A Case for Participatory Disease Surveillance of the COVID-19 Pandemic in India

Garg, Suneela; Bhatnagar, Nidhi; Gangadharan, Navya

doi:10.2196/18795

Cited by 75 publications

(61 citation statements)

References 10 publications

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“… 12 , 15 Technology could be used to address some of these limitations, including by automating the processing of test results or symptom reports and by use of smartphone capabilities (eg, Bluetooth) to identify and notify contacts instantaneously who are at risk of infection. 14 , 16 , 17 Automated contact tracing for COVID-19 has been deployed in several countries, 18 , 19 but in the UK, its introduction has been delayed by technical setbacks. 20 The practical, technical, legal, and ethical considerations involved are complex; 20 , 21 , 22 and uptake, privacy, security, and testing access have been identified as potential barriers to effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Automated and partly automated contact tracing: a systematic review to inform the control of COVID-19

Braithwaite¹,

Callender²,

Bullock³

et al. 2020

The Lancet Digital Health

272

254

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Summary Evidence for the use of automated or partly automated contact-tracing tools to contain severe acute respiratory syndrome coronavirus 2 is scarce. We did a systematic review of automated or partly automated contact tracing. We searched PubMed, EMBASE, OVID Global Health, EBSCO Medical COVID Information Portal, Cochrane Library, medRxiv, bioRxiv, arXiv, and Google Advanced for articles relevant to COVID-19, severe acute respiratory syndrome, Middle East respiratory syndrome, influenza, or Ebola virus, published from Jan 1, 2000, to April 14, 2020. We also included studies identified through professional networks up to April 30, 2020. We reviewed all full-text manuscripts. Primary outcomes were the number or proportion of contacts (or subsequent cases) identified. Secondary outcomes were indicators of outbreak control, uptake, resource use, cost-effectiveness, and lessons learnt. This study is registered with PROSPERO (CRD42020179822). Of the 4036 studies identified, 110 full-text studies were reviewed and 15 studies were included in the final analysis and quality assessment. No empirical evidence of the effectiveness of automated contact tracing (regarding contacts identified or transmission reduction) was identified. Four of seven included modelling studies that suggested that controlling COVID-19 requires a high population uptake of automated contact-tracing apps (estimates from 56% to 95%), typically alongside other control measures. Studies of partly automated contact tracing generally reported more complete contact identification and follow-up compared with manual systems. Automated contact tracing could potentially reduce transmission with sufficient population uptake. However, concerns regarding privacy and equity should be considered. Well designed prospective studies are needed given gaps in evidence of effectiveness, and to investigate the integration and relative effects of manual and automated systems. Large-scale manual contact tracing is therefore still key in most contexts.

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Section: Introductionmentioning

confidence: 99%

Automated and partly automated contact tracing: a systematic review to inform the control of COVID-19

Braithwaite¹,

Callender²,

Bullock³

et al. 2020

The Lancet Digital Health

272

254

View full text Add to dashboard Cite

show abstract

“…Smartphone apps may approximate pathogen exposure risk through the use of geolocation technologies such as GPS, and/or via proximity-based approaches using localized Radio Frequency (RF) transmissions like Bluetooth. Location-based approaches attempt to compare the places a user has been with a database of high-risk locations or overlaps with infected people ( 6 ) , while proximity-based approaches directly detect nearby smartphones that can later be checked for "too close for too long" exposure to infected people ( 7 ) . In either approach, users who are deemed to be at risk are then notified, and in some implementations, health authorities also receive this information for follow-up.…”

Section: Introductionmentioning

confidence: 99%

Modeling the combined effect of digital exposure notification and non-pharmaceutical interventions on the COVID-19 epidemic in Washington state

Abueg

Hinch

et al. 2020

Preprint

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Contact tracing is increasingly being used to combat COVID-19, and digital implementations are now being deployed, many of them based on Apple and Google's Exposure Notification System. These systems are new and are based on smartphone technology that has not traditionally been used for this purpose, presenting challenges in understanding possible outcomes. In this work, we use individual-based computational models to explore how digital exposure notifications can be used in conjunction with non-pharmaceutical interventions, such as traditional contact tracing and social distancing, to influence COVID-19 disease spread in a population. Specifically, we use a representative model of the household and occupational structure of three counties in the state of Washington together with a proposed digital exposure notifications deployment to quantify impacts under a range of scenarios of adoption, compliance, and mobility. In a model in which 15% of the population participated, we found that digital exposure notification systems could reduce infections and deaths by approximately 8% and 6%, effectively complementing traditional contact tracing. We believe this can serve as guidance to health authorities in Washington state and beyond on how exposure notification systems can complement traditional public health interventions to suppress the spread of COVID-19.

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“…7,8 Moreover, these applications also enable a system of participatory disease surveillance wherein the users are expected to self-report specific symptoms to assist public health personnel in analyzing and responding to the emerging health threats. 9 The feasibility of implementing such technology is also driven by the exponential worldwide growth of mobile phone and information technology.…”

Section: Introductionmentioning

confidence: 99%

Effective Contact Tracing for COVID-19 Using Mobile Phones: An Ethical Analysis of the Mandatory Use of the Aarogya Setu Application in India

Basu¹

2020

Camb Q Healthc Ethics

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Several digital contact tracing smartphone applications have been developed worldwide in the effort to combat COVID-19 that warn users of potential exposure to infectious patients and generate big data that helps in early identification of hotspots, complementing the manual tracing operations. In most democracies, concerns over a breach in data privacy have resulted in severe opposition toward their mandatory adoption. This paper examines India as a noticeable exception, where the compulsory installation of such a government-backed application, the “Aarogya Setu” has been deemed mandatory in certain situations. We argue that the mandatory app requirement constitutes a legitimate public health intervention during a public health emergency.

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A Case for Participatory Disease Surveillance of the COVID-19 Pandemic in India

Cited by 75 publications

References 10 publications

Automated and partly automated contact tracing: a systematic review to inform the control of COVID-19

Automated and partly automated contact tracing: a systematic review to inform the control of COVID-19

Modeling the combined effect of digital exposure notification and non-pharmaceutical interventions on the COVID-19 epidemic in Washington state

Effective Contact Tracing for COVID-19 Using Mobile Phones: An Ethical Analysis of the Mandatory Use of the Aarogya Setu Application in India

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