Integrating Machine Learning and Epidemiological Data to Forecast Zoonotic Disease Outbreaks from Avian Influenza and Other Animal

Abstract

There are growing concerns about zoonotic diseases, such as avian influenza and other zoonotic pathogens, that increasingly threaten global health security. Advanced methods for predicting outbreaks are thus needed. This article pools epidemiological data with machine learning in order to develop a time series forecasting model capable of predicting the potential of new outbreaks using historical information. Analyses of past outbreaks, pathogen strains, geographic patterns, and temporal tr capture these relations and allow for early warnings of future zoonotic spill overs. Yet another important advantage for using machine learning in time-series analysis is that it can more effectively process complex high-dimensional data, and it might just be the key to finding some of these hidden patterns. In this way, the tools for dynamic and accurate outbreak prediction will be met, given how unpredictable and ever more frequent zoonotic diseases have become. It would be able to empower public health authorities better to foresee the outbreak potential, allocate appropriate resources beforehand, prepare relevant preventive measures, and mitigate more significant risks of transmission among the whole population. Second, this framework can be used to predict all other zoonotic diseases, including further enhancement of global public health surveillance and pandemic preparedness efforts

Topics & Concepts