Investigation of the validity of two Bayesian ancestral state reconstruction models for estimating Salmonella transmission during outbreaks.
ABSTRACT: Ancestral state reconstruction models use genetic data to characterize a group of organisms' common ancestor. These models have been applied to salmonellosis outbreaks to estimate the number of transmissions between different animal species that share similar geographical locations, with animal host as the state. However, as far as we are aware, no studies have validated these models for outbreak analysis. In this study, salmonellosis outbreaks were simulated using a stochastic Susceptible-Infected-Recovered model, and the host population and transmission parameters of these simulated outbreaks were estimated using Bayesian ancestral state reconstruction models (discrete trait analysis (DTA) and structured coalescent (SC)). These models were unable to accurately estimate the number of transmissions between the host populations or the amount of time spent in each host population. The DTA model was inaccurate because it assumed the number of isolates sampled from each host population was proportional to the number of individuals infected within each host population. The SC model was inaccurate possibly because it assumed that each host population's effective population size was constant over the course of the simulated outbreaks. This study highlights the need for phylodynamic models that can take into consideration factors that influence the characteristics and behavior of outbreaks, e.g. changing effective population sizes, variation in infectious periods, intra-population transmissions, and disproportionate sampling of infected individuals.
Project description:Foodborne illnesses in Australia, including salmonellosis, are estimated to cost over $A1.25 billion annually. The weather has been identified as being influential on salmonellosis incidence, as cases increase during summer, however time series modelling of salmonellosis is challenging because outbreaks cause strong autocorrelation. This study assesses whether switching models is an improved method of estimating weather-salmonellosis associations.We analysed weather and salmonellosis in South-East Queensland between 2004 and 2013 using 2 common regression models and a switching model, each with 21-day lags for temperature and precipitation.The switching model best fit the data, as judged by its substantial improvement in deviance information criterion over the regression models, less autocorrelated residuals and control of seasonality. The switching model estimated a 5 °C increase in mean temperature and 10 mm precipitation were associated with increases in salmonellosis cases of 45.4% (95% CrI 40.4%, 50.5%) and 24.1% (95% CrI 17.0%, 31.6%), respectively.Switching models improve on traditional time series models in quantifying weather-salmonellosis associations. A better understanding of how temperature and precipitation influence salmonellosis may identify where interventions can be made to lower the health and economic costs of salmonellosis.
Project description:Pathogen phylogenies are often used to infer spread among hosts. There is, however, not an exact match between the pathogen phylogeny and the host transmission history. Here, we examine in detail the limitations of this relationship. First, all splits in a pathogen phylogeny of more than 1 host occur within hosts, not at the moment of transmission, predating the transmission events as described by the pretransmission interval. Second, the order in which nodes in a phylogeny occur may be reflective of the within-host dynamics rather than epidemiologic relationships. To investigate these phenomena, motivated by within-host diversity patterns, we developed a two-phase coalescent model that includes a transmission bottleneck followed by linear outgrowth to a maximum population size followed by either stabilization or decline of the population. The model predicts that the pretransmission interval shrinks compared with predictions based on constant population size or a simple transmission bottleneck. Because lineages coalesce faster in a small population, the probability of a pathogen phylogeny to resemble the transmission history depends on when after infection a donor transmits to a new host. We also show that the probability of inferring the incorrect order of multiple transmissions from the same host is high. Finally, we compare time of HIV-1 infection informed by genetic distances in phylogenies to independent biomarker data, and show that, indeed, the pretransmission interval biases phylogeny-based estimates of when transmissions occurred. We describe situations where caution is needed not to misinterpret which parts of a phylogeny that may indicate outbreaks and tight transmission clusters.
Project description:Infectious disease often occurs in small, independent outbreaks in populations with varying characteristics. Each outbreak by itself may provide too little information for accurate estimation of epidemic model parameters. Here we show that using standard stochastic epidemic models for each outbreak and allowing parameters to vary between outbreaks according to a linear predictor leads to a generalized linear model that accurately estimates parameters from many small and diverse outbreaks. By estimating initial growth rates in addition to transmission rates, we are able to characterize variation in numbers of initially susceptible individuals or contact patterns between outbreaks. With simulation, we find that the estimates are fairly robust to the data being collected at discrete intervals and imputation of about half of all infectious periods. We apply the method by fitting data from 75 norovirus outbreaks in health-care settings. Our baseline regression estimates are 0.0037 transmissions per infective-susceptible day, an initial growth rate of 0.27 transmissions per infective day, and a symptomatic period of 3.35 days. Outbreaks in long-term-care facilities had significantly higher transmission and initial growth rates than outbreaks in hospitals.
Project description:Salmonella enterica is the leading etiologic agent of bacterial food-borne outbreaks worldwide. This ubiquitous species contains more than 2,600 serovars that may differ in their host specificity, clinical manifestations, and epidemiology. To characterize salmonellosis epidemiology in Israel and to study the association of nontyphoidal Salmonella (NTS) serovars with invasive infections, 48,345 Salmonella cases reported and serotyped at the National Salmonella Reference Center between 1995 and 2012 were analyzed. A quasi-Poisson regression was used to identify irregular clusters of illness, and pulsed-field gel electrophoresis in conjunction with whole-genome sequencing was applied to molecularly characterize strains of interest. Three hundred twenty-nine human salmonellosis clusters were identified, representing an annual average of 23 (95% confidence interval [CI], 20 to 26) potential outbreaks. We show that the previously unsequenced S. enterica serovar 9,12:l,v:- belongs to the B clade of Salmonella enterica subspecies enterica, and we show its frequent association with extraintestinal infections, compared to other NTS serovars. Furthermore, we identified the dissemination of two prevalent Salmonella enterica serovar Typhimurium DT104 clones in Israel, which are genetically distinct from other global DT104 isolates. Accumulatively, these findings indicate a severe underreporting of Salmonella outbreaks in Israel and provide insights into the epidemiology and genomics of prevalent serovars, responsible for recurring illness.
Project description:Episodic high-risk sexual behavior is common and can have a profound effect on HIV transmission. In a model of HIV transmission among men who have sex with men (MSM), changing the frequency, duration and contact rates of high-risk episodes can take endemic prevalence from zero to 50% and more than double transmissions during acute HIV infection (AHI). Undirected test and treat could be inefficient in the presence of strong episodic risk effects. Partner services approaches that use a variety of control options will be likely to have better effects under these conditions, but the question remains: What data will reveal if a population is experiencing episodic risk effects? HIV sequence data from Montreal reveals genetic clusters whose size distribution stabilizes over time and reflects the size distribution of acute infection outbreaks (AIOs). Surveillance provides complementary behavioral data. In order to use both types of data efficiently, it is essential to examine aspects of models that affect both the episodic risk effects and the shape of transmission trees. As a demonstration, we use a deterministic compartmental model of episodic risk to explore the determinants of the fraction of transmissions during acute HIV infection (AHI) at the endemic equilibrium. We use a corresponding individual-based model to observe AIO size distributions and patterns of transmission within AIO. Episodic risk parameters determining whether AHI transmission trees had longer chains, more clustered transmissions from single individuals, or different mixes of these were explored. Encouragingly for parameter estimation, AIO size distributions reflected the frequency of transmissions from acute infection across divergent parameter sets. Our results show that episodic risk dynamics influence both the size and duration of acute infection outbreaks, thus providing a possible link between genetic cluster size distributions and episodic risk dynamics.
Project description:Background Belimumab is a recombinant, human, IgG1λ monoclonal antibody that targets B-lymphocyte stimulator. The intravenous formulation is indicated for the treatment of active, autoantibody-positive systemic lupus erythematosus (SLE). Belimumab has been formulated for subcutaneous (SC) administration to improve patient convenience. This post-hoc modeling and simulation analysis characterizes the population pharmacokinetics (PK) of SC belimumab, and compares the exposure profiles of the approved belimumab IV dose-10 mg/kg every four weeks-to the 200 mg SC weekly dose in SLE patients, highlighting key pharmacological differences relevant for clinicians. Methods Data from two Phase 1 studies in US American and Japanese healthy subjects were analyzed with a non-linear mixed effects modeling approach. The resulting SC population PK model and a previously developed IV population PK model were used to conduct simulation trials in a Phase 3 IV belimumab SLE patient population, comparing chronic exposure profiles and exposure ranges stratified by body weight tertiles for IV vs SC dosing. Results The PK of belimumab following SC administration was best described by a linear two-comment model. The estimates for clearance, steady-state volume of distribution, and bioavailability were 208 mL/day, 5250 mL, and 76%, respectively. After four weeks of SC dosing, simulated belimumab concentrations exceeded the steady-state trough concentrations of the IV dosing regimen. At steady state simulated serum profiles demonstrated comparable average belimumab concentrations (Cavg,ss) after IV and SC dosing. Simulated belimumab exposures demonstrated largely overlapping concentration ranges following 200 mg SC weekly and 10 mg/kg IV every four weeks dosing. Discussion The predicted Cavg,ss of belimumab in SLE patients was comparable following 200 mg SC weekly and 10 mg/kg IV every four weeks dosing. The simulated belimumab accumulation following SC weekly dosing indicated that administration of a loading dose was not required. Similar Cavg,ss ranges were predicted for fixed dose SC and weight-proportional IV regimens in the simulated SLE population, albeit with a reversed body-size-to-exposure relationship for the SC regimen. These findings provide rheumatologists with a better understanding of expected differences in belimumab exposure when comparing IV and SC dosing regimens.
Project description:Motivation:Genomic analysis has become one of the major tools for disease outbreak investigations. However, existing computational frameworks for inference of transmission history from viral genomic data often do not consider intra-host diversity of pathogens and heavily rely on additional epidemiological data, such as sampling times and exposure intervals. This impedes genomic analysis of outbreaks of highly mutable viruses associated with chronic infections, such as human immunodeficiency virus and hepatitis C virus, whose transmissions are often carried out through minor intra-host variants, while the additional epidemiological information often is either unavailable or has a limited use. Results:The proposed framework QUasispecies Evolution, Network-based Transmission INference (QUENTIN) addresses the above challenges by evolutionary analysis of intra-host viral populations sampled by deep sequencing and Bayesian inference using general properties of social networks relevant to infection dissemination. This method allows inference of transmission direction even without the supporting case-specific epidemiological information, identify transmission clusters and reconstruct transmission history. QUENTIN was validated on experimental and simulated data, and applied to investigate HCV transmission within a community of hosts with high-risk behavior. It is available at https://github.com/skumsp/QUENTIN. Contact:firstname.lastname@example.org or email@example.com or firstname.lastname@example.org or email@example.com. Supplementary information:Supplementary data are available at Bioinformatics online.
Project description:Background. Absent adaptive, individualized dose-finding in early-phase oncology trials, subsequent 'confirmatory' Phase III trials risk suboptimal dosing, with resulting loss of statistical power and reduced probability of technical success for the investigational therapy. While progress has been made toward explicitly adaptive dose-finding and quantitative modeling of dose-response relationships, most such work continues to be organized around a concept of 'the' maximum tolerated dose (MTD). The purpose of this paper is to demonstrate concretely how the aim of early-phase trials might be conceived, not as 'dose-finding', but as dose titration algorithm (DTA)-finding. Methods. A Phase I dosing study is simulated, for a notional cytotoxic chemotherapy drug, with neutropenia constituting the critical dose-limiting toxicity. The drug's population pharmacokinetics and myelosuppression dynamics are simulated using published parameter estimates for docetaxel. The amenability of this model to linearization is explored empirically. The properties of a simple DTA targeting neutrophil nadir of 500 cells/mm 3 using a Newton-Raphson heuristic are explored through simulation in 25 simulated study subjects. Results. Individual-level myelosuppression dynamics in the simulation model approximately linearize under simple transformations of neutrophil concentration and drug dose. The simulated dose titration exhibits largely satisfactory convergence, with great variance in individualized optimal dosing. Some titration courses exhibit overshooting. Conclusions. The large inter-individual variability in simulated optimal dosing underscores the need to replace 'the' MTD with an individualized concept of MTD i . To illustrate this principle, the simplest possible DTA capable of realizing such a concept is demonstrated. Qualitative phenomena observed in this demonstration support discussion of the notion of tuning such algorithms. Although here illustrated specifically in relation to cytotoxic chemotherapy, the DTAT principle appears similarly applicable to Phase I studies of cancer immunotherapy and molecularly targeted agents.
Project description:The incidence of salmonellosis, a costly foodborne disease, is rising in Australia. Salmonellosis increases during high temperatures and rainfall, and future incidence is likely to rise under climate change. Allocating funding to preventative strategies would be best informed by accurate estimates of salmonellosis costs under climate change and by knowing which population subgroups will be most affected.We used microsimulation models to estimate the health and economic costs of salmonellosis in Central Queensland under climate change between 2016 and 2036 to inform preventative strategies.We projected the entire population of Central Queensland to 2036 by simulating births, deaths, and migration, and salmonellosis and two resultant conditions, reactive arthritis and postinfectious irritable bowel syndrome. We estimated salmonellosis risks and costs under baseline conditions and under projected climate conditions for Queensland under the A1FI emissions scenario using composite projections from 6 global climate models (warm with reduced rainfall). We estimated the resulting costs based on direct medical expenditures combined with the value of lost quality-adjusted life years (QALYs) based on willingness-to-pay.Estimated costs of salmonellosis between 2016 and 2036 increased from 456.0 QALYs (95% CI: 440.3, 473.1) and AUD29,900,000?million (95% CI: AUD28,900,000, AUD31,600,000), assuming no climate change, to 485.9 QALYs (95% CI: 469.6, 503.5) and AUD31,900,000 (95% CI: AUD30,800,000, AUD33,000,000) under the climate change scenario.We applied a microsimulation approach to estimate the costs of salmonellosis and its sequelae in Queensland during 2016-2036 under baseline conditions and according to climate change projections. This novel application of microsimulation models demonstrates the models' potential utility to researchers for examining complex interactions between weather and disease to estimate future costs. https://doi.org/10.1289/EHP1370.
Project description:Cross-species transmission of viruses poses a sustained threat to public health. Due to increased contact between humans and other animal species the possibility exists for cross-species transmissions and ensuing disease outbreaks. By using conventional PCR amplification and next generation sequencing, we obtained 130 partial or full genome kobuvirus sequences from humans in a sentinel cohort in Vietnam and various mammalian hosts including bats, rodents, pigs, cats, and civets. The evolution of kobuviruses in different hosts was analysed using Bayesian phylogenetic methods. We estimated and compared time of origin of kobuviruses in different host orders; we also examined the cross-species transmission of kobuviruses within the same host order and between different host orders. Our data provide new knowledge of rodent and bat kobuviruses, which are most closely related to human kobuviruses. The novel bat kobuviruses isolated from bat roosts in Southern Vietnam were genetically distinct from previously described bat kobuviruses, but closely related to kobuviruses found in rodents. We additionally found evidence of frequent cross-species transmissions of kobuviruses within rodents. Overall, our phylogenetic analyses reveal multiple cross-species transmissions both within and among mammalian species, which increases our understanding of kobuviruses genetic diversity and the complexity of their evolutionary history.