posted an update


Lynch, S., (2020)., - Open Data on Coronavirus Symptoms as a Proxy for Modelling the Spatiotemporal Characteristics of N-COVID19.

Little is known about the distribution and spatiotemporal characteristics of the coronavirus. Authorities do not have the resources to test large numbers of suspected cases. To complement institutional data collection efforts, we are repurposing our 10+ year citizen science methodology ( / as OpenCoronaMap. We are actively crowdsourcing self-reported clustered and semi-anonymous geospatial information about symptoms (HSE defined; GDPR compliant) to map a proxy of the potential distribution of the virus. Such data could give users a probability of having contracted the virus, what actions to take, and measure performance over time. Our anonymised and clustered data will be open meeting FAIR principles. This gives institutions access to a democratised research community, who will be able to advance science, and potentially improve supply chains, which might not be possible with more limited teams, who can also participate in the research. As we already have a system in production to capture geographic information about litter across the globe in hyper-resolution across space and time, it was relatively easy for us to change “litter” to “symptoms” to achieve similar results. As citizen science is new, largely unexplored, and not supported, and as we do not have a means to verify information, we can only guess about the quality of the crowdsourced data. However, it is important that we begin to train society to become better data collectors. OpenCoronaMap has potential to become a catalyst for citizen science, continuously improve responsible behaviour and protect public health.

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