Nov 9, 2018

A dynamic neural network model for predicting risk of Zika in real-time

BioRxiv : the Preprint Server for Biology
Mahmood AkhtarLauren Gardner


Background In 2015 the Zika virus spread from Brazil throughout the Americas, posing an unprecedented challenge to the public health community. During the epidemic, international public health officials lacked reliable predictions of the outbreak’s expected geographic scale and prevalence of cases, and were therefore unable to plan and allocate surveillance resources in a timely and effective manner. Methods In this work we present a dynamic neural network model to predict the geographic spread of outbreaks in real-time. The modeling framework is flexible in three main dimensions i) selection of the chosen risk indicator, i.e. , case counts or incidence rate, ii) risk classification scheme, which defines the high risk group based on a relative or absolute threshold, and iii) prediction forecast window (one up to 12 weeks). The proposed model can be applied dynamically throughout the course of an outbreak to identify the regions expected to be at greatest risk in the future. Results The model is applied to the recent Zika epidemic in the Americas at a weekly temporal resolution and country spatial resolution, using epidemiological data, passenger air travel volumes, vector habitat suitability, socioeconomic and population data...Continue Reading

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Mentioned in this Paper

Community Health Centers
Supervision (Regime/Therapy)
Neural Network Simulation
Cell Growth
Genetic Vectors
Zika Virus Disease (Disorder)

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