IJREH | Assessment of the Spatial and Temporal Trend of the COVID-19 Pandemic in Senegal

Indexing and Abstracting

Assessment of the Spatial and Temporal Trend of the COVID-19 Pandemic in Senegal

Cheikh Faye , Eddy Nilsone Gomis , Sidy Diéye , René Ndimag Diouf , Fall Aïdara Cherif Amadou Lamine

International Journal of Rural Development, Environment and Health Research(IJREH), Vol-4,Issue-4, July - August 2020, Pages 113-125, 10.22161/ijreh.4.4.1

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Abstract:

Following the declaration of COVID-19 as a global pandemic and the reporting of one case in Senegal, the number of regions with confirmed cases of infection increased considerably, with the disease now being reported throughout the country after 3 months of evolution. It is therefore necessary to assess the evolution of the disease in the country as the situation evolves in order to rapidly identify best practices for adoption. The objective of this paper is to make a preliminary spatial and temporal assessment and comparison of the results of the COVID-19 pandemic in the regions of Senegal. Data on the evolution of COVID-19 (confirmed cases of infection, deaths, recoveries), population, density and area of each region were analysed using a set of statistical tools. The results show that the COVID-19 pandemic has spread stubbornly in Senegal. In the space of 112 days (from March 2 to June 21), Senegal reached a number of 5888 infected cases for 3919 cured, 1885 active and 84 deaths for a total of 67855 tests performed. About 40 people out of 10,000 have been tested so far and 4 out of 10,000 have tested positive. The Mann-Kendall test indicates that the number of confirmed daily cases is slowly increasing, with the slope of Sen estimated at about 1.2 person/day across the country. In addition, the Pettitt test indicates a sharp change in the upward trend across the country on April 26, 2020. Among the main affected regions, Dakar, Thies and Touba are noted with an extremely high rate of increase. Principal component analysis and hierarchical ascending classification have made it possible to divide Senegal's 14 regions into 3 groups in terms of the number of confirmed cases, active cases, recovered cases and reported deaths, and the population, area and density of the region. The 1st group concerns the Dakar region, the 2nd Diourbel and Thies and the 3rd the other regions. Furthermore, statistics related to COVID-19 in the regions of Senegal are highly correlated with population size and density. This study revealed convincing spatial differences in the evolution of the pandemic between the regions of Senegal. The study recommends that the approaches adopted by regions that have achieved very low levels of COVID-19 be incorporated into health care management plans for the pandemic throughout the country, even as the situation evolves.

Keywords:

COVID-19, global pandemic, assessment, trend, Senegal.

References:

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[2] Agence Nationale de la Statistique et de la Démographie (ANSD), 2020 : La production statistique dans le contexte de pandémie du Covid-19 : les mesures prises par l'ANSD. http://www.ansd.sn/index.php?option=com_content&view=article&id=596.

[3] Baba Hamed K. et Bouanan. A., 2016 : Caractérisation d’un bassin versant par l’analyse statistique des paramètres morphométriques : Cas du bassin versant de la Tafna. (Nord-ouest algérien). Geo-Eco-Trop., 40, 4 : 277-286.

[4] Boko M., I., Niang A., Nyong C., Vogel A., Githeko M., Medany B., Osman-Elasha R., Tabo and Yanda P., 2007 : Africa - Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge UK, 433-467.

[5] Cucinotta D. and Vanelli M., 2020 : WHO declares COVID-19 a pandemic. Acta Biomed 91(1): 157 – 160. doi: 10.23750/abm.v91i1.9397.

[6] Faye C., Wade C.T., Dione I. D., 2020 : A dissymmetry in the Fig.s related to the Covid-19 pandemic in the World: What factors explain the difference between Africa and the rest of the World?. medRxiv 2020.05.17.20104687; doi: https://doi.org/10.1101/2020.05.17.20104687

[7] Faye, C., 2014 : Méthode d’analyse statistique de données morphométriques : corrélation de paramètres morphométriques et influence sur l’écoulement des sous-bassins du fleuve Sénégal. Cinq Continents, 4 (10): 80-108.

[8] Gatto M., Bertuzzo E., Mari L., Miccoli S., Carraro L., Casagrandi R., Rinaldo A., 2020 : Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures. PNAS 117, 10484–10491. https://doi.org/10.1073/pnas.2004978117.

[9] Gibbs E. P. J., 2005 : Emerging zoonotic epidemics in the interconnected global community. Veterinary Record, 157(22), 673-679.

[10] Gilbert M., Pullano G., Pinotti F., Valdano E., Poletto C., Boelle P., D’Ortenzio E., Yazdanpanah Y., Eholie S.P., Altmann M., Gutierrez B., Kraemer M.U.G. and Colizza V., 2020 : Preparedness and vulnerability of African countries against importations of COVID- 19: a modelling study. Lancet, 395(10227): 871-7. DOI: https://doi.org/10.1016/S0140-6736(20)30411-6.

[11] Gupta A., Pradhan B., 2020 : Assessment of temporal trend of COVID-19 outbreak in India. https://www.researchgate.net/publication/342179820.

[12] Hoogeveen J., Tesliuc E., Vakis R. and Dercon S., 2004 : A guide to the analysis of risk, vulnerability and vulnerable groups. Washington, DC, USA: The World Bank.

[13] Kendall, M., 1975: Multivariate Analysis. Charles Griffin & Company, London, 202 p.

[14] Leung K., Wu J.T., Liu D., Leung G.M., 2020 : First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: a modelling impact assessment. The Lancet 395, 1382–1393. https://doi.org/10.1016/S0140-6736(20)30746-7.

[15] Lubes-Niel H., Masson J.M., Paturel J.E., Servat E., 1998 : Variabilité climatique et statistiques. Etude par simulation de la puissance et de la robustesse de quelques tests utilisés pour vérifier l'homogénéité de chroniques. Revue des Sciences de l’Eau, N° 3, 383-408.

[16] Makoni M., 2020 : Keeping COVID-19 at bay in Africa. Lancet Respir Med. https://doi.org/10.1016/S2213-2600(20)30219-8

[17] Mann, H.B., 1945: Nonparametric Tests against Trend, Econometrica. 13 (3): 245-259.

[18] Martinez-Alvarez M., Jarde A., Usuf E., Brotherton H., Bittaye M., Samateh A.L., Antonio M., Vives-Tomas J., D’Alessandro U. and Roca A., 2020 : COVID-19 pandemic in West Africa. Lancet Glob Health Online First. DOI: https://doi.org/10.1016/S2214-109X(20)30123-6.

[19] Ministère de la Santé et de l’Action sociale (MSAS), 2020 : Informations sur le coronavirus. http://www.sante.gouv.sn/Pr%C3%A9sentation/coronavirus-informations-officielles-et-quotidiennes-du-msas

[20] Nkengasong J.N. and Mankoula, 2020 : Looming threat of COVID-19 infection in Africa: act collectively, and fast. Lancet 395 (10227): P841-842. doi: 10.1016/S0140-6736(20)30464-5. Epub 2020 Feb 27.

[21] OMS, 2020d : World Health Organization. https://www.worldometers.info/coronavirus/

[22] Pettitt A. N., 1979: A non-parametric approach to the change-point problem. Appl. Statist., 28 (2), 126-135.

[23] Sen, P.K., 1968 : Estimates of the Regression Coefficient Based on Kendall’s Tau. Journal of the American Statistical Association, 63, 1379-1389.

[24] Servat E., Paturel J. E., Kouame B., Travaglio M., Ouedraogo M., Boyer J. F., Lubes-NieL H., Fritsch J. M., Masson J.M., Marieu B., 1998 : Identification, caractérisation et conséquences d’une variabilité hydrologique en Afrique de l’Ouest et centrale. IAHS Publication, N°252, 323-337.

[25] The World Bank, 2020 : In the face of Coronavirus, African countries apply lessons from Ebola response. https://www.worldbank.org/en/news/feature/2020/04/03/.

[26] UNESCO, 2020 : COVID-19 au Sénégal : Des mesures fortes pour endiguer la contagion. https://fr.unesco.org/news/covid-19-au-senegal-mesures-fortes-endiguer-contagion.

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