Correction: Multilocus Sequence Typing as a Replacement for Serotyping in Salmonella enterica.

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

Detecting critical slowing down in high-dimensional epidemiological systems.

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.

Multilocus sequence typing as a replacement for serotyping in Salmonella enterica.

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.

Modeling the Impact of COVID-19 Mitigation Strategies in Pennsylvania, USA.

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.

Three outbreak-causing Neisseria meningitidis serogroup C clones, Brazil(1.).

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.

The Impact of the Good Behavior Game on Risk for Drug Use Disorder in an Agent-Based Model of Southern Sweden.

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.

Agent-based model of the impact of higher influenza vaccine efficacy on seasonal influenza burden.

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 âˆ¼ 28 million total yearly cases in the US, while the most effective vaccine modeled (95 % efficacy) decreased estimated cases to âˆ¼ 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.

Capsular switching in invasive Neisseria meningitidis, Brazil(1).

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.

Impact of Low Rates of Influenza on Next-Season Influenza Infections.

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.

Can a Two-Dose Influenza Vaccine Regimen Better Protect Older Adults? An Agent-Based Modeling Study.

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.