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Bilancia M, Vitale D, Manca F, Perchinunno P, Santacroce L. A dynamic causal modeling of the second outbreak of COVID-19 in Italy. ADVANCES IN STATISTICAL ANALYSIS : ASTA : A JOURNAL OF THE GERMAN STATISTICAL SOCIETY 2023:1-30. [PMID: 36776481 PMCID: PMC9904269 DOI: 10.1007/s10182-023-00469-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 01/04/2023] [Indexed: 02/10/2023]
Abstract
While the vaccination campaign against COVID-19 is having its positive impact, we retrospectively analyze the causal impact of some decisions made by the Italian government on the second outbreak of the SARS-CoV-2 pandemic in Italy, when no vaccine was available. First, we analyze the causal impact of reopenings after the first lockdown in 2020. In addition, we also analyze the impact of reopening schools in September 2020. Our results provide an unprecedented opportunity to evaluate the causal relationship between the relaxation of restrictions and the transmission in the community of a highly contagious respiratory virus that causes severe illness in the absence of prophylactic vaccination programs. We present a purely data-analytic approach based on a Bayesian methodology and discuss possible interpretations of the results obtained and implications for policy makers.
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Affiliation(s)
- Massimo Bilancia
- Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro, Policlinic University Hospital – Piazza G. Cesare 11, 70124 Bari, Italy
| | - Domenico Vitale
- MEMOTEF Department, University of Roma La Sapienza, Via del Castro Laurenziano 9, 00161 Rome, Italy
| | - Fabio Manca
- Department of Education, Psychology, Communication (ForPsiCom), University of Bari Aldo Moro, Palazzo Chiaia Napolitano – Via S. Crisanzio 42, 70122 Bari, Italy
| | - Paola Perchinunno
- Department of Business and Law Studies (DEMDI), University of Bari Aldo Moro, Largo Abbazia di Santa Scolastica 53, 70124 Bari, Italy
| | - Luigi Santacroce
- Department of Interdisciplinary Medicine (DIM) and Microbiology and Virology Unit, University of Bari Aldo Moro, Policlinic University Hospital – Piazza G. Cesare 11, 70124 Bari, Italy
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Moazeni M, Rahimi M, Ebrahimi A. What are the Effects of Climate Variables on COVID-19 Pandemic? A Systematic Review and Current Update. Adv Biomed Res 2023; 12:33. [PMID: 37057247 PMCID: PMC10086649 DOI: 10.4103/abr.abr_145_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 01/05/2022] [Accepted: 01/19/2022] [Indexed: 04/15/2023] Open
Abstract
The climatological parameters can be different in various geographical locations. Moreover, they have possible impacts on COVID-19 incidence. Therefore, the purpose of this systematic review article was to describe the effects of climatic variables on COVID-19 pandemic in different countries. Systematic literature search was performed in Scopus, ISI Web of Science, and PubMed databases using ("Climate" OR "Climate Change" OR "Global Warming" OR "Global Climate Change" OR "Meteorological Parameters" OR "Temperature" OR "Precipitation" OR "Relative Humidity" OR "Wind Speed" OR "Sunshine" OR "Climate Extremes" OR "Weather Extremes") AND ("COVID" OR "Coronavirus disease 2019" OR "COVID-19" OR "SARS-CoV-2" OR "Novel Coronavirus") keywords. From 5229 articles, 424 were screened and 149 were selected for further analysis. The relationship between meteorological parameters is variable in different geographical locations. The results indicate that among the climatic indicators, the temperature is the most significant factor that influences on COVID-19 pandemic in most countries. Some studies were proved that warm and wet climates can decrease COVID-19 incidence; however, the other studies represented that warm location can be a high risk of COVID-19 incidence. It could be suggested that all climate variables such as temperature, humidity, rainfall, precipitation, solar radiation, ultraviolet index, and wind speed could cause spread of COVID-19. Thus, it is recommended that future studies will survey the role of all meteorological variables and interaction between them on COVID-19 spread in specific small areas such as cities of each country and comparison between them.
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Affiliation(s)
- Malihe Moazeni
- Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
- Student Research Committee, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohammad Rahimi
- Department of Combat Desertification, Faculty of Desert Studies, Semnan University, Semnan, Iran
| | - Afshin Ebrahimi
- Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
- Environment Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
- Address for correspondence: Dr. Afshin Ebrahimi, Department of Environmental Health Engineering, School of Health, Hezar-Jerib Ave., Isfahan University of Medical Sciences, Isfahan, 81676 − 36954, Iran. E-mail:
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Chaitanya P, Upadhyay E, Kulkarni A, Raju PVS. Effect of association of temperature and pollutant levels on COVID-19 spread over Jaipur. VEGETOS (BAREILLY, INDIA) 2022; 36:133-140. [PMID: 36312873 PMCID: PMC9592543 DOI: 10.1007/s42535-022-00500-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 09/26/2022] [Accepted: 10/01/2022] [Indexed: 11/07/2022]
Abstract
The association of temperature and air pollutants is a very prominent factor which significantly affects human health and may cause diseases such as respiratory illness, cardiovascular mortality in spreading of different pathogenic diseases. The pandemic due to covid-19 infection may be affected by temperature and concentration of pollutants. Jaipur is one of the most polluted cities in Rajasthan of India as per World Health Organization, 2016; also, Jaipur city has a hot semi-arid climate with extremely hot summers. This fact tempered us to examine the impact of the association of temperature and pollutants on corona-virus infection in humans over Jaipur. Analysis was conducted by correlating air pollutants (PM10, PM2.5, NO2, SO2, CO) on seasonal variations because the temperature is one of the major factors in changing seasons. Association between the number of Covid cases and temperature in Jaipur was observed during December 2019 to December 2020. Seasonal analysis indicated that the intensity of Covid-19 infection varied according to increase or decrease in temperature.
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Affiliation(s)
- P. Chaitanya
- Amity Institute of Biotechnology, Amity University Rajasthan, Jaipur, India
| | - Era Upadhyay
- Amity Institute of Biotechnology, Amity University Rajasthan, Jaipur, India
| | - Akshay Kulkarni
- Centre for Ocean Atmospheric Science and Technology, Amity University Rajasthan, Jaipur, India
| | - P. V. S. Raju
- Centre for Ocean Atmospheric Science and Technology, Amity University Rajasthan, Jaipur, India
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Jin B, Ji J, Yang W, Yao Z, Huang D, Xu C. Analysis on the spatio-temporal characteristics of COVID-19 in mainland China. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION : TRANSACTIONS OF THE INSTITUTION OF CHEMICAL ENGINEERS, PART B 2021; 152:291-303. [PMID: 34121818 PMCID: PMC8183012 DOI: 10.1016/j.psep.2021.06.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 06/01/2021] [Accepted: 06/02/2021] [Indexed: 05/10/2023]
Abstract
COVID-19 has brought many unfavorable effects on humankind and taken away many lives. Only by understanding it more profoundly and comprehensively can it be soundly defeated. This paper is dedicated to studying the spatial-temporal characteristics of the epidemic development at the provincial-level in mainland China and the civic-level in Hubei Province. Moreover, a correlation analysis on the possible factors that cause the spatial differences in the epidemic's degree is conducted. After completing these works, three different methods are adopted to fit the daily-change tendencies of the number of confirmed cases in mainland China and Hubei Province. The three methods are the Logical Growth Model (LGM), Polynomial fitting, and Fully Connected Neural Network (FCNN). The analysis results on the spatial-temporal differences and their influencing factors show that: (1) The Chinese government has contained the domestic epidemic in early March 2020, indicating that the number of newly diagnosed cases has almost zero increase since then. (2) Throughout the entire mainland of China, effective manual intervention measures such as community isolation and urban isolation have significantly weakened the influence of the subconscious factors that may impact the spatial differences of the epidemic. (3) The classification results based on the number of confirmed cases also prove the effectiveness of the isolation measures adopted by the governments at all levels in China from another aspect. It is reflected in the small monthly grade changes (even no change) in the provinces of mainland China and the cities in Hubei Province during the study period. Based on the experimental results of curve-fitting and considering the time cost and goodness of fit comprehensively, the Polynomial(Degree = 18) model is recommended in this paper for fitting the daily-change tendency of the number of confirmed cases.
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Affiliation(s)
- Biao Jin
- College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350108, China
- Digital Fujian Institute of Big Data Security Technology, Fuzhou 350108, China
| | - Jianwan Ji
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wuheng Yang
- School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China
| | - Zhiqiang Yao
- College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350108, China
| | - Dandan Huang
- College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350108, China
| | - Chao Xu
- College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350108, China
- Digital Fujian Institute of Big Data Security Technology, Fuzhou 350108, China
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Li J, Chu B, Chai N, Wu B, Shi B, Ou F. Work Resumption Rate and Migrant Workers' Income During the COVID-19 Pandemic. Front Public Health 2021; 9:678934. [PMID: 34095076 PMCID: PMC8175901 DOI: 10.3389/fpubh.2021.678934] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 03/31/2021] [Indexed: 12/02/2022] Open
Abstract
The COVID-19 public health crisis has quickly led to an economic crisis, impacting many people and businesses in the world. This study examines how the pandemic affects workforces and workers' income. We quantify the impact of staggered resumption of work, after the coronavirus lockdowns, on the migrant workers' income. Using data on population movements of 366 Chinese cities at the daily level from the Baidu Maps-Migration Big Data Platform and historical data on the average monthly income of migrant workers, we find that the average work resumption rate (WRR) during the period of the Chinese Lantern Festival was 25.25%, which was only 30.67% of that in the same matched lunar calendar period in 2019. We then apply Gray Model First Order One Variable [GM (1, 1)] to predict the monthly income of migrant workers during the period of the COVID-19 pandemic. We show that, if without the influence of the COVID-19 pandemic, the average monthly income of migrant workers in 2020 will be expected to increase by 12% compared with 2019. We further conduct scenario analysis and show that the average monthly income of migrant workers in 2020 under the conservative scenario (COS), medium scenario (MES), and worse scenario (WOS) will be predicted to decrease by 2, 21, and 44%, respectively. Through testing, our prediction error is <5%. Our findings will help policymakers to decide when and how they implement a plan to ease the coronavirus lockdown and related financial support policies.
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Affiliation(s)
- Jiaxiang Li
- College of Economics and Management, Northwest A&F University, Yangling, China
| | - Baoju Chu
- State Grid Fujian Electric Power Co. Ltd., Fujian, China
- School of Economics, University of Nottingham, Ningbo, China
| | - Nana Chai
- College of Economics and Management, Northwest A&F University, Yangling, China
| | - Bi Wu
- Research Center for Rural Economy, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Baofeng Shi
- College of Economics and Management, Northwest A&F University, Yangling, China
| | - Feiya Ou
- Business School, University of Manchester, Manchester, United Kingdom
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