: 33509422 PMCID: PMC7648890 DOI: 10.1016/j.sste.2020.100390] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 10/26/2020] [Accepted: 11/06/2020] [Indexed: 12/23/2022]
Abstract
Examines the spread of the COVID-19 pandemic in India in four separate time steps. Uses geospatial and geostatistical measure to identify viral hotspots and clusters. Analyses COVID-19′s correlates at the district level, eliciting detailed outputs. Gauges epidemiological susceptibility and socioeconomic vulnerability to COVID-19. Provides a framework for denoting districts where lockdown measures can be eased.
In this study, we trace the COVID-19 pandemic's footprint across India's districts. We identify its primary epicentres and the outbreak's imprint in India's hinterlands in four separate time-steps, signifying the different lockdown stages. We also identify hotspots and predict areas where the pandemic may spread next. Significant clusters in the country's western and northern parts pose risk, along with the threat of rising numbers in the east. We also perform epidemiological and socioeconomic susceptibility and vulnerability analyses, identifying resident populations that may be physiologically weaker, leading to a high incidence of cases and pinpoint regions that may report high fatalities due to ambient poor demographic and health-related factors. Districts with a high share of urban population and high population density face elevated COVID-19 risks. Aspirational districts have a higher magnitude of transmission and fatality. Discerning such locations can allow targeted resource allocation to combat the pandemic's next phase in India.
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Affiliation(s)
- Kashif Imdad
- Department of Geography, Pandit Prithi Nath PG College (affiliated to Chhatrapati Shahu Ji Maharaj University), 96/12, Mahatma Gandhi Marg, Kanpur 208001, Uttar Pradesh, India.
| | - Mehebub Sahana
- School of Environment, Education and Development, University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom.
| | - Md Juel Rana
- Centre for the Study of Regional Development, School of Social Sciences, Jawaharlal Nehru University, New Delhi 110067, India; International Institute for Population Sciences, Mumbai 400088, India.
| | - Ismail Haque
- Centre for the Study of Regional Development, School of Social Sciences, Jawaharlal Nehru University, New Delhi 110067, India; Indian Council for Research on International Economic Relations (ICRIER) Plot No. 16-17, Sector-6, Pushp Vihar Institutional Area, Saket, New Delhi 110017, India.
| | - Priyank Pravin Patel
- Department of Geography, Presidency University, 86/1, College Street, Kolkata 700073, West Bengal, India.
| | - Malay Pramanik
- Department of Development and Sustainability, School of Environment, Resources and Development, Asian Institute of Technology (AIT), PO. Box 4, Klong Luang, Pathumthani 12120, Thailand; Centre of International Politics, Organization, and Disarmament, School of International Studies, Jawaharlal Nehru University, New Delhi 110067, India.
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