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ZENODO
Article . 2021
License: CC BY
Data sources: Datacite
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ZENODO
Article . 2021
License: CC BY
Data sources: Datacite
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Covid-19 Pandemic: Nigeria's Health Sector and the Imperatives of Reform

Authors: Isa, Mohammed; Sakue-Collins Yimovie;

Covid-19 Pandemic: Nigeria's Health Sector and the Imperatives of Reform

Abstract

This paper examines the impact of Coronavirus Disease 2019 (Covid-19) on Nigeria���s health sector. The novel Covid-19 which allegedly originated from Wuhan in Hubei Province of the People���s Republic of China in December, 2019, has been declared a pandemic ��� a global public health emergency ��� by the World Health Organization (WHO). The disease has been reported in over 192 countries across Asia, Europe, America, Africa, and Latin America, and the John Hopkins University Centre for Systems Science and Engineering (CSSE) currently estimates 119,875,420 infected persons, 67,895,864 recovered, and 2,653,652 deaths globally, with Nigeria having over 13,245 active cases, 145,399 recovered, and 2,013 deaths. Without disregard for the overwhelming effect of the pandemic on healthcare systems globally, Nigeria���s casualty is not unconnected to the sordid state of her healthcare sector. This paper argues that successive governments have neglected the country���s health sector and this has impacted its preparedness to respond to the Covid-19 pandemic. The study adopts content analysis and contends that the poor state of the healthcare system in Nigeria is associated with the phenomenon of medical tourism. The paper calls for the imperatives of reform in the health sector, emphasizing the integration of traditional medicine into mainstream healthcare delivery (vaccine nationalism) while prioritizing recruitment and re-training of health sector personnel and increase funding of the health sector in budgetary allocations.

Keywords

health sector, pandemic, medical tourism, Nigeria, Covid-19

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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