Pandemic Research
Ad Astra Per Aspera
Blindspots: Pandemic Response Atlas
Pandemic response depends on accurate detection and measurement — but testing data has social blindspots. In an ideal surveillance system, the decision to test would depend only on infection risk. In reality, testing behavior also depends on income, education, housing, ability to isolate, distance to testing facilities, work constraints, and trust in public-health systems. This means measured incidence may partly reflect who gets tested, not only who is infected.
This project builds a prototype pandemic response atlas using COVID-19 France and the greater Paris area as a case study. The prototype is an R Shiny web application providing three interactive map layers: COVID testing visibility across Île-de-France, IRIS-level socioeconomic conditions, and a high-resolution 200m Paris socioeconomic layer. Its main contribution is enabling better public-health surveillance interpreted with local socioeconomic context — so policymakers can preventively protect blindspot areas.
Working Prototype
R Shiny app mapping COVID testing visibility alongside socioeconomic burden indicators.
Multi-Scale Pipeline
Combines IRIS COVID data, IRIS socioeconomic data, and 200m Filosofi grid data into one coherent atlas.
Policy Framework
Combines undervisibility signals with local conditions to inform targeted interventions over broad lockdowns.
