Influence des paramètres climatiques sur l’incidence de la COVID-19 de mars 2020 à décembre 2022 dans la région de Niamey au Niger
Abstract
Introduction : La maladie à coronavirus 2019 (COVID-19) est une maladie infectieuse due au nouveau coronavirus. Son émergence est un enjeu de santé publique mondial. Le rôle des facteurs climatiques dans sa transmission n’est pas déterminé avec précision. Objectif : Cette étude analyse la corrélation entre les paramètres climatiques (température, humidité relative, durée d’ensoleillement, vitesse du vent) et la dynamique de la COVID-19. Méthodologie : C’est une étude rétrospective, analytique, utilisant les données épidémiologiques de la COVID 19 (nombre quotidien de cas confirmés et des décès) de mars 2020 à décembre 2022. Ils ont été recueillis à la Direction de la Surveillance et la Riposte aux Epidémies (DSRE). Les données climatiques ont été recueillies à la Direction de la Météorologie Nationale (DMN). La méthode statistique utilisée est le coefficient de corrélation ‘’r’’ entre les variables climatiques et morbi-mortalité de la COVID-19. Résultats : L’analyse uni variée montre que le nombre de cas le plus élevé était observé en septembre. L’humidité relative minimale la plus élevée fut observée en août. L’analyse multivariée révèle une corrélation forte et positive entre la vitesse moyenne du vent et la morbidité (r= 0,14). La vitesse maximale du vent (r= 0,13), la vitesse minimale du vent (r= 0,15) influencent la survenue des nouveaux cas de la COVID-19. Conclusion : L’humidité relative a impact sur la morbi-mortalité, par opposition à la température. Le plus grand nombre des cas intervient en novembre, décembre et janvier, les mois durant lesquelles les températures sont plus basses. Ces informations sont utiles pour planifier et soutenir la lutte contre la COVID-19.
Introduction: Coronavirus 2019 (COVID-19) is an infectious disease caused by a new coronavirus. Its emergence is a global public health issue. The role of climatic factors in its transmission is not precisely determined. Objective: This study analyzes the correlation between climatic parameters (temperature, relative humidity, sunshine duration, wind speed) and the dynamics of COVID-19. Methodology: This is a retrospective, analytical study using COVID 19 epidemiological data (daily number of confirmed cases and deaths) from March 2020 to December 2022. They were collected at the Direction de la Surveillance et la Riposte aux Epidémies (DSRE). Climatic data were collected from the Direction de la Météorologie Nationale (DMN). The statistical method used was the "r" correlation coefficient between the COVID-19 climate and morbidity-mortality variables. Results: Univariate analysis shows that the highest number of cases occurred in September. Minimum relative humidity was highest in August. Multivariate analysis revealed a strong, positive correlation between mean wind speed and morbidity (r= 0.14). Maximum wind speed (r= 0.13) and minimum wind speed (r= 0.15) influenced the occurrence of new cases of COVID-19. Conclusion: Relative humidity has an impact on morbidity and mortality, as opposed to temperature. The greatest number of cases occurs in November, December and January, the months when temperatures are lowest. This information is useful for planning and supporting the fight against COVID-19.
Downloads
Metrics
References
2. Bochenek, B., Jankowski, M., Gruszczynska, M., Nykiel, G., Gruszczynski, M., Jaczewski, A., Ziemianski, M., Pyrc, R., Figurski, M., & Pinkas, J. (2021). Impact of Meteorological Conditions on the Dynamics of the COVID-19 Pandemic in Poland. International Journal of Environmental Research and Public Health, 18(8), Article 8. https://doi.org/10.3390/ijerph18083951
3. Candel, F. J., Barreiro, P., San Román, J., Abanades, J. C., Barba, R., Barberán, J., Bibiano, C., Canora, J., Cantón, R., Calvo, C., Carretero, M., Cava, F., Delgado, R., García-Rodríguez, J., González Del Castillo, J., González de Villaumbrosia, C., Hernández, M., Losa, J. E., Martínez-Peromingo, F. J., … Zapatero, A. (2020). Recommendations for use of antigenic tests in the diagnosis of acute SARS-CoV-2 infection in the second pandemic wave : Attitude in different clinical settings. Revista Espanola De Quimioterapia: Publicacion Oficial De La Sociedad Espanola De Quimioterapia, 33(6), 466‑484. https://doi.org/10.37201/req/120.2020
4. CDC. (2020, février 11). Coronavirus Disease 2019 (COVID-19). Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/index.html
5. Chung, H. W., Apio, C., Goo, T., Heo, G., Han, K., Kim, T., Kim, H., Ko, Y., Lee, D., Lim, J., Lee, S., & Park, T. (2021). Effects of government policies on the spread of COVID-19 worldwide. Scientific Reports, 11(1), 20495. https://doi.org/10.1038/s41598-021-99368-9
6. Corripio, J. G., & Raso, L. (2020). Weather variables impact on COVID-19 incidence (p. 2020.06.08.20125377). medRxiv. https://doi.org/10.1101/2020.06.08.20125377
7. COVID - Statistiques Coronavirus—Worldometer. (s. d.). Consulté 28 mai 2023, à l’adresse https://www.worldometers.info/coronavirus/
8. Dieng, A., Diouf, J. B. N., & Ndiaye, S. M. L. (2020). COVID-19 au Sénégal : Réflexion d´un microbiologiste. The Pan African Medical Journal, 35(Suppl 2), 31. https://doi.org/10.11604/pamj.supp.2020.35.2.22860
9. Dye, C. (2014). After 2015 : Infectious diseases in a new era of health and development. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1645), 20130426. https://doi.org/10.1098/rstb.2013.0426
10. Faranda, D., & Alberti, T. (2020). Modeling the second wave of COVID-19 infections in France and Italy via a stochastic SEIR model. Chaos (Woodbury, N.Y.), 30(11), 111101. https://doi.org/10.1063/5.0015943
11. Hardy, É. J. L., & Flori, P. (2021). Spécificités épidémiologiques de la COVID-19 en Afrique : Préoccupation de santé publique actuelle ou future ? Annales Pharmaceutiques Françaises, 79(2), 216‑226. https://doi.org/10.1016/j.pharma.2020.10.011
12. Hariharan, R. (2021). Random forest regression analysis on combined role of meteorological indicators in disease dissemination in an Indian city : A case study of New Delhi. Urban Climate, 36, 100780. https://doi.org/10.1016/j.uclim.2021.100780
13. Islam, A., Pakrashi, D., Vlassopoulos, M., & Wang, L. C. (2021). Stigma and misconceptions in the time of the COVID-19 pandemic : A field experiment in India. Social Science & Medicine, 278, 113966. https://doi.org/10.1016/j.socscimed.2021.113966
14. Islam, A. R. Md. T., Hasanuzzaman, Md., Shammi, M., Salam, R., Bodrud-Doza, Md., Rahman, Md. M., Mannan, Md. A., & Huq, S. (2021). Are meteorological factors enhancing COVID-19 transmission in Bangladesh? Novel findings from a compound Poisson generalized linear modeling approach. Environmental Science and Pollution Research, 28(9), 11245‑11258. https://doi.org/10.1007/s11356-020-11273-2
15. Islam, A., Sayeed, M. A., Rahman, M. K., Ferdous, J., Shano, S., Choudhury, S. D., & Hassan, M. M. (2021). Spatiotemporal patterns and trends of community transmission of the pandemic COVID-19 in South Asia : Bangladesh as a case study. Biosafety and Health, 3(1), 39‑49. https://doi.org/10.1016/j.bsheal.2020.09.006
16. Liu, D., Tai, Q., Wang, Y., Pu, M., Ge, S., Ji, T., Zhang, L., & Su, B. (2020). The impact of containment measures and air temperature on mitigating COVID-19 transmission : Non-classical SEIR modeling and analysis (p. 2020.05.12.20099267). medRxiv. https://doi.org/10.1101/2020.05.12.20099267
17. Liu, Y., Carlisle, E., Zhang, H., Yang, B., Steiner, M., Shao, T., Duan, B., Marone, F., Xiao, S., & Donoghue, P. C. J. (2022). Saccorhytus is an early ecdysozoan and not the earliest deuterostome. Nature, 609(7927), 541‑546. https://doi.org/10.1038/s41586-022-05107-z
18. Lyu, Y., Wang, Y., Jiang, C., Ding, C., Zhai, M., Xu, K., Wei, L., & Wang, J. (2023). Random forest regression on joint role of meteorological variables, demographic factors, and policy response measures in COVID-19 daily cases : Global analysis in different climate zones. Environmental Science and Pollution Research, 30(32), 79512‑79524. https://doi.org/10.1007/s11356-023-27320-7
19. Mutombo, P. N., Fallah, M. P., Munodawafa, D., Kabel, A., Houeto, D., Goronga, T., Mweemba, O., Balance, G., Onya, H., Kamba, R. S., Chipimo, M., Kayembe, J.-M. N., & Akanmori, B. (2022). COVID-19 vaccine hesitancy in Africa : A call to action. The Lancet. Global Health, 10(3), e320‑e321. https://doi.org/10.1016/S2214-109X(21)00563-5
20. Paraskevis, D., Kostaki, E. G., Alygizakis, N., Thomaidis, N. S., Cartalis, C., Tsiodras, S., & Dimopoulos, M. A. (2021). A review of the impact of weather and climate variables to COVID-19 : In the absence of public health measures high temperatures cannot probably mitigate outbreaks. The Science of the Total Environment, 768, 144578. https://doi.org/10.1016/j.scitotenv.2020.144578
21. Sehra, S. T., Salciccioli, J. D., Wiebe, D. J., Fundin, S., & Baker, J. F. (2020). Maximum Daily Temperature, Precipitation, Ultraviolet Light, and Rates of Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 in the United States. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America, 71(9), 2482‑2487. https://doi.org/10.1093/cid/ciaa681
22. Talbot, N., Takada, A., Bingham, A. H., Elder, D., Lay Yee, S., & Golubiewski, N. E. (2021). An investigation of the impacts of a successful COVID-19 response and meteorology on air quality in New Zealand. Atmospheric Environment, 254, 118322. https://doi.org/10.1016/j.atmosenv.2021.118322
23. Wan, X., Cheng, C., & Zhang, Z. (2020). Early transmission of COVID-19 has an optimal temperature but late transmission decreases in warm climate (p. 2020.05.14.20102459). medRxiv. https://doi.org/10.1101/2020.05.14.20102459
24. Wang, B., Liu, J., Li, Y., Fu, S., Xu, X., Li, L., Zhou, J., Liu, X., He, X., Yan, J., Shi, Y., Niu, J., Yang, Y., Li, Y., Luo, B., & Zhang, K. (2020). Airborne particulate matter, population mobility and COVID-19 : A multi-city study in China. BMC Public Health, 20(1), 1585. https://doi.org/10.1186/s12889-020-09669-3
25. Who-china-joint-mission-on-COVID-19-final-report.pdf. (s. d.). Consulté 28 mai 2023, à l’adresse https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-COVID-19-final-report.pdf
26. Yao, Y., Pan, J., Wang, W., Liu, Z., Kan, H., Qiu, Y., Meng, X., & Wang, W. (2020). Association of particulate matter pollution and case fatality rate of COVID-19 in 49 Chinese cities. The Science of the Total Environment, 741, 140396. https://doi.org/10.1016/j.scitotenv.2020.140396
27. Yeşilkanat, C. M. (2020). Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm. Chaos, Solitons & Fractals, 140, 110210. https://doi.org/10.1016/j.chaos.2020.110210
Copyright (c) 2024 Tcholi Idrissa Emoud, Hannadé Houma Ismaguil, Salifou Ibrahim Alkassoum, Halawen Mohamed Alassane, Ibrahim Maman Laminou, Eric Omar Adehossi
This work is licensed under a Creative Commons Attribution 4.0 International License.
.jpg)
