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

MacIntyre, C Raina; Lim, Samsung; Quigley, Ashley

doi:10.1016/j.xcrm.2022.100867

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…Originating in the 1950s, the concept of AI has evolved significantly, gaining momentum with the advent of the big data era and the subsequent digital revolution. Despite its potential, the adoption of AI across various organizational functions has been gradual, with many institutions failing to fully leverage AI capabilities in emulating human intelligence across diverse data inputs, such as text, numerical data, images, and sound, to facilitate decisionmaking processes (2)(3)(4). This limited application often restricts AI's integration to singular organizational functions, thereby underutilizing its potential to revolutionize business operations comprehensively (4)(5)(6).…”

Section: Introductionmentioning

confidence: 99%

AI Strategy in Healthcare CHRM: Analyzing the Influence Organization Effective Performance Evidence from the Private Hospitals of Lahore Pakistan

Ghaffar,

Arshad,

Siddqiue

et al. 2024

JHRR

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Background: The advent of Artificial Intelligence (AI) within organizational frameworks, particularly in the realm of Human Resource Management (HRM), has initiated a transformative shift in operational efficiencies and strategies across various sectors. This integration aims to leverage AI's capabilities to augment human decision-making, enhance operational efficiency, and foster a culture of innovation within organizations. Despite the potential benefits, the practical application and tangible impact of AI strategies on organizational effectiveness remain areas of significant academic and practical interest. Objective: This study aimed to investigate the influence of AI strategies and creativity oriented HRM practices on organizational effective performance. It sought to explore the synergistic relationship between AI implementation and innovative HR practices, and their collective impact on enhancing organizational efficiency and performance metrics. Methods: Employing a cross-sectional survey design, the study collected data from employees working in private hospitals in Lahore, Pakistan. A total of 144 valid responses were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the relationships between AI strategy implementation, creativity-oriented HRM practices, and organizational effective performance. Reliability and validity of the constructs were evaluated through Cronbach's alpha, composite reliability, and average variance extracted (AVE) measures. Results: The findings revealed that AI strategy implementation (Cronbach's alpha = 0.942, AVE = 0.611) and creativity oriented HRM practices (Cronbach's alpha = 0.932, AVE = 0.585) were both significantly associated with enhanced organizational effective performance (Cronbach's alpha = 0.932, AVE = 0.533). The path analysis indicated strong positive relationships between AI strategies and creativity-oriented HRM practices (β = 0.688, p < 0.001), between AI strategies and organizational performance (β = 0.228, p = 0.004), and between creativity-oriented HRM practices and organizational performance (β = 0.597, p < 0.001). The model explained 59% of the variance in organizational effective performance. Conclusion: The study concludes that the strategic integration of AI within HRM frameworks significantly contributes to organizational effectiveness. Emphasizing creativity-oriented HRM practices in conjunction with AI strategies can lead to substantial improvements in organizational performance. These findings underscore the importance of a strategic approach to AI integration in HRM, highlighting the need for organizations to foster environments that promote innovation and creativity.

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Section: Introductionmentioning

confidence: 99%

AI Strategy in Healthcare CHRM: Analyzing the Influence Organization Effective Performance Evidence from the Private Hospitals of Lahore Pakistan

Ghaffar,

Arshad,

Siddqiue

et al. 2024

JHRR

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Section: Introductionmentioning

confidence: 99%

“…Open-source data obtained from EPIWATCH, an Artificial Intelligence (AI)-driven disease surveillance system, has proven capability in early outbreak detection [7][8][9]. A previous study indicated that the 2014 West African Ebola epidemic could have been identified months prior to WHO notification [7]. Unstructured open-source data contains valuable information that can be harnessed by EISs and used to detect early warnings of serious epidemics [10].…”

Section: Introductionmentioning

confidence: 99%

Respiratory illness of unknown cause as an early signal of the COVID-19 pandemic

Chen,

Kunasekaran,

Moa

et al. 2024

Global Biosecurity

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Background: As the COVID-19 pandemic began, cases of respiratory illness of unknown cause could have presented early disease signals. The detection of such signals can provide useful clues regarding disease emergence and opportunities for early outbreak control. This study assessed trends in the global incidence of reports of respiratory illness of unknown cause as early indicators of COVID-19 in late 2019, using the early warning system, EPIWATCH, to investigate the disease’s origins and early epidemiology. Methods: Global news articles published between 1-Oct and 31-Dec in 2019 and collected by EPIWATCH, an open-source Artificial Intelligence (AI)-driven epidemic surveillance system, were searched for keywords relating to unknown respiratory illness. Control periods representing the pre-COVID period (1-Oct to 31-Dec 2016-2018) were used to compare report trends over time. Data on report location, case demographics, outbreak setting, and type of illness, were extracted and descriptively analysed. Fisher’s exact tests were conducted to assess proportional differences between years. Results: Overall, 113 EPIWATCH reports of unknown respiratory illness were identified from 12 countries in 2019. Most reports (n=111 [98.2%]) were published in Northern hemisphere countries, in particular Russia (n=A72 [63.7%]) and the United States (n=17 [15.0%]). China experienced a month-on-month increase in reports. Schools were the most reported outbreak setting (n=49 [43.4%]). The proportion of included reports in 2019 was significantly higher compared to 2016-2018 (p<.001). Conclusion: Potential early COVID-19 signals were detected from various countries before the end of 2019. AI-driven open-source disease surveillance systems, such as EPIWATCH, can provide early warnings to enhance disease surveillance and improve outbreak prevention and response.

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“…Epidemic open-source intelligence (OSINT) systems provide new approaches to public health surveillance and are increasingly used for epidemic early warning [ 6 ]. Early warning OSINT systems can complement and improve the performance of formal surveillance systems by enabling early detection of serious events or fill a gap when routine surveillance systems fail or are absent.…”

Section: Introductionmentioning

confidence: 99%

“…Through the use of specialized processes and algorithms, early warning of potential outbreaks of diseases in populations can be flagged from unstructured sources such as new articles and social media [ 7 ]. EPIWATCH and Epitweetr are examples of such systems [ 6 , 8 ]. While OSINT lacks verification, an early warning can be followed by a formal investigation to verify a signal.…”

Section: Introductionmentioning

confidence: 99%

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

Stone¹,

Heslop²,

Lim³

et al. 2023

JMIR Infodemiology

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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.

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

Cited by 7 publications

References 14 publications

AI Strategy in Healthcare CHRM: Analyzing the Influence Organization Effective Performance Evidence from the Private Hospitals of Lahore Pakistan

AI Strategy in Healthcare CHRM: Analyzing the Influence Organization Effective Performance Evidence from the Private Hospitals of Lahore Pakistan

Respiratory illness of unknown cause as an early signal of the COVID-19 pandemic

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

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