Artificial intelligence in public health: the potential of epidemic early warning systems.

The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.

Epidemic intelligence studies: A research agenda for political scientists.

This research letter introduces readers to health intelligence by conceptualizing critical components and providing a primer for research within political science broadly considered. Accordingly, a brief review of the literature is provided, concluding with possible future research agendas. The aim is to elaborate on the importance of public health intelligence to national security studies, and to political science more generally.

Timeliness of online COVID-19 reports from official sources.

Introduction: Making epidemiological indicators for COVID-19 publicly available through websites and social media can support public health experts in the near-real-time monitoring of the situation worldwide, and in the establishment of rapid response and public health measures to reduce the consequences of the pandemic. Little is known, however, about the timeliness of such sources. Here, we assess the timeliness of official public COVID-19 sources for the WHO regions of Europe and Africa. Methods: We monitored official websites and social media accounts for updates and calculated the time difference between daily updates on COVID-19 cases. We covered a time period of 52 days and a geographic range of 62 countries, 28 from the WHO African region and 34 from the WHO European region. Results: The most prevalent categories were social media updates only (no website reporting) in the WHO African region (32.7% of the 1,092 entries), and updates in both social media and websites in the WHO European region (51.9% of the 884 entries for EU/EEA countries, and 73.3% of the 884 entries for non-EU/EEA countries), showing an overall clear tendency in using social media as an official source to report on COVID-19 indicators. We further show that the time difference for each source group and geographical region were statistically significant in all WHO regions, indicating a tendency to focus on one of the two sources instead of using both as complementary sources. Discussion: Public health communication via social media platforms has numerous benefits, but it is worthwhile to do it in combination with other, more traditional means of communication, such as websites or offline communication.

Implementing epidemic intelligence in the WHO African region for early detection and response to acute public health events.

Epidemic intelligence activities are undertaken by the WHO Regional Office for Africa to support member states in early detection and response to outbreaks to prevent the international spread of diseases. We reviewed epidemic intelligence activities conducted by the organisation from 2017 to 2020, processes used, key results and how lessons learned can be used to strengthen preparedness, early detection and rapid response to outbreaks that may constitute a public health event of international concern. A total of 415 outbreaks were detected and notified to WHO, using both indicator-based and event-based surveillance. Media monitoring contributed to the initial detection of a quarter of all events reported. The most frequent outbreaks detected were vaccine-preventable diseases, followed by food-and-water-borne diseases, vector-borne diseases and viral haemorrhagic fevers. Rapid risk assessments generated evidence and provided the basis for WHO to trigger operational processes to provide rapid support to member states to respond to outbreaks with a potential for international spread. This is crucial in assisting member states in their obligations under the International Health Regulations (IHR) (2005). Member states in the region require scaled-up support, particularly in preventing recurrent outbreaks of infectious diseases and enhancing their event-based surveillance capacities with automated tools and processes.

Giants on Clay Feet-COVID-19, infection control and public health laboratory networks in England, the USA and (West-)Germany (1945-2020).

In early 2020, COVID-19 exposed differences in public health laboratory systems' testing abilities. Focusing on Germany, the USA and the UK between 1900 and 2020, this article argues that studying the distinct evolution of laboratory infrastructures is critical to understanding the history of infection control and the limits of template-based reforms in global health. While each analysed laboratory infrastructure was shaped by a unique national context, neoliberal visions of lean public services and declining resources led to significant reform pressure from the 1970s. The US Center of Disease Control's model of epidemic intelligence provided an attractive template to integrate resources and focus planning on preparedness scenarios. It also helped justify cuts to local laboratory infrastructures. Effects were uneven: in the USA and the UK, improved integration failed to compensate for local laboratory cuts and loss of autonomy. By contrast, Germany's subsidiary principle allowed for limited federal integration while leaving local services mostly intact.

Epidemic Intelligence Service Alumni in Public Health Leadership Roles.

Since 1951, the Epidemic Intelligence Service (EIS) of the U.S. Centers for Disease Control and Prevention (CDC) has trained physicians, nurses, scientists, veterinarians, and other allied health professionals in applied epidemiology. To understand the program's effect on graduates' leadership outcomes, we examined the EIS alumni representation in five select leadership positions. These positions were staffed by 353 individuals, of which 185 (52%) were EIS alumni. Among 12 CDC directors, four (33%) were EIS alumni. EIS alumni accounted for 29 (58%) of the 50 CDC center directors, 61 (35%) of the 175 state epidemiologists, 27 (56%) of the 48 Field Epidemiology Training Program resident advisors, and 70 (90%) of the 78 Career Epidemiology Field Officers. Of the 185 EIS alumni in leadership positions, 136 (74%) were physicians, 22 (12%) were scientists, 21 (11%) were veterinarians, 6 (3%) were nurses, and 94 (51%) were assigned to a state or local health department. Among the 61 EIS alumni who served as state epidemiologists, 40 (66%) of them were assigned to a state or local health department during EIS. Our evaluation suggests that epidemiology training programs can serve as a vital resource for the public health workforce, particularly given the capacity strains brought to light by the COVID-19 pandemic.

An intelligent early warning system of analyzing Twitter data using machine learning on COVID-19 surveillance in the US.

The World Health Organization (WHO) declared on 11th March 2020 the spread of the coronavirus disease 2019 (COVID-19) a pandemic. The traditional infectious disease surveillance had failed to alert public health authorities to intervene in time and mitigate and control the COVID-19 before it became a pandemic. Compared with traditional public health surveillance, harnessing the rich data from social media, including Twitter, has been considered a useful tool and can overcome the limitations of the traditional surveillance system. This paper proposes an intelligent COVID-19 early warning system using Twitter data with novel machine learning methods. We use the natural language processing (NLP) pre-training technique, i.e., fine-tuning BERT as a Twitter classification method. Moreover, we implement a COVID-19 forecasting model through a Twitter-based linear regression model to detect early signs of the COVID-19 outbreak. Furthermore, we develop an expert system, an early warning web application based on the proposed methods. The experimental results suggest that it is feasible to use Twitter data to provide COVID-19 surveillance and prediction in the US to support health departments' decision-making.

Monitoring online media reports for early detection of unknown diseases: Insight from a retrospective study of COVID-19 emergence.

Event-based surveillance (EBS) systems monitor a broad range of information sources to detect early signals of disease emergence, including new and unknown diseases. In December 2019, a newly identified coronavirus emerged in Wuhan (China), causing a global coronavirus disease (COVID-19) pandemic. A retrospective study was conducted to evaluate the capacity of three event-based surveillance (EBS) systems (ProMED, HealthMap and PADI-web) to detect early COVID-19 emergence signals. We focused on changes in online news vocabulary over the period before/after the identification of COVID-19, while also assessing its contagiousness and pandemic potential. ProMED was the timeliest EBS, detecting signals one day before the official notification. At this early stage, the specific vocabulary used was related to 'pneumonia symptoms' and 'mystery illness'. Once COVID-19 was identified, the vocabulary changed to virus family and specific COVID-19 acronyms. Our results suggest that the three EBS systems are complementary regarding data sources, and all require timeliness improvements. EBS methods should be adapted to the different stages of disease emergence to enhance early detection of future unknown disease outbreaks.