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1.
Biostatistics ; 2020 Mar 02.
Artigo em Inglês | MEDLINE | ID: mdl-32118253

RESUMO

Infectious disease models can be of great use for understanding the underlying mechanisms that influence the spread of diseases and predicting future disease progression. Modeling has been increasingly used to evaluate the potential impact of different control measures and to guide public health policy decisions. In recent years, there has been rapid progress in developing spatio-temporal modeling of infectious diseases and an example of such recent developments is the discrete-time individual-level models (ILMs). These models are well developed and provide a common framework for modeling many disease systems; however, they assume the probability of disease transmission between two individuals depends only on their spatial separation and not on their spatial locations. In cases where spatial location itself is important for understanding the spread of emerging infectious diseases and identifying their causes, it would be beneficial to incorporate the effect of spatial location in the model. In this study, we thus generalize the ILMs to a new class of geographically dependent ILMs, to allow for the evaluation of the effect of spatially varying risk factors (e.g., education, social deprivation, environmental), as well as unobserved spatial structure, upon the transmission of infectious disease. Specifically, we consider a conditional autoregressive (CAR) model to capture the effects of unobserved spatially structured latent covariates or measurement error. This results in flexible infectious disease models that can be used for formulating etiological hypotheses and identifying geographical regions of unusually high risk to formulate preventive action. The reliability of these models is investigated on a combination of simulated epidemic data and Alberta seasonal influenza outbreak data ($2009$). This new class of models is fitted to data within a Bayesian statistical framework using Markov chain Monte Carlo methods.

2.
Int J Biostat ; 2019 Dec 10.
Artigo em Inglês | MEDLINE | ID: mdl-31812945

RESUMO

Infectious disease transmission between individuals in a heterogeneous population is often best modelled through a contact network. However, such contact network data are often unobserved. Such missing data can be accounted for in a Bayesian data augmented framework using Markov chain Monte Carlo (MCMC). Unfortunately, fitting models in such a framework can be highly computationally intensive. We investigate the fitting of network-based infectious disease models with completely unknown contact networks using approximate Bayesian computation population Monte Carlo (ABC-PMC) methods. This is done in the context of both simulated data, and data from the UK 2001 foot-and-mouth disease epidemic. We show that ABC-PMC is able to obtain reasonable approximations of the underlying infectious disease model with huge savings in computation time when compared to a full Bayesian MCMC analysis.

3.
ACR Open Rheumatol ; 1(8): 471-479, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31777827

RESUMO

Objective: To jointly estimate American College of Rheumatology (ACR50) response (a more commonly reported outcome) and remission (a more clinically relevant outcome) for methotrexate (MTX)-based treatment options in rheumatoid arthritis (RA). Methods: We conducted a bivariate network meta-analysis (NMA) to compare MTX monotherapy and MTX-based conventional and biologic disease-modifying antirheumatic drug (DMARD) combinations for RA. The correlation between the outcomes was derived from an incident RA cohort study, whereas the treatment effects were derived from randomized trials in the network of evidence. The analyses were conducted separately for MTX-naïve and MTX-inadequate response (IR) populations in a Bayesian framework with uninformative priors. Results: From the cohort study, the correlation between ACR50 response and Disease Activity Score 28 remission at 6 months was moderate (Pearson correlation coefficient = 0.58). In the bivariate NMA for MTX-naïve populations, most combinations of MTX with either biologic or tofacitinib were statistically superior to MTX alone for both ACR50 response and remission. Triple therapy (MTX + sulfasalazine + hydroxychloroquine) was the only nonbiologic DMARD statistically superior to MTX for either ACR50 response (odds ratio [OR] 95% credible interval: 2.1 [1.0, 4.3]) or remission (OR: 2.5 [1.0, 5.8]). In the MTX-IR analysis, all treatments except MTX + sulfasalazine were statistically superior to MTX alone. Compared to analyzing the outcomes separately, the bivariate model often resulted in more precise estimates and allowed remission to be estimated for all treatments. Conclusion: Borrowing the strength of correlation between outcomes allowed us to demonstrate a statistically significant benefit for remission across most MTX-based DMARD combinations, including triple therapy.

4.
BMC Public Health ; 19(1): 1232, 2019 Sep 05.
Artigo em Inglês | MEDLINE | ID: mdl-31488092

RESUMO

BACKGROUND: School absenteeism data have been collected daily by the public health unit in Wellington-Dufferin-Guelph, Ontario since 2008. To date, a threshold-based approach has been implemented to raise alerts for community-wide and within-school illness outbreaks. We investigate several statistical modelling approaches to using school absenteeism for influenza surveillance at the regional level, and compare their performances using two metrics. METHODS: Daily absenteeism percentages from elementary and secondary schools, and report dates for influenza cases, were obtained from Wellington-Dufferin-Guelph Public Health. Several absenteeism data aggregations were explored, including using the average across all schools or only using schools of one type. A 10% absence threshold, exponentially weighted moving average model, logistic regression with and without seasonality terms, day of week indicators, and random intercepts for school year, and generalized estimating equations were used as epidemic detection methods for seasonal influenza. In the regression models, absenteeism data with various lags were used as predictor variables, and missing values in the datasets used for parameter estimation were handled either by deletion or linear interpolation. The epidemic detection methods were compared using a false alarm rate (FAR) as well as a metric for alarm timeliness. RESULTS: All model-based epidemic detection methods were found to decrease the FAR when compared to the 10% absence threshold. Regression models outperformed the exponentially weighted moving average model and including seasonality terms and a random intercept for school year generally resulted in fewer false alarms. The best-performing model, a seasonal logistic regression model with random intercept for school year and a day of week indicator where parameters were estimated using absenteeism data that had missing values linearly interpolated, produced a FAR of 0.299, compared to the pre-existing threshold method which at best gave a FAR of 0.827. CONCLUSIONS: School absenteeism can be a useful tool for alerting public health to upcoming influenza epidemics in Wellington-Dufferin-Guelph. Logistic regression with seasonality terms and a random intercept for school year was effective at maximizing true alarms while minimizing false alarms on historical data from this region.


Assuntos
Absenteísmo , Epidemias , Influenza Humana/epidemiologia , Vigilância da População/métodos , Instituições Acadêmicas , Adolescente , Criança , Humanos , Ontário/epidemiologia , Estações do Ano
5.
JAMA Neurol ; 2019 Aug 05.
Artigo em Inglês | MEDLINE | ID: mdl-31380987

RESUMO

Importance: Patients with epilepsy are at an elevated risk of premature mortality. Interventions to reduce this risk are crucial. Objective: To determine if the level of care (non-neurologist, neurologist, or comprehensive epilepsy program) is negatively associated with the risk of premature mortality. Design, Setting, and Participants: In this retrospective open cohort study, all adult patients 18 years or older who met the administrative case definition for incident epilepsy in linked databases (Alberta Health Services administrative health data and the Comprehensive Calgary Epilepsy Programme Registry [CEP]) inclusive of the years 2002 to 2016 were followed up until death or loss to follow-up. The final analyses were performed on May 1, 2019. Exposures: Evaluation by a non-neurologist, neurologist, or epileptologist. Main Outcomes and Measures: The outcome was all-cause mortality. We used extended Cox models treating exposure to a neurologist or the CEP as time-varying covariates. Age, sex, socioeconomic deprivation, disease severity, and comorbid burden at index date were modeled as fixed-time coefficients. Results: A total 23 653 incident cases were identified (annual incidence of 89 per 100 000); the mean age (SD) at index date was 50.8 (19.1) years and 12 158 (50.3%) were women. A total of 14 099 (60%) were not exposed to specialist neurological care, 9554 (40%) received care by a neurologist, and 2054 (9%) received care in the CEP. In total, 4098 deaths (71%) occurred in the nonspecialist setting, 1481 (26%) for those seen by a neurologist, and 176 (3%) for those receiving CEP care. The standardized mortality rate was 7.2% for the entire cohort, 9.4% for those receiving nonspecialist care, 5.6% for those seen by a neurologist, and 2.8% for those seen in the CEP. The hazard ratio (HR) of mortality was lower in those receiving neurologist (HR, 0.85; 95% CI, 0.77-0.93) and CEP (HR, 0.49; 95% CI, 0.38-0.62) care. In multivariable modeling, specialist care, the age at index, and disease severity were retained in the final model of the association between specialist care and mortality. Conclusions and Relevance: Exposure to specialist care is associated with incremental reductions in the hazard of premature mortality. Those referred to a comprehensive epilepsy program received the greatest benefit.

6.
Open Forum Infect Dis ; 6(6): ofz234, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-31263731

RESUMO

Background: It is unclear if malaria causes deranged liver enzymes. This has implications both in clinical practice and in research, particularly for antimalarial drug development. Method: We performed a retrospective cohort study of returning travelers (n = 4548) who underwent a malaria test and had enzymes measured within 31 days in Calgary, Canada, from 2010 to 2017. Odds ratios of having an abnormal alkaline phosphatase (ALP), alanine aminotransferases (ALT), aspartate aminotransferases (AST), and total bilirubin (TB) were calculated using multivariable longitudinal analysis with binomial response. Results: After adjusting for gender, age, and use of hepatotoxic medications, returning travelers testing positive for malaria had higher odds of having an abnormal TB (odds ratio [OR], 12.64; 95% confidence interval [CI], 6.32-25.29; P < .001) but not ALP (OR, 0.32; 95% CI, 0.09-1.10; P = .072), ALT (OR, 1.01; 95% CI, 0.54-1.89; P = .978) or AST (OR, 1.26; 95% CI, 0.22-7.37; P = .794), compared with those who tested negative. TB was most likely to be abnormal in the "early" period (day 0-day 3) but then normalized in subsequent intervals. Returning travelers with severe malaria (OR, 2.56; 95% CI, 0.99-6.62; P = .052) had borderline increased odds of having an abnormal TB, but malaria species (OR, 0.70; 95% CI, 0.24-2.05; P = .511) did not. Conclusions: In malaria-exposed returning travelers, the TB is abnormal, especially in the early period, but no abnormalities are seen for ALT, AST, or ALP.

7.
Transbound Emerg Dis ; 66(5): 1910-1919, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31059200

RESUMO

We use swine shipping data from Manitoba to construct one-mode dynamic contact networks of swine locations and two-mode location-to-truck networks at four time scales: daily, weekly, monthly and for the entire two-year study period. We provide measures of graph evolution and graph characterization for each, useful in the development of statistical models related to infectious disease transmission. We find that Manitoba shipping practices differ from those in other Canadian regions, and particularly that truck sharing is more common in Manitoba than elsewhere in the country.


Assuntos
Criação de Animais Domésticos , Doenças dos Suínos/transmissão , Transportes , Animais , Feminino , Masculino , Manitoba , Modelos Teóricos , Fatores de Risco , Sus scrofa , Suínos , Transportes/métodos
8.
Spat Spatiotemporal Epidemiol ; 29: 187-198, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-31128628

RESUMO

In an emerging epidemic, public health officials must move quickly to contain the spread. Information obtained from statistical disease transmission models often informs the development of containment strategies. Inference procedures such as Bayesian Markov chain Monte Carlo allow researchers to estimate parameters of such models, but are computationally expensive. In this work, we explore supervised statistical and machine learning methods for fast inference via supervised classification, with a focus on deep learning. We apply our methods to simulated epidemics through two populations of swine farms in Iowa, and find that the random forest performs well on the denser population, but is outperformed by a deep learning model on the sparser population.

9.
Gastroenterology ; 156(5): 1345-1353.e4, 2019 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-30639677

RESUMO

BACKGROUND & AIMS: Inflammatory bowel diseases (IBDs) exist worldwide, with high prevalence in North America. IBD is complex and costly, and its increasing prevalence places a greater stress on health care systems. We aimed to determine the past current, and future prevalences of IBD in Canada. METHODS: We performed a retrospective cohort study using population-based health administrative data from Alberta (2002-2015), British Columbia (1997-2014), Manitoba (1990-2013), Nova Scotia (1996-2009), Ontario (1999-2014), Quebec (2001-2008), and Saskatchewan (1998-2016). Autoregressive integrated moving average regression was applied, and prevalence, with 95% prediction intervals (PIs), was forecasted to 2030. Average annual percentage change, with 95% confidence intervals, was assessed with log binomial regression. RESULTS: In 2018, the prevalence of IBD in Canada was estimated at 725 per 100,000 (95% PI 716-735) and annual average percent change was estimated at 2.86% (95% confidence interval 2.80%-2.92%). The prevalence in 2030 was forecasted to be 981 per 100,000 (95% PI 963-999): 159 per 100,000 (95% PI 133-185) in children, 1118 per 100,000 (95% PI 1069-1168) in adults, and 1370 per 100,000 (95% PI 1312-1429) in the elderly. In 2018, 267,983 Canadians (95% PI 264,579-271,387) were estimated to be living with IBD, which was forecasted to increase to 402,853 (95% PI 395,466-410,240) by 2030. CONCLUSION: Forecasting prevalence will allow health policy makers to develop policy that is necessary to address the challenges faced by health systems in providing high-quality and cost-effective care.


Assuntos
Doenças Inflamatórias Intestinais/epidemiologia , Modelos Estatísticos , Demandas Administrativas em Assistência à Saúde , Adolescente , Adulto , Distribuição por Idade , Canadá/epidemiologia , Criança , Pré-Escolar , Bases de Dados Factuais , Feminino , Previsões , História do Século XXI , Humanos , Lactente , Recém-Nascido , Doenças Inflamatórias Intestinais/diagnóstico , Doenças Inflamatórias Intestinais/história , Masculino , Pessoa de Meia-Idade , Prevalência , Estudos Retrospectivos , Distribuição por Sexo , Fatores de Tempo , Adulto Jovem
10.
Can J Surg ; 61(4): 244-250, 2018 08.
Artigo em Inglês | MEDLINE | ID: mdl-30067182

RESUMO

BACKGROUND: Despite supporting evidence, many staff surgeons and surgical trainees do not routinely double glove. We performed a study to assess rates of and attitudes toward double gloving and the use of eye protection in the operating room. METHODS: We conducted an electronic survey among all staff surgeons and surgical trainees at 2 tertiary care centres in Alberta between September and November 2015.We analyzed the data using log-binomial regression for binary outcomes to account for multiple independent variables and interactions. For 2-group comparisons, we used a 2-group test of proportions. RESULTS: The response rate was 34.3% (361/1051); 205/698 staff surgeons (29.4%) and 156/353 surgical trainees (44.2%) responded. Trainees were more likely than staff surgeons to ever double glove in the operating room (p = 0.01) and to do so routinely (p = 0.01). Staff surgeons were more likely than trainees to never double glove (p = 0.01). A total of 300/353 respondents (85.0%) reported using eye protection routinely in the operating room. Needle-stick injury was common (184 staff surgeons [92.5%], 115 trainees [74.7%]). Reduced tactile feedback, decreased manual dexterity and discomfort/poor fit were perceived barriers to double gloving. CONCLUSION: Rates of double gloving leave room for improvement. Surgical trainees were more likely than staff surgeons to double glove. Barriers remain to routine double gloving among staff surgeons and trainees. Increased education on the benefits of double gloving and early introduction of this practice may increase uptake.


Assuntos
Atitude do Pessoal de Saúde , Luvas Cirúrgicas , Transmissão de Doença Infecciosa do Paciente para o Profissional/prevenção & controle , Padrões de Prática Médica , Adulto , Idoso , Canadá , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Ferimentos Penetrantes Produzidos por Agulha , Adulto Jovem
11.
PLoS One ; 13(6): e0198313, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29856881

RESUMO

Influenza A virus commonly circulating in swine (IAV-S) is characterized by large genetic and antigenic diversity and, thus, improvements in different aspects of IAV-S surveillance are needed to achieve desirable goals of surveillance such as to establish the capacity to forecast with the greatest accuracy the number of influenza cases likely to arise. Advancements in modeling approaches provide the opportunity to use different models for surveillance. However, in order to make improvements in surveillance, it is necessary to assess the predictive ability of such models. This study compares the sensitivity and predictive accuracy of the autoregressive integrated moving average (ARIMA) model, the generalized linear autoregressive moving average (GLARMA) model, and the random forest (RF) model with respect to the frequency of influenza A virus (IAV) in Ontario swine. Diagnostic data on IAV submissions in Ontario swine between 2007 and 2015 were obtained from the Animal Health Laboratory (University of Guelph, Guelph, ON, Canada). Each modeling approach was examined for predictive accuracy, evaluated by the root mean square error, the normalized root mean square error, and the model's ability to anticipate increases and decreases in disease frequency. Likewise, we verified the magnitude of improvement offered by the ARIMA, GLARMA and RF models over a seasonal-naïve method. Using the diagnostic submissions, the occurrence of seasonality and the long-term trend in IAV infections were also investigated. The RF model had the smallest root mean square error in the prospective analysis and tended to predict increases in the number of diagnostic submissions and positive virological submissions at weekly and monthly intervals with a higher degree of sensitivity than the ARIMA and GLARMA models. The number of weekly positive virological submissions is significantly higher in the fall calendar season compared to the summer calendar season. Positive counts at weekly and monthly intervals demonstrated a significant increasing trend. Overall, this study shows that the RF model offers enhanced prediction ability over the ARIMA and GLARMA time series models for predicting the frequency of IAV infections in diagnostic submissions.


Assuntos
Previsões/métodos , Vírus da Influenza A , Modelos Estatísticos , Infecções por Orthomyxoviridae/epidemiologia , Doenças dos Suínos/epidemiologia , Animais , Incidência , Vírus da Influenza A/fisiologia , Ontário/epidemiologia , Infecções por Orthomyxoviridae/veterinária , Análise de Regressão , Suínos
12.
Can J Surg ; 61(4): 13616, 2018 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-29806804

RESUMO

BACKGROUND: Despite supporting evidence, many staff surgeons and surgical trainees do not routinely double glove. We performed a study to assess rates of and attitudes toward double gloving and the use of eye protection in the operating room. METHODS: We conducted an electronic survey among all staff surgeons and surgical trainees at 2 tertiary care centres in Alberta between September and November 2015.We analyzed the data using log-binomial regression for binary outcomes to account for multiple independent variables and interactions. For 2-group comparisons, we used a 2-group test of proportions. RESULTS: The response rate was 34.3% (361/1051); 205/698 staff surgeons (29.4%) and 156/353 surgical trainees (44.2%) responded. Trainees were more likely than staff surgeons to ever double glove in the operating room (p = 0.01) and to do so routinely (p = 0.01). Staff surgeons were more likely than trainees to never double glove (p = 0.01). A total of 300/353 respondents (85.0%) reported using eye protection routinely in the operating room. Needle-stick injury was common (184 staff surgeons [92.5%], 115 trainees [74.7%]). Reduced tactile feedback, decreased manual dexterity and discomfort/poor fit were perceived barriers to double gloving. CONCLUSION: Rates of double gloving leave room for improvement. Surgical trainees were more likely than staff surgeons to double glove. Barriers remain to routine double gloving among staff surgeons and trainees. Increased education on the benefits of double gloving and early introduction of this practice may increase uptake.

13.
J Biol Eng ; 11: 35, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-29213303

RESUMO

"To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of." - R.A. Fisher While this idea is relevant across research scales, its importance becomes critical when dealing with the inherently large, complex and expensive process of preparing material for cell-based therapies (CBTs). Effective and economically viable CBTs will depend on the establishment of optimized protocols for the production of the necessary cell types. Our ability to do this will depend in turn on the capacity to efficiently search through a multi-dimensional problem space of possible protocols in a timely and cost-effective manner. In this review we discuss approaches to, and illustrate examples of the application of statistical design of experiments to stem cell bioprocess optimization.

14.
Spat Spatiotemporal Epidemiol ; 17: 95-104, 2016 05.
Artigo em Inglês | MEDLINE | ID: mdl-27246276

RESUMO

A class of complex statistical models, known as individual-level models, have been effectively used to model the spread of infectious diseases. These models are often fitted within a Bayesian Markov chain Monte Carlo framework, which can have a sig nificant computational expense due to the complex nature of the likelihood function associated with this class of models. Increases in population size or duration of the modeled epidemic can contribute to this computational burden. Here, we explore the effect of reducing this computational expense by aggregating the data into spatial clusters, and therefore reducing the overall population size. Individual-level models, reparameterized to account for this aggregation effect, may then be fitted to the spatially aggregated data. The ability of two reparameterized individual-level models, when fitted to this reduced data set, to identify a covariate effect is investigated through a simulation study.


Assuntos
Doenças Transmissíveis/epidemiologia , Coleta de Dados/estatística & dados numéricos , Modelos Estatísticos , Análise Espaço-Temporal , Doenças Transmissíveis/transmissão , Simulação por Computador/estatística & dados numéricos , Humanos , Modelos Teóricos
15.
PLoS One ; 11(1): e0146253, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-26731666

RESUMO

A class of discrete-time models of infectious disease spread, referred to as individual-level models (ILMs), are typically fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework. These models quantify probabilistic outcomes regarding the risk of infection of susceptible individuals due to various susceptibility and transmissibility factors, including their spatial distance from infectious individuals. The infectious pressure from infected individuals exerted on susceptible individuals is intrinsic to these ILMs. Unfortunately, quantifying this infectious pressure for data sets containing many individuals can be computationally burdensome, leading to a time-consuming likelihood calculation and, thus, computationally prohibitive MCMC-based analysis. This problem worsens when using data augmentation to allow for uncertainty in infection times. In this paper, we develop sampling methods that can be used to calculate a fast, approximate likelihood when fitting such disease models. A simple random sampling approach is initially considered followed by various spatially-stratified schemes. We test and compare the performance of our methods with both simulated data and data from the 2001 foot-and-mouth disease (FMD) epidemic in the U.K. Our results indicate that substantial computation savings can be obtained--albeit, of course, with some information loss--suggesting that such techniques may be of use in the analysis of very large epidemic data sets.


Assuntos
Doenças Transmissíveis/transmissão , Modelos Teóricos , Algoritmos , Simulação por Computador , Humanos , Método de Monte Carlo , Probabilidade , Fatores de Tempo , Incerteza
16.
J Math Biol ; 72(5): 1195-224, 2016 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-26084408

RESUMO

Well formulated models of disease spread, and efficient methods to fit them to observed data, are powerful tools for aiding the surveillance and control of infectious diseases. Our project considers the problem of the simultaneous spread of two related strains of disease in a context where spatial location is the key driver of disease spread. We start our modeling work with the individual level models (ILMs) of disease transmission, and extend these models to accommodate the competing spread of the pathogens in a two-tier hierarchical population (whose levels we refer to as 'farm' and 'animal'). The postulated interference mechanism between the two strains is a period of cross-immunity following infection. We also present a framework for speeding up the computationally intensive process of fitting the ILM to data, typically done using Markov chain Monte Carlo (MCMC) in a Bayesian framework, by turning the inference into a two-stage process. First, we approximate the number of animals infected on a farm over time by infectivity curves. These curves are fit to data sampled from farms, using maximum likelihood estimation, then, conditional on the fitted curves, Bayesian MCMC inference proceeds for the remaining parameters. Finally, we use posterior predictive distributions of salient epidemic summary statistics, in order to assess the model fitted.


Assuntos
Doenças dos Animais/epidemiologia , Epidemias/veterinária , Modelos Biológicos , Doenças dos Animais/transmissão , Animais , Animais Domésticos , Teorema de Bayes , Simulação por Computador , Epidemias/estatística & dados numéricos , Fazendas/estatística & dados numéricos , Funções Verossimilhança , Cadeias de Markov , Conceitos Matemáticos , Método de Monte Carlo , Dinâmica não Linear
17.
Spat Spatiotemporal Epidemiol ; 11: 59-77, 2014 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-25457597

RESUMO

Parameter estimation for mechanistic models of infectious disease can be computationally intensive. Nsoesie et al. (2011) introduced an approach for inference on infectious disease data based on the idea of supervised learning. Their method involves simulating epidemics from various infectious disease models, and using classifiers built from the epidemic curve data to predict which model were most likely to have generated observed epidemic curves. They showed that the classification approach could fairly identify underlying characteristics of the disease system, without fitting various transmission models via, say, Bayesian Markov chain Monte Carlo. We extend this work to the case where the underlying infectious disease model is inherently spatial. Our goal is to compare the use of global epidemic curves for building the classifier, with the use of spatially stratified epidemic curves. We demonstrate these methods on simulated data and apply the method to analyze a tomato spotted wilt virus epidemic dataset.


Assuntos
Teorema de Bayes , Doenças Transmissíveis/epidemiologia , Epidemias/estatística & dados numéricos , Modelos Teóricos , Análise Espacial , Simulação por Computador , Humanos , Cadeias de Markov , Método de Monte Carlo , Análise de Regressão , Reprodutibilidade dos Testes , Tospovirus
18.
Can J Vet Res ; 77(4): 241-53, 2013 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-24124266

RESUMO

Risk-based surveillance is becoming increasingly important in the veterinary and public health fields. It serves as a means of increasing surveillance sensitivity and improving cost-effectiveness in an increasingly resource-limited environment. Our approach for developing a tool for the risk-based geographical surveillance of contagious diseases of swine incorporates information about animal density and external biosecurity practices within swine herds in southern Ontario. The objectives of this study were to group the sample of herds into discrete biosecurity groups, to develop a map of southern Ontario that can be used as a tool in the risk-based geographical surveillance of contagious swine diseases, and to identify significant predictors of biosecurity group membership. A subset of external biosecurity variables was selected for 2-step cluster analysis and latent class analysis (LCA). It was determined that 4 was the best number of groups to describe the data, using both analytical approaches. The authors named these groups: i) high biosecurity herds that were open with respect to replacement animals; ii) high biosecurity herds that were closed with respect to replacement animals; iii) moderate biosecurity herds; and iv) low biosecurity herds. The risk map was developed using information about the geographic distribution of herds in the biosecurity groups, as well as the density of swine sites and of grower-finisher pigs in the study region. Finally, multinomial logistic regression identified heat production units (HPUs), number of incoming pig shipments per month, and herd type as significant predictors of biosecurity group membership. It was concluded that the ability to identify areas of high and low risk for disease may improve the success of surveillance and eradication projects.


Assuntos
Monitoramento Epidemiológico/veterinária , Síndrome Respiratória e Reprodutiva Suína/prevenção & controle , Vírus da Síndrome Respiratória e Reprodutiva Suína/crescimento & desenvolvimento , Medição de Risco/métodos , Gestão da Segurança/métodos , Animais , Análise por Conglomerados , Modelos Logísticos , Ontário/epidemiologia , Síndrome Respiratória e Reprodutiva Suína/epidemiologia , Inquéritos e Questionários , Suínos
19.
Spat Spatiotemporal Epidemiol ; 6: 59-70, 2013 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-23973181

RESUMO

Often, when modeling infectious disease spread, the complex network through which the disease propagates is approximated by simplified spatial information. Here, we simulate epidemic spread through various contact networks and fit spatial-based models in a Bayesian framework using Markov chain Monte Carlo methods. These spatial models are individual-level models which account for the spatio-temporal dynamics of infectious disease. The focus here is on choosing a spatial model which best predicts the true probabilities of infection, as well as determining under which conditions such spatial models fail. Spatial models tend to predict infection probability reasonably well when disease spread is propagated through contact networks in which contacts are only within a certain distance of each other. If contacts exist over long distances, the spatial models tend to perform worse when compared to the network model.


Assuntos
Doenças Transmissíveis/epidemiologia , Busca de Comunicante/estatística & dados numéricos , Modelos Teóricos , Análise Espacial , Doenças Transmissíveis/transmissão , Simulação por Computador , Humanos , Cadeias de Markov , Método de Monte Carlo
20.
Int J Biostat ; 9(1)2013 Aug 03.
Artigo em Inglês | MEDLINE | ID: mdl-23917477

RESUMO

Individual-level models (ILMs) have previously been used to model the spatiotemporal spread of infectious diseases. These models can incorporate individual-level covariate information, to account for population heterogeneity. However, incomplete or unreliable data are a common problem in infectious disease modeling, and models that are explicitly dependent on such information may not be robust to these inherent uncertainties. In this investigation, we assess an adaptation to a spatial ILM that incorporates a latent grouping structure based on some trait heterogeneous in the population. The resulting latent conditional ILM is then only dependent upon a discrete latent grouping variable, rather than precise covariate information. The posterior predictive ability of this proposed model is tested through a simulation study, in which the model is fitted to epidemic data simulated from a true model that utilizes explicit covariate information. In addition, the posterior predictive ability of the proposed ILM is also compared to that of an ILM that assumes population homogeneity. The application of these models to data from the 2001 UK foot-and-mouth disease epidemic is also explored. This study demonstrates that the use of a discrete latent grouping variable can be an effective alternative to utilizing covariate information, particularly when such information may be unreliable.


Assuntos
Doenças Transmissíveis/epidemiologia , Epidemias , Modelos Biológicos , Modelos Estatísticos , Animais , Simulação por Computador , Febre Aftosa/epidemiologia , Humanos , Cadeias de Markov , Método de Monte Carlo
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