Geospatial analysis of open-source intelligence data to early detect laboratory-acquired infections, using the 2019 brucellosis laboratory leak in China as a case study.

Atalay Goshu Muluneh, Samsung Lim, Aye Moa, Chandini Raina Maclntyre

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Article in Infection, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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Atalay Goshu MulunehBiosecurity Program, The Kirby Institute, Faculty of Medicine and Health, University of New South Wales, Sydney, Australia.

Samsung LimSchool of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia. s.lim@unsw.edu.au.

Aye MoaBiosecurity Program, The Kirby Institute, Faculty of Medicine and Health, University of New South Wales, Sydney, Australia.

Chandini Raina MaclntyreBiosecurity Program, The Kirby Institute, Faculty of Medicine and Health, University of New South Wales, Sydney, Australia.

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6 · The paper itself

Abstract

purposeThis study aimed to use geospatial analysis to retrospectively determine whether earlier detection of the 2019 brucellosis laboratory leak in China could have been achieved using open-source intelligence data.

methodsWe used open-source intelligence data of brucellosis outbreaks from EPIWATCH

resultsThe central Gansu province, where the 2019 laboratory leak occurred, has China's densest signal of brucellosis outbreaks. In the multiple ring buffer analysis, outbreak signals were identified within a five-km radius of the Zhongmu Lanzhou Biopharmaceutical Plant Laboratory (ZLBPL) with an event date in July 2019. This compares to the official identification date of 6 December 2019. We identified 10,528 cases among 68,571 tests associated with the 2019 ZLBPL leak. This matches the 10,528 cases recorded in official reports among 79,357 tests, with 1,604 seeking medical treatment. This corresponds to a 13.3% test-positive rate among suspected cases and a 15.2% rate of medical treatment among confirmed cases.

conclusionA prolonged brucellosis laboratory leak occurred in China, with a delay of nearly six months before formal acknowledgment was received from local authorities. Geospatial analysis of open-source intelligence data identified the outbreak early, the likely source and a nearby laboratory affected by the outbreak.

Indexed as

BrucellosisDisease OutbreaksLaboratoriesChinaHumansRetrospective StudiesSpatial AnalysisAnd geospatial analysis techniquesBiosafetyBrucellosis leak

Identifiers

PMID41085603

DOI10.1007/s15010-025-02666-x

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