Evaluation of WGS-subtyping methods for epidemiological surveillance of foodborne salmonellosis

Background: Salmonellosis is one of the most common foodborne diseases worldwide. Although human infection by non-typhoidal Salmonella (NTS) enterica subspecies enterica is associated primarily with a self-limiting diarrhoeal illness, invasive bacterial infections (such as septicaemia, bacteraemia and meningitis) were also reported. Human outbreaks of NTS were reported in several countries all over the world including developing as well as high-income countries. Conventional laboratory methods such as pulsed field gel electrophoresis (PFGE) do not display adequate discrimination and have their limitations in epidemiological surveillance. It is therefore very crucial to use accurate, reliable and highly discriminative subtyping methods for epidemiological characterisation and outbreak investigation. Methods: Here, we used different whole genome sequence (WGS)-based subtyping methods for retrospective investigation of two different outbreaks of Salmonella Typhimurium and Salmonella Dublin that occurred in 2013 in UK and Ireland respectively. Results: Single nucleotide polymorphism (SNP)-based cluster analysis of Salmonella Typhimurium genomes revealed well supported clades, that were concordant with epidemiologically defined outbreak and confirmed the source of outbreak is due to consumption of contaminated mayonnaise. SNP-analyses of Salmonella Dublin genomes confirmed the outbreak however the source of infection could not be determined. The core genome multilocus sequence typing (cgMLST) was discriminatory and separated the outbreak strains of Salmonella Dublin from the non-outbreak strains that were concordant with the epidemiological data however cgMLST could neither discriminate between the outbreak and non-outbreak strains of Salmonella Typhimurium nor confirm that contaminated mayonnaise is the source of infection, On the other hand, other WGS-based subtyping methods including multilocus sequence typing (MLST), ribosomal MLST (rMLST), whole genome MLST (wgMLST), clustered regularly interspaced short palindromic repeats (CRISPRs), prophage sequence profiling, antibiotic resistance profile and plasmid typing methods were less discriminatory and could not confirm the source of the outbreak. Conclusions: Foodborne salmonellosis is an important concern for public health therefore, it is crucial to use accurate, reliable and highly discriminative subtyping methods for epidemiological surveillance and outbreak investigation. In this study, we showed that SNP-based analyses do not only have the ability to confirm the occurrence of the outbreak but also to provide definitive evidence of the source of the outbreak in real-time

A pragmatic harm reduction approach to manage a large outbreak of wound botulism in people who inject drugs, Scotland 2015

Abstract Background People who inject drugs (PWID) are at an increased risk of wound botulism, a potentially fatal acute paralytic illness. During the first 6 months of 2015, a large outbreak of wound botulism was confirmed among PWID in Scotland, which resulted in the largest outbreak in Europe to date. Methods A multidisciplinary Incident Management Team (IMT) was convened to conduct an outbreak investigation, which consisted of enhanced surveillance of cases in order to characterise risk factors and identify potential sources of infection. Results Between the 24th of December 2014 and the 30th of May 2015, a total of 40 cases were reported across six regions in Scotland. The majority of the cases were male, over 30 and residents in Glasgow. All epidemiological evidence suggested a contaminated batch of heroin or cutting agent as the source of the outbreak. There are significant challenges associated with managing an outbreak among PWID, given their vulnerability and complex addiction needs. Thus, a pragmatic harm reduction approach was adopted which focused on reducing the risk of infection for those who continued to inject and limited consequences for those who got infected. Conclusions The management of this outbreak highlighted the importance and need for pragmatic harm reduction interventions which support the addiction needs of PWID during an outbreak of spore-forming bacteria. Given the scale of this outbreak, the experimental learning gained during this and similar outbreaks involving spore-forming bacteria in the UK was collated into national guidance to improve the management and investigation of future outbreaks among PWID

The impact of prior information on estimates of disease transmissibility using Bayesian tools

The basic reproductive number (R₀) and the distribution of the serial interval (SI) are often used to quantify transmission during an infectious disease outbreak. In this paper, we present estimates of R₀ and SI from the 2003 SARS outbreak in Hong Kong and Singapore, and the 2009 pandemic influenza A(H1N1) outbreak in South Africa using methods that expand upon an existing Bayesian framework. This expanded framework allows for the incorporation of additional information, such as contact tracing or household data, through prior distributions. The results for the R₀ and the SI from the influenza outbreak in South Africa were similar regardless of the prior information (R0 = 1.36-1.46, μ = 2.0-2.7, μ = mean of the SI). The estimates of R₀ and μ for the SARS outbreak ranged from 2.0-4.4 and 7.4-11.3, respectively, and were shown to vary depending on the use of contact tracing data. The impact of the contact tracing data was likely due to the small number of SARS cases relative to the size of the contact tracing sample

Projections of Ebola outbreak size and duration with and without vaccine use in Équateur, Democratic Republic of Congo, as of May 27, 2018.

As of May 27, 2018, 6 suspected, 13 probable and 35 confirmed cases of Ebola virus disease (EVD) had been reported in Équateur Province, Democratic Republic of Congo. We used reported case counts and time series from prior outbreaks to estimate the total outbreak size and duration with and without vaccine use. We modeled Ebola virus transmission using a stochastic branching process model that included reproduction numbers from past Ebola outbreaks and a particle filtering method to generate a probabilistic projection of the outbreak size and duration conditioned on its reported trajectory to date; modeled using high (62%), low (44%), and zero (0%) estimates of vaccination coverage (after deployment). Additionally, we used the time series for 18 prior Ebola outbreaks from 1976 to 2016 to parameterize the Thiel-Sen regression model predicting the outbreak size from the number of observed cases from April 4 to May 27. We used these techniques on probable and confirmed case counts with and without inclusion of suspected cases. Probabilistic projections were scored against the actual outbreak size of 54 EVD cases, using a log-likelihood score. With the stochastic model, using high, low, and zero estimates of vaccination coverage, the median outbreak sizes for probable and confirmed cases were 82 cases (95% prediction interval [PI]: 55, 156), 104 cases (95% PI: 58, 271), and 213 cases (95% PI: 64, 1450), respectively. With the Thiel-Sen regression model, the median outbreak size was estimated to be 65.0 probable and confirmed cases (95% PI: 48.8, 119.7). Among our three mathematical models, the stochastic model with suspected cases and high vaccine coverage predicted total outbreak sizes closest to the true outcome. Relatively simple mathematical models updated in real time may inform outbreak response teams with projections of total outbreak size and duration

Enterohemorrhagic Escherichia coli with particular attention to the German outbreak strain O104:H4

This review deals with the epidemiology and ecology of enterohemorrhagic Escherichia coli (EHEC), a subset of the verocytotoxigenic Escherichia coli (VTEC), and subsequently discusses its public health concern. Attention is also given to the outbreak strain O104:H4, which has been isolated as causative agent of the second largest outbreak of the hemolytic uremic syndrome worldwide, which started in Germany in May 2011. This outbreak strain is not an EHEC as such but possesses an unusual combination of EHEC and enteroaggregative E. coli (EAggEC) virulence properties

Size of Outbreaks Near the Epidemic Threshold

The spread of infectious diseases near the epidemic threshold is investigated. Scaling laws for the size and the duration of outbreaks originating from a single infected individual in a large susceptible population are obtained. The maximal size of an outbreak n_* scales as N^{2/3} with N the population size. This scaling law implies that the average outbreak size scales as N^{1/3}. Moreover, the maximal and the average duration of an outbreak grow as t_* ~ N^{1/3} and ~ ln N, respectively.Comment: 4 pages, 5 figure

A review of epidemiological parameters from Ebola outbreaks to inform early public health decision-making.

The unprecedented scale of the Ebola outbreak in West Africa has, as of 29 April 2015, resulted in more than 10,884 deaths among 26,277 cases. Prior to the ongoing outbreak, Ebola virus disease (EVD) caused relatively small outbreaks (maximum outbreak size 425 in Gulu, Uganda) in isolated populations in central Africa. Here, we have compiled a comprehensive database of estimates of epidemiological parameters based on data from past outbreaks, including the incubation period distribution, case fatality rate, basic reproduction number (R 0), effective reproduction number (R t) and delay distributions. We have compared these to parameter estimates from the ongoing outbreak in West Africa. The ongoing outbreak, because of its size, provides a unique opportunity to better understand transmission patterns of EVD. We have not performed a meta-analysis of the data, but rather summarize the estimates by virus from comprehensive investigations of EVD and Marburg outbreaks over the past 40 years. These estimates can be used to parameterize transmission models to improve understanding of initial spread of EVD outbreaks and to inform surveillance and control guidelines

Genomic dissection of the 1994 Cronobacter sakazakii outbreak in a French neonatal intensive care unit

Background: Cronobacter sakazakii is a member of the genus Cronobacter that has frequently been isolated from powdered infant formula (PIF) and linked with rare but fatal neonatal infections such as meningitis and necrotising enterocolitis. The Cronobacter MLST scheme has reported over 400 sequence types and 42 clonal complexes; however C. sakazakii clonal complex 4 (CC4) has been linked strongly with neonatal infections, especially meningitis. There have been a number of reported Cronobacter outbreaks over the last three decades. The largest outbreak of C. sakazakii was in a neonatal intensive care unit (NICU) in France (1994) that lasted over 3 months and claimed the lives of three neonates. The present study used whole genome sequencing data of 26 isolates obtained from this outbreak to reveal their relatedness. This study is first of its kind to use whole genome sequencing data to analyse a Cronobacter outbreak. Methods: Whole genome sequencing data was generated for 26 C. sakazakii isolates on the Illumina MiSeq platform. The whole genome phylogeny was determined using Mugsy and RaxML. SNP calls were determined using SMALT and SAMtools, and filtered using VCFtools. Results: The whole genome phylogeny suggested 3 distant clusters of C. sakazakii isolates were associated with the outbreak. SNP typing and phylogeny indicate the source of the C. sakazakii could have been from extrinsic contamination of reconstituted infant formula from the NICU environment and personnel. This pool of strains would have contributed to the prolonged duration of the outbreak, which was up to 3 months. Furthermore 3 neonates were co-infected with C. sakazakii from two different genotype clusters. Conclusion: The genomic investigation revealed the outbreak consisted of an heterogeneous population of C. sakazakii isolates. The source of the outbreak was not identified, but probably was due to environmental and personnel reservoirs resulting in extrinsic contamination of the neonatal feeds. It also indicated that C. sakazakii isolates from different genotype clusters have the ability to co-infect neonates

Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019.

BackgroundAs of February 25, 2019, 875 cases of Ebola virus disease (EVD) were reported in North Kivu and Ituri Provinces, Democratic Republic of Congo. Since the beginning of October 2018, the outbreak has largely shifted into regions in which active armed conflict has occurred, and in which EVD cases and their contacts have been difficult for health workers to reach. We used available data on the current outbreak, with case-count time series from prior outbreaks, to project the short-term and long-term course of the outbreak.MethodsFor short- and long-term projections, we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission rates estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used two regression models to estimate similar projection periods. Short- and long-term projections were estimated using negative binomial autoregression and Theil-Sen regression, respectively. We also used Gott's rule to estimate a baseline minimum-information projection. We then constructed an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20, 2018 to February 25, 2019, short-term model projections were validated against known case counts.ResultsDuring validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of February 25, the stochastic model projected a median case count of 933 cases by February 18 (95% prediction interval: 872-1054) and 955 cases by March 4 (95% prediction interval: 874-1105), while the auto-regression model projects median case counts of 889 (95% prediction interval: 876-933) and 898 (95% prediction interval: 877-983) cases for those dates, respectively. Projected median final counts range from 953 to 1,749. Although the outbreak is already larger than all past Ebola outbreaks other than the 2013-2016 outbreak of over 26,000 cases, our models do not project that it is likely to grow to that scale. The stochastic model estimates that vaccination coverage in this outbreak is lower than reported in its trial setting in Sierra Leone.ConclusionsOur projections are concentrated in a range up to about 300 cases beyond those already reported. While a catastrophic outbreak is not projected, it is not ruled out, and prevention and vigilance are warranted. Prospective validation of our models in real time allowed us to generate more accurate short-term forecasts, and this process may prove useful for future real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response challenges