Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 314

The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.

A Review and Discussion of Prospective Statistical Surveillance in Public Health

Open access
Sonesson, Christian, Bock, David·2003·Journal of the Royal Statistical Society Series A: Statistics in Society

Abstract

Summary: A review of methods suggested in the literature for sequential detection of changes in public health surveillance data is presented. Many researchers have noted the need for prospective methods. In recent years there has been an increased interest in both the statistical and the epidemiological literature concerning this type of problem. However, most of the vast literature in public health monitoring deals with retrospective methods, especially spatial methods. Evaluations with respect to the statistical properties of interest for prospective surveillance are rare. The special aspects of prospective statistical surveillance and different ways of evaluating such methods are described. Attention is given to methods that include only the time domain as well as methods for detection where observations have a spatial structure. In the case of surveillance of a change in a Poisson process the likelihood ratio method and the Shiryaev–Roberts method are derived.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/1467-985x.00256

Cites

43 of 43 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 43 references

Metadata

Authors
Sonesson, C., & Bock, D.
Venue
Journal of the Royal Statistical Society Series A: Statistics in Society
Published
2003-01-08
Volume
166
Issue
1
Pages
5-21
ISSN
  • 0964-1998 (ISSN-L)
  • 1467-985X
OpenAlex
W2070539966
Cited by
314
References
43
FWCI
10.09
Type
journal article
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A Review and Discussion of Prospective Statistical Surveillance in Public Health. https://app.cochat.ai/papers/10.1111/1467-985x.00256
The paper
Sonesson, C., & Bock, D. (2003). A Review and Discussion of Prospective Statistical Surveillance in Public Health. Journal of the Royal Statistical Society Series A: Statistics in Society. https://doi.org/10.1111/1467-985x.00256

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.