Estimation of force of infection based on different epidemiological proxies : 2009/2010 Influenza epidemic in Malta

Abstract

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