A decision support tool for evaluating the impact of a diagnostic-capacity and antiviral-delivery constrained intervention strategy on an influenza pandemic

The 2009 H1N1 experience in Australia and elsewhere highlighted the difficulties faced by public health authorities in diagnosing infections and delivering antiviral agents (e.g. oseltamivir) as treatment for cases and prophylaxis for contacts in a timely manner. Consequently, forecasts from mathematical models of the possible benefits of widespread antiviral interventions were largely unmet. We summarise results from a recently developed model that includes realworld constraints, such as finite diagnostic and antiviral distribution capacities. We find that use of antiviral agents might be capable of containing or substantially mitigating an epidemic in only a small proportion of epidemic scenarios given Australia's existing public health capacities. We then introduce a statistical model that, based on just three characteristics of a hypothetical outbreak [(i) the basic reproduction number, (ii) the reduction in infectiousness of cases when provided with antiviral agents as treatment, and (iii) the proportion of cases that present for medical attention], accurately predicts whether or not an antiviral intervention strategy will be successful. The model highlights the importance of having data collection tools in place prior to a pandemic outbreak, so as to make accurate and timely estimates of key epidemiological parameters unique (in both time and place) to any particular epidemic.