Alternative scan-based approaches to identify space-time clusters of highly pathogenic avian influenza virus H5N1 in wild birds in Denmark and Sweden in 2006

Abstract

The objective of this study was to demonstrate the effects of the nature of the information collected through passive surveillance on the detection of space-time clusters of highly pathogenic avian influenza virus (HPAIV) H5N1 cases reported among dead wild birds in Denmark and Sweden in 2006. Data included 1469 records (109 cases, 1360 controls) collected during the regional epidemic between February and June by passive surveillance of dead wild birds. Laboratory diagnoses were obtained by PCR methods and/or virus isolation. The nature of available information influences both the type of model suitable for analysis and its parameterization. Here, we explored four alternative scan-based methods, suitable for detection of clusters only when case data (univariate permutation model), case and hypothesized epidemiological variables (multivariate permutation model), case and control data (univariate Bernoulli model), and case, control, and hypothesized epidemiological variables (multivariate Bernoulli model) are available. Tufted ducks were particularly common among infected wild bird species detected in Denmark and Sweden during the initial phases of this epidemic, and species group (tufted ducks [62 cases, 57 controls] vs. other wild bird species [47 cases, 1303 controls]) was considered in the multivariate models as a covariate potentially associated with clustering. Bernoulli and permutation scan analyses both detected multiple significant (P < 0.01) clusters with similar locations, but with certain differences in their numbers and sizes. The observed-to-expected case ratios in the two clusters detected by the multivariate Bernoulli scan model were substantially heterogeneous. However, the permutation model detected only one of the Swedish clusters and only pinpointed the heterogeneity between species on clustering in the same Danish cluster as detected by the Bernoulli model. The output of the methods described here were shown to be highly sensitive to the choice of the probability model for cases and the choice of plausible assumptions to parameterize the scan statistic tests. The results of the multivariate Bernoulli suggest that with noncase information regarding a potential risk factor, such as species of birds, this method is sensitive and efficient in identifying high-risk areas and time periods for regional occurrence of HPAIV and potentially for similar infectious diseases. Results here demonstrate the impact that the nature of the collected information has on the epidemiological investigation of outbreaks. Results show the importance of collecting information on control data and on variables hypothesized to influence disease risk on the identification of periods of time and locations at high risk for the disease and risk factors associated with clustering as part of the national and international surveillance systems.