Whether due to improved surveillance or an increasing rate of emergence, our awareness about the number of viruses that can infect humans continues to increase. Viral zoonoses are a predominant reason why infectious diseases continue to increase in human populations.
Zoonotic viral pathogens have the potential to jump from animal hosts to humans pose a significant public health concern. As the pressure on healthcare to prevent novel zoonotic disease outbreaks continues to increase, experts look to infectious disease modelling to examine host-virus networks to possibly identify future pathogens and predict potential emergence.
Predictive modeling tools that identify host-virus relationships that pose significant threats to human populations currently rely upon a pipeline of data from numerous scientific disciplines and sources of data. However, existing models that produce actionable forecasts that enable preventative decision making by public health authorities and other leaders have limited utility.
In this paper, CEID research scientists and contributing authors Daniel Becker and Max Farrell describe and analyze six modeling approaches to predicting the host-virus network: link prevention, host interference, zoonotic risk, viral sharing, viral diversity, and host range modeling. Each modeling approach shows significant potential despite some limitations.
Through the evaluation of host-virus network modelling, researchers were able to examine the possibilities of more complex disease models. Researchers stated that the collaborative use of transparent and accountable model development along with the continuation of building understanding of the global virome increases the possibilities for infectious disease modeling.
For more information about host-virus networks and modeling please click here to review the article.