Machine Learning in Public Health Forecasting and Monitoring the Zika Virus

The Zika virus is a severe public health threat all across the world, owing to its spreading mechanism through Aedes mosquitoes and its ability to result in extreme neurological diseases, which include the congenital Zika syndrome and the Guillain-Barré syndrome, amongst others. Conventional monitoring techniques often fail because many asymptomatic cases render early diagnosis challenging. Machine learning (ML) techniques can be seen as a constructive development in addressing this challenge, which entails predicting and tracking the spread of diseases such as Zika through extensive and complex datasets. Data analytic ML systems also enhance early warning systems and situational uplift by using data from social media, climate history, and genetics. This helps reasonably to predict the mosquito population biologically and the environmental factors that favor the spread of the virus for a more practical approach from the public health sector. Over and above, some issues are still pending, especially regarding the quality of data, understanding the models and how to apply such models within the current health systems. These factors must be solved to implement ML successfully in surveillance practice. This review provides an overview of the issue, stating the potential of machine learning applications in the development of public health, whose actions focus on Zika and other diseases transmitted by vectors.