Danger zones for avian influenza H7N9 revealed

The higher risk areas in Asia for avian influenza H7N9 have been mapped by a research group. Their study shows that Bangladesh, India and Vietnam could easily sustain the virus, as could parts of Indonesia and the Philippines.

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Higher peaks represent greater risk of H7N9 becoming established.
Higher peaks represent greater risk of H7N9 becoming established.

The higher risk areas in Asia for avian influenza H7N9 have been mapped by a research group. Their study shows that Bangladesh, India and Vietnam could easily sustain the virus, as could parts of Indonesia and the Philippines.

Some areas of China, the country most affected by the virus to date, are not thought to be at high risk, while Thailand is not thought to be at risk due to cultural differences. 

The study, published in Nature Communications, used data sets of the 8,943 live poultry markets in China and maps of environmental correlates. From this, the research team developed a statistical model to predict the risk of H7N9 infection in other Asian countries.

Local density of live poultry markets is considered the most important predictor variable for H7N9 infection risk in markets, and is a key factor in the spatial epidemiology of the virus, along with the presence of other poultry, land cover and anthropogenic predictor variables.

Live poultry markets key

To date, the majority of positive H7N9 isolations have come from human, chicken or environmental samples, and they were directly or indirectly linked to live poultry markets.

The diversity of genetic sequences obtained from these samples suggests that H7N9 had spread extensively and largely undetected in poultry flocks prior to its appearance in markets - and transmission to humans. Investigations of other wild or domestic virus reservoirs have proved inconclusive.

To develop the model, the researchers conducted an extensive countrywide census of live poultry markets in China, updated and improved high spatial resolution surfaces for poultry and human population distribution across the country, and built a live poultry distribution model to allow extrapolation of their predictions to other areas within and beyond China.

The team believes the density of local live markets is the most important predictor variable of H7N9 infection at market level. Other predictor variables include the population densities of chickens reared in extensive and intensive systems, bodies of water, accessibility to major cities, the domestic duck population density and rice cultivation.

Evaluation of the environmental space occupied by markets, as determined by the values of key predictor variables for avian influenza, revealed that although infected markets were present only in a limited area, their influence extended far further. Additionally, while the locations of newly infected markets spread geographically, the area of influence of infected markets was already wide from the early stages of the epidemic. 

This would suggest that, to date, the environmental spread of the pathogen has been fairly conservative and has not expanded.

Network of transmission

Live poultry markets bring together birds from large catchment areas, and unsold birds are commonly traded to other markets. This results in market networks with numerous connections. 

The nature of this market network, and the characteristics of the individual markets within the network, influence the spread and persistence of the disease over long periods of time, even if disease is not reintroduced from farms. Higher densities of markets within an area are thought to elevate the risk.

The epidemiological importance of dense networks of susceptible markets is also supported by the effectiveness of massive control efforts that have been implemented by China’s authorities involving the closure of markets and the banning or regulation of trade in poultry.

Characterizing the population size, species composition, trade volumes and connectivity of live poultry markets in a region or country would allow better quantification of the role played by various types of markets in disease spread and persistence, and the this would allow control efforts to be better targeted. 

Not all areas, production methods equal

The negative association between H7N9 presence and intensively raised chickens probably can be explained by the absence of H7N9 cases in Northeastern China, an area where chicken production is highly intensive.

Other variables include the suitability of infection in the presence of medium to high areas of land covered in water, and peri-urban areas. These characteristics reflect the area where the disease first emerged.

Building on H5N1

Earlier studies of the distribution of the H5N1 virus strain had shown that several epidemic waves of that virus in Asia were strongly correlated with the distribution of domestic ducks, human populations and wetlands. These associations were then used to map the distribution of the disease.

However, this work was carried out after the fact, and the ability to predict risk in new areas when only the first few cases have been recorded would significantly improve contingency planning. The team’s work may make this easier.

Since the H7N9 virus jumped from birds to people and was first detected in early 2013, there have been at least 433 reported cases in humans and more than 60 deaths have been attributed to the disease, mostly within China.

The model does not explain how or when the disease will spread, only where the virus could successfully establish itself, yet it offers a warning ahead of possible infection which could be useful to government authorities and poultry producers alike. With the unfolding H7N9 epidemic, which many believe could spread significantly further, establishing the capacity of the model to extrapolate findings to other geographic regions is necessary to assess the utility of its predictions and use them to take preventative action if necessary.

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