Scaling up Laboratory Testing Capacity in the Context of Managing Emerging Pandemic: Lessons Learned from Scaling Up SARS-COV-2 Testing in Rwanda

Authors : Emil Ivan; Patrick Gad Iradukunda; Pierre Gashema; Ingabire Angelique; Alice Kabanda; Enatha Mukantwari; Anicet Rucogoza; Jules Christian Ishimwe; Kamwesiga Julius; Emile Musoni; Eliah Shema; Tafadzwa Dzinamarira; Eugene Mutimura

Volume/Issue : Volume 6 - 2021, Issue 2 - February

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The coronavirus disease 2019 (COVID-19) has challenged health systems globally. In low and middleincome countries, a unique challenge ensuring the widespread testing that is critical to the response toward the pandemic has persisted. The pandemic has accentuated the need for rapid scale-up of real-time polymerase chain reaction (RT-PCR), a molecular testing technique that was often used for research purposes only, especially in limited-resource settings. Rwanda is a lowincome country that has managed to scale up RT-PCR laboratory testing capacity by 15-fold within the first four-month of the COVID-19 pandemic. Rwanda has been in line with the measures to contain COVID-19 even before 14th March 2020, when the first case of COVID-19 was detected. Due to the transmission of the infection, the scale-up was immediately in place, relying on public, private, nongovernmental, and voluntary students from universities to boost the surveillance of the pandemic in the population. In COVID-19 testing, this scale-up relied on population and government goodwill; research-based actions that included adopting a pooling strategy; optimized use of available human resources; and the use of limited resource funding models to support the established health system governance structure. Initially, the national reference laboratory was the only testing site for COVID-19; however, later, the country implemented decentralized testing, setting up COVID-19 testing centers countrywide. In this article, we highlight the lessons learned from the Rwandan COVID-19 laboratory testing response to guide effective laboratory response in limited-resource settings. We recommend an appropriate epidemic response algorithm to be developed to classify cases based on epidemiological, clinical, and laboratory information in line with diagnosis methods and sustain quality.



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