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The recent warning that children are bearing a disproportionate burden of the Ebola outbreak in the Democratic Republic of Congo highlights the difficulty of obtaining reliable health data in crisis zones. For quantitative traders, the same challenges appear when building models on sparse, noisy, or biased datasets. This article shows how to think about data quality, model risk, and out‑of‑sample testing using concepts from applied finance mathematics. The Data Landscape in Crisis‑Driven Markets When a health emergency erupts, news feeds, social‑media chatter, and limited official reports become the primary sources of information. These sources are often incomplete, delayed, or deliberately censored....