The ongoing pandemic of the recently-emerged SARS-CoV-2 is critically challenging health systems worldwide. The virus is characterized by fever and severe acute respiratory syndrome 1, 2 . As of March 17, the World Health Organization (WHO) has reported over 170,000 cases with over 10,000 new diagnoses added in 24 hours 3 .
Detecting carriers of the virus is fundamental to response efforts. It ensures the quarantine of COVID-19 patients to prevent local spread 1 , and more broadly informs national response measures 4 .
Nevertheless, as monitoring capacity is limited, testing in most countries is generally focused on acutely ill patients, while potentially infectious carriers at the community remain undiagnosed. As many countries are already experiencing shortages of diagnosis kits and factories struggling to keep with the demand [5] [6] [7] , it has become important to come up with new ways to conserve the reagents used for diagnostic tests. At the same time, as the disease is novel, it is of value to validate any modifications to the testing process before universal adoption 8,9 . Pooling diagnostic tests has been applied in other infectious diseases and is especially attractive as it requires no additional training, equipment, or materials. In this method, first suggested by Dorfman in 1943 10 and perfected over the years [11] [12] [13] , samples are mixed and tested at a single pool, and subsequent individual tests are made only if the pool tests positive. In addition to being used in the clinic for infectious disease diagnostics in previous epidemics 14, 15 , pooling has been proven to work for RT-qPCR 16, 17 , a time-consuming step for which the reagents are expected to be in short supply 18 .
Nonetheless, as SARS-CoV-2 is a novel pathogen, it is unclear how diluting a sample containing its RNA would affect the sensitivity of this assay and the false-negative rate.
A c c e p t e d M a n u s c r i p t 5 Here, we test the ability of the standard RT-qPCR test for detecting a single positive sample within a pool of negative samples. First, pooling RNA extracts of clinical samples, we tested previouslyconfirmed positive samples alone and combined with an increasing number of previously-confirmed negative samples and found that positive samples can still be well observed in pools of up to 32 samples, and possibly even 64 with additional PCR cycles. Second, pooling prior to RNA extraction was tested and successfully demonstrated for 8-sample pools.
Swabs from both nostrils and the throat were previously collected by healthcare providers and sent to the Virology laboratory at the Rambam Health Care Campus, Haifa, Israel. A volume of 130 microliter of the transport swab buffer was mixed with 270 microliter lysis buffer and RNA was extracted using magLEAD (Precision System Science). We obtained samples tested between March 4-15, 2020. 
This study was granted exemption from IRB approval for use of deidentified discarded RNA samples of COVID-19 tests by The Rambam Health Care Campus IRB committee.
The original diagnostic run at the Rambam Health Care Campus was robust. Positive samples had on average 135 ± 32 -fold stronger fluorescence relative to negatives, and the positive samples reached the threshold, which we set at a fluorescence of 300 according to CDC guidelines 8 , at the 25.5± 6.1 cycle ( Figure 1A) . The five positive samples selected similarly averaged at Ct of 24.5±3.1 and maximum fluorescence of 5164±912 ( Figure 1B) . Compared to unpooled tests, the Ct of pools increased, on average, by 3.6±2.1, 2.9±0.45 and 2.4±0.11 for genes E, RdRp and N, respectively (Fig. 4, Supplementary Fig. 2) . The average increase in Ct was 2.9, which is in accordance with an 8 fold dilution of the initial samples.
We found that a single clinical sample with SARS-CoV-2 RNA can be consistently detected in a pool of up to 16 to 32 samples. Pooling this way leads to only a linear increase in the threshold cycle (Ct).
Even for a relatively restrictive cutoff, pooling would reach sensitivity of 96% for a pool size of 16 samples. For high-fold pooling, our data shows an estimated false negative rate of 10% (1 out of 10), which is relatively small compared to the inherent clinical sensitivity of the standard assay 20 . We further demonstrate that pooling works well also when applied prior to RNA extraction.
RT-qPCR could be further optimized for the detection of low-concentration RNA. For instance, additional amplification cycles could lower detection limit allowing better detection for pools of more than 32 samples, which based on extrapolation of the data we expect would allow the 64sample pools of positive sample #5 to cross the threshold. In addition, some abnormalities as with duplicate B in positive samples #5 and #2 could have been due to interference from contamination in one or more of the samples. The unusual peaks for positive sample #2 could be due to a changing salt concentration that disturbed the TaqMan probe. Since both these issues could be solved by A c c e p t e d M a n u s c r i p t 10 further diluting the RNA samples with water, it is worthwhile to explore whether diluting samples with different ratios of water could improve the integrity of the signal in pooled results. While we tested samples with a range of different signal strengths, the detection of samples with even lower signals may warrant the use of smaller pools. Alternatively, adding a few additional PCR cycles could be considered as a means to increase detection rate of such low viral load samples. In general, as RT-qPCR kits and protocols vary internationally, use of suggested pooling may require validation for each specific setting.
These results can be used not only for pooling, but also in multiplexing and any other signal compression techniques where samples are mixed to reduce the number of tests. We hope that this proof-of-concept will encourage others to develop mathematical and computational tools tailored for the pooling of SARS-CoV-2 tests.
Pooling is especially useful for routine community survey and for monitoring of cohesive groups.
Local and global epidemic response critically depend on determining carriage frequency in the population, which is greatly enabled by pooling techniques. Furthermore, pooling techniques can be used for routine monitoring of essential work groups, such as hospital staff, military units, and factory workers. While the frequency of infection in these groups may be low, diagnosing even a single positive person typically requires quarantine of the entire group to prevent further spread in the community. In these surveillance applications, pooling may allow more routine monitoring and detection of low frequency of carriage thereby informing policy makers, reducing transmission, and alleviating the strain on healthcare services. 