We obtained R 0 by first turning off all intervention policies prior to simulation. We then randomly infect one susceptible person and simulate the model, noting down the number of people he/she transmits the virus to. This process is repeated 1000 times, and the average number of transmissions is reported as the R 0 value. With the default parameters, we computed R 0 in the model to be approximately 1.9. To see the effectiveness of the various diagnostic approaches and intervention policies under different viral transmissibility, we vary R 0 from 1.6 (low transmissibility) to 2.3 (high transmissibility) by manually adjusting the transmission coefficients b h , b w and b c (equation (2.1)), while ensuring that the transmission proportion between the different location types remains relatively constant. Figure 7 shows the changes of various indicators (AR Total , AR Peak , C d , C t , C w and C h ) for different diagnostic methods and intervention policies with varying transmissibility. In general, there is a monotonic increase across all indicators as R 0 increases. Again, within each graph, it is observed that values from the same intervention policy tend to cluster together, regardless of viral transmissibility (with the exception of testkit usage for diagnostic approach D1, figure 7d ). Hence during the onset of an outbreak, where the transmissibility is still unknown-which is often the case-it may be more prudent to first implement proper social distancing intervention policies, followed by the use of testkits to ensure that drug wastage is minimized.