Prediction of Infectious Disease Outbreaks Based on Limited Information

Information about infectious disease outbreaks is often gathered indirectly, from doctor’s reports and health board records. It also typically underestimates the actual number of cases, but the relationship between the observed proxies and the numbers that drive the diseases is complicated, nonlinear and potentially timeand state-dependent. We use a combination of data collection from the 2009-2010 H1N1 outbreak in Malta, compartmental modelling and Bayesian inference to explore the effect of using various sources of information (consultations, doctor’s diagnose, swabbing and molecular testing) on estimation of the effective basic reproduction ratio, Rt. Different proxies and different sampling rates (daily and weekly) lead to similar behaviour of Rt as the epidemic unfolds, although individual parameters (force of infection, length of latent and infectious period) vary. We also demonstrate that the relationship between different proxies varies as epidemic progresses, with the first period characterised by high ratio of consultations and influenza diagnoses to actual confirmed cases of H1N1. This has important consequences for modelling that is based on reconstructing influenza cases from doctor’s reports.