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利用开源传染病监测数据应对传染病疫情暴发,乌克兰,2022 年。

Use of Open-Source Epidemic Intelligence for Infectious Disease Outbreaks, Ukraine, 2022.

出版信息

Emerg Infect Dis. 2024 Sep;30(9):1865-1871. doi: 10.3201/eid3009.240082.

Abstract

Formal infectious disease surveillance in Ukraine has been disrupted by Russia's 2022 invasion, leading to challenges with tracking and containing epidemics. To analyze the effects of the war on infectious disease epidemiology, we used open-source data from EPIWATCH, an artificial intelligence early-warning system. We analyzed patterns of infectious diseases and syndromes before (November 1, 2021-February 23, 2022) and during (February 24-July 31, 2022) the conflict. We compared case numbers for the most frequently reported diseases with numbers from formal sources and found increases in overall infectious disease reports and in case numbers of cholera, botulism, tuberculosis, HIV/AIDS, rabies, and salmonellosis during compared with before the invasion. During the conflict, although open-source intelligence captured case numbers for epidemics, such data (except for diphtheria) were unavailable/underestimated by formal surveillance. In the absence of formal surveillance during military conflicts, open-source data provide epidemic intelligence useful for infectious disease control.

摘要

乌克兰的正式传染病监测因俄罗斯 2022 年的入侵而中断,导致追踪和控制疫情的工作面临挑战。为了分析战争对传染病流行病学的影响,我们使用了人工智能预警系统 EPIWATCH 的开源数据。我们分析了冲突前后(2021 年 11 月 1 日至 2022 年 2 月 23 日和 2022 年 2 月 24 日至 7 月 31 日)传染病和综合征的模式。我们将最常报告的疾病的病例数与正式来源的数字进行了比较,发现与入侵前相比,传染病报告总数和霍乱、肉毒中毒、结核病、艾滋病毒/艾滋病、狂犬病和沙门氏菌病的病例数有所增加。在冲突期间,尽管开源情报捕捉到了流行病的病例数,但正式监测没有/低估了此类数据(除白喉外)。在军事冲突期间没有正式监测的情况下,开源数据为传染病控制提供了有用的疫情情报。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edd6/11346974/f35505a7a0b7/24-0082-F1.jpg

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