Loading data…Automated screening of 1,200+ notified infectious disease time series (diagnosis × region, ÚZIS ISIN) for exceedances of the expected level using the Farrington/Noufaily method.
Infectious disease notifications form more than 1,200 time series (114 diagnoses × 14 regions plus national totals) — far too many for anyone to watch by eye. This page therefore screens them automatically: for every series it computes the expected endemic level from seasonality and history, and shows the months where the notified count exceeded the threshold.
Where the signal is — by region
Series above the expected level
Loading…Note: diagnosis and region names in the table come from the Czech source data.
How to read the table
| Column | Meaning |
|---|---|
| Reported | How many cases were actually notified in the given month. |
| Of which via the new channel | How many of the reported cases arrived through EWS notifications — a reporting route that ÚZIS introduced in July 2025 and that is absent from the history. |
| Usually | The model’s endemic level: how many cases this disease, in this region, at this time of year, would have in an ordinary year. Computed from the 2018–present history with past epidemics down-weighted. |
| Still normal up to | The upper limit of what ordinary fluctuation can still explain (99th percentile of the prediction interval). A count between Usually and this limit is business as usual; a signal starts above it. |
| Exceeded by | How many times further past the limit of normal than that limit is from the usual level. 1× = exactly at the limit, 2× = twice as far beyond it. The higher, the less likely it is chance. |
An example from the table: hepatitis A in the South Moravian region — usually 1 case, still normal up to 5, notified 80. The observation is ~19× further beyond the threshold than ordinary fluctuation reaches (strength 19×) — chance practically cannot explain that.
Two badges replace strength where a statistical model makes no sense:
- rare disease — a disease with at most ~5 cases in the entire history (diphtheria, yellow fever…). The threshold here is “more than an isolated case”: every cluster of 2+ cases is flagged.
- outside prior occurrence — cases in a period where the disease previously did not occur at all (no history for this part of the year). It may be a genuine novelty or a change in reporting.
What to look at first: high strength together with high case counts (an epidemic under way). A row with small counts and strength just above 1× may be chance — with 1,200 series scored, we expect a few such rows every month.
The new reporting channel: why some signals “cannot be decided”
Since July 2025 ÚZIS has also accepted cases through a new route, EWS notifications. For some diseases most cases now arrive this way — 62% for herpes zoster, 59% for Lyme borreliosis (“other spirochaetal infections”), 51% for infectious mononucleosis. The series jumps without any more people being ill: we simply learn of more cases.
They cannot just be subtracted. Some notifications moved to the new channel from the old one (zoster reported the old way fell from ~340 to ~175 cases a month), so the count that is comparable with earlier years is unknown — we only know it lies somewhere between “reported without the new channel” and “reported in total”. The table therefore splits signals in two:
- at the top, signals that hold even if the new-channel cases are left out entirely — a real exceedance whatever moved between channels;
- under the heading “cannot be decided”, series that are above the normal range only thanks to new-channel cases. There may be a real rise behind them or merely a change in reporting, and the data cannot tell the two apart. On the map these regions are grey, not shaded by strength.
What a signal means — and what it does not
A signal says one thing only: the number of notified cases is statistically well above what would be usual for this disease, in this region, at this time of year. It is not a confirmed outbreak. An exceedance may reflect a genuine cluster, but also a change in reporting practice, delayed notifications catching up, or plain chance. A signal is an invitation to look closer, not a conclusion.
The reverse also holds: silence is not evidence of calm. Data for the most recent months are always incomplete (notifications arrive with a delay), so a fresh rise may only become visible retrospectively.
How the expected level is computed
We use the Farrington/Noufaily method — the same algorithm the UK Health Security Agency runs weekly across thousands of laboratory reporting series:
- the expectation comes from a quasi-Poisson regression model over the full available history (2018–present) with seasonality and trend,
- past epidemics in the history are down-weighted, so that last year’s wave does not raise this year’s “normal”,
- the threshold is the upper bound of the prediction interval (99th percentile); the strength of a signal states how many times the observation exceeded the threshold distance from the expectation,
- diseases too rare for a statistical model (e.g. diphtheria) have their own rule: every cluster of cases is flagged.
How well the method works
We measured the method’s operating characteristics with a simulation study with known ground truth, and by backtesting against real epidemics:
- an epidemic adding ten times the usual monthly variation is caught with a probability of ~96%, usually in its first or second month; five times the usual variation is caught in ~3 cases out of 5,
- the 2024 pertussis epidemic (37,918 cases): first signal in November 2023, three months before the epidemic became a public topic — with no false alarm in the calm period 2021–2023,
- real major epidemics are far stronger than these detection limits; November 2023 for pertussis was roughly fifteen to twenty times the usual variation.
Method: Farrington et al. (1996), Noufaily et al. (2012) · implementation, backtests and the simulation study live in the pathogensportal-db repository · Data source: ÚZIS CZ — ISIN, monthly aggregation