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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. [ABSTRACT FROM AUTHOR]

Purpose. To estimate the impact on mortality of nonpharmaceutical interventions (NPIs) implemented early in the COVID-19 pandemic. Methods. We implemented an agent-based modified SEIR model of COVID-19, calibrated to match death numbers reported in Pennsylvania from January 2020 to April 2021 and including representations of NPIs implemented in Pennsylvania. To investigate the impact of these strategies, we ran the calibrated model with no interventions and with varying combinations, timings, and levels of interventions. Results. The model closely replicated death outcomes data for Pennsylvania. Without NPIs, deaths in the early months of the pandemic were estimated to be much higher (67,718 deaths compared to actual 6,969). Voluntary interventions alone were relatively ineffective at decreasing mortality. Delaying implementation of interventions led to higher deaths (∼9,000 more deaths with just a 1-week delay). School closure was insufficient as a single intervention but was an important part of a combined intervention strategy. Conclusions. NPIs were effective at reducing deaths early in the COVID-19 pandemic. Agent-based models can incorporate substantial detail on infectious disease spread and the impact of mitigations. Policy Implications. The model supports the importance and effectiveness of NPIs to decrease morbidity from respiratory pathogens. This is particularly important for emerging pathogens for which no vaccines or treatments exist, but such strategies are applicable to a variety of respiratory pathogens., Highlights: Nonpharmaceutical interventions were used extensively during the early period of the COVID-19 pandemic, but their use has remained controversial.Agent-based modeling of the impact of these mitigation strategies early in the COVID-19 pandemic supports the effectiveness of nonpharmaceutical interventions at decreasing mortality.Since such interventions are not specific to a particular pathogen, they can be used to protect against any respiratory pathogen, known or emerging. They can be applied rapidly when conditions warrant., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding for this work was provided by the Centers for Disease Control and Prevention through the Pennsylvania Department of Health and by the Center for Disease Control and Prevention U01-IP001141-01. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report., (© The Author(s) 2024.)

Objective: Drug use disorder (DUD) is a worldwide problem, and strategies to reduce its incidence are central to decreasing its burden. This investigation seeks to provide a proof of concept for the ability of agent-based modeling to predict the impact of the introduction of an effective school-based intervention, the Good Behavior Game (GBG), on reducing DUD in Scania, Sweden, primarily through increasing school achievement., Method: We modified an existing agent-based simulation model of opioid use disorder to represent DUD in Scania County, southern Sweden. The model represents every individual in the population and is calibrated with the linked individual data from multiple sources including demographics, education, medical care, and criminal history. Risks for developing DUD were estimated from the population in Scania. Scenarios estimated the impact of introducing the GBG in schools located in disadvantaged areas., Results: The model accurately reflected the growth of DUD in Scania over a multiyear period and reproduced the levels of affected individuals in various socioeconomic strata over time. The GBG was estimated to improve school achievement and lower DUD registrations over time in males residing in disadvantaged areas by 10%, reflecting a decrease of 540 cases of DUD. Effects were considerably smaller in females., Conclusions: This work provides support for the impact of improving school achievement on long-term risks of developing DUD. It also demonstrated the value of using simulation modeling calibrated with data from a real population to estimate the impact of an intervention applied at a population level.

Introduction: Current influenza vaccines have limited effectiveness. COVID-19 vaccines using mRNA technology have demonstrated very high efficacy, suggesting that mRNA vaccines could be more effective for influenza. Several such influenza vaccines are in development. FRED, an agent-based modeling platform, was used to estimate the impact of more effective influenza vaccines on seasonal influenza burden., Methods: Simulations were performed using an agent-based model of influenza that included varying levels of vaccination efficacy (40-95 % effective). In some simulations, level of infectiousness and/or length of infectious period in agents with breakthrough infections was also decreased. Impact of increased and decreased levels of vaccine uptake were also modeled. Outcomes included number of symptomatic influenza cases estimated for the US., Results: Highly effective vaccines significantly reduced estimated influenza cases in the model. When vaccine efficacy was increased from 40 % to a maximum of 95 %, estimated influenza cases in the US decreased by 43 % to > 99 %. The base simulation (40 % efficacy) resulted in ∼ 28 million total yearly cases in the US, while the most effective vaccine modeled (95 % efficacy) decreased estimated cases to ∼ 22,000., Discussion: Highly effective vaccines could dramatically reduce influenza burden. Model estimates suggest that even modest increases in vaccine efficacy could dramatically reduce seasonal influenza disease burden., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mary G Krauland reports financial support was provided by Centers for Disease Control and Prevention. Richard K Zimmerman reports a relationship with Sanofi Pasteur that includes: funding grants. Jonathan M Raviotta reports a relationship with Sanofi Pasteur that includes: funding grants. John V. Williams reports a relationship with Quidel Corp that includes: board membership. John V. Williams reports a relationship with GlaxoSmithKline that includes: consulting or advisory., (© 2022 The Author(s).)

Older adults (age ≥ 65) are at high risk of influenza morbidity and mortality. This study evaluated the impact of a hypothetical two-dose influenza vaccine regimen per season to reduce symptomatic flu cases by providing preseason (first dose) and mid-season (second dose) protection to offset waning vaccine effectiveness (VE). The Framework for Reconstructing Epidemiological Dynamics (FRED), an agent-based modeling platform, was used to compare typical one-dose vaccination to a two-dose vaccination strategy. Primary models incorporated waning VE of 10% per month and varied influenza season timing (December through March) to estimate cases and hospitalizations in older adults. Additional scenarios modeled reductions in uptake and VE of the second dose, and overall waning. In seasons with later peaks, two vaccine doses had the largest potential to reduce cases (14.4% with February peak, 18.7% with March peak) and hospitalizations (13.1% with February peak, 16.8% with March peak). Reductions in cases and hospitalizations still resulted but decreased when 30% of individuals failed to receive a second dose, second dose VE was reduced, or overall waning was reduced to 7% per month. Agent-based modeling indicates that two influenza vaccine doses could decrease cases and hospitalizations in older individuals. The highest impact occurred in the more frequently observed late-peak seasons. The beneficial impact of the two-dose regimen persisted despite model scenarios of reduced uptake of the second dose, decreased VE of the second dose, or overall VE waning.

Introduction: 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., Methods: 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., Results: 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., Conclusions: 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., (Copyright © 2021 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

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.

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.

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., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: JJG is a principal in Epistemix Inc., which has been licensed by the University of Pittsburgh to develop commercial applications of the FRED modeling technology mentioned in this study.

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.

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.

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.

During 2003-2012, 8 clusters of meningococcal disease were identified in Rio de Janeiro State, Brazil, all caused by serogroup C Neisseria meningitidis. The isolates were assigned to 3 clonal complexes (cc): cc11, cc32, and cc103. These hyperinvasive disease lineages were associated with endemic disease, outbreaks, and high case-fatality rates.

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.

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.