Influenza A viruses are able to infect both birds and certain mammals, with the resulting infection commonly referred to as ‘bird flu’. One subtype, H5N1, gained particular notoriety following outbreaks of a highly pathogenic variant in 2003 that resulted in human fatalities. More recently another subtype of influenza A has been shown to spread from birds to humans. In April 2013 the World Health Organization announced that H7N9 has for the first time been detected in a number of individuals in China, raising concerns over the potential for a new pandemic. Researchers are conscious of the need to better predict the spread of these viruses. Jiming Liu of Hong Kong Baptist University and colleagues generated risk maps for the spread of H7N9 in the eastern provinces of China, as explained in their recent article published in Infectious Diseases of Poverty. Liu told us more about how they incorporated data on poultry distribution and bird migratory patterns – both factors implicated in H7N9 transmission – into computational models to predict infection risks.
Negatively-stained transmission electron micrograph of H7N9 virus. Image source: US Centers for Disease Control and Prevention, Cynthia Goldsmith and Thomas Rowe
What does your article set out to investigate and what was the main goal?
We aim to investigate the potential risks of H7N9 infection in China by using a data-driven computational model to characterize the spatiotemporal patterns of both bird migration and poultry distribution. In our current model, we have considered the environmental and meteorological factors that may affect the route and timing of bird migration, and poultry production and consumption in different provinces/municipalities in China.
We believe this will enable public health authorities to better plan necessary intervention measures, such as the monitoring and regulation of poultry markets in some specific regions at risk. While our modeling has considered the factors of bird migration and poultry distribution, the presented methodology can also be used to incorporate additional factors once identified.
How did you become interested in vector-borne disease epidemiology?
Compared with influenza, vector-borne diseases are more prevalent and dangerous. Their transmission is also more challenging to model. For example, malaria is one of the most common vector-borne infectious diseases, which causes enormous public health problems. The World Health Organization (WHO) reported that there are approximately 515 million cases of malaria a year worldwide, killing about 1 million people each year.
The transmission of malaria can be affected by many factors, including environmental factors such as temperature and rainfall, biological factors such as the daily survival rate of mosquitoes and the sporogonic cycle length of sporozoits in their bodies, as well as various human behavior related factors – this makes modeling transmission difficult. Over the years, we have been working in the area of data-driven complex systems modeling. Modeling the dynamics of infectious disease spread is well-suited to the methodology used in complex systems modeling.
Can you explain the approach you took in building your model for H7N9 transmission?
Working closely with Professor Xiao-Nong Zhou of the Chinese Center for Disease Control and Prevention, we started with surveillance-based observations of the transmission ‘system’, and then, based on the empirical knowledge available, identified the potential factors or ‘entities’, as well as their interactions, which may contribute to transmission. Thereafter, we developed computational models of the interaction mechanisms to gain insights into the emergent behavior of the ‘system’ over time and space, such as the evolution of potential H7N9 risk patterns. By doing so, our ultimate goals are to be able to predict the possible outcomes and to develop effective or even optimal intervention strategies for disease control or prevention.
What do you consider to be the most interesting findings of your study?
By computationally modeling patterns of bird migration and poultry distribution, we can indirectly infer the potential risks for human infection with H7N9. Our preliminary results have provided insights into the relative infection risks of the H7N9 virus over different periods of time for the eastern provinces in mainland China. We have shown how such a risk map could change over a certain period of time. It should be noted that the risk map itself does not directly predict the number of human infections (or the number of cases to be found). However, it can readily be used to better understand and foresee the relative risks of different regions over time. For instance, based on the estimated risk map, we could easily identify the emerging relative risk of infection in Shandong, due to the bird migration and poultry distribution there.
What impact do you think your model could have in controlling the spread of novel avian viruses?
In the absence of clear information on H7N9 transmission at the moment, our study has shown that active surveillance of significant stopovers of migratory birds and intensive intervention in poultry markets could effectively contribute to the control of the virus spread. Moreover, the methodology of modeling used in our study can immediately be applied in real-world risk estimation and mapping. To make it more informative and useful, it would be helpful to incorporate further fine-grained data sources, for example, with respect to specific types and quantities of bird migration and poultry distribution.
What challenges remain to be addressed in modelling the spread of avian viruses in China?
It is important to address the migratory patterns of different categories of birds (more than 1000 categories) under different environmental conditions, as well as the spread of virus in these different migratory birds. Another challenge is how to describe the route of virus spread among birds, poultry, and human beings.
Do you have plans to continue your work into H7N9?
Yes, our research team plans to further study the spread of H7N9. We will analyze historical bird migration data in mainland China, and if possible, the whole world. Then, we will extend our model to study the potential infection risks in China when migratory birds come back this autumn.