Algorithms; COVID-19/diagnosis; COVID-19/epidemiology; COVID-19/virology; COVID-19 Nucleic Acid Testing/methods; Humans; Population Surveillance/methods; Prevalence; Rwanda/epidemiology; SARS-CoV-2/isolation & purification; Sensitivity and Specificity; COVID-19; COVID-19 Nucleic Acid Testing; Population Surveillance; Rwanda; SARS-CoV-2; Multidisciplinary
Abstract :
[en] Suppressing infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will probably require the rapid identification and isolation of individuals infected with the virus on an ongoing basis. Reverse-transcription polymerase chain reaction (RT-PCR) tests are accurate but costly, which makes the regular testing of every individual expensive. These costs are a challenge for all countries around the world, but particularly for low-to-middle-income countries. Cost reductions can be achieved by pooling (or combining) subsamples and testing them in groups1-7. A balance must be struck between increasing the group size and retaining test sensitivity, as sample dilution increases the likelihood of false-negative test results for individuals with a low viral load in the sampled region at the time of the test8. Similarly, minimizing the number of tests to reduce costs must be balanced against minimizing the time that testing takes, to reduce the spread of the infection. Here we propose an algorithm for pooling subsamples based on the geometry of a hypercube that, at low prevalence, accurately identifies individuals infected with SARS-CoV-2 in a small number of tests and few rounds of testing. We discuss the optimal group size and explain why, given the highly infectious nature of the disease, largely parallel searches are preferred. We report proof-of-concept experiments in which a positive subsample was detected even when diluted 100-fold with negative subsamples (compared with 30-48-fold dilutions described in previous studies9-11). We quantify the loss of sensitivity due to dilution and discuss how it may be mitigated by the frequent re-testing of groups, for example. With the use of these methods, the cost of mass testing could be reduced by a large factor. At low prevalence, the costs decrease in rough proportion to the prevalence. Field trials of our approach are under way in Rwanda and South Africa. The use of group testing on a massive scale to monitor infection rates closely and continually in a population, along with the rapid and effective isolation of people with SARS-CoV-2 infections, provides a promising pathway towards the long-term control of coronavirus disease 2019 (COVID-19).
Disciplines :
Genetics & genetic processes
Author, co-author :
Mutesa, Leon ; Centre for Human Genetics, College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda ; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
Ndishimye, Pacifique; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda ; African Institute for Mathematical Sciences, Kigali, Rwanda
Butera, Yvan ; Université de Liège - ULiège > GIGA ; Centre for Human Genetics, College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda ; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
Souopgui, Jacob ; Centre for Human Genetics, College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda ; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda ; Department of Molecular Biology, Institute of Biology and Molecular Medicine, IBMM, Université Libre de Bruxelles, Gosselies, Belgium
Uwineza, Annette; Centre for Human Genetics, College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda ; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
Rutayisire, Robert; Centre for Human Genetics, College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda ; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
Ndoricimpaye, Ella Larissa; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
Musoni, Emile; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
Rujeni, Nadine; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
Nyatanyi, Thierry; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
Ntagwabira, Edouard; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
Semakula, Muhammed; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
Musanabaganwa, Clarisse; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
Nyamwasa, Daniel; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
Ndashimye, Maurice; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda ; African Institute for Mathematical Sciences, Kigali, Rwanda
Ujeneza, Eva ; African Institute for Mathematical Sciences, Kigali, Rwanda
Mwikarago, Ivan Emile; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
Muvunyi, Claude Mambo; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
Mazarati, Jean Baptiste; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
Nsanzimana, Sabin; Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
Turok, Neil ; African Institute for Mathematical Sciences, Kigali, Rwanda. Neil.Turok@ed.ac.uk ; School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK. Neil.Turok@ed.ac.uk ; Perimeter Institute for Theoretical Physics, Waterloo, Ontario, Canada. Neil.Turok@ed.ac.uk
Ndifon, Wilfred ; African Institute for Mathematical Sciences, Kigali, Rwanda. wndifon@nexteinstein.org
Acknowledgements We thank the Rwanda Ministry of Health through RBC for discussions and correspondence, K. Smith and C. Squire for providing encouragement and references, and A. Jackson for discussions. Research at AIMS is supported in part by the Carnegie Corporation of New York and by the Government of Canada through the International Development Research Centre and Global Affairs Canada. Research at the Perimeter Institute is supported in part by the Government of Canada through the Department of Innovation, Science and Economic Development Canada and by the Province of Ontario through the Ministry of Colleges and Universities. The data collection, SARS-CoV-2 molecular experiments and analysis of the study were supported by the Government of Rwanda (Rwanda Biomedical Centre/Ministry of Health) and the Académie de Recherche et d’Enseignement Supérieur in collaboration with the University of Rwanda (ARES-UR Programme). All statistical and mathematical analyses were supported by the African Institute for Mathematical Sciences (AIMS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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