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

Abstract

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 (1 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, and vector habitat suitability, socioeconomic, and population data for all affected countries and territories in the Americas. The model performance is quantitatively evaluated based on the predictive accuracy of the model. We show that the model can accurately predict the geographic expansion of Zika in the Americas with the overall average accuracy remaining above 85% even for prediction windows of up to 12 weeks. Conclusions Sensitivity analysis illustrated the model performance to be robust across a range of features. Critically, the model performed consistently well at various stages throughout the course of the outbreak, indicating its potential value at any time during an epidemic. The predictive capability was superior for shorter forecast windows and geographically isolated locations that are predominantly connected via air travel. The highly flexible nature of the proposed modeling framework enables policy makers to develop and plan vector control programs and case surveillance strategies which can be tailored to a range of objectives and resource constraints. Electronic supplementary material The online version of this article (10.1186/s12916-019-1389-3) contains supplementary material, which is available to authorized users.

Network for subclinical prognostication of COVID 19 Patients from data of thoracic roentgenogram: A feasible alternative screening technology

“…Nevertheless, the only drawback comes from the dataset development part as developing a dataset during a pandemic is troublesome but very crucial for strategic planning in healthcare organisations [5][6][7] . However, we predict that our model along with case record form (CRF)…”

“…As the onset of disease is primarily in the lung of the infected person, so images of the lung could give us a quicker and better predictive approach. But to make it quicker, we need to use artificial intelligence and machine learning as a front-line analytical tool of the images [5][6][7] .…”

Artificial intelligence vs COVID-19: limitations, constraints and pitfalls

“…The first is that AI requires data on COVID-19 to train. An example of how this can be done is the case of the 2015 Zika-virus, whose spread was ex post predicted using a dynamic neural network (Akhtar et al 2019). Because COVID-19 is different from Zika, or other infections, and because there are at the time of writing still not sufficient data to build AI models that can track and forecast its spread.…”

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

“…With the case of the 2015 Zika virus epidemic, a dynamic neural network was developed to predict its spread [47]. Predictive models have been developed such as the seasonal auto-regressive integrated moving average (SARIMA) model to predict influenza changes [48], SARIMA and a support vector regression model to predict the incidence of hand-foot-mouth disease (HFMD) [49] and many more.…”