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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 groups 1 – 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 test 8 . 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 studies 9 – 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). A mathematical algorithm for population-wide screening of SARS-CoV-2 infections using pooled parallel RT–PCR tests requires considerably fewer tests than individual testing procedures and has minimal delays in the identification of individuals infected with SARS-CoV-2.

The management of COVID-19 in Rwanda has been dynamic, and the use of COVID-19 therapeutics has gradually been updated based on scientific discoveries. The treatment for COVID-19 remained patient-centered and entirely state-sponsored during the first and second waves. From the time of identification of the index case in March 2020 up to August 2021, three versions of the clinical management guidelines were developed, with the aim of ensuring that the COVID-19 patients treated in Rwanda were receiving care based on the most recent therapeutic discoveries. As the case load increased and imposed imminent heavy burdens on the healthcare system, a smooth transition was made to enable that the asymptomatic and mild COVID-19 cases could continue to be closely observed and managed while they remained in their homes. The care provided to patients requiring facility-based interventions mainly focused on the provision of anti-inflammatory drugs, anticoagulation, broad-spectrum antibiotic therapy, management of hyperglycemia and the provision of therapeutics with a direct antiviral effect such as favipiravir and neutralizing monoclonal antibodies. The time to viral clearance was observed to be shortest among eligible patients treated with neutralizing monoclonal antibodies (bamlanivimab). Moving forward, as we strive to continue detecting COVID-19 cases as early as possible, and promptly initiate supportive interventions, the use of neutralizing monoclonal antibodies constitutes an attractive and cost-effective therapeutic approach. If this approach is used strategically along with other measures in place (i.e., COVID-19 vaccine roll out, etc.), it will enable us to bring this global battle against the COVID-19 pandemic under full control and with a low case fatality rate.

Suppressing SARS-CoV-2 will likely require the rapid identification and isolation of infected individuals, on an ongoing basis. RT-PCR (reverse transcription polymerase chain reaction) tests are accurate but costly, making regular testing of every individual expensive. The costs are a challenge for all countries and particularly for developing countries. Cost reductions can be achieved by combining samples and testing them in groups. We propose an algorithm for grouping subsamples, prior to testing, based on the geometry of a hypercube. At low prevalence, this testing procedure uniquely identifies infected individuals in a small number of tests. We discuss the optimal group size and explain why, given the highly infectious nature of the disease, parallel searches are preferred. We report proof of concept experiments in which a positive sample was detected even when diluted a hundred-fold with negative samples. Using these methods, the costs of mass testing could be reduced by a factor of ten to a hundred or more. If infected individuals are quickly and effectively quarantined, the prevalence will fall and so will the costs of regularly testing everyone. Such a strategy provides a possible pathway to the longterm elimination of SARS-CoV-2. Field trials of our approach are now under way in Rwanda and initial data from these are reported here.