Abstract
Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens.This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control‘. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.
Highlights
Emerging infectious diseases are a constant threat to public health worldwide
That have been developed and applied in other fields to rigorously assess not just the accuracy of forecasts and how well models quantify the inherent uncertainty in making predictions, are only rarely applied in infectious disease epidemiology [111,112]
We reviewed methodological and technological resources forming the basis of outbreak analytics, an emerging data science for informing outbreak response
Summary
Emerging infectious diseases are a constant threat to public health worldwide. In the past decade, several major outbreaks, such as the 2009 influenza pandemic [1],. That have been developed and applied in other fields to rigorously assess not just the accuracy of forecasts and how well models quantify the inherent uncertainty in making predictions, are only rarely applied in infectious disease epidemiology [111,112] Whether it is to estimate R or predict future incidence, the most appropriate method depends on the particular epidemiological setting, existing knowledge of the transmission dynamics and data availability. A further step towards integrating WGS alongside epidemiological data is the reconstruction of transmission trees (who infects whom) using evidence synthesis approaches This methodological field has been growing fast over the past decade [25,142,143,144,145,146,147,148], but most applications of these methods remain within academia and their usefulness in the field in an outbreak response context needs to be critically assessed. The complex nature of the problem requires the use of Bayesian methods for model fitting, making these approaches difficult to interpret by non-experts [145,146,148]
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