  Background: Rapid testing for an infection is paramount during a pandemic to
prevent continued viral spread and excess morbidity and mortality. This study
aimed to determine whether alternative testing strategies based on sample
pooling can increase the speed and throughput of screening for SARS-CoV-2.
  Methods: A mathematical modelling approach was chosen to simulate six
different testing strategies based on key input parameters (infection rate,
test characteristics, population size, testing capacity etc.). The situations
in five countries (US, DE, UK, IT and SG) currently experiencing COVID-19
outbreaks were simulated to reflect a broad variety of population sizes and
testing capacities. The primary study outcome measurements that were finalised
prior to any data collection were time and number of tests required; number of
cases identified; and number of false positives.
  Findings: The performance of all tested methods depends on the input
parameters, i.e. the specific circumstances of a screening campaign. To screen
one tenth of each country's population at an infection rate of 1% - e.g. when
prioritising frontline medical staff and public workers -, realistic optimised
testing strategies enable such a campaign to be completed in ca. 29 days in the
US, 71 in the UK, 25 in Singapore, 17 in Italy and 10 in Germany (ca. eight
times faster compared to individual testing). When infection rates are
considerably lower, or when employing an optimal, yet logistically more complex
pooling method, the gains are more pronounced. Pool-based approaches also
reduces the number of false positive diagnoses by 50%.
  Interpretation: The results of this study provide a clear rationale for
adoption of pool-based testing strategies to increase speed and throughput of
testing for SARS-CoV-2. The current individual testing approach unnecessarily
wastes valuable time and resources.
