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Simpson RB, Babool S, Tarnas MC, et al. Signatures of Cholera Outbreak during the Yemeni Civil War, 2016-2019. Int J Environ Res Public Health. 2021;19(1)doi: 10.3390/ijerph19010378.
Simpson, R. B., Babool, S., Tarnas, M. C., Kaminski, P. M., Hartwick, M. A., & Naumova, E. N. (2021). Signatures of Cholera Outbreak during the Yemeni Civil War, 2016-2019. International journal of environmental research and public health, 19(1), . https://doi.org/10.3390/ijerph19010378
Simpson, Ryan B, et al. "Signatures of Cholera Outbreak during the Yemeni Civil War, 2016-2019." International journal of environmental research and public health vol. 19,1 (2021). doi: https://doi.org/10.3390/ijerph19010378
Simpson RB, Babool S, Tarnas MC, Kaminski PM, Hartwick MA, Naumova EN. Signatures of Cholera Outbreak during the Yemeni Civil War, 2016-2019. Int J Environ Res Public Health. 2021 Dec 30;19(1). doi: 10.3390/ijerph19010378. PMID: 35010649.
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Int J Environ Res Public Health. 2021 Dec 30;19(1). doi: 10.3390/ijerph19010378.

Signatures of Cholera Outbreak during the Yemeni Civil War, 2016-2019.

International journal of environmental research and public health

Ryan B Simpson, Sofia Babool, Maia C Tarnas, Paulina M Kaminski, Meghan A Hartwick, Elena N Naumova

Affiliations

  1. Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA 02111, USA.
  2. Department of Neuroscience, The University of Texas at Dallas, 800 W Campbell Road, Richardson, TX 75080, USA.
  3. Department of Community Health, School of Arts and Sciences, Tufts University, 574 Boston Avenue, Medford, MA 02155, USA.

PMID: 35010649 DOI: 10.3390/ijerph19010378

Abstract

The Global Task Force on Cholera Control (GTFCC) created a strategy for early outbreak detection, hotspot identification, and resource mobilization coordination in response to the Yemeni cholera epidemic. This strategy requires a systematic approach for defining and classifying outbreak signatures, or the profile of an epidemic curve and its features. We used publicly available data to quantify outbreak features of the ongoing cholera epidemic in Yemen and clustered governorates using an adaptive time series methodology. We characterized outbreak signatures and identified clusters using a weekly time series of cholera rates in 20 Yemeni governorates and nationally from 4 September 2016 through 29 December 2019 as reported by the World Health Organization (WHO). We quantified critical points and periods using Kolmogorov-Zurbenko adaptive filter methodology. We assigned governorates into six clusters sharing similar outbreak signatures, according to similarities in critical points, critical periods, and the magnitude of peak rates. We identified four national outbreak waves beginning on 12 September 2016, 6 March 2017, 28 May 2018, and 28 January 2019. Among six identified clusters, we classified a core regional hotspot in Sana'a, Sana'a City, and Al-Hudaydah-the expected origin of the national outbreak. The five additional clusters differed in Wave 2 and Wave 3 peak frequency, timing, magnitude, and geographic location. As of 29 December 2019, no governorates had returned to pre-Wave 1 levels. The detected similarity in outbreak signatures suggests potentially shared environmental and human-made drivers of infection; the heterogeneity in outbreak signatures implies the potential traveling waves outwards from the core regional hotspot that could be governed by factors that deserve further investigation.

Keywords: Kolmogorov–Zurbenko filter; Yemen; cholera; critical periods; outbreak signature; time series

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