Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks

Building a pathway to One Health surveillance and response in Asian countries

To detect and respond to emerging diseases more effectively, an integrated surveillance strategy needs to be applied to both human and animal health. Current programs in Asian countries operate separately for the two sectors and are principally concerned with detection of events that represent a short-term disease threat. It is not realistic to either invest only in efforts to detect emerging diseases, or to rely solely on event-based surveillance. A comprehensive strategy is needed, concurrently investigating and managing endemic zoonoses, studying evolving diseases which change their character and importance due to influences such as demographic and climatic change, and enhancing understanding of factors which are likely to influence the emergence of new pathogens. This requires utilisation of additional investigation tools that have become available in recent years but are not yet being used to full effect. As yet there is no fully formed blueprint that can be applied in Asian countries. Hence a three-step pathway is proposed to move towards the goal of comprehensive One Health disease surveillance and response.

Deep Similarity Analysis and Forecasting of Actual Outbreak of Major Infectious Diseases using Internet-Sourced Data

Perhaps no other generation in the span of recorded human history has endured the risks of infectious diseases as has the current generation. The prevalence of infectious diseases is caused mainly by unlimited contact between people in a highly globalized world. Disease control and prevention (CDC) promptly collect and produce disease outbreak statistics, but CDCs rely on a curated, centralized collection system, and requires up to two weeks of lead time. Consequently, the quick release of disease outbreak information has become a great challenge. Infectious disease outbreak information is recorded and spread somewhere on the Internet much faster than CDC announcements, and Internet-sourced data have shown non-substitutable potential to watch and predict infectious disease outbreaks in advance. In this study, we performed a thorough analysis to show the similarity between the Korean Center of Disease Control (KCDC) infectious disease datasets and three Internet-sourced data for nine major infectious diseases in terms of time-series volume. The results show that many of infectious disease outbreak have strongly related to Internet-sourced data. We analyzed several factors that affect the similarity. Our analysis shows that the increase in the number of Internet-sourced data correlates with the increase in the number of infected people and thus, show the positive similarity. We also found that the greater the number of infectious disease outbreaks corresponds to having a wider spread of outbreak regions, in which it also proves to have higher similarity. We presented the prediction result of infectious disease outbreak using various Internet-sourced data and an effective deep learning algorithm. It showed that there are positive correlations between the number of infected people or the number of related web data and the prediction accuracy. We developed and currently operate a web-based system to show the similarity between KCDC and related Internet-sourced data for infectious diseases. This paper helps people to identify what kind of Internet-sourced data they need to use to predict and track a specific infectious disease outbreak. We considered as much as nine major diseases and three kinds of Internet-sourced data together, and we can say that our finding did not depend on specific infectious disease nor specific Internet-sourced data.

Warning Signs of Potential Black Swan Outbreaks in Infectious Disease

Black swan events in infectious disease describe rare but devastatingly large outbreaks. While experts are skeptical that such events are predictable, it might be possible to identify the warning signs of a black swan event. Specifically, following the initiation of an outbreak, key differentiating features could serve as alerts. Such features could be derived from meta-analyses of large outbreaks for multiple infectious diseases. We hypothesized there may be common features among the pathogen, environment, and host epidemiological triad that characterize an infectious disease black swan event. Using Los Alamos National Laboratory’s tool, Analytics for Investigation of Disease Outbreaks, we investigated historical disease outbreak information and anomalous events for several infectious diseases. By studying 32 different infectious diseases and global outbreaks, we observed that in the past 20–30 years, there have been potential black swan events in the majority of infectious diseases analyzed. Importantly, these potential black swan events cannot be attributed to the first introduction of the disease to a susceptible host population. This paper describes our observations and perspectives and illustrates the value of broad analysis of data across the infectious disease realm, providing insights that may not be possible when we focus on singular infectious agents or diseases. Data analytics could be developed to warn health authorities at the beginning of an outbreak of an impending black swan event. Such tools could complement traditional epidemiological modeling to help forecast future large outbreaks and facilitate timely warning and effective, targeted resource allocation for mitigation efforts.

Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study

BACKGROUND

Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence.

OBJECTIVE

Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence?

METHODS

We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies.

RESULTS

Our supervised learning models were able to identify 82%-90% of the local re-emergence events, although with 18%-31% (except 46% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence.

CONCLUSIONS

To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.