Outbreak analytics: a developing data science for informing the response to emerging pathogens

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’.

Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study

“…During the period of an epidemic when human-to-human transmission is established and reported case numbers are rising exponentially, nowcasting and forecasting are of crucial importance for public health planning and control domestically and internationally. 39,40 In this study, we have estimated the outbreak size of 2019-nCoV thus far in Wuhan and the probable extent of disease spread to other cities domestically. Our findings suggest that independent self-sustaining human-to-human spread is already present in multiple major Chinese cities, many of which are global transport hubs with huge numbers of both inbound and outbound passengers (eg, Beijing, Shanghai, Guangzhou, and Shenzhen).…”

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

“…Quantifying disease transmissibility during outbreaks is crucial for designing effective control measures and assessing their effectiveness once implemented. This assessment forms a critical part of real-time situational awareness (Cauchemez et al, 2006a;Cowling et al, 2010;Christaki, 2015;Cori et al, 2017;Polonsky et al, 2019). Indeed, in circumstances in which the incidence of cases is still increasing, but the time-dependent reproduction number is dropping, there might be a very different outlook compared to if the incidence of cases and the reproduction number are both increasing.…”

“…Infectious disease epidemics are a recurring threat worldwide (Daszak et al, 2000;Taylor et al, 2001;Morens et al, 2004;Jones et al, 2008;Fisher et al, 2012;Allen et al, 2017;Thompson and Brooks-Pollock, 2019). A key challenge during outbreaks is designing appropriate control interventions, and mathematical models are increasingly used to guide this decision-making (Lofgren et al, 2014;Cunniffe et al, 2016;Cori et al, 2017;Polonsky et al, 2019). Recent examples of the real-time use of models during outbreaks https://doi.org/10.…”

Infectious disease pandemic planning and response: Incorporating decision analysis

“…As our understanding of the probable impact of a pandemic improves, policy makers can then use this information to help decide on the overall scale of response, which control measures to implement, and when to deploy them [29]. Given the dependency of response plans and decision-making on assessments of situational awareness, gathering the appropriate information as early as possible in an outbreak has been identified as a priority for surveillance and realtime data analysis activities [30,31].…”

“…The need for data collection and analysis pipelines to be made routine in epidemic response practice has been the topic of recent widespread discussion [30,31,43,44]. Furthermore, it is clear that situational evidence should be used with intervention models to assess the likely effectiveness of response options.…”