Evaluation of local media surveillance for improved disease recognition and monitoring in global hotspot regions

Health Reporting Characteristics among Journalists in Nepal Utilizing a One Health Framework

Journalists play a crucial role in the dissemination of health-related information. In developing countries, such as Nepal, the media are integral in shaping the national agenda and informing the public of important health issues. With an increasing need for a collaborative effort to attain optimal health for people, animals, and the environment, the One Health approach was used to characterize health reporting in Nepal. A comprehensive survey was administered to health journalists regarding their public, animal, and environmental health reporting habits. Seventy-one journalists completed the survey across three study sites. Many journalists indicated a history of reporting across all three sectors but did not routinely focus on health reporting in general. The majority of journalists perceived the quality and overall coverage of health-related topics increased over the last five years. However, few journalists reported receiving specialized training in any health sector. Although the overall quality of health reporting in the Nepali media showed improvements, many journalists acknowledged a lack of understanding of common health topics and a desire to learn more skills related to accurate health reporting. One Health provides a conceptual framework for understanding and promoting health communication through mass media to benefit humans, animals, and ecosystems.

Development of a global infectious disease activity database using natural language processing, machine learning, and human expertise

OBJECTIVE

We assessed whether machine learning can be utilized to allow efficient extraction of infectious disease activity information from online media reports.

MATERIALS AND METHODS

We curated a data set of labeled media reports (n = 8322) indicating which articles contain updates about disease activity. We trained a classifier on this data set. To validate our system, we used a held out test set and compared our articles to the World Health Organization Disease Outbreak News reports.

RESULTS

Our classifier achieved a recall and precision of 88.8% and 86.1%, respectively. The overall surveillance system detected 94% of the outbreaks identified by the WHO covered by online media (89%) and did so 43.4 (IQR: 9.5-61) days earlier on average.

DISCUSSION

We constructed a global real-time disease activity database surveilling 114 illnesses and syndromes. We must further assess our system for bias, representativeness, granularity, and accuracy.

CONCLUSION

Machine learning, natural language processing, and human expertise can be used to efficiently identify disease activity from digital media reports.

An overview of mobile applications (apps) to support the coronavirus disease 2019 response in India

Background & objectives:

The potential benefits of mobile health (mHealth) initiatives to manage the coronavirus disease 2019 (COVID-19) pandemic have been explored. The Government of India, State governments, and healthcare organizations have developed various mobile apps for the containment of COVID-19. This study was aimed to systematically review COVID-19 related mobile apps and highlight gaps to inform the development of future mHealth initiatives.

Methods:

Google Play and the Apple app stores were searched using the terms 'COVID-19', 'coronavirus', 'pandemic', and 'epidemic' in the first week of April 2020. A list of COVID-19-specific functions was compiled based on the review of the selected apps, the literature on epidemic surveillance, and national and international media reports. The World Health Organization guideline on Digital Health Interventions was used to classify the app functions under the categories of the general public, health workers, health system managers, and data services.

Results:

The search yielded 346 potential COVID-19 apps, of which 50 met the inclusion criteria. Dissemination of untargeted COVID-19-related information on preventative strategies and monitoring the movements of quarantined individuals was the function of 27 (54%) and 19 (32%) apps, respectively. Eight (16%) apps had a contact tracing and hotspot identification function.

Interpretation & conclusions:

Our study highlights the current emphasis on the development of self-testing, quarantine monitoring, and contact tracing apps. India's response to COVID-19 can be strengthened by developing comprehensive mHealth solutions for frontline healthcare workers, rapid response teams and public health authorities. Among this unprecedented global health emergency, the Governments must ensure the necessary but least intrusive measures for disease surveillance.

DOT: a crowdsourcing Mobile application for disease outbreak detection and surveillance in Mauritius

Early detection of disease outbreaks is crucial and even small improvements in detection can significantly impact on a country’s public health. In this work, we investigate the use of a crowdsourcing application and a real-time disease outbreak surveillance system for five diseases; Influenza, Gastroenteritis, Upper Respiratory Tract Infection (URTI), Scabies and Conjunctivitis, that are closely monitored in Mauritius. We also analyze and correlate the collected data with past statistics. A crowdsourcing mobile application known as Disease Outbreak Tracker (DOT) was implemented and made public. A real-time disease surveillance system using the Early Aberration Reporting System algorithm (EARS) for analysis of the collected data was also implemented. The collected data were correlated to historical data for 2017. Data were successfully collected and plotted on a daily basis. The results show that a few cases of Flu and Scabies were reported in some districts. The EARS methods C1, C2 and C3 also depicted spikes above the set threshold on some days. The study covers data collected over a period of one month. Once symptoms data were collected using DOT, probabilistic methods were used to find the disease or diseases that the user was suffering from. The data were further processed to find the extent of the disease outbreak district-wise, per disease. These data were represented graphically for a rapid understanding of the situation in each district. Our findings concur with existing data for the same period for previous years showing that the crowdsourcing application can aid in the detection of disease outbreaks.

A systematic review on integration mechanisms in human and animal health surveillance systems with a view to addressing global health security threats

BACKGROUND

Health surveillance is an important element of disease prevention, control, and management. During the past two decades, there have been several initiatives to integrate health surveillance systems using various mechanisms ranging from the integration of data sources to changing organizational structures and responses. The need for integration is caused by an increasing demand for joint data collection, use and preparedness for emerging infectious diseases.

OBJECTIVE

To review the integration mechanisms in human and animal health surveillance systems and identify their contributions in strengthening surveillance systems attributes.

METHOD

The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) 2015 checklist. Peer-reviewed articles were searched from PubMed, HINARI, Web of Science, Science Direct and advanced Google search engines. The review included articles published in English from 1900 to 2018. The study selection considered all articles that used quantitative, qualitative or mixed research methods. Eligible articles were assessed independently for quality by two authors using the QualSyst Tool and relevant information including year of publication, field, continent, addressed attributes and integration mechanism were extracted.

RESULTS

A total of 102 publications were identified and categorized into four pre-set integration mechanisms: interoperability (35), convergent integration (27), semantic consistency (21) and interconnectivity (19). Most integration mechanisms focused on sensitivity (44.1%), timeliness (41.2%), data quality (23.5%) and acceptability (17.6%) of the surveillance systems. Generally, the majority of the surveillance system integrations were centered on addressing infectious diseases and all hazards. The sensitivity of the integrated systems reported in these studies ranged from 63.9 to 100% (median = 79.6%, n = 16) and the rate of data quality improvement ranged from 73 to 95.4% (median = 87%, n = 4). The integrated systems were also shown improve timeliness where the recorded changes were reported to be ranging from 10 to 91% (median = 67.3%, n = 8).

CONCLUSION

Interoperability and semantic consistency are the common integration mechanisms in human and animal health surveillance systems. Surveillance system integration is a relatively new concept but has already been shown to enhance surveillance performance. More studies are needed to gain information on further surveillance attributes.

Symptom-based data preprocessing for the detection of disease outbreak

Early warning systems for outbreak detection is a challenge topic for researchers in the epidemiology and biomedical informatics fields. We are proposing a new method for detecting disease epidemics using a symptom-based approach. The data was collected from developed mobile applications which include users' demographic information and a list of chief complaint symptoms. Deliberated outbreaks are differentiated from seasonal outbreak by specific symptoms that represent a sign of infection. These symptoms were grouped, classified, and then converted to a time-series digital signal using the consensus scoring approach. Through the syndromic grouping method, the system digitized each data package into a single independent variable that is ready for further one-dimensional signal processing to predict disease outbreaks in the future.

Towards a genomics-informed, real-time, global pathogen surveillance system

The recent Ebola and Zika epidemics demonstrate the need for the continuous surveillance, rapid diagnosis and real-time tracking of emerging infectious diseases. Fast, affordable sequencing of pathogen genomes - now a staple of the public health microbiology laboratory in well-resourced settings - can affect each of these areas. Coupling genomic diagnostics and epidemiology to innovative digital disease detection platforms raises the possibility of an open, global, digital pathogen surveillance system. When informed by a One Health approach, in which human, animal and environmental health are considered together, such a genomics-based system has profound potential to improve public health in settings lacking robust laboratory capacity.

Online surveillance of media health event reporting in Nepal: digital disease detection from a One Health perspective

Background

Traditional media and the internet are crucial sources of health information. Media can significantly shape public opinion, knowledge and understanding of emerging and endemic health threats. As digital communication rapidly progresses, local access and dissemination of health information contribute significantly to global disease detection and reporting.

Methods

Health event reports in Nepal (October 2013–December 2014) were used to characterize Nepal’s media environment from a One Health perspective using HealthMap - a global online disease surveillance and mapping tool. Event variables (location, media source type, disease or risk factor of interest, and affected species) were extracted from HealthMap.

Results

A total of 179 health reports were captured from various sources including newspapers, inter-government agency bulletins, individual reports, and trade websites, yielding 108 (60%) unique articles. Human health events were reported most often (n = 85; 79%), followed by animal health events (n = 23; 21%), with no reports focused solely on environmental health.

Conclusions

By expanding event coverage across all of the health sectors, media in developing countries could play a crucial role in national risk communication efforts and could enhance early warning systems for disasters and disease outbreaks.

Utility and potential of rapid epidemic intelligence from internet-based sources

OBJECTIVES

Rapid epidemic detection is an important objective of surveillance to enable timely intervention, but traditional validated surveillance data may not be available in the required timeframe for acute epidemic control. Increasing volumes of data on the Internet have prompted interest in methods that could use unstructured sources to enhance traditional disease surveillance and gain rapid epidemic intelligence. We aimed to summarise Internet-based methods that use freely-accessible, unstructured data for epidemic surveillance and explore their timeliness and accuracy outcomes.

METHODS

Steps outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist were used to guide a systematic review of research related to the use of informal or unstructured data by Internet-based intelligence methods for surveillance.

RESULTS

We identified 84 articles published between 2006-2016 relating to Internet-based public health surveillance methods. Studies used search queries, social media posts and approaches derived from existing Internet-based systems for early epidemic alerts and real-time monitoring. Most studies noted improved timeliness compared to official reporting, such as in the 2014 Ebola epidemic where epidemic alerts were generated first from ProMED-mail. Internet-based methods showed variable correlation strength with official datasets, with some methods showing reasonable accuracy.

CONCLUSION

The proliferation of publicly available information on the Internet provided a new avenue for epidemic intelligence. Methodologies have been developed to collect Internet data and some systems are already used to enhance the timeliness of traditional surveillance systems. To improve the utility of Internet-based systems, the key attributes of timeliness and data accuracy should be included in future evaluations of surveillance systems.

Social Media Listening for Routine Post-Marketing Safety Surveillance

INTRODUCTION

Post-marketing safety surveillance primarily relies on data from spontaneous adverse event reports, medical literature, and observational databases. Limitations of these data sources include potential under-reporting, lack of geographic diversity, and time lag between event occurrence and discovery. There is growing interest in exploring the use of social media ('social listening') to supplement established approaches for pharmacovigilance. Although social listening is commonly used for commercial purposes, there are only anecdotal reports of its use in pharmacovigilance. Health information posted online by patients is often publicly available, representing an untapped source of post-marketing safety data that could supplement data from existing sources.

OBJECTIVES

The objective of this paper is to describe one methodology that could help unlock the potential of social media for safety surveillance.

METHODS

A third-party vendor acquired 24 months of publicly available Facebook and Twitter data, then processed the data by standardizing drug names and vernacular symptoms, removing duplicates and noise, masking personally identifiable information, and adding supplemental data to facilitate the review process. The resulting dataset was analyzed for safety and benefit information.

RESULTS

In Twitter, a total of 6,441,679 Medical Dictionary for Regulatory Activities (MedDRA(®)) Preferred Terms (PTs) representing 702 individual PTs were discussed in the same post as a drug compared with 15,650,108 total PTs representing 946 individual PTs in Facebook. Further analysis revealed that 26 % of posts also contained benefit information.

CONCLUSION

Social media listening is an important tool to augment post-marketing safety surveillance. Much work remains to determine best practices for using this rapidly evolving data source.

One Health proof of concept: Bringing a transdisciplinary approach to surveillance for zoonotic viruses at the human-wild animal interface

As the world continues to react and respond inefficiently to emerging infectious diseases, such as Middle Eastern Respiratory Syndrome and the Ebola and Zika viruses, a growing transdisciplinary community has called for a more proactive and holistic approach to prevention and preparedness - One Health. Such an approach presents important opportunities to reduce the impact of disease emergence events and also to mitigate future emergence through improved cross-sectoral coordination. In an attempt to provide proof of concept of the utility of the One Health approach, the US Agency for International Development's PREDICT project consortium designed and implemented a targeted, risk-based surveillance strategy based not on humans as sentinels of disease but on detecting viruses early, at their source, where intervention strategies can be implemented before there is opportunity for spillover and spread in people or food animals. Here, we share One Health approaches used by consortium members to illustrate the potential for successful One Health outcomes that can be achieved through collaborative, transdisciplinary partnerships. PREDICT's collaboration with partners around the world on strengthening local capacity to detect hundreds of viruses in wild animals, coupled with a series of cutting-edge virological and analytical activities, have significantly improved our baseline knowledge on the zoonotic pool of viruses and the risk of exposure to people. Further testament to the success of the project's One Health approach and the work of its team of dedicated One Health professionals are the resulting 90 peer-reviewed, scientific publications in under 5 years that improve our understanding of zoonoses and the factors influencing their emergence. The findings are assisting in global health improvements, including surveillance science, diagnostic technologies, understanding of viral evolution, and ecological driver identification. Through its One Health leadership and multi-disciplinary partnerships, PREDICT has forged new networks of professionals from the human, animal, and environmental health sectors to promote global health, improving our understanding of viral disease spillover from wildlife and implementing strategies for preventing and controlling emerging disease threats.

Avian Influenza Risk Surveillance in North America with Online Media

The use of Internet-based sources of information for health surveillance applications has increased in recent years, as a greater share of social and media activity happens through online channels. The potential surveillance value in online sources of information about emergent health events include early warning, situational awareness, risk perception and evaluation of health messaging among others. The challenge in harnessing these sources of data is the vast number of potential sources to monitor and developing the tools to translate dynamic unstructured content into actionable information. In this paper we investigated the use of one social media outlet, Twitter, for surveillance of avian influenza risk in North America. We collected AI-related messages over a five-month period and compared these to official surveillance records of AI outbreaks. A fully automated data extraction and analysis pipeline was developed to acquire, structure, and analyze social media messages in an online context. Two methods of outbreak detection; a static threshold and a cumulative-sum dynamic threshold; based on a time series model of normal activity were evaluated for their ability to discern important time periods of AI-related messaging and media activity. Our findings show that peaks in activity were related to real-world events, with outbreaks in Nigeria, France and the USA receiving the most attention while those in China were less evident in the social media data. Topic models found themes related to specific AI events for the dynamic threshold method, while many for the static method were ambiguous. Further analyses of these data might focus on quantifying the bias in coverage and relation between outbreak characteristics and detectability in social media data. Finally, while the analyses here focused on broad themes and trends, there is likely additional value in developing methods for identifying low-frequency messages, operationalizing this methodology into a comprehensive system for visualizing patterns extracted from the Internet, and integrating these data with other sources of information such as wildlife, environment, and agricultural data.

Can Digital Tools Be Used for Improving Immunization Programs?

In order to successfully control and eliminate vaccine-preventable infectious diseases, an appropriate vaccine coverage has to be achieved and maintained. This task requires a high level of effort as it may be compromised by a number of barriers. Public health agencies have issued specific recommendations to address these barriers and therefore improve immunization programs. In the present review, we characterize issues and challenges of immunization programs for which digital tools are a potential solution. In particular, we explore previously published research on the use of digital tools in the following vaccine-related areas: immunization registries, dose tracking, and decision support systems; vaccine-preventable diseases surveillance; surveillance of adverse events following immunizations; vaccine confidence monitoring; and delivery of information on vaccines to the public. Subsequently, we analyze the limits of the use of digital tools in such contexts and envision future possibilities and challenges.

Drivers of Emerging Infectious Disease Events as a Framework for Digital Detection

Improved and expanded data collection is required to fulfil the promise of an early-warning digital system.

The growing field of digital disease detection, or epidemic intelligence, attempts to improve timely detection and awareness of infectious disease (ID) events. Early detection remains an important priority; thus, the next frontier for ID surveillance is to improve the recognition and monitoring of drivers (antecedent conditions) of ID emergence for signals that precede disease events. These data could help alert public health officials to indicators of elevated ID risk, thereby triggering targeted active surveillance and interventions. We believe that ID emergence risks can be anticipated through surveillance of their drivers, just as successful warning systems of climate-based, meteorologically sensitive diseases are supported by improved temperature and precipitation data. We present approaches to driver surveillance, gaps in the current literature, and a scientific framework for the creation of a digital warning system. Fulfilling the promise of driver surveillance will require concerted action to expand the collection of appropriate digital driver data.