Architecture & national scale
InvisibleSignal is intentionally Lyme-only. No other condition is implemented. What makes it reusable is that 8 of its 12 layers carry no condition-specific assumptions — and that the 4 that do are named rather than glossed over.
National scale of the Lyme implementation
Scalability is not only about other conditions. A Lyme product that works in Connecticut and breaks in Texas is not nationally deployable, and handling the places with no data is the harder half.
51
States + DC covered
688
Counties county-coded
548
With a 2-year pair
16
States county-coding none
50 states plus DC are covered by the state series. 688 counties are county-coded by CDC; 16 states report Lyme cases but county-code none of them, and those states resolve to a labelled state fallback rather than to zero.
Shared infrastructure — 8 layers
A layer is listed here only when the Lyme implementation calls it through an interface carrying no Lyme assumptions — not because it could be generalised with work.
Lyme implementation today
CDC NNDSS county-coded Lyme, 2022-2023. 152,018 source cases reconcile exactly: 148,156 county-assigned, 3,694 state residual, 168 national residual.
What a future condition supplies
The reconciliation and suppression logic makes no reference to Lyme. A new dataset supplies its own Socrata resource and denominator vintage.
data/scripts/ingest_cdc_nndss_county.py
Lyme-specific logic — 4 layers
Why we did not ship a second condition
It would score worse and be less honest. The layers that generalise cleanly are the ones handling provenance, geography, evidence and export. The risk index does not: its weights were chosen for county incidence and Ixodes seasonality and validated against Lyme case data. Pointed at another condition’s data the engine would still return a number, and that number would mean nothing. Shipping it to claim breadth would undo the reason the Lyme index is defensible.
The current product is intentionally Lyme-focused. The underlying intelligence infrastructure is modular and standards-based, which is what makes future reuse possible without changing the core platform.