A Review of the Important Weapons against Antimicrobial Resistance in Sub-Saharan Africa

Author:

Gahamanyi Noel1ORCID,Umuhoza Therese2ORCID,Saeed Shamsaldeen Ibrahim34ORCID,Mayigane Landry Ndriko5ORCID,Hakizimana Jean Nepomuscene6ORCID

Affiliation:

1. Biology Department, School of Science, College of Science and Technology, University of Rwanda, Kigali P.O. Box 3900, Rwanda

2. Institute of Tropical and Infectious Diseases, University of Nairobi, Nairobi P.O. Box 30197-00100, Kenya

3. Faculty of Veterinary Medicine, University Malaysia Kelantan, Pengkalan Chepa 16100, Kelantan, Malaysia

4. Faculty of Veterinary Science, University of Nyala, Nyala P.O. Box 155, Sudan

5. Rwanda Field Epidemiology and Laboratory Alumni Network (RWAFELAN), Kigali P.O. Box 3286, Rwanda

6. OR Tambo Africa Research Chair for Viral Epidemics, SACIDS Foundation for One Health, Sokoine University of Agriculture, Morogoro P.O. Box 3297, Tanzania

Abstract

Antimicrobial resistance (AMR) is one of the top 10 global health threats facing humanity, and the sub-Saharan Africa (SSA) is among the heavily affected regions due to its weak health systems and limited resources. Due to an escalating number of AMR pathogens and the scarcity of new antimicrobials, efforts in the prevention of infections and the search for alternative treatment options are ongoing. The objective of this review was to assess important weapons against AMR in SSA. The highlighted weapons include vaccines, education and awareness, infection prevention and control (IPC) using water, sanitation, and hygiene (WASH), alternative treatment options, the One Health (OH) approach, AMR surveillance, operational national action plans (NAPs) on AMR, antimicrobial stewardship (AMS) programs, and good governance and regulations. Despite not being used at a satisfactory level in SSA, advanced techniques in dealing with AMR in SSA include (i) metagenomics, (ii) whole-genome sequencing (WGS) in AMR surveillance to track resistance trends and know when to intervene, and (iii) use of artificial intelligence in AMR prediction based on genomics data. The fight against AMR threat in SSA has embraced a number of currently available strategies, and developing new ones will lower the consequences of such a threat for future generations.

Publisher

MDPI AG

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