Abstract

It is widely feared that a novel, highly pathogenic, human transmissible influenza virus may evolve that could cause the next global pandemic. Mitigating the spread of such an influenza pandemic would require not only the timely administration of antiviral drugs to those infected, but also the implementation of suitable intervention policies for stunting the spread of the virus. Towards this end, mathematical modelling and simulation studies are crucial as they allow us to evaluate the predicted effectiveness of the various intervention policies before enforcing them. Diagnosis plays a vital role in the overall pandemic management framework by detecting and distinguishing the pathogenic strain from the less threatening seasonal strains and other influenza-like illnesses. This allows treatment and intervention to be deployed effectively, given limited antiviral supplies and other resources. However, the time required to design a fast and accurate testkit for novel strains may limit the role of diagnosis. Herein, we aim to investigate the cost and effectiveness of different diagnostic methods using a stochastic agent-based city-scale model, and then address the issue of whether conventional testing approaches, when used with appropriate intervention policies, can be as effective as fast testkits in containing a pandemic outbreak. We found that for mitigation purposes, fast and accurate testkits are not necessary as long as sufficient medication is given, and are generally recommended only when used with extensive contact tracing and prophylaxis. Additionally, in the event of insufficient medication and fast testkits, the use of slower, conventional testkits together with proper isolation policies while waiting for the diagnostic results can be an equally effective substitute.