Data analysis and predictive modelling of HPAI H5 and H7 outbreaks in the EU 2005‐2015

Abstract

All data from reported HPAI outbreaks in the period 2005 -2015 were collected and analysed with respect to its value for quantifying transmission of HPAI to other flocks, preferably in the form of a transmission kernel. The strain type, farm type and host species was taken into account. In total 345 outbreaks were registered. To quantify transmission to other flocks, the traced contacts are relevant. Even more interesting than the traced contacts, is the transmission to neighbouring farms, since it helps in quantifying the unspecified routes of infections, which cannot easily be prevented with a transport ban. Many different transmission routes may play a role, and by combining all these small routes into one transmission kernel, a valuable tool for analysis of transmission under control conditions is created. Unfortunately, data was only available on the density of flocks in the outbreak area for only 33 of the 345 outbreaks. This number is insufficient for a robust estimation of a transmission kernel, and it certainly does not allow for distinction between strains, types and species. However, it still has value for evaluating the suitability of existing kernel estimates for extrapolation to other countries, other virus strains and other host species. We found that these data indicate that the two available transmission kernels show higher transmission than fits with the many outbreaks in this dataset. We find that the transmission could easily be 5 times lower than the lower kernel estimate from the Netherlands, whereas the other available kernel, (from Italy) shows 5 times higher transmission than the Dutch kernel. Thus, using the Dutch kernel with an uncertainty of a factor 5, is a good indication of the potential transmission range between flocks of incoming new HPAI viruses.

A review of estimated transmission parameters for the spread of avian influenza viruses

“…We found a large variation in the estimated R 0 , latency and infectiousness between the studies, supporting a previous study of estimates restricted to experimental data (Central Veterinary Institute et al., 2017 ). These parameters are crucial when planning the response to an outbreak, especially if the response is based on simulation studies.…”

“…( 2021 ) found differences in the infectiousness of AIV between turkeys and chickens, with viremia higher in turkeys than in chickens in the case of LPAI. In contrast, a previous review of experimental data found that the range of R 0 for chickens, turkeys and ducks overlapped considerably, indicating that there might be no real difference (Central Veterinary Institute et al., 2017 ). The different R 0 estimates between species should be utilized when designing the response to outbreaks: biosecurity could be focused on high risk species such as ducks and turkeys, while chickens and poultry in general might exhibit a reduced risk of transmission.…”

“… 2021 ). In addition to predictive models for between‐flock transmission, within‐flock models are useful for decision makers when acting on specific outbreaks, as well as in combination with between‐flock simulation models (Central Veterinary Institute et al., 2017 ). Several models have been developed to predict the risk and spread of AIV within poultry populations, so as to inform decision‐makers (Stegeman et al.…”

“…However, the usefulness of such models depends on valid parameterization. One previous study collated parameter estimates for within‐flock transmission based on experimental studies, and found some differences in the basic reproduction number and infectious period between bird species (Central Veterinary Institute et al., 2017 ). However, in general, models have been calibrated using data collected from specific outbreaks.…”

Inferring within‐flock transmission dynamics of highly pathogenic avian influenza H5N8 virus in France, 2020

“…Because the flock was culled on 6 December, the number of dead ducks observed on the morning of that day (300) did not represent a full day's mortality and, hence, was not used for fitting the model. As shown in Table 1, the transmission rate and the mean durations of the latent and infectious periods were assigned informative gamma priors, calibrated from the literature (Hobbelen et al., 2020 ; Koeijer et al., 2017 ). In the absence of relevant information, the shape parameters of the latent and infectious periods were given uniform distributions between 0 and 5 and between 0 and 20, respectively.…”