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Background: While overall rates of meningococcal disease have been declining in the United States for the past several decades, New York City (NYC) has experienced two serogroup C meningococcal disease outbreaks in 2005-2006 and in 2010-2013. The outbreaks were centered within drug use and sexual networks, were difficult to control, and required vaccine campaigns., Methods: Whole Genome Sequencing (WGS) was used to analyze preserved meningococcal isolates collected before and during the two outbreaks. We integrated and analyzed epidemiologic, geographic, and genomic data to better understand transmission networks among patients. Betweenness centrality was used as a metric to understand the most important geographic nodes in the transmission networks. Comparative genomics was used to identify genes associated with the outbreaks., Results: Neisseria meningitidis serogroup C (ST11/ET-37) was responsible for both outbreaks with each outbreak having distinct phylogenetic clusters. WGS did identify some misclassifications of isolates that were more distant from the outbreak strains, as well as those that should have been included based on high genomic similarity. Genomes for the second outbreak were more similar than the first and no polymorphism was found to either be unique or specific to either outbreak lineage. Betweenness centrality as applied to transmission networks based on phylogenetic analysis demonstrated that the outbreaks were transmitted within focal communities in NYC with few transmission events to other locations., Conclusions: Neisseria meningitidis is an ever changing pathogen and comparative genomic analyses can help elucidate how it spreads geographically to facilitate targeted interventions to interrupt transmission., Competing Interests: All authors have no competing interests to declare. After the project data collection and analysis was completed, during the final phase of manuscript editing, D.A.J. was appointed to the board of the North Carolina Biotechnology Center. D.A.J. reaffirmed that he does not have any competing interests. The affiliation with DCS [A.T.L and P.A.R.] does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: Using a novel combination of whole-genome sequencing (WGS) analysis and geographic metadata, we traced the origins of Salmonella Bareilly isolates collected in 2012 during a widespread food-borne outbreak in the United States associated with scraped tuna imported from India., Methods: Using next-generation sequencing, we sequenced the complete genome of 100 Salmonella Bareilly isolates obtained from patients who consumed contaminated product, from natural sources, and from unrelated historically and geographically disparate foods. Pathogen genomes were linked to geography by projecting the phylogeny on a virtual globe and produced a transmission network., Results: Phylogenetic analysis of WGS data revealed a common origin for outbreak strains, indicating that patients in Maryland and New York were infected from sources originating at a facility in India., Conclusions: These data represent the first report fully integrating WGS analysis with geographic mapping and a novel use of transmission networks. Results showed that WGS vastly improves our ability to delimit the scope and source of bacterial food-borne contamination events. Furthermore, these findings reinforce the extraordinary utility that WGS brings to global outbreak investigation as a greatly enhanced approach to protecting the human food supply chain as well as public health in general., (Published by Oxford University Press for the Infectious Diseases Society of America 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Background: In Spring 2009, a novel reassortant strain of H1N1 influenza A emerged as a lineage distinct from seasonal H1N1. On June 11, the World Heath Organization declared a pandemic - the first since 1968. There are currently two main branches of H1N1 circulating in humans, a seasonal branch and a pandemic branch. The primary treatment method for pandemic and seasonal H1N1 is the antiviral drug Tamiflu (oseltamivir). Although many seasonal H1N1 strains around the world are resistant to oseltamivir, initially, pandemic H1N1 strains have been susceptible to oseltamivir. As of February 3, 2010, there have been reports of resistance to oseltamivir in 225 cases of H1N1 pandemic influenza. The evolution of resistance to oseltamivir in pandemic H1N1 could be due to point mutations in the neuraminidase or a reassortment event between seasonal H1N1 and pandemic H1N1 viruses that provide a neuraminidase carrying an oseltamivir-resistant genotype to pandemic H1N1., Results: Using phylogenetic analysis of neuraminidase sequences, we show that both seasonal and pandemic lineages of H1N1 are evolving to direct selective pressure for resistance to oseltamivir. Moreover, seasonal lineages of H1N1 that are resistant to oseltamivir co-circulate with pandemic H1N1 throughout the globe. By combining phylogenetic and geographic data we have thus far identified 53 areas of co-circulation where reassortment can occur. At our website POINTMAP, http://pointmap.osu.edu we make available a visualization and an application for updating these results as more data are released., Conclusions: As oseltamivir is a keystone of preparedness and treatment for pandemic H1N1, the potential for resistance to oseltamivir is an ongoing concern. Reassortment and, more likely, point mutation have the potential to create a strain of pandemic H1N1 against which we have a reduced number of treatment options.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Recent disease outbreaks have raised awareness of tropical pathogens, especially mosquito‐borne viruses. Dengue virus (DENV) is a widely studied mammalian pathogen transmitted by various species of mosquito in the genus Aedes, especially Aedes aegypti and Aedes albopictus. The prevailing view of the research community is that endemic viral lineages that cause epidemics of DENV in humans have emerged over time from sylvatic viral lineages, which persist in wild, non‐human primates. These notions have been examined by researchers through phylogenetic analyses of the envelope gene (E) from the four serotypes of DENV (serotypes DENV‐1 to DENV‐4). In these previous reports, researchers used visual inspection of a phylogeny in order to assert that sylvatic lineages lead to endemic clades. In making this assertion, these researchers also reasserted the model of periodic sylvatic to endemic disease outbreaks. Since that study, there has been a significant increase in data both in terms of metadata (e.g., place and host of isolation) and genetic sequences of DENV. Here, we re‐examine the model of sylvatic to endemic shifts in viral lineages through a phylogenetic tree search and character evolution study of metadata on the tree. We built a dataset of nucleotide sequences for 188 isolates of DENV that have metadata on sylvatic or endemic sampling along with three orthologous sequences from West Nile virus as the outgroup for the phylogenetic analysis. In contrast to previous research, we find that there are several shifts from endemic to sylvatic lineages as well as sylvatic to endemic lineages, indicating a much more dynamic model of evolution. We propose that a model that allows oscillation between sylvatic and endemic hosts better captures the dynamics of DENV transmission. [ABSTRACT FROM AUTHOR]