• Brucella Pinnipedialis in grey seals (Halichoerus grypus) and harbor seals (Phoca vitulina) in the Netherlands

      Kroese, Michiel V; Beckers, Lisa; Bisselink, Yvette J W M; Brasseur, Sophie; van Tulden, Peter W; Koene, Miriam G J; Roest, Hendrik I J; Ruuls, Robin C; Backer, Jantien A; IJzer, Jooske; et al. (2018-04-26)
      Brucellosis is a zoonotic disease with terrestrial or marine wildlife animals as potential reservoirs for the disease in livestock and human populations. The primary aim of this study was to assess the presence of Brucella pinnipedialis in marine mammals living along the Dutch coast and to observe a possible correlation between the presence of B. pinnipedialis and accompanying pathology found in infected animals. The overall prevalence of Brucella spp. antibodies in sera from healthy wild grey seals ( Halichoerus grypus; n=11) and harbor seals ( Phoca vitulina; n=40), collected between 2007 and 2013 ranged from 25% to 43%. Additionally, tissue samples of harbor seals collected along the Dutch shores between 2009 and 2012, were tested for the presence of Brucella spp. In total, 77% (30/39) seals were found to be positive for Brucella by IS 711 real-time PCR in one or more tissue samples, including pulmonary nematodes. Viable Brucella was cultured from 40% (12/30) real-time PCR-positive seals, and was isolated from liver, lung, pulmonary lymph node, pulmonary nematode, or spleen, but not from any PCR-negative seals. Tissue samples from lung and pulmonary lymph nodes were the main source of viable Brucella bacteria. All isolates were typed as B. pinnipedialis by multiple-locus variable number of tandem repeats analysis-16 clustering and matrix-assisted laser desorption ionization-time of flight mass spectrometry, and of sequence type ST25 by multilocus sequence typing analysis. No correlation was observed between Brucella infection and pathology. This report displays the isolation and identification of B. pinnipedialis in marine mammals in the Dutch part of the Atlantic Ocean.
    • Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks.

      Klinkenberg, Don; Backer, Jantien A; Didelot, Xavier; Colijn, Caroline; Wallinga, Jacco (2017-05)
      Whole-genome sequencing of pathogens from host samples becomes more and more routine during infectious disease outbreaks. These data provide information on possible transmission events which can be used for further epidemiologic analyses, such as identification of risk factors for infectivity and transmission. However, the relationship between transmission events and sequence data is obscured by uncertainty arising from four largely unobserved processes: transmission, case observation, within-host pathogen dynamics and mutation. To properly resolve transmission events, these processes need to be taken into account. Recent years have seen much progress in theory and method development, but existing applications make simplifying assumptions that often break up the dependency between the four processes, or are tailored to specific datasets with matching model assumptions and code. To obtain a method with wider applicability, we have developed a novel approach to reconstruct transmission trees with sequence data. Our approach combines elementary models for transmission, case observation, within-host pathogen dynamics, and mutation, under the assumption that the outbreak is over and all cases have been observed. We use Bayesian inference with MCMC for which we have designed novel proposal steps to efficiently traverse the posterior distribution, taking account of all unobserved processes at once. This allows for efficient sampling of transmission trees from the posterior distribution, and robust estimation of consensus transmission trees. We implemented the proposed method in a new R package phybreak. The method performs well in tests of both new and published simulated data. We apply the model to five datasets on densely sampled infectious disease outbreaks, covering a wide range of epidemiological settings. Using only sampling times and sequences as data, our analyses confirmed the original results or improved on them: the more realistic infection times place more confidence in the inferred transmission trees.
    • Visual tools to assess the plausibility of algorithm-identified infectious disease clusters: an application to mumps data from the Netherlands dating from January 2009 to June 2016.

      Soetens, Loes; Backer, Jantien A; Hahné, Susan; van Binnendijk, Rob; Gouma, Sigrid; Wallinga, Jacco (2019-03-01)
      IntroductionWith growing amounts of data available, identification of clusters of persons linked to each other by transmission of an infectious disease increasingly relies on automated algorithms. We propose cluster finding to be a two-step process: first, possible transmission clusters are identified using a cluster algorithm, second, the plausibility that the identified clusters represent genuine transmission clusters is evaluated.AimTo introduce visual tools to assess automatically identified clusters.MethodsWe developed tools to visualise: (i) clusters found in dimensions of time, geographical location and genetic data; (ii) nested sub-clusters within identified clusters; (iii) intra-cluster pairwise dissimilarities per dimension; (iv) intra-cluster correlation between dimensions. We applied our tools to notified mumps cases in the Netherlands with available disease onset date (January 2009 - June 2016), geographical information (location of residence), and pathogen sequence data (n = 112). We compared identified clusters to clusters reported by the Netherlands Early Warning Committee (NEWC).ResultsWe identified five mumps clusters. Three clusters were considered plausible. One was questionable because, in phylogenetic analysis, genetic sequences related to it segregated in two groups. One was implausible with no smaller nested clusters, high intra-cluster dissimilarities on all dimensions, and low intra-cluster correlation between dimensions. The NEWC reports concurred with our findings: the plausible/questionable clusters corresponded to reported outbreaks; the implausible cluster did not.ConclusionOur tools for assessing automatically identified clusters allow outbreak investigators to rapidly spot plausible transmission clusters for mumps and other human-to-human transmissible diseases. This fast information processing potentially reduces workload.