Discrimination between bycatch and other causes of cetacean and pinniped stranding

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Diseases of Aquatic Organisms 127 (2018): 83-95, doi:10.3354/dao03189.The challenge of identifying cause of death in discarded bycaught marine mammals stems from a combination of the non-specific nature of the lesions of drowning, the complex physiologic adaptations unique to breath-holding marine mammals, lack of case histories, and the diverse nature of fishing gear. While no pathognomonic lesions are recognized, signs of acute external entanglement, bulging or reddened eyes, recently ingested gastric contents, pulmonary changes, and decompression-associated gas bubbles have been identified in the condition of peracute underwater entrapment (PUE) syndrome in previous studies of marine mammals. We reviewed the gross necropsy and histopathology reports of 36 cetaceans and pinnipeds including 20 directly observed bycaught and 16 live stranded animals that were euthanized between 2005 and 2011 for lesions consistent with PUE. We identified 5 criteria which present at significantly higher rates in bycaught marine mammals: external signs of acute entanglement, red or bulging eyes, recently ingested gastric contents, multi-organ congestion, and disseminated gas bubbles detected grossly during the necropsy and histologically. In contrast, froth in the trachea or primary bronchi, and lung changes (i.e. wet, heavy, froth, edema, congestion, and hemorrhage) were poor indicators of PUE. This is the first study that provides insight into the different published parameters for PUE in bycatch. For regions frequently confronted by stranded marine mammals with non-specific lesions, this could potentially aid in the investigation and quantification of marine fisheries interactions.This work was supported by the Nat - ional Oceanic and Atmospheric Administration (NOAA) John H. Prescott Program NA12NMF4390144. The WHOI Marine Mammal Center, Wick and Sloan Simmons, and the University of Las Palmas de Gran Canaria provided postdoctoral funding for Y.B.Q

Forecasting Seasonal Vibrio parahaemolyticus Concentrations in New England Shellfish

Seafood-borne Vibrio parahaemolyticus illness is a global public health issue facing resource managers and the seafood industry. The recent increase in shellfish-borne illnesses in the Northeast United States has resulted in the application of intensive management practices based on a limited understanding of when and where risks are present. We aim to determine the contribution of factors that affect V. parahaemolyticus concentrations in oysters (Crassostrea virginica) using ten years of surveillance data for environmental and climate conditions in the Great Bay Estuary of New Hampshire from 2007 to 2016. A time series analysis was applied to analyze V. parahaemolyticus concentrations and local environmental predictors and develop predictive models. Whereas many environmental variables correlated with V. parahaemolyticus concentrations, only a few retained significance in capturing trends, seasonality and data variability. The optimal predictive model contained water temperature and pH, photoperiod, and the calendar day of study. The model enabled relatively accurate seasonality-based prediction of V. parahaemolyticus concentrations for 2014–2016 based on the 2007–2013 dataset and captured the increasing trend in extreme values of V. parahaemolyticus concentrations. The developed method enables the informative tracking of V. parahaemolyticus concentrations in coastal ecosystems and presents a useful platform for developing area-specific risk forecasting models

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

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

Dynamic mapping of cholera outbreak during the Yemeni Civil War, 2016-2019.

Widespread destruction from the Yemeni Civil War (2014-present) triggered the world's largest cholera outbreak. We compiled a comprehensive health dataset and created dynamic maps to demonstrate spatiotemporal changes in cholera infections and war conflicts. We aligned and merged daily, weekly, and monthly epidemiological bulletins of confirmed cholera infections and daily conflict events and fatality records to create a dataset of weekly time series for Yemen at the governorate level (subnational regions administered by governors) from 4 January 2016 through 29 December 2019. We demonstrated the use of dynamic mapping for tracing the onset and spread of infection and manmade factors that amplify the outbreak. We report curated data and visualization techniques to further uncover associations between infectious disease outbreaks and risk factors and to better coordinate humanitarian aid and relief efforts during complex emergencies

From hospitalization records to surveillance: The use of local patient profiles to characterize cholera in Vellore, India

<div><p>Despite availability of high quality medical records, health care systems often do not have the resources or tools to utilize these data efficiently. Yet, hospital-based, laboratory-confirmed records may pave the way for building reliable surveillance systems capable of monitoring temporal trends of emerging infections. In this communication, we present a new tool to compress and visualize medical records with a local population profile (LPP) approach, which transforms information into statistically comparable patterns. We provide a step-by-step tutorial on how to build, interpret, and expand the use of LPP using hospitalization records of laboratory-confirmed cholera. We abstracted case information from the databases maintained by the Department of Clinical Microbiology at Christian Medical College in Vellore, India. We used a single-year age distribution to construct LPPs for O1, O139, and non O1/O139 serotypes of <i>Vibrio cholerae</i>. Disease counts and hospitalization rates were converted into fitted kernel-based probability densities. We formally compared LPPs with the Kolmogorov-Smirnov test, and created multi-panel visuals to depict temporal trend, age distribution, and hospitalization rates simultaneously. Our first implementation of LPPs revealed information that is typically gathered from surveillance systems such as: i) estimates of the demographic distribution of diseases and identification of a population at risk, ii) changes in the dominant pathogen presence; and iii) trends in disease occurrence. The LPP demonstrated the benefit of increased resolution in pattern detection of disease for different <i>Vibrio cholerae</i> serotypes and two demographic categories by showing patterns and anomalies that would be obscured by traditional methods of analysis and visualization. LPP can be used effectively to compile basic patient information such as age, sex, diagnosis, location, and time into compact visuals. Future development of the proposed approach will allow public health researchers and practitioners to broadly utilize and efficiently compress large volumes of medical records without loss of information.</p></div

Descriptive statistics^* of cholera patient ages from Vellore.

<p>Descriptive statistics^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182642#t001fn001" target="_blank">*</a>} of cholera patient ages from Vellore.</p

Location-specific patient profile plots for cholera patients.

<p>a) plot highlights the inflection points of disease probability of all Vellore patients; b) probability plots by sex with subscripts M and F referring to males and females; c) probability plot by serotype with subscripts 1, 2 and 3 referring to serotype O1, serotype O139, and non O1/O139 serotypes; d) probability plot by sex and serotype with subscripts as a combination from plot b and c. N is the total number of patients.</p

Population and outcome pyramids.

<p>a) original census data for Vellore population in 2011 and b) cholera patients, respectively.</p

Before and after data smoothing.

<p>a) probability density plots of original and adjusted census data for 2011; b) comparison of original Vellore cholera patient disease count by age and adjusted disease count by age; c) original hospitalization rate calculated with original census data and patient values compared with adjusted rate based on adjusted census and adjusted patient data.</p