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Interventions to curb the spread of COVID-19 during the 2020-2021 influenza season essentially eliminated influenza during that season. Given waning antibody titers over time, future residual population immunity against influenza will be reduced. The implication for the subsequent 2021-2022 influenza season is unknown.An agent-based model of influenza implemented in the Framework for Reconstructing Epidemiological Dynamics simulation platform was used to estimate cases and hospitalizations over 2 successive influenza seasons. The impact of reduced residual immunity owing to protective measures in the first season was estimated over varying levels of similarity (cross-immunity) between influenza strains over the seasons.When cross-immunity between first- and second-season strains was low, a decreased first season had limited impact on second-season cases. High levels of cross-immunity resulted in a greater impact on the second season. This impact was modified by the transmissibility of strains in the 2 seasons. The model estimated a possible increase of 13.52%-46.95% in cases relative to that in a normal season when strains have the same transmissibility and 40%-50% cross-immunity in a season after a very low one.Given the light 2020-2021 influenza season, cases may increase by as much as 50% in 2021-2022, although the increase could be much less, depending on cross-immunity from past infection and transmissibility of strains. Enhanced vaccine coverage or continued interventions to reduce transmission could reduce this high season. Young children may have a higher risk in 2021-2022 owing to limited exposure to infection in the previous year.

Background Influenza activity in the 2020–2021 season was remarkably low, likely due to implementation of public health preventive measures such as social distancing, mask wearing, and school closure. With waning immunity, the impact of low influenza activity in the 2020–2021 season on the following season is unknown. Methods We built a multistrain compartmental model that captures immunity over multiple influenza seasons in the United States. Compared with the counterfactual case, where influenza activity remained at the normal level in 2020–2021, we estimated the change in the number of hospitalizations when the transmission rate was decreased by 20% in 2020–2021. We varied the level of vaccine uptake and effectiveness in 2021–2022. We measured the change in population immunity over time by varying the number of seasons with lowered influenza activity. Results With the lowered influenza activity in 2020–2021, the model estimated 102 000 (95% CI, 57 000–152 000) additional hospitalizations in 2021–2022, without changes in vaccine uptake and effectiveness. The estimated changes in hospitalizations varied depending on the level of vaccine uptake and effectiveness in the following year. Achieving a 50% increase in vaccine coverage was necessary to avert the expected increase in hospitalization in the next influenza season. If the low influenza activity were to continue over several seasons, population immunity would remain low during those seasons, with 48% of the population susceptible to influenza infection. Conclusions Our study projected a large compensatory influenza season in 2021–2022 due to a light season in 2020–2021. However, higher influenza vaccine uptake would reduce this projected increase in influenza.

BackgroundsThe influenza season of 2020-21 was remarkably low, likely due to implementation of public health preventive measures such as social distancing, mask-wearing, and school closure and due to decreased international travel. This leads to a key public health question: what will happen in the 2021-22 influenza season? To answer this, we developed a multi-season influenza model that accounted for residual immunity from prior infection.MethodWe built a multi-strain, non-age structured compartmental model that captures immunity over multiple influenza seasons. By the end of the influenza season, we sorted the population based on their experience of natural infection and/or vaccination, which determines the susceptibility to influenza infection in the following season. Because the exact parameters of transmission rates and immunity are unknown, we implemented Bayesian calibration against the observed influenza epidemics (influenza hospitalization rates from 2012 to 2020 in the US) to estimate those parameters. In forward projections, we simulated low influenza activity in 2020-21 season by lowering transmission rate by 20%. Compared to the counterfactual case, in which influenza activity remained at the normal level in 2020-21, we estimated the change in the number of hospitalizations in the following seasons with varying level of vaccine uptake and effectiveness. We measured the change in population immunity over time by varying the number of seasons with low influenza activity.ResultWith the low influenza activity in 2020-21, the model estimated 102,000 [95% CI: 57,000-152,000] additional hospitalizations in 2021-22, without change in vaccine uptake and effectiveness. The expected change in hospitalization varied depending on the level of vaccine uptake and effectiveness in the following year. Achieving 50% increase in one of two measures (1.5X vaccine uptake with 1X vaccine efficacy or 1.5X vaccine efficacy with 1X vaccine uptake) was necessary to avert the expected increase in hospitalization in the next influenza season. Otherwise, increases in both measures by 25% averted the expected increase in influenza-hospitalization. If the low influenza activity seasons continue, population immunity would remain low during those seasons, with 48% the population susceptible to influneza infection.ConclusionWe predicted a large compensatory influenza season in 2021-2 due to a light season in 2020-21. However, higher influenza vaccine uptake would reduce this projected increase in influenza.

IntroductionInterventions to curb the spread of SARS-CoV-2 during the 2020-21 influenza season essentially eliminated influenza during that season. Given waning antibody titers over time, future residual population immunity against influenza will be reduced. The implication for the subsequent 2021-22 influenza season is unknown.MethodsWe used an agent-based model of influenza implemented in the FRED (Framework for Reconstructing Epidemiological Dynamics) simulation platform to estimate cases and hospitalization over two succeeding influenza seasons. The model uses a synthetic population to represent an actual population, and individual interactions in workplaces, school, households and neighborhoods. The impact of reduced residual immunity was estimated as a consequence of increased protective measures (e.g., social distancing and school closure) in the first season. The impact was contrasted by the level of similarity (cross-immunity) between influenza strains over the seasons.ResultsWhen the second season strains were dissimilar to the first season (have a low level of cross immunity), a low first season has limited impact on second season cases. When a high level of cross-immunity exists between strains in the 2 seasons, the first season has a much greater impact on the second season. In both cases this is modified by the transmissibility of strains in the 2 seasons. In the context of the 2021-22 season, the worst case scenario is a highly transmissible strain causing increased cases and hospitalizations over average influenza seasons, with a possible significant increase in cases in some scenarios. The most likely overall scenario for 2021-22 is a more modest increase in flu cases over an average season.DiscussionGiven the light 2020-21 season, we found that a large, compensatory second season might occur in 2021-22, depending on cross-immunity from past infection and transmissibility of strains. Furthermore, we found that enhanced vaccine coverage could reduce this high, compensatory season. Young children may be especially at risk in 2021-22 since very young children were unlikely to have had any exposure to infection and most immunity in that age group would be from vaccination, which wanes quickly.

[This corrects the article DOI: 10.1371/journal.ppat.1002776.].

Background. The use of electronic cigarettes (e-cigarette) offers potential to facilitate cigarette smoking cessation, yet potentially increases risk of cigarette smoking initiation. This relationship has been primarily modeled in mathematical ways that often do not represent real-world complexities, which could inform decisions regarding local prevention programs or policies. Aims. To develop a model of cigarette and e-cigarette use that combines current research on tobacco use and incorporates real-world geographic and demographic data. Method. We used a platform for developing agent-based models with demographic information representative of the population in Pennsylvania. We developed three models of cigarette and e-cigarette use. The primary outcome for each was the total number of users for cigarette, e-cigarette, and total nicotine. The first model applied current cigarette and e-cigarette data, the second tested the effect of implementing a program of e-cigarette education and policies, and the third considered a social contagion factor, where local schools functioned as a transmission vector. Results. The baseline and social contagion models found an overall decline in cigarette use but an increase in e-cigarette and total nicotine use. The education/policies model had declines in all categories. Sensitivity analysis suggested the importance of nuanced e-cigarette/cigarette interactions when modeling tobacco use. Discussion. Public health campaigns that focus on reducing youth e-cigarette usage can have a large effect. Social contagion should be strongly considered when studying e-cigarette spread. Conclusion. Targeted public health campaigns focused on reducing school prevalence of e-cigarette use may be particularly valuable.

Key Points Question What is the expected size of measles outbreaks in Texas at current (ie, 2018) and decreased vaccination rates? Findings In this decision analytical model study of metropolitan statistical areas in Texas, 1 in 20 measles introductions was forecasted to be associated with outbreaks of more than 400 cases in the Austin and Dallas metropolitan areas, according to 1000 simulated outbreaks in each metropolitan area; 64% of cases were forecasted to occur in children for whom a vaccine has been refused and 36% in others (ie, bystanders). Increased vaccine exemptions were forecasted to be associated with increases in the expected size of measles outbreaks. Meaning The 2018 vaccination rates in multiple metropolitan areas may permit large measles outbreaks, which could infect not only vaccine refusers but also other members of the population., This study uses a decision analytical model to simulate measles outbreaks in metropolitan areas in Texas using 2018 and future reduced vaccination rates., Importance Vaccine exemptions, which allow unvaccinated children to attend school, have increased by a factor of 28 since 2003 in Texas. Geographic clustering of unvaccinated children facilitates the spread of measles introductions, but the potential size of outbreaks is unclear. Objective To forecast the range of measles outbreak sizes in each metropolitan area of Texas at 2018 and future reduced school vaccination rates. Design, Setting, and Participants An agent-based decision analytical model using a synthetic population of Texas, derived from the 2010 US Census, was used to simulate measles transmission in the Texas population. Real schools were represented in the simulations, and the 2018 vaccination rate of each real school was applied to a simulated hypothetical equivalent. Single cases of measles were introduced, daily activities and interactions were modeled for each population member, and the number of infections over the course of 9 months was counted for 1000 simulated runs in each Texas metropolitan area. Interventions To determine the outcomes of further decreases in vaccination coverage, additional simulations were performed with vaccination rates reduced by 1% to 10% in schools with populations that are currently undervaccinated. Main Outcomes and Measures Expected distributions of outbreak sizes in each metropolitan area of Texas at 2018 and reduced vaccination rates. Results At 2018 vaccination rates, the median number of cases in large metropolitan areas was typically small, ranging from 1 to 3 cases, which is consistent with outbreaks in Texas 2006 to 2017. However, the upper limit of the distribution of plausible outbreaks (the 95th percentile, associated with 1 in 20 measles introductions) exceeded 400 cases in both the Austin and Dallas metropolitan areas, similar to the largest US outbreaks since measles was eliminated in 2000. Decreases in vaccination rates in schools with undervaccinated populations in 2018 were associated with exponential increases in the potential size of outbreaks: a 5% decrease in vaccination rate was associated with a 40% to 4000% increase in potential outbreak size, depending on the metropolitan area. A mean (SD) of 64% (11%) of cases occurred in students for whom a vaccine had been refused, but a mean (SD) of 36% (11%) occurred in others (ie, bystanders). Conclusions and Relevance This study suggests that vaccination rates in some Texas schools are currently low enough to allow large measles outbreaks. Further decreases are associated with dramatic increases in the probability of large outbreaks. Limiting vaccine exemptions could be associated with a decrease in the risk of large measles outbreaks.

Neisseria meningitidis is an important cause of meningococcal disease globally. Sequence type (ST)-11 clonal complex (cc11) is a hypervirulent meningococcal lineage historically associated with serogroup C capsule and is believed to have acquired the W capsule through a C to W capsular switching event. We studied the sequence of capsule gene cluster (cps) and adjoining genomic regions of 524 invasive W cc11 strains isolated globally. We identified recombination breakpoints corresponding to two distinct recombination events within W cc11: A 8.4-kb recombinant region likely acquired from W cc22 including the sialic acid/glycosyl-transferase gene, csw resulted in a C→W change in capsular phenotype and a 13.7-kb recombinant segment likely acquired from Y cc23 lineage includes 4.5 kb of cps genes and 8.2 kb downstream of the cps cluster resulting in allelic changes in capsule translocation genes. A vast majority of W cc11 strains (497/524, 94.8%) retain both recombination events as evidenced by sharing identical or very closely related capsular allelic profiles. These data suggest that the W cc11 capsular switch involved two separate recombination events and that current global W cc11 meningococcal disease is caused by strains bearing this mosaic capsular switch.

ObjectiveHurricane Sandy in the Rockaways, Queens, forced residents to evacuate and primary care providers to close or curtail operations. A large deficit in primary care access was apparent in the immediate aftermath of the storm. Our objective was to build a computational model to aid responders in planning to situate primary care services in a disaster-affected area.MethodsUsing an agent-based modeling platform, HAZEL, we simulated the Rockaways population, its evacuation behavior, and primary care providers’ availability in the aftermath of Hurricane Sandy. Data sources for this model included post-storm and community health surveys from New York City, a survey of the Rockaways primary care providers, and research literature. The model then tested geospatially specific interventions to address storm-related access deficits.ResultsThe model revealed that areas of high primary care access deficit were concentrated in the eastern part of the Rockaways. Placing mobile health clinics in the most populous census tracts reduced the access deficit significantly, whereas increasing providers’ capacity by 50% reduced the deficit to a lesser degree.ConclusionsAn agent-based model may be a useful tool to have in place so that policy makers can conduct scenario-based analyses to plan interventions optimally in the event of a disaster. (Disaster Med Public Health Preparedness. 2016;10:386–393)

This decision analytical model describes the use of a semisynthetic population to identify the distribution of excess cardiovascular death risk and its correlation with social and biological risk factors., Key Points Question Can synthetic populations that statistically mimic real populations in characteristics and spatial distribution of disease be constructed with real and synthetic data, and are these synthetic populations useful in designing and targeting interventions? Findings In this decision analytical model of a synthetic population of 1 188 112 individuals with an excess risk for cardiovascular disease death, the modeling process created was validated through identifying the correlation of cardiovascular disease death risk with social and biological risk factors. Meaning This study suggests that spatially explicit modeling using a synthetic population is a feasible way to estimate disease risk and the implication of population-level interventions., Importance Evaluating the association of social determinants of health with chronic diseases at the population level requires access to individual-level factors associated with disease, which are rarely available for large populations. Synthetic populations are a possible alternative for this purpose. Objective To construct and validate a synthetic population that statistically mimics the characteristics and spatial disease distribution of a real population, using real and synthetic data. Design, Setting, and Participants This population-based decision analytical model used data for Allegheny County, Pennsylvania, collected from January 2015 to December 2016, to build a semisynthetic population based on the synthetic population used by the modeling and simulation platform FRED (A Framework for Reconstructing Epidemiological Dynamics). Disease status was assigned to this population using health insurer claims data from the 3 major insurance providers in the county or from the National Health and Nutrition Examination Survey. Biological, social, and other variables were also obtained from the National Health Interview Survey, Allegheny County, and public databases. Data analysis was performed from November 2016 to February 2020. Exposures Risk of cardiovascular disease (CVD) death. Main Outcomes and Measures Difference between expected and observed CVD death risk. A validated risk equation was used to estimate CVD death risk. Results The synthetic population comprised 1 188 112 individuals with demographic characteristics similar to those of the 2010 census population in the same county. In the synthetic population, the mean (SD) age was 40.6 (23.3) years, and 622 997 were female individuals (52.4%). Mean (SD) observed 4-year rate of excess CVD death risk at the census tract level was –40 (523) per 100 000 persons. The correlation of social determinant data with difference between expected and observed CVD death risk indicated that income- and education-based social determinants were associated with risk. Estimating improved social determinants of health and biological factors associated with disease did not entirely remove the excess in CVD death rates. That is, a 20% improvement in the most significant determinants still resulted in 105 census tracts with excess CVD death risk, which represented 24% of the county population. Conclusions and Relevance The results of this study suggest that creating a geographically explicit synthetic population from real and synthetic data is feasible and that synthetic populations are useful for modeling disease in large populations and for estimating the outcome of interventions.

Despite medical advances, the emergence and re-emergence of infectious diseases continue to pose a public health threat. Low-dimensional epidemiological models predict that epidemic transitions are preceded by the phenomenon of critical slowing down (CSD). This has raised the possibility of anticipating disease (re-)emergence using CSD-based early-warning signals (EWS), which are statistical moments estimated from time series data. For EWS to be useful at detecting future (re-)emergence, CSD needs to be a generic (model-independent) feature of epidemiological dynamics irrespective of system complexity. Currently, it is unclear whether the predictions of CSD—derived from simple, low-dimensional systems—pertain to real systems, which are high-dimensional. To assess the generality of CSD, we carried out a simulation study of a hierarchy of models, with increasing structural complexity and dimensionality, for a measles-like infectious disease. Our five models included: i) a nonseasonal homogeneous Susceptible-Exposed-Infectious-Recovered (SEIR) model, ii) a homogeneous SEIR model with seasonality in transmission, iii) an age-structured SEIR model, iv) a multiplex network-based model (Mplex) and v) an agent-based simulator (FRED). All models were parameterised to have a herd-immunity immunization threshold of around 90% coverage, and underwent a linear decrease in vaccine uptake, from 92% to 70% over 15 years. We found evidence of CSD prior to disease re-emergence in all models. We also evaluated the performance of seven EWS: the autocorrelation, coefficient of variation, index of dispersion, kurtosis, mean, skewness, variance. Performance was scored using the Area Under the ROC Curve (AUC) statistic. The best performing EWS were the mean and variance, with AUC > 0.75 one year before the estimated transition time. These two, along with the autocorrelation and index of dispersion, are promising candidate EWS for detecting disease emergence., Author summary Emerging and re-emerging infectious diseases, such as Ebola and measles, present urgent public health challenges and threaten the progress made towards eliminating the global burden of disease. Consequently, a crucial activity in modern epidemiology is developing methods of anticipating (re-)emerging disease outbreaks. Early-warning signals (EWS) are a proposed method for detecting disease (re-)emergence, based on critical slowing down (CSD), a dynamical phenomenon present in systems approaching transition points. The presence of CSD preceding disease (re-)emergence has been comprehensively demonstrated in a range of low-dimensional epidemiological models. For EWS to be useful, however, CSD needs to be a generic feature of (re-)emerging disease transmission dynamics, rather than being limited to specific models. To assess the generality of CSD, we carried out a simulation study of a hierarchy of models of a re-emerging measles-like infectious disease. We found that CSD is present in the dynamics of all the models studied, supporting its generality. In addition, we studied seven candidate EWS, and found that four are strong candidates for use in monitoring systems to detect disease (re-)emergence.

Neisseria meningitidis is a leading bacterial cause of sepsis and meningitis globally with dynamic strain distribution over time. Beginning with an epidemic among Hajj pilgrims in 2000, serogroup W (W) sequence type (ST) 11 emerged as a leading cause of epidemic meningitis in the African ‘meningitis belt’ and endemic cases in South America, Europe, Middle East and China. Previous genotyping studies were unable to reliably discriminate sporadic W ST-11 strains in circulation since 1970 from the Hajj outbreak strain (Hajj clone). It is also unclear what proportion of more recent W ST-11 disease clusters are caused by direct descendants of the Hajj clone. Whole genome sequences of 270 meningococcal strains isolated from patients with invasive meningococcal disease globally from 1970 to 2013 were compared using whole genome phylogenetic and major antigen-encoding gene sequence analyses. We found that all W ST-11 strains were descendants of an ancestral strain that had undergone unique capsular switching events. The Hajj clone and its descendants were distinct from other W ST-11 strains in that they shared a common antigen gene profile and had undergone recombination involving virulence genes encoding factor H binding protein, nitric oxide reductase, and nitrite reductase. These data demonstrate that recent acquisition of a distinct antigen-encoding gene profile and variations in meningococcal virulence genes was associated with the emergence of the Hajj clone. Importantly, W ST-11 strains unrelated to the Hajj outbreak contribute a significant proportion of W ST-11 cases globally. This study helps illuminate genomic factors associated with meningococcal strain emergence and evolution., Highlights • Genomic characterization of serogroup W ST-11 of Neisseria meningitidis. . • Epidemic W ST-11 strain (Hajj clone) emerged through recombination affecting virulence genes. • Both the Hajj clone and W ST-11 strains unrelated to the Hajj outbreak have persisted globally. Neisseria meningitidis, a bacterial cause of frequently fatal brain (meningitis) and blood stream (sepsis) infections, has variable strain distribution over time. Serogroup W sequence type 11 (W ST-11) lineage is associated on one hand with strains causing only rare (sporadic) disease cases, and the Hajj clone – a major global cause of epidemic and endemic meningococcal disease. In this study we analyzed complete genome sequences of a global collection of 270 W ST-11 isolates causing meningococcal disease from 1970-2013. The Hajj clone acquired novel gene sequences within genes involved in nitrogen metabolism (nitrogen oxide reductase, nitrite reductase) and evasion of human immune response (factor H binding protein). These genes may be the cause of increased virulence of the Hajj clone and can be used to trace continuing spread of the clone. These results shed light on mechanisms of meningococcal strain emergence.

Horizontal gene transfer and clonal expansion contribute substantially to the dissemination of resistant strains, Salmonella enterica bacteria have become increasingly resistant to antimicrobial agents, partly as a result of genes carried on integrons. Clonal expansion and horizontal gene transfer may contribute to the spread of antimicrobial drug–resistance integrons in these organisms. We investigated this resistance and integron carriage among 90 isolates with the ACSSuT phenotype (resistance to ampicillin, chloramphenicol, streptomycin, sulfamethoxazole, and tetracycline) in a global collection of S. enterica isolates. Four integrons, dfrA12/orfF/aadA2, dfrA1/aadA1, dfrA7, and arr2/blaOXA30/cmlA5/aadA2, were found in genetically unrelated isolates from 8 countries on 4 continents, which supports a role for horizontal gene transfer in the global dissemination of S. enterica multidrug resistance. Serovar Typhimurium isolates containing identical integrons with the gene cassettes blaPSE1 and aadA2 were found in 4 countries on 3 continents, which supports the role of clonal expansion. This study demonstrates that clonal expansion and horizontal gene transfer contribute to the global dissemination of antimicrobial drug resistance in S. enterica.

Increased incidence of infections due to Klebsiella pneumoniae carbapenemase (KPC)-producing Klebsiella pneumoniae (KPC-Kp) was noted among patients undergoing endoscopic retrograde cholangiopancreatography (ERCP) at a single hospital. An epidemiologic investigation identified KPC-Kp and non-KPC-producing, extended-spectrum β-lactamase (ESBL)-producing Kp in cultures from 2 endoscopes. Genotyping was performed on patient and endoscope isolates to characterize the microbial genomics of the outbreak. Genetic similarity of 51 Kp isolates from 37 patients and 3 endoscopes was assessed by pulsed-field gel electrophoresis (PFGE) and multi-locus sequence typing (MLST). Five patient and 2 endoscope isolates underwent whole genome sequencing (WGS). Two KPC-encoding plasmids were characterized by single molecule, real-time sequencing. Plasmid diversity was assessed by endonuclease digestion. Genomic and epidemiologic data were used in conjunction to investigate the outbreak source. Two clusters of Kp patient isolates were genetically related to endoscope isolates by PFGE. A subset of patient isolates were collected post-ERCP, suggesting ERCP endoscopes as a possible source. A phylogeny of 7 Kp genomes from patient and endoscope isolates supported ERCP as a potential source of transmission. Differences in gene content defined 5 ST258 subclades and identified 2 of the subclades as outbreak-associated. A novel KPC-encoding plasmid, pKp28 helped define and track one endoscope-associated ST258 subclade. WGS demonstrated high genetic relatedness of patient and ERCP endoscope isolates suggesting ERCP-associated transmission of ST258 KPC-Kp. Gene and plasmid content discriminated the outbreak from endemic ST258 populations and assisted with the molecular epidemiologic investigation of an extended KPC-Kp outbreak.

During the 1990s, an epidemic of B:4 Neisseria meningitidis infections affected Brazil. Subsequent increase in C:4 disease suggested B → C capsular switching. This study identified B → C switches within the sequence type 32 complex. Substantial disease related to capsular switching emphasizes the need for surveillance of circulating meningococcal strains to optimize disease control.

In the United States, serogroup Y, ST-23 clonal complex Neisseria meningitidis was responsible for an increase in meningococcal disease incidence during the 1990s. This increase was accompanied by antigenic shift of three outer membrane proteins, with a decrease in the population that predominated in the early 1990s as a different population emerged later in that decade. To understand factors that may have been responsible for the emergence of serogroup Y disease, we used whole genome pyrosequencing to investigate genetic differences between isolates from early and late N. meningitidis populations, obtained from meningococcal disease cases in Maryland in the 1990s. The genomes of isolates from the early and late populations were highly similar, with 1231 of 1776 shared genes exhibiting 100% amino acid identity and an average π(N) = 0.0033 and average π(S) = 0.0216. However, differences were found in predicted proteins that affect pilin structure and antigen profile and in predicted proteins involved in iron acquisition and uptake. The observed changes are consistent with acquisition of new alleles through horizontal gene transfer. Changes in antigen profile due to the genetic differences found in this study likely allowed the late population to emerge due to escape from population immunity. These findings may predict which antigenic factors are important in the cyclic epidemiology of meningococcal disease.

Salmonella enterica subspecies enterica is traditionally subdivided into serovars by serological and nutritional characteristics. We used Multilocus Sequence Typing (MLST) to assign 4,257 isolates from 554 serovars to 1092 sequence types (STs). The majority of the isolates and many STs were grouped into 138 genetically closely related clusters called eBurstGroups (eBGs). Many eBGs correspond to a serovar, for example most Typhimurium are in eBG1 and most Enteritidis are in eBG4, but many eBGs contained more than one serovar. Furthermore, most serovars were polyphyletic and are distributed across multiple unrelated eBGs. Thus, serovar designations confounded genetically unrelated isolates and failed to recognize natural evolutionary groupings. An inability of serotyping to correctly group isolates was most apparent for Paratyphi B and its variant Java. Most Paratyphi B were included within a sub-cluster of STs belonging to eBG5, which also encompasses a separate sub-cluster of Java STs. However, diphasic Java variants were also found in two other eBGs and monophasic Java variants were in four other eBGs or STs, one of which is in subspecies salamae and a second of which includes isolates assigned to Enteritidis, Dublin and monophasic Paratyphi B. Similarly, Choleraesuis was found in eBG6 and is closely related to Paratyphi C, which is in eBG20. However, Choleraesuis var. Decatur consists of isolates from seven other, unrelated eBGs or STs. The serological assignment of these Decatur isolates to Choleraesuis likely reflects lateral gene transfer of flagellar genes between unrelated bacteria plus purifying selection. By confounding multiple evolutionary groups, serotyping can be misleading about the disease potential of S. enterica. Unlike serotyping, MLST recognizes evolutionary groupings and we recommend that Salmonella classification by serotyping should be replaced by MLST or its equivalents., Author Summary Microbiologists have used serological and nutritional characteristics to subdivide pathogenic bacteria for nearly 100 years. These subdivisions in Salmonella enterica are called serovars, some of which are thought to be associated with particular diseases and epidemiology. We used MultiLocus Sequence-based Typing (MLST) to identify clusters of S. enterica isolates that are related by evolutionary descent. Some clusters correspond to serovars on a one to one basis. But many clusters include multiple serovars, which is of public health significance, and most serovars span multiple, unrelated clusters. Despite its broad usage, serological typing of S. enterica has resulted in confusing systematics, with a few exceptions. We recommend that serotyping for strain discrimination of S. enterica be replaced by a DNA-based method, such as MLST. Serotyping and other non-sequence based typing methods are routinely used for detecting outbreaks and to support public health responses. Moving away from these methods will require a major shift in thinking by public health microbiology laboratories as well as national and international agencies. However, a transition to the routine use of MLST, supplemented where appropriate by even more discriminatory sequence-based typing methods based on entire genomes, will provide a clearer picture of long-term transmission routes of Salmonella, facilitate data transfer and support global control measures.

Investigation of integron carriage in a global collection of multi-drug resistant Salmonella enterica identified 3 unique class 1 integron gene cassette arrays not previously reported in this species. The present study used PCR and DNA sequence analysis to characterize the structure of these gene cassette arrays. A approximately 4.0 kb integron containing the gene cassette array arr2/cmlA5/bla (OXA10) /aadA1 was found in isolates belonging to serovars Isangi and Typhimurium from South Africa. A approximately 6.0 kb integron containing the gene cassettes aac(6')IIc/ereA2/IS1247/aac/arr/ereA2 was found in isolates belonging to serovar Heidelberg from the Philippines. In this gene cassette array, the insertion sequence, IS1247, and two putative resistance genes, disrupt the erythromycin resistance gene cassette. Finally, a approximately 6.0 kb integron containing the gene cassette qacH/dfrA32/ereA1/aadA2/cmlA/aadA1 was found in serovar Stanley isolates from Taiwan. This integron, which has not been previously reported in any bacterial species, contains a new dihydrofolate reductase gene cassette sequence designated dfrA32, with only 90% sequence similarity to previously reported dfrA cassettes. The S. enterica integrons described in the present study represent novel collections of resistance genes which confer multi-drug resistance and have the potential to be widely disseminated among S. enterica as well as other bacterial species.

Since late 1970, nine isolates expressing dual capsules (W and Y) have been described in North America and Europe ([1][1][–][2][4][3]). Although the origin of such isolates and the precise composition of the capsular polysaccharide expressed by them are open issues, meningococcal vaccines that