Taking connected mobile-health diagnostics of infectious diseases to the field

Wood, Christopher S.; Thomas, Michael R.; Budd, Jobie; Mashamba-Thompson, Tivani P.; Herbst, Kobus; Pillay, Deenan; Peeling, Rosanna W.; Johnson, Anne M; McKendry, Rachel A.; Stevens, Molly M.

doi:10.1038/s41586-019-0956-2

Cited by 285 publications

(249 citation statements)

References 81 publications

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“…The increasing prevalence of mobile phones, wireless internet connectivity and social media activity raises the possibility of using these tools to gather data for epidemiological studies, diagnostics 45 , population mobility during an Ebola epidemic 46 or influenza incidence in real time 47 . Future developments in predictive technology, machine learning and artificial intelligence will bring more opportunities to move towards 'precision public health' (Box 2).…”

Section: Emerging Technologiesmentioning

confidence: 99%

A new twenty-first century science for effective epidemic response

Bedford

Farrar²,

Ihekweazu

et al. 2019

Nature

264

204

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With rapidly changing ecology, urbanization, climate change, increased travel and fragile public health systems, epidemics will become more frequent, more complex and harder to prevent and contain. Here we argue that our concept of epidemics must evolve from crisis response during discrete outbreaks to an integrated cycle of preparation, response and recovery. This is an opportunity to combine knowledge and skills from all over the world-especially at-risk and affected communities. Many disciplines need to be integrated, including not only epidemiology but also social sciences, research and development, diplomacy, logistics and crisis management. This requires a new approach to training tomorrow's leaders in epidemic prevention and response.

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Section: Emerging Technologiesmentioning

confidence: 99%

A new twenty-first century science for effective epidemic response

Bedford

Farrar²,

Ihekweazu

et al. 2019

Nature

264

204

View full text Add to dashboard Cite

show abstract

“…Smartphones can be leveraged for this purpose as they possess the connectivity, computational power, and hardware to facilitate electronic reporting, epidemiological databasing, and point-of-care testing ( Figure 4). 84,85 An exponential rise in worldwide smartphone adoption, including in sub-Saharan Africa, makes smartphones a widely accessible technology to coordinate responses during large outbreaks like COVID-19. 84 The global spread of COVID-19 has been catalyzed by insufficient communication and underreporting.…”

mentioning

confidence: 99%

Diagnosing COVID-19: The Disease and Tools for Detection

et al. 2020

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COVID-19 has spread globally since its discovery in Hubei province, China in December 2019. A combination of computed tomography imaging, whole genome sequencing, and electron microscopy were initially used to screen and identify SARS-CoV-2, the viral etiology of COVID-19. The aim of this review article is to inform the audience of diagnostic and surveillance technologies for SARS-CoV-2 and their performance characteristics. We describe point-of-care diagnostics that are on the horizon and encourage academics to advance their technologies beyond conception. Developing plug-and-play diagnostics to manage the SARS-CoV-2 outbreak would be useful in preventing future epidemics.

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“…From the hospital's perspective, an important characteristic of sleep apnoea patient management is that it is predominantly ambulatory . Approaches including multimodal remote telemonitoring and telemedicine, smartphone engagement tools and internet‐based management platforms need to be supported by Big Data analysis and AI . Innovative Big Data analytics will allow us to better identify high‐risk populations and clinical scenarios that require immediate intervention .…”

Section: Opportunities Of Big Data In Understanding Sleep Breathing Dmentioning

confidence: 99%

Big Data in sleep apnoea: Opportunities and challenges

2019

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Sleep apnoea is now regarded as a highly prevalent systemic, multimorbid, chronic disease requiring a combination of long-term home-based treatments. Optimization of personalized treatment strategies requires accurate patient phenotyping. Data to describe the broad variety of phenotypes can come from electronic health records, health insurance claims, socio-economic administrative databases, environmental monitoring, social media, etc. Connected devices in and outside homes collect vast amount of data amassed in databases. All this contributes to 'Big Data' that, if used appropriately, has great potential for the benefit of health, well-being and therapeutics. Sleep apnoea is particularly well placed with regards to Big Data because the primary treatment is positive airway pressure (PAP). PAP devices, used every night over long periods by millions of patients across the world, generate an enormous amount of data. In this review, we discuss how different types of Big Data have, and could be, used to improve our understanding of sleep-disordered breathing, to identify undiagnosed sleep apnoea, to personalize treatment and to adapt health policies and better allocate resources. We discuss some of the challenges of Big Data including the need for appropriate data management, compilation and analysis techniques employing innovative statistical approaches alongside machine learning/artificial intelligence; closer collaboration between data scientists and physicians; and respect of the ethical and regulatory constraints of collecting and using Big Data. Lastly, we consider how Big Data can be used to overcome the limitations of randomized clinical trials and advance real-life evidence-based medicine for sleep apnoea.Big Data and sleep apnoea tory, Grenoble Alpes University) for the literature search and contribution to the pharmacovigilance and environmentalRespirology (2020) 25, 486-494

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Taking connected mobile-health diagnostics of infectious diseases to the field

Cited by 285 publications

References 81 publications

A new twenty-first century science for effective epidemic response

A new twenty-first century science for effective epidemic response

Diagnosing COVID-19: The Disease and Tools for Detection

Big Data in sleep apnoea: Opportunities and challenges

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