Abstract
Highly pathogenic avian influenza H5N1 remains a persistent public health threat, capable of causing infection in humans with a high mortality rate while simultaneously negatively impacting the livestock industry. A central question is to determine regions that are likely sources of newly emerging influenza strains with pandemic causing potential. A suitable candidate is Bangladesh, being one of the most densely populated countries in the world and having an intensifying farming system. It is therefore vital to establish the key factors, specific to Bangladesh, that enable both continued transmission within poultry and spillover across the human–animal interface. We apply a modelling framework to H5N1 epidemics in the Dhaka region of Bangladesh, occurring from 2007 onwards, that resulted in large outbreaks in the poultry sector and a limited number of confirmed human cases. This model consisted of separate poultry transmission and zoonotic transmission components. Utilising poultry farm spatial and population information a set of competing nested models of varying complexity were fitted to the observed case data, with parameter inference carried out using Bayesian methodology and goodness-of-fit verified by stochastic simulations. For the poultry transmission component, successfully identifying a model of minimal complexity, which enabled the accurate prediction of the size and spatial distribution of cases in H5N1 outbreaks, was found to be dependent on the administration level being analysed. A consistent outcome of non-optimal reporting of infected premises materialised in each poultry epidemic of interest, though across the outbreaks analysed there were substantial differences in the estimated transmission parameters. The zoonotic transmission component found the main contributor to spillover transmission of H5N1 in Bangladesh was found to differ from one poultry epidemic to another. We conclude by discussing possible explanations for these discrepancies in transmission behaviour between epidemics, such as changes in surveillance sensitivity and biosecurity practices.
Highlights
The H5N1 subtype of highly pathogenic avian influenza (HPAI) has caused considerable concern since the initial observation of the virus in southern China during 1996 (Sims et al, 2005)
With a number of countries in South and Southeast Asia, including China, Vietnam, Indonesia and Bangladesh, being gravely affected, a number of studies have predominately focused on either spatio-temporal analysis of outbreaks (Pfeiffer et al, 2007; Ahmed et al, 2010, 2011; Minh et al, 2011; Dhingra et al, 2014), or on determining ecological/environmental risk factors for H5N1 avian influenza emergence and spread at region-wide (Gilbert et al, 2008), national (Gilbert et al, 2007; Van Boeckel et al, 2012; Loth et al, 2010) and sub-national levels (Henning et al, 2009)
We extended the model by including a “spark” term parameter i to allow for spontaneous, non-distance dependent infections that were unexplained by the susceptibility, transmissibility and kernel components of the model (Deardon et al, 2010)
Summary
The H5N1 subtype of highly pathogenic avian influenza (HPAI) has caused considerable concern since the initial observation of the virus in southern China during 1996 (Sims et al, 2005). With a number of countries in South and Southeast Asia, including China, Vietnam, Indonesia and Bangladesh, being gravely affected, a number of studies have predominately focused on either spatio-temporal analysis of outbreaks (Pfeiffer et al, 2007; Ahmed et al, 2010, 2011; Minh et al, 2011; Dhingra et al, 2014), or on determining ecological/environmental risk factors for H5N1 avian influenza emergence and spread at region-wide (Gilbert et al, 2008), national (Gilbert et al, 2007; Van Boeckel et al, 2012; Loth et al, 2010) and sub-national levels (Henning et al, 2009). Across studies and regions three types of variables with similar statistical association with H5N1 were identified: domestic waterfowl, human related variables (e.g. human population density) and indicators of water presence (Gilbert and Pfeiffer, 2012)
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