Using Open-Source Intelligence to Detect Early Signals of COVID-19 in China: Descriptive Study

Use of Open-Source Epidemic Intelligence for Infectious Disease Outbreaks, Ukraine, 2022

Formal infectious disease surveillance in Ukraine has been disrupted by Russia's 2022 invasion, leading to challenges with tracking and containing epidemics. To analyze the effects of the war on infectious disease epidemiology, we used open-source data from EPIWATCH, an artificial intelligence early-warning system. We analyzed patterns of infectious diseases and syndromes before (November 1, 2021-February 23, 2022) and during (February 24-July 31, 2022) the conflict. We compared case numbers for the most frequently reported diseases with numbers from formal sources and found increases in overall infectious disease reports and in case numbers of cholera, botulism, tuberculosis, HIV/AIDS, rabies, and salmonellosis during compared with before the invasion. During the conflict, although open-source intelligence captured case numbers for epidemics, such data (except for diphtheria) were unavailable/underestimated by formal surveillance. In the absence of formal surveillance during military conflicts, open-source data provide epidemic intelligence useful for infectious disease control.

Use of a risk assessment tool to determine the origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

The origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is contentious. Most studies have focused on a zoonotic origin, but definitive evidence such as an intermediary animal host is lacking. We used an established risk analysis tool for differentiating natural and unnatural epidemics, the modified Grunow-Finke assessment tool (mGFT) to study the origin of SARS-COV-2. The mGFT scores 11 criteria to provide a likelihood of natural or unnatural origin. Using published literature and publicly available sources of information, we applied the mGFT to the origin of SARS-CoV-2. The mGFT scored 41/60 points (68%), with high inter-rater reliability (100%), indicating a greater likelihood of an unnatural than natural origin of SARS-CoV-2. This risk assessment cannot prove the origin of SARS-CoV-2 but shows that the possibility of a laboratory origin cannot be easily dismissed.

Open-Source Intelligence for Detection of Radiological Events and Syndromes Following the Invasion of Ukraine in 2022: Observational Study

BACKGROUND

On February 25, 2022, Russian forces took control of the Chernobyl power plant after continuous fighting within the Chernobyl exclusion zone. Continual events occurred in the month of March, which raised the risk of potential contamination of previously uncontaminated areas and the potential for impacts on human and environmental health. The disruption of war has caused interruptions to normal preventive activities, and radiation monitoring sensors have been nonfunctional. Open-source intelligence can be informative when formal reporting and data are unavailable.

OBJECTIVE

This paper aimed to demonstrate the value of open-source intelligence in Ukraine to identify signals of potential radiological events of health significance during the Ukrainian conflict.

METHODS

Data were collected from search terminology for radiobiological events and acute radiation syndrome detection between February 1 and March 20, 2022, using 2 open-source intelligence (OSINT) systems, EPIWATCH and Epitweetr.

RESULTS

Both EPIWATCH and Epitweetr identified signals of potential radiobiological events throughout Ukraine, particularly on March 4 in Kyiv, Bucha, and Chernobyl.

CONCLUSIONS

Open-source data can provide valuable intelligence and early warning about potential radiation hazards in conditions of war, where formal reporting and mitigation may be lacking, to enable timely emergency and public health responses.

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.

Preventing the next pandemic: Use of artificial intelligence for epidemic monitoring and alerts

Emerging infections are a continual threat to public health security, which can be improved by use of rapid epidemic intelligence and open-source data. Artificial intelligence systems to enable earlier detection and rapid response by governments and health can feasibly mitigate health and economic impacts of serious epidemics and pandemics. EPIWATCH is an artificial intelligence-driven outbreak early-detection and monitoring system, proven to provide early signals of epidemics before official detection by health authorities.

SARS-CoV-2: International Investigation Under the WHO or BWC

In late 2019, the novel and highly infectious coronavirus SARS-CoV-2 caused a worldwide outbreak of a severe respiratory infectious disease, known as COVID-19. The disease has started in China and turned into one of the worst pandemics in human history. Due to the very fast global spread of the pathogen, COVID-19 is a great challenge for the Public Health Systems. It had led to a variety of severe limitations in private and public life worldwide. There is a lively public debate about possible sources of SARS-CoV-2. This article aims at providing a better understanding of controversial biological and political issues regarding COVID-19. Recommendations are made for possible actions under the umbrella of the World Health Organization and in respect to the Biological Weapons Convention.

Lifestyle Variations during and after the COVID-19 Pandemic: A Cross-Sectional Study of Diet, Physical Activities, and Weight Gain among the Jordanian Adult Population

The way that COVID-19 has been handled since its inception in 2019 has had a significant impact on lifestyle-related behaviors, such as physical activities, diet, and sleep patterns. This study measures lifestyle-related behavior during the COVID-19 pandemic lockdown using a 22-item questionnaire. The responses were collected from March 2021 to September 2021. A total of four hundred and sixty-seven Jordanian participants were engaged in assessing the changes caused by the pandemic and their effect on BMI. The validity and reliability of the questionnaire were tested for 71 participants. Cronbach's alpha values for the questionnaire exceeded 0.7, demonstrating good reliability and internal consistency. The effect of each question regarding physical activity and dietary habits over the BMI difference was studied using ANOVA. The study shows that more than half of the participants reported snacking more between meals and increased their sitting and screen time, while 74% felt more stressed and anxious. BMI difference among the individuals throughout the lockdown was significantly associated with these variables. In contrast, 62% of the participants showed more awareness about their health by increasing the intake of immunity-boosting foods, and 56% of the participants showed an increase in the consumption of nutrition supplements. Females and married individuals tended to be healthier. Therefore, their BMI showed stability compared to others based on their gender and marital status. Exercise, sleep, and avoiding 'junk' food, which contributes to weight gain and COVID-19 vulnerability, are strongly recommended.

COVID-19 Vaccination Strategy in China: A Case Study

The coronavirus disease 2019 (COVID-19) outbreak in China was first reported to the World Health Organization on 31 December 2019, after the first cases were officially identified around 8 December 2019. However, the case of an infected patient of 55 years old can probably be traced back on 17 November. The spreading has been rapid and heterogeneous. Economic, political and social impacts have not been long overdue. This paper, based on English, French and Chinese research in national and international databases, aims to study the COVID-19 situation in China through the management of the outbreak and the Chinese response to vaccination strategy. The coronavirus disease pandemic is under control in China through non-pharmaceutical interventions, and the mass vaccination program has been launched to further prevent the disease and progressed steadily with 483.34 million doses having been administered across the country by 21 May 2021. China is also acting as an important player in the development and production of SARS-CoV-2 vaccines.

Rapid diagnostic methods for SARS-CoV-2 (COVID-19) detection: an evidence-based report

Since December 2019, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been a global health concern. The transmission method is human-to-human. Since this second wave of SARS-CoV-2 is more aggressive than the first wave, rapid testing is warranted to use practical diagnostics to break the transfer chain. Currently, various techniques are used to diagnose SARS-CoV-2 infection, each with its own set of advantages and disadvantages. A full review of online databases such as PubMed, EMBASE, Web of Science, and Google Scholar was analyzed to identify relevant articles focusing on SARS-CoV-2 and diagnosis and therapeutics. The most recent article search was on May 10, 2021. We summarize promising methods for detecting the novel Coronavirus using sensor-based diagnostic technologies that are sensitive, cost-effective, and simple to use at the point of care. This includes loop-mediated isothermal amplification and several laboratory protocols for confirming suspected 2019-nCoV cases, as well as studies with non-commercial laboratory protocols based on real-time reverse transcription-polymerase chain reaction and a field-effect transistor-based bio-sensing device. We discuss a potential discovery that could lead to the mass and targeted SARS-CoV-2 detection needed to manage the COVID-19 pandemic through infection succession and timely therapy.

Using open-source intelligence to identify early signals of COVID-19 in Indonesia

Objective

Open-source data from online news reports and informal sources may provide information about outbreaks before official notification. This study aims to evaluate the use of open-source data from the epidemic observatory, EpiWATCH, to identify the early signals of pneumonia of unknown cause as a proxy for COVID-19 in Indonesia.

Methods

Using open-source data on pneumonia of unknown cause in Indonesia between 1 November 2019 and 31 March 2020 (extracted from EpiWATCH, an open-source epidemic observatory), a descriptive analysis was performed to identify the trend of pneumonia of unknown cause in Indonesia before official notification of COVID-19 cases.

Results

A rise in reports of pneumonia of unknown cause was identified in Indonesia, starting from late January 2020. There were 304 reported cases of pneumonia of unknown cause, 30 of which occurred before the identification of the first COVID-19 cases on 2 March 2020. The early signals of pneumonia of unknown cause in Indonesia may indicate possible unrecognized circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) before official detection.

Discussion

Open-source data may provide rapid, unvalidated information for early detection of outbreaks. Although unvalidated, such information may be used to supplement or trigger investigation and testing. As EpiWATCH sources global information, this methodology can be repeated for other countries within the Western Pacific Region, and for other diseases.