Tailored intelligence to detect unusual epidemic activity following the explosion at Vector, Russia

Authors

  • C Raina MacIntyre University of New South Wales https://orcid.org/0000-0002-3060-0555
  • Xin Chen University of New South Wales
  • Mohana Kunasekaran University of New South Wales
  • Anjali Kannan University of New South Wales
  • Veethika Nayak University of New South Wales
  • Khulan Bayaraa University of New South Wales
  • Elena Sitnikova University of New South Wales
  • Phi Yen Nguyen University of New South Wales
  • Tracey German Kings College London

DOI:

https://doi.org/10.31646/gbio.85

Keywords:

outbreak, Russia, Vector, BSL4, laboratory, COVID-19, pneumonia

Abstract

We used open source data from the Epiwatch observatory to monitor for early disease signals in Russia and surrounding countries following an explosion at a BSL 4 laboratory, Vector, in Siberia in September 2019. Upon news of the explosion at Vector on September 16th 2019, the Epiwatch team added the Russian language and key words Russia, Siberia, Novosibirsk, and Koltsovo to the Standard Operating Procedures, in addition to the usual epidemic-specific keywords used in Epiwatch. We also searched for outbreak reports in countries bordering Siberia, including Mongolia, Kazakhstan and China. Given local spread of an epidemic could manifest in these countries, we included searching in Chinese, Mongolian and Kazakh. We added “Ukraine” as a key word, given current conflict between Russia and Ukraine. Data collection began in September 2019, one week after the explosion, with this considered the baseline. We demonstrate a method for rapid epidemic intelligence following an incident of concern, the explosion at Vector. There were some unexplained outbreaks in Russia in the three months following the explosion. No unexplained outbreaks were detected in countries bordering Russia, nor in Ukraine in the three months following the explosion. We detected an accidental release of brucella from a laboratory in China in early December 2019 and two reports of severe pneumonia prior to official reports, which could have been early COVID-19 cases. Best practice in preparedness should include surveillance for disease events in the months following an event of concern at local, national and global levels. In the absence of official surveillance data, open source intelligence may be the only available means of detecting outbreaks and enabling early response and mitigation for the rest of the world. Epiwatch was able to identify reports of Russian outbreaks in the weeks and months following the Vector explosion, which allowed monitoring of outbreaks of concern without a known cause.

Author Biography

C Raina MacIntyre, University of New South Wales

Professor Raina MacIntyre (MBBS Hons 1, M App Epid, PhD, FRACP, FAFPHM) is Professor of Global Biosecurity at the Kirby Institute, UNSW Sydney. She is a dual-specialist physician with extensive track record in infectious diseases, vaccines and transmission dynamics of pathogens. As Head of the Biosecurity Program, she leads research in epidemiology, vaccinology, bioterrorism prevention, mathematical modelling, public health and clinical trials in infectious diseases. Her research includes personal protective equipment, vaccinology, epidemics of emerging infectious diseases and bioterrorism prevention. She is an expert in influenza epidemiology, adult vaccination, bioterrorism and rapid epidemic intelligence and has led the largest body of research internationally on face masks and respirators in health care workers. She has a 20 year track record in public health control of infectious diseases including vaccinology, surveillance and program design. She has over 370 publications in peer-reviewed journals. Her research is underpinned by extensive field outbreak investigation experience. She is a graduate of the Australian Field Epidemiology Training program and has extensive experience in shoe-leather epidemiology of infectious diseases outbreaks. Her in-depth understanding of the science of outbreak investigation draws from this experience combined with her clinical training as a specialist physician and her academic training through a Masters and PhD in Epidemiology. Her passion for field epidemiology led her to co-found the ARM network for Australian field outbreak response. She also has an interest in the ethics of medicine, and specifically in dual-use research of concern in the fields of synthetic biology and genetic engineering, and the risk this poses to biosecurity. She is on editorial boards for Vaccine, BMJ Open and Epidemiology & Infection, and has served on numerous expert committees including for WHO, IOM and OIE. She was won numerous awards for her research including the Sir Henry Wellcome Medal from the Association of Military Surgeons of the United States, the National Immunisation Award from Public Health Association of Australia, and the Frank Fenner Prize for advanced research in infectious diseases. See: https://research.unsw.edu.au/people/professor-raina-macintyre

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Published

2020-08-31

How to Cite

MacIntyre, C. R., Chen, X., Kunasekaran, M., Kannan, A., Nayak, V., Bayaraa, K., … German, T. (2020). Tailored intelligence to detect unusual epidemic activity following the explosion at Vector, Russia. Global Biosecurity, 2(1). https://doi.org/10.31646/gbio.85

Issue

Vol. 2 (2020)

Section

Rapid Reports and Perspectives From the Field

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Copyright (c) 2020 The Author(s)

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Received 2020-08-21
Accepted 2020-08-21
Published 2020-08-31