Maps of the 2019-nCoV coronavirus outbreak

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Feb 4, 2020

Maps of the 2019-nCoV coronavirus outbreak

Mapbox
No items found.

Feb 4, 2020

As the new coronavirus (2019 n-COv) changes rapidly, we’ve noticed quite a few maps emerging to monitor the outbreak. We know that maps have the power to inform, but can just as easily dehumanize an outbreak or strike fear when it’s unnecessary. To learn more about the responsible use of maps for diseases, we sat down with Amanda Makulec, the Senior Data Visualization Lead at Excella and Operations Director for the Data Visualization Society, to understand the right way to build and interpret visualization of public health data.

Investigate the data source

Before even building the map, Amanda encourages developers to evaluate the data, saying:

Develop an understanding of the data — How is the data collected? How is it checked for accuracy? When was it last updated? Interactive maps with a live-updating data layer (sourced from a reputable organization) can serve an important need in providing real-time information, compared to static maps and graphics that quickly become outdated.”

Healthmap, an infectious disease surveillance platform out of Boston Children’s Hospital, keeps its map up to date by aggregating several sources, including online publications, official reports, and eyewitness accounts. Check out the repository for a full list of sources.

For developers looking to build a map of the new coronavirus, Amanda suggests reliable resources like the CDC page for the 2019 Novel Coronavirus and the 2019-nCoV Situation Summary, Next Strain, and Be Outbreak Prepared. For more on disease patterns and understanding the metrics used to quantify the reach of a disease (e.g. prevalence, incidence, mortality rate), the New York Times recently explained key epidemiological principles, and the CDC has an open source textbook on Principles of Epidemiology.

Contextualize the data

Public health data without context can easily misinform, increase fear, or lead to poor decision making. Amanda instead suggests adding context by including other data to help make sense of the numbers, saying:

“With an emergent disease that’s making headlines, it can be helpful to offer a comparison to other more familiar diseases, like influenza, to help put prevalence and mortality rate data into perspective. Patterns of disease transmission are also important to consider. For example, are cases travel-related or the result of local person-to-person transmission?”

EpiRisk, a computational platform built by GLEAMviz team from Northeastern University and the ISI Foundation, is contextualizing the risk of the coronavirus by modeling how it might spread in relation to airlines and commuting patterns. The platform allows a quick estimate of the probability of exporting infected individuals from sites affected by a disease outbreak to other areas and is being used for some preliminary analysis of the 2019-nCOV outbreak in Wuhan.

Remember the human component

Most importantly, when building maps for outbreaks or public health concerns like the new coronavirus, we have to remember that it is at its core about, and for, humans. Amanda encourages,

“Be mindful of how people will react to, use, and understand (or misunderstand) what is presented. We don’t want visualizations to incite fear — instead, we want them to inform and increase understanding of what’s happening. Color, text, and other design decisions you make can evoke a visceral reaction in your audience. Don’t reinforce xenophobia or stereotypes with how you present information about disease demographics, or create inflammatory headlines. Think about how someone directly affected by this disease might feel looking at a visualization.”

Emergent Epidemics Lab, a live-updating visualization built by Professor Samuel Scarpino from Northeastern University, keeps the human component top of mind for this visualization. The platform allows users to filter the map by confirmed cases, country name, travel history, and relationship to Wuhan. The map then displays incidents by date of confirmation using a red-blue color ramp with the most recently confirmed cases showing as red and older cases displaying blue — a differentiation that takes care to not evoke a visceral reaction. For more on the data or to contribute, visit the lab’s Github.

We’re watching the 2019-n-CoV coronavirus outbreak very closely, concerned for those personally impacted as well as the health and wellbeing of our teammates in our China office. If you’re building maps to monitor this outbreak, share it with us on Twitter, and we’ll add it to our post.

Our Community team is also here to help with technical support, feedback, and discount coupons for your positive impact projects. Learn more about how Mapbox supports social impact initiatives on our Community page.

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