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.
Highlights
We examined similarities in outbreak features to define clusters
Based on the estimated onset, peak, and resolution critical points and acceleration, deceleration, and steady-state period durations for the Yemeni cholera outbreak from 4 September 2016 through 29 December 2019, we identified four distinct national outbreak waves (Figure 2, Tables 1 and 2; Supplementary Excel Tables S3 and S4)
We proposed an application of non-parametric KZ adaptive filters to define outbreak signatures and identified regional hotspots of cholera outbreaks
Summary
The inability of the Yemeni government, Houthi forces, and other involved actors and international agencies to mitigate this epidemic demonstrates the challenges that many countries face when trying to manage infectious disease outbreaks amidst other humanitarian emergencies [1,2,3,4,5]. The ongoing outbreak coincides with the Yemeni Civil War (2015–ongoing), which has crippled existing health infrastructure and depleted medical resource stockpiles [5,6]. Even before these crises, Yemen was one of the poorest countries in the Arabian Peninsula, with widespread malnutrition and rampant poverty [5]
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More From: International Journal of Environmental Research and Public Health
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