Geospatial analysis of open-source intelligence data to early detect laboratory-acquired infections, using the 2019...

This 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.

We used open-source intelligence data of brucellosis outbreaks from EPIWATCH^@, from late 2016 to mid-2024. The spatial distribution of brucellosis was mapped using heatmap analysis in China to identify the provinces with the densest outbreak signals. Multiple-ring buffer analysis of outbreak signals within a five and 10-kilometer radius of BSL-3 laboratories in Gansu province was implemented to examine the geospatial analysis techniques' capability to detect the 2019 brucellosis laboratory leak early.

The 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.

A 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.