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Ben Snaith

About this Project | COVID-19 County Social Distancing Reporter - 0 views

covid19.cambersystems.com/about

Data Mobility Coronavirus

shared by Ben Snaith on 27 May 20 - No Cached
  • In achieving these goals, we started by applying our experience at Camber and the experience of the epidemiological teams, and taking into account the differences between aggregated human movements that are predictable and movements that are not, notably socialization. Presenting both radius of gyration and entropy gives a more complete picture of socialization patterns within a county. For example, a high RoG and low entropy could indicate a population that needs to travel far to go to work or the grocery store, but are otherwise staying home; a low RoG and high entropy could indicate a more dense area where people are staying near home but are still moving in their neighborhood. Both are important signals needing different interventions.*
    • Ben Snaith
      Ben Snaith on 27 May 20
       
      yo @fionntan this seems helpful, I just don't really get it
  • In collaboration with epidemiologists within the COVID-19 Mobility Data Network including the Harvard School of Public Health, Direct Relief, Princeton, and many others, we built this dashboard as an enhanced offering to public health officials that builds further on others’ early work product. This effort provides a more accurate and actionable understanding of the effectiveness of social distancing and other policy interventions aimed at reducing or slowing the spread of COVID-19.
  • In developing this dashboard, we began with several goals. First, we worked with public-health researchers to understand what they needed and provide them with the most-important data and metrics. Second, we worked with experts to ensure that we used privacy-forward practices in developing these metrics. Finally, we aim to iterate based on new information and feedback from the public and researchers to continue to aid the fight against COVID-19.
davetaz

Potential Coronavirus (COVID-19) symptoms reported through NHS Pathways and 111 online ... - 0 views

digital.nhs.uk/...latest

NHS Coronavirus Anonymisation Open-Data

shared by davetaz on 27 Apr 20 - No Cached
  • Potential Coronavirus (COVID-19) symptoms reported through NHS Pathways and 111 online
  • Summary Data published on potential COVID-19 symptoms reported through NHS Pathways and 111 online Dashboard shows the total number of NHS Pathways triages through 111 and 999, and online assessments in 111 online which have received a potential COVID-19 final disposition. This data is based on potential COVID-19 symptoms reported by members of the public to NHS Pathways through NHS 111 or 999 and 111 online,  and is not based on the outcomes of tests for coronavirus. This is not a count of people.
Ben Snaith

Which Covid-19 Data Can You Trust? - 2 views

hbr.org/...ch-covid-19-data-can-you-trust

Data Mobility Coronavirus Contact-Tracing Health Algorithms Models

shared by Ben Snaith on 11 May 20 - No Cached
  • In a crisis situation like the one we are in, data can be an essential tool for crafting responses, allocating resources, measuring the effectiveness of interventions, such as social distancing, and telling us when we might reopen economies. However, incomplete or incorrect data can also muddy the waters, obscuring important nuances within communities, ignoring important factors such as socioeconomic realities, and creating false senses of panic or safety, not to mention other harms such as needlessly exposing private information.
  • Unfortunately, many of these technological solutions — however well intended — do not provide the clear picture they purport to. In many cases, there is insufficient engagement with subject-matter experts, such as epidemiologists who specialize in modeling the spread of infectious diseases or front-line clinicians who can help prioritize needs. But because technology and telecom companies have greater access to mobile device data, enormous financial resources, and larger teams of data scientists, than academic researchers do, their data products are being rolled out at a higher volume than high quality studies.
  • To some extent, all data risk breaching the privacy of individual or group identities, but publishing scorecards for specific neighborhoods risks shaming or punishing communities, while ignoring the socioeconomic realities of people’s lives that make it difficult for them to stay home.
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  • Even more granular examples, such as footfalls at identifiable business locations, risks de-identifying religious groups; patients visiting cancer hospitals, HIV clinics, or reproductive health clinics; or those seeking public assistance. The medical and public health communities long ago deemed the un-masking of such information without consent unacceptable, but companies have recently been releasing it on publicly available dashboards.
  • Until we know more about how these changing movement patterns impact epidemiological aspects of the disease, we should use these data with caution.
  • Simply presenting them, or interpreting them without a proper contextual understanding, could inadvertently lead to imposing or relaxing restrictions on lives and livelihoods, based on incomplete information.
  • In the absence of a tightly coupled testing and treatment plan, however, these apps risk either providing false reassurance to communities where infectious but asymptomatic individuals can continue to spread disease, or requiring an unreasonably large number of people to quarantine. The behavioral response of the population to these apps is therefore unknown and likely to vary significantly across societies.
  • Some contact-tracing apps follow black-box algorithms, which preclude the global community of scientists from refining them or adopting them elsewhere. These non-transparent, un-validated interventions — which are now being rolled out (or rolled back) in countries such as China, India, Israel and Vietnam — are in direct contravention to the open cross-border collaboration that scientists have adopted to address the Covid-19 pandemic.
  • pidemiological models that can help predict the burden and pattern of spread of Covid-19 rely on a number of parameters that are, as yet, wildly uncertain. We still lack many of the basic facts about this disease, including how many people have symptoms, whether people who have been infected are immune to reinfection, and — crucially — how many people have been infected so far. In the absence of reliable virological testing data, we cannot fit models accurately, or know confidently what the future of this epidemic will look like for all these reasons, and yet numbers are being presented to governments and the public with the appearance of certainty
  • Telenor, the Norwegian telco giant has led the way in responsible use of aggregated mobility data from cell phone tower records. Its data have been used, in close collaboration with scientists and local practitioners, to model, predict, and respond to outbreaks around the world. Telenor has openly published its methods and provided technological guidance on how telco data can be used in public health emergencies in a responsible, anonymized format that does not risk de-identification.
  • The Covid-19 Mobility Data Network, of which we are part, comprises a voluntary collaboration of epidemiologists from around the world analyzes aggregated data from technology companies to provide daily insights to city and state officials from California to Dhaka, Bangladesh.
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