Past Meetup

Poor, Sick and Unpredictable: High-tech disease surveillance misses the poor

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To kick off our fall season, we're super excited to host a talk that's about disease, and also about addressing the familiar challenges faced most of us working in data science: How do you make the most of of the data you have, develop a good model and translate the results into terms that influencers will trust and use?

Poor, Sick and Unpredictable...

If you’re poor and you get the flu, you’re more likely to develop complications and end up in an expensive hospital bed than someone who’s rich. And, ironically, the hospital is less likely to know you’ll need that bed than they will for someone who’s rich.

We’re hosting Sam Scarpino this month to talk about his extensive work forecasting disease dynamics among the poor. Sam is a new UVM professor ( http://scarpino.github.io/ ) who works at the intersection of biology, behavior, disease and full stack data science to improve predictions and provide decision makers with more timely insights into the dynamics of many diseases, including influenza, Ebola, Zika, and others.

Sam will speak about the general challenges of understanding and managing disease outbreaks by highlighting work he’s done on forecasting inlfuenza.

With poverty comes less access to outpatient healthcare, less sick leave, and often less support within the home. And, with these things, a greater chance that you will not just get the flu, but that you will also develop complications and end up in hospital.

Making matters worse, the primary surveillance system used by many agencies and healthcare providers in the United States (ILINet) relies heavily on data from outpatient healthcare providers, the very places less likely to see poor people who are sick.

See THIS ARTICLE (http://www.santafenewmexican.com/news/health_and_science/researcher-to-discuss-poverty-s-role-in-infectious-disease-outbreaks/article_79e12d8f-8375-5ab2-9513-a15633b784fe.html) more in Sam’s work.