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Topaloglu MS, Sogut O, Az A, Ergenc H, Akdemir T, Dogan Y. The impact of meteorological factors on the spread of COVID-19. Niger J Clin Pract 2023; 26:485-490. [PMID: 37203114 DOI: 10.4103/njcp.njcp_591_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Background Clinical studies suggest that warmer climates slow the spread of viral infections. In addition, exposure to cold weakens human immunity. Aim This study describes the relationship between meteorological indicators, the number of cases, and mortality in patients with confirmed coronavirus disease 2019 (COVID-19). Patients and Methods This was a retrospective observational study. Adult patients who presented to the emergency department with confirmed COVID-19 were included in the study. Meteorological data [mean temperature, minimum (min) temperature, maximum (max) temperature, relative humidity, and wind speed] for the city of Istanbul were collected from the Istanbul Meteorology 1st Regional Directorate. Results The study population consisted of 169,058 patients. The highest number of patients were admitted in December (n = 21,610) and the highest number of deaths (n = 46) occurred in November. In a correlation analysis, a statistically significant, negative correlation was found between the number of COVID-19 patients and mean temperature (rho = -0.734, P < 0.001), max temperature (rho = -0.696, P < 0.001) or min temperature (rho = -0.748, P < 0.001). Besides, the total number of patients correlated significantly and positively with the mean relative humidity (rho = 0.399 and P = 0.012). The correlation analysis also showed a significant negative relationship between the mean, maximum, and min temperatures and the number of deaths and mortality. Conclusion Our results indicate an increased number of COVID-19 cases during the 39-week study period when the mean, max, and min temperatures were consistently low and the mean relative humidity was consistently high.
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Affiliation(s)
- M S Topaloglu
- Department of Emergency Medicine, Haseki Training and Research Hospital, University of Health Sciences, Istanbul, Turkey
| | - O Sogut
- Department of Emergency Medicine, Haseki Training and Research Hospital, University of Health Sciences, Istanbul, Turkey
| | - A Az
- Department of Emergency Medicine, Haseki Training and Research Hospital, University of Health Sciences, Istanbul, Turkey
| | - H Ergenc
- Department of Emergency Medicine, Haseki Training and Research Hospital, University of Health Sciences, Istanbul, Turkey
| | - T Akdemir
- Department of Emergency Medicine, Haseki Training and Research Hospital, University of Health Sciences, Istanbul, Turkey
| | - Y Dogan
- Department of Emergency Medicine, Haseki Training and Research Hospital, University of Health Sciences, Istanbul, Turkey
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Zhang S, Wang B, Yin L, Wang S, Hu W, Song X, Feng H. Novel Evidence Showing the Possible Effect of Environmental Variables on COVID-19 Spread. Geohealth 2022; 6:e2021GH000502. [PMID: 35317468 PMCID: PMC8923516 DOI: 10.1029/2021gh000502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 11/09/2021] [Accepted: 11/17/2021] [Indexed: 06/09/2023]
Abstract
Coronavirus disease (COVID-19) remains a serious issue, and the role played by meteorological indicators in the process of virus spread has been a topic of academic discussion. Previous studies reached different conclusions due to inconsistent methods, disparate meteorological indicators, and specific time periods or regions. This manuscript is based on seven daily meteorological indicators in the NCEP reanalysis data set and COVID-19 data repository of Johns Hopkins University from 22 January 2020 to 1 June 2021. Results showed that worldwide average temperature and precipitable water (PW) had the strongest correlation (ρ > 0.9, p < 0.001) with the confirmed COVID-19 cases per day from 22 January to 31 August 2020. From 22 January to 31 August 2020, positive correlations were observed between the temperature/PW and confirmed COVID-19 cases/deaths in the northern hemisphere, whereas negative correlations were recorded in the southern hemisphere. From 1 September to 31 December 2020, the opposite results were observed. Correlations were weak throughout the near full year, and weak negative correlations were detected worldwide (|ρ| < 0.4, p ≤ 0.05); the lag time had no obvious effect. As the latitude increased, the temperature and PW of the maximum confirmed COVID-19 cases/deaths per day generally showed a decreasing trend; the 2020-year fitting functions of the response latitude pattern were verified by the 2021 data. Meteorological indicators, although not a decisive factor, may influence the virus spread by affecting the virus survival rates and enthusiasm of human activities. The temperature or PW threshold suitable for the spread of COVID-19 may increase as the latitude decreases.
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Affiliation(s)
- Sixuan Zhang
- College of Atmospheric ScienceChengdu University of Information TechnologyChengduChina
| | - Bingyun Wang
- College of Atmospheric ScienceChengdu University of Information TechnologyChengduChina
| | - Li Yin
- Panzhihua Central HospitalPanzhihuaChina
| | - Shigong Wang
- College of Atmospheric ScienceChengdu University of Information TechnologyChengduChina
- Zunyi Academician Work CenterZunyiChina
| | - Wendong Hu
- College of Atmospheric ScienceChengdu University of Information TechnologyChengduChina
| | - Xueqian Song
- College of ManagementChengdu University of Information TechnologyChengduChina
| | - Hongmei Feng
- College of Atmospheric ScienceChengdu University of Information TechnologyChengduChina
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Ateba FF, Febrero-Bande M, Sagara I, Sogoba N, Touré M, Sanogo D, Diarra A, Magdalene Ngitah A, Winch PJ, Shaffer JG, Krogstad DJ, Marker HC, Gaudart J, Doumbia S. Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models. Int J Environ Res Public Health 2020; 17:E6339. [PMID: 32878174 PMCID: PMC7504016 DOI: 10.3390/ijerph17176339] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 08/19/2020] [Accepted: 08/26/2020] [Indexed: 01/22/2023]
Abstract
Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012-2017) from 1400 persons who sought treatment at Dangassa's community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.
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Affiliation(s)
- François Freddy Ateba
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.S.); (A.D.)
- Department of Mathematics, University of Quebec at Montreal (UQAM), Montréal, QC H2X 3Y7, Canada
- Faculty of Health Sciences, University of Buea, Buea BP 63, Cameroon;
| | - Manuel Febrero-Bande
- Department of Statistics, Mathematical Analysis and Optimization, University of Santiago de Compostela, Santiago de Compostela, 15782 Galicia, Spain;
| | - Issaka Sagara
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.S.); (A.D.)
- Department of Public Health Education and Research, Faculty of Medicine and Odonto-Stomatology, University of Sciences, Techniques and Technologies of Bamako, Bamako 1805, Mali
| | - Nafomon Sogoba
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.S.); (A.D.)
| | - Mahamoudou Touré
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.S.); (A.D.)
| | - Daouda Sanogo
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.S.); (A.D.)
| | - Ayouba Diarra
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.S.); (A.D.)
| | | | - Peter J. Winch
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; (P.J.W.); (H.C.M.)
| | - Jeffrey G. Shaffer
- Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street New Orleans, New Orleans, Louisiana, LA 70112, USA; (J.G.S.); (D.J.K.)
| | - Donald J. Krogstad
- Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street New Orleans, New Orleans, Louisiana, LA 70112, USA; (J.G.S.); (D.J.K.)
| | - Hannah C. Marker
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; (P.J.W.); (H.C.M.)
| | - Jean Gaudart
- Aix Marseille University, APHM, INSERM, IRD, SESSTIM, Hop Timone, BioSTIC, Biostatistics & ICT, 13005 Marseille, France;
| | - Seydou Doumbia
- Malaria Research and Training Center, Faculty of Medicine, Pharmacy and Dentistry, University of Sciences, Techniques and Technologies of Bamako, Bamako BP 1805, Mali; (F.F.A.); (I.S.); (N.S.); (M.T.); (D.S.); (A.D.)
- Department of Public Health Education and Research, Faculty of Medicine and Odonto-Stomatology, University of Sciences, Techniques and Technologies of Bamako, Bamako 1805, Mali
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