Detection of mortality clusters associated with highly pathogenic avian influenza in poultry: a theoretical analysis

Identification of High-Risk Areas for the Spread of Highly Pathogenic Avian Influenza in Central Luzon, Philippines

Highly pathogenic avian influenza virus (HPAIV) is a major problem in the poultry industry. It is highly contagious and is associated with a high mortality rate. The Philippines experienced an outbreak of avian influenza (AI) in 2017. As there is always a risk of re-emergence, efforts to manage disease outbreaks should be optimal. Linked to this is the need for an effective surveillance procedure to capture disease outbreaks at their early stage. Risk-based surveillance is the most effective and economical approach to outbreak management. This study evaluated the potential of commercial poultry farms in Central Luzon to transmit HPAI by calculating their respective reproductive ratios (R0). The reproductive number for each farm is based on the spatial kernel and the infectious period. A risk map has been created based on the calculated R₀. There were 882 (76.63%) farms with R0 < 1. Farms with R0 ≥ 1 were all located in Pampanga Province. These farms were concentrated in the towns of San Luis (n = 12) and Candaba (n = 257). This study demonstrates the utility of mapping farm-level R0 estimates for informing HPAI risk management activities.

Mortality-Based Triggers and Premovement Testing Protocols for Detection of Highly Pathogenic Avian Influenza Virus Infection in Commercial Upland Game Birds

Outbreaks involving avian influenza viruses are often devastating to the poultry industry economically and otherwise. Disease surveillance is critically important because it facilitates timely detection and generates confidence that infected birds are not moved during business continuity intended to mitigate associated economic losses. The possibility of using an abnormal increase in daily mortality to levels that exceed predetermined thresholds as a trigger to initiate further diagnostic investigations for highly pathogenic avian influenza (HPAI) virus infection in the flock is explored. The range of optimal mortality thresholds varies by bird species, trigger type, and mortality thresholds, and these should be considered when assessing sector-specific triggers. The study uses purposefully collected data and data from the literature to determine optimal mortality triggers for HPAI detection in commercial upland game bird flocks. Three trigger types were assessed for the ability to detect rapidly both HPAI (on the basis of disease-induced and normal mortality data) and false alarm rate (on the basis of normal mortality data); namely, 1) exceeding a set absolute threshold on one day, 2) exceeding a set absolute threshold on two consecutive days, or 3) exceeding a multiple of a seven-day moving average. The likelihood of disease detection using some of these triggers together with premovement real-time reverse transcription PCR (rRT-PCR) testing was examined. Results indicate that the performance of the two consecutive days trigger had the best metrics (i.e., rapid detection with few false alarms) in the trade-off analysis. The collected normal mortality data was zero on 66% of all days recorded, with an overall mean of 0.6 dead birds per day. In the surveillance scenario analyses, combining the default protocol that relied only on active surveillance (i.e., premovement testing of oropharyngeal swab samples from dead birds by rRT-PCR) together with either of the mortality-based triggers improved detection rates on all days postexposure before scheduled movement. For exposures occurring within 8 days of movement, the protocol that combined the default with single-day triggers had slightly more detections than that with two consecutive days triggers. However, all assessed protocol combinations were able to detect all infections that occurred more than 10 days before scheduled movement. These findings can inform risk-based decisions pertaining to continuity of business in the commercial upland game bird industry.

Mathematical models used to inform study design or surveillance systems in infectious diseases: a systematic review

BACKGROUND

Mathematical models offer the possibility to investigate the infectious disease dynamics over time and may help in informing design of studies. A systematic review was performed in order to determine to what extent mathematical models have been incorporated into the process of planning studies and hence inform study design for infectious diseases transmitted between humans and/or animals.

METHODS

We searched Ovid Medline and two trial registry platforms (Cochrane, WHO) using search terms related to infection, mathematical model, and study design from the earliest dates to October 2016. Eligible publications and registered trials included mathematical models (compartmental, individual-based, or Markov) which were described and used to inform the design of infectious disease studies. We extracted information about the investigated infection, population, model characteristics, and study design.

RESULTS

We identified 28 unique publications but no registered trials. Focusing on compartmental and individual-based models we found 12 observational/surveillance studies and 11 clinical trials. Infections studied were equally animal and human infectious diseases for the observational/surveillance studies, while all but one between humans for clinical trials. The mathematical models were used to inform, amongst other things, the required sample size (n = 16), the statistical power (n = 9), the frequency at which samples should be taken (n = 6), and from whom (n = 6).

CONCLUSIONS

Despite the fact that mathematical models have been advocated to be used at the planning stage of studies or surveillance systems, they are used scarcely. With only one exception, the publications described theoretical studies, hence, not being utilised in real studies.

Using quantitative disease dynamics as a tool for guiding response to avian influenza in poultry in the United States of America

Wild birds are the primary source of genetic diversity for influenza A viruses that eventually emerge in poultry and humans. Much progress has been made in the descriptive ecology of avian influenza viruses (AIVs), but contributions are less evident from quantitative studies (e.g., those including disease dynamic models). Transmission between host species, individuals and flocks has not been measured with sufficient accuracy to allow robust quantitative evaluation of alternate control protocols. We focused on the United States of America (USA) as a case study for determining the state of our quantitative knowledge of potential AIV emergence processes from wild hosts to poultry. We identified priorities for quantitative research that would build on existing tools for responding to AIV in poultry and concluded that the following knowledge gaps can be addressed with current empirical data: (1) quantification of the spatio-temporal relationships between AIV prevalence in wild hosts and poultry populations, (2) understanding how the structure of different poultry sectors impacts within-flock transmission, (3) determining mechanisms and rates of between-farm spread, and (4) validating current policy-decision tools with data. The modeling studies we recommend will improve our mechanistic understanding of potential AIV transmission patterns in USA poultry, leading to improved measures of accuracy and reduced uncertainty when evaluating alternative control strategies.

Investigation into sampling strategies in response to potential outbreaks of low pathogenicity notifiable avian influenza initiated in commercial duck holdings in Great Britain

The aim of this study was to evaluate potential sampling strategies for detection of infected flocks that could be applied during an outbreak of low pathogenicity notifiable avian influenza (LPNAI) initiated in duck holdings, following initial detection. A simulation model of avian influenza virus transmission and spread within and between holdings, respectively, was used to predict the impact on the size and duration of an outbreak of (i) changing the tracing window within which premises that might be the source of infection or that may have been infected by the index premises were sampled and (ii) changing the number of birds sampled in the flock being tested. It has shown that there is potential benefit in increasing the tracing window in terms of reducing the likelihood of a large outbreak. It has also shown that there is comparatively little benefit from increasing the number of birds sampled per flock.

Implications of within-farm transmission for network dynamics: consequences for the spread of avian influenza

•

Cross-scale dynamics were investigated for avian influenza in British poultry.

•

Transmission risk is dependent on the assumed within-flock transmission mode.

•

Transmission risk may not scale with transmissibility or flock size.

•

Transmission risk corresponds with between-farm impact for 28% of farms.

•

These results have implications for targeted disease control at the farm-level.

The importance of considering coupled interactions across multiple population scales has not previously been studied for highly pathogenic avian influenza (HPAI) in the British commercial poultry industry. By simulating the within-flock transmission of HPAI using a deterministic S-E-I-R model, and by incorporating an additional environmental class representing infectious faeces, we tracked the build-up of infectious faeces within a poultry house over time. A measure of the transmission risk (TR) was computed for each farm by linking the amount of infectious faeces present each day of an outbreak with data describing the daily on-farm visit schedules for a major British catching company. Larger flocks tended to have greater levels of these catching-team visits. However, where density-dependent contact was assumed, faster outbreak detection (according to an assumed mortality threshold) led to a decreased opportunity for catching-team visits to coincide with an outbreak. For this reason, maximum TR-levels were found for mid-range flock sizes (~25,000–35,000 birds). When assessing all factors simultaneously using multivariable linear regression on the simulated outputs, those related to the pattern of catching-team visits had the largest effect on TR, with the most important movement-related factor depending on the mode of transmission. Using social network analysis on a further database to inform a measure of between-farm connectivity, we identified a large fraction of farms (28%) that had both a high TR and a high potential impact at the between farm level. Our results have counter-intuitive implications for between-farm spread that could not be predicted based on flock size alone, and together with further knowledge of the relative importance of transmission risk and impact, could have implications for improved targeting of control measures.

Moving-average trigger for early detection of rapidly increasing mortality in caged table-egg layers

Rapidly increasing and unexplained mortality in commercial poultry flocks may signal the presence of a highly transmissible and reportable disease. Activation of an infectious-disease surveillance system occurs when a key production parameter, i.e., mortality, changes. Various triggers have been proposed to alert producers when mortality exceeds normal limits for a given production system to enable early detection of such diseases. In this article we demonstrate that a simple moving-average trigger is useful for detecting any disease syndrome in caged table-egg layer flocks that manifests itself as sudden, rapidly increasing mortality. We superimposed HPAI disease mortality output data derived from a disease transmission model and from a naturally occurring HPAI outbreak onto normal mortality data from 12 healthy commercial egg-layer flocks, and compared the performance of 7-day moving-average triggers to previously proposed triggers. The moving-average trigger is more efficient, resulting in fewer false-positive alerts and an earlier time to disease detection. It can be easily calculated by using a computer spreadsheet providing only 7 days of mortality data and can be practically and inexpensively implemented by large commercial poultry integrators. A moving-average trigger can be an active component of a production-based surveillance system.

Use of epidemiologic models in the control of highly pathogenic avian influenza

In the past decades, mathematical models have become more and more accepted as a tool to develop surveillance programs and to evaluate the efficacy of intervention measures for the control of infectious diseases such as highly pathogenic avian influenza. Predictive models are used to simulate the effect of various control measures on the course of an epidemic; analytical models are used to analyze data from outbreaks or from experiments. A key parameter in both types of models is the reproductive ratio, which indicates whether virus can be transmitted in the population, resulting in an epidemic, or not. Parameters obtained from real data using the analytical models can subsequently be used in predictive models to evaluate control strategies or surveillance programs. Examples of the use of these models are described here.

Stochastic model of the potential spread of highly pathogenic avian influenza from an infected commercial broiler operation in Georgia

The potential spread of highly pathogenic avian influenza among commercial broiler farms in Georgia, U. S. A., was mathematically modeled. The dynamics of the spread within the first infected flock were estimated using an SEIR (susceptible-exposed-infectious-recovered) deterministic model, and predicted that grower detection of flock infection is most likely 5 days after virus introduction. Off-farm spread of virus was estimated stochastically for this period, predicting a mean range of exposed farms from 0-5, depending on the density of farms in the area. Modeled off-farm spread was most frequently associated with feed trucks (highest daily probability and number of farm visits) and with company personnel or hired help (highest level of bird contact).

Risk assessment to estimate the probability of a chicken flock infected with H5N1 highly pathogenic avian influenza virus reaching slaughter undetected

Highly pathogenic avian influenza (HPAI) H5N1 is an infectious disease of fowl that can cause rapid and pervasive mortality resulting in complete flock loss. It has also been shown to cause death in humans. Although H5N1 HPAI virus (HPAIV) has not been identified in the United States, there are concerns about whether an infected flock could remain undetected long enough to pose a risk to consumers. This paper considers exposure from an Asian lineage H5N1 HPAIV-infected chicken flock given that no other flocks have been identified as H5N1 HPAIV positive (the index flock). A state-transition model is used to evaluate the probability of an infected flock remaining undetected until slaughter. This model describes three possible states within the flock: susceptible, infected, and dead, and the transition probabilities that predict movements between the possible states. Assuming a 20,000-bird house with 1 bird initially infected, the probability that an H5N1 HPAIV-infected flock would be detected before slaughter is approximately 94%. This is because H5N1 HPAIV spreads rapidly through a flock, and bird mortality quickly reaches high levels. It is assumed that approximately 2% or greater bird mortality due to H5N1 HPAIV would result in on-farm identification of the flock as infected. The only infected flock likely to reach slaughter undetected is one that was infected within approximately 3.5 days of shipment. In this situation, there is not enough time for high mortality to present. These results suggest that the probability of an infected undetected flock going to slaughter is low, yet such an event could occur if a flock is infected at the most opportune time.

The role of pre-emptive culling in the control of foot-and-mouth disease

The 2001 foot-and-mouth disease epidemic was controlled by culling of infectious premises and pre-emptive culling intended to limit the spread of disease. Of the control strategies adopted, routine culling of farms that were contiguous to infected premises caused the most controversy. Here we perform a retrospective analysis of the culling of contiguous premises as performed in 2001 and a simulation study of the effects of this policy on reducing the number of farms affected by disease. Our simulation results support previous studies and show that a national policy of contiguous premises (CPs) culling leads to fewer farms losing livestock. The optimal national policy for controlling the 2001 epidemic is found to be the targeting of all contiguous premises, whereas for localized outbreaks in high animal density regions, more extensive fixed radius ring culling is optimal. Analysis of the 2001 data suggests that the lowest-risk CPs were generally prioritized for culling, however, even in this case, the policy is predicted to be effective. A sensitivity analysis and the development of a spatially heterogeneous policy show that the optimal culling level depends upon the basic reproductive ratio of the infection and the width of the dispersal kernel. These analyses highlight an important and probably quite general result: optimal control is highly dependent upon the distance over which the pathogen can be transmitted, the transmission rate of infection and local demography where the disease is introduced.

A novel approach to real-time risk prediction for emerging infectious diseases: a case study in Avian Influenza H5N1

Mathematical simulation modelling of epidemic processes has recently become a popular tool in guiding policy decisions for potential disease outbreaks. Such models all rely on various parameters in order to specify quantities such as transmission and detection rates. However, the values of these parameters are peculiar to an individual outbreak, and estimating them in advance of an epidemic has been the major difficulty in the predictive credibility of such approaches. The obstruction to classical approaches in estimating model parameters has been that of missing data: (i) an infected individual is only detected after the onset of clinical signs, we never observe the time of infection directly; (ii) if we wish to make inference on an epidemic while it is in progress (in order to predict how it might unfold in the future), we must take into account the fact that there may be individuals who are infected but not yet detected. In this paper we apply a reversible-jump Markov chain Monte Carlo algorithm to a combined spatial and contact network model constructed in a Bayesian context to provide a real-time risk prediction during an epidemic. Using the example of a potential Avian H5N1 epidemic in the UK poultry industry, we demonstrate how such a technique can be used to give real-time predictions of quantities such as the probability of individual poultry holdings becoming infected, the risk that individual holdings pose to the population if they become infected, and the number and whereabouts of infected, but not yet detected, holdings. Since the methodology generalises easily to many epidemic situations, we anticipate its use as a real-time decision-support tool for targetting disease control to critical transmission processes, and for monitoring the efficacy of current control policy.

Estimation of transmission parameters of H5N1 avian influenza virus in chickens

Despite considerable research efforts, little is yet known about key epidemiological parameters of H5N1 highly pathogenic influenza viruses in their avian hosts. Here we show how these parameters can be estimated using a limited number of birds in experimental transmission studies. Our quantitative estimates, based on Bayesian methods of inference, reveal that (i) the period of latency of H5N1 influenza virus in unvaccinated chickens is short (mean: 0.24 days; 95% credible interval: 0.099–0.48 days); (ii) the infectious period of H5N1 virus in unvaccinated chickens is approximately 2 days (mean: 2.1 days; 95%CI: 1.8–2.3 days); (iii) the reproduction number of H5N1 virus in unvaccinated chickens need not be high (mean: 1.6; 95%CI: 0.90–2.5), although the virus is expected to spread rapidly because it has a short generation interval in unvaccinated chickens (mean: 1.3 days; 95%CI: 1.0–1.5 days); and (iv) vaccination with genetically and antigenically distant H5N2 vaccines can effectively halt transmission. Simulations based on the estimated parameters indicate that herd immunity may be obtained if at least 80% of chickens in a flock are vaccinated. We discuss the implications for the control of H5N1 avian influenza virus in areas where it is endemic.

Outbreaks of highly pathogenic H5N1 avian influenza in poultry first occurred in China in 1996. Since that time, the virus has become endemic in Asia, and has been the cause of outbreaks in Africa and Europe. Although many aspects of H5N1 virus biology have been studied in detail, surprisingly little is known about the key epidemiological parameters of the virus in its avian hosts (the length of time from infection until a bird becomes infectious, the duration of infectiousness, how many birds each infectious bird will infect). In this paper we show, using experimental transmission studies with unvaccinated and vaccinated chickens, that H5N1 avian influenza induces a short duration of infectiousness (∼2 days) and a very short period of time from infection until infectiousness (∼0.25 day) in unvaccinated chickens. Furthermore, while transmission was efficient among unvaccinated birds, no bird-to-bird transmission was observed in vaccinated chickens. Our results indicate that it may be difficult to curb outbreaks by vaccination after an introduction in a flock has been detected. On the other hand, preventive vaccination could be effective in preventing virus introductions and limiting the size of outbreaks.