Dataset Information

A bayesian approach for modeling cattle movements in the United States: scaling up a partially observed network.

ABSTRACT: Networks are rarely completely observed and prediction of unobserved edges is an important problem, especially in disease spread modeling where networks are used to represent the pattern of contacts. We focus on a partially observed cattle movement network in the U.S. and present a method for scaling up to a full network based on bayesian inference, with the aim of informing epidemic disease spread models in the United States. The observed network is a 10% state stratified sample of Interstate Certificates of Veterinary Inspection that are required for interstate movement; describing approximately 20,000 movements from 47 of the contiguous states, with origins and destinations aggregated at the county level. We address how to scale up the 10% sample and predict unobserved intrastate movements based on observed movement distances. Edge prediction based on a distance kernel is not straightforward because the probability of movement does not always decline monotonically with distance due to underlying industry infrastructure. Hence, we propose a spatially explicit model where the probability of movement depends on distance, number of premises per county and historical imports of animals. Our model performs well in recapturing overall metrics of the observed network at the node level (U.S. counties), including degree centrality and betweenness; and performs better compared to randomized networks. Kernel generated movement networks also recapture observed global network metrics, including network size, transitivity, reciprocity, and assortativity better than randomized networks. In addition, predicted movements are similar to observed when aggregated at the state level (a broader geographic level relevant for policy) and are concentrated around states where key infrastructures, such as feedlots, are common. We conclude that the method generally performs well in predicting both coarse geographical patterns and network structure and is a promising method to generate full networks that incorporate the uncertainty of sampled and unobserved contacts.

SUBMITTER: Lindstrom T

PROVIDER: S-EPMC3537632 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A bayesian approach for modeling cattle movements in the United States: scaling up a partially observed network.

Lindström Tom T Grear Daniel A DA Buhnerkempe Michael M Webb Colleen T CT Miller Ryan S RS Portacci Katie K Wennergren Uno U

PloS one 20130104 1

Networks are rarely completely observed and prediction of unobserved edges is an important problem, especially in disease spread modeling where networks are used to represent the pattern of contacts. We focus on a partially observed cattle movement network in the U.S. and present a method for scaling up to a full network based on bayesian inference, with the aim of informing epidemic disease spread models in the United States. The observed network is a 10% state stratified sample of Interstate C ...[more]

PMID: 23308223

Similar Datasets

Project description:BACKGROUND AND AIMS:Effective strategies are needed to address dramatic increases in hepatitis C virus (HCV) infection among people who inject drugs (PWID) in rural settings of the United States. We determined the required scale-up of HCV treatment with or without scale-up of HCV prevention interventions to achieve a 90% reduction in HCV chronic prevalence or incidence by 2025 and 2030 in a rural US setting. DESIGN:An ordinary differential equation model of HCV transmission calibrated to HCV epidemiological data obtained primarily from an HIV outbreak investigation in Indiana. SETTING:Scott County, Indiana (population 24?181), USA, a rural setting with negligible baseline interventions, increasing HCV epidemic since 2010, and 55.3% chronic HCV prevalence among PWID in 2015. PARTICIPANTS:PWID. MEASUREMENTS:Required annual HCV treatments per 1000 PWID (and initial annual percentage of infections treated) to achieve a 90% reduction in HCV chronic prevalence or incidence by 2025/30, either with or without scaling-up syringe service programmes (SSPs) and medication-assisted treatment (MAT) to 50% coverage. Sensitivity analyses considered whether this impact could be achieved without re-treatment of re-infections, and whether greater intervention scale-up was required due to the increasing epidemic in this setting. FINDINGS:To achieve a 90% reduction in incidence and prevalence by 2030, without MAT and SSP scale-up, 159 per 1000 PWID (initially 24.9% of infected PWID) need to be HCV-treated annually. However, with MAT and SSP scaled-up, treatment rates are halved (89 per 1000 annually or 14.5%). To reach the same target by 2025 with MAT and SSP scaled-up, 121 per 1000 PWID (19.9%) need treatment annually. These treatment requirements are threefold higher than if the epidemic was stable, and the impact targets are unattainable without retreatment. CONCLUSIONS:Combined scale-up of hepatitis C virus treatment and prevention interventions is needed to decrease the increasing burden of hepatitis C virus incidence and prevalence in rural Indiana, USA, by 90% by 2025/30.

Project description:AimsTo examine the cost-effectiveness of hepatitis C virus (HCV) treatment of people who inject drugs (PWID), combined with medication-assisted treatment (MAT) and syringe-service programs (SSP), to tackle the increasing HCV epidemic in the United States.DesignHCV transmission and disease progression models with cost-effectiveness analysis using a health-care perspective.SettingRural Perry County, KY (PC) and urban San Francisco, CA (SF), USA. Compared with PC, SF has a greater proportion of PWID with access to MAT or SSP. HCV treatment of PWID is negligible in both settings.ParticipantsPWID data were collected between 1998 and 2015 from Social Networks Among Appalachian People, U Find Out, Urban Health Study and National HIV Behavioral Surveillance System studies.Interventions and comparatorThree intervention scenarios modeled: baseline-existing SSP and MAT coverage with HCV screening and treatment with direct-acting antiviral for ex-injectors only as per standard of care; intervention 1-scale-up of SSP and MAT without changes to treatment; and intervention 2-scale-up as intervention 1 combined with HCV screening and treatment for current PWID.MeasurementsIncremental cost-effectiveness ratios (ICERs) and uncertainty using cost-effectiveness acceptability curves. Benefits were measured in quality-adjusted life-years (QALYs).FindingsFor both settings, intervention 2 is preferred to intervention 1 and the appropriate comparator for intervention 2 is the baseline scenario. Relative to baseline, for PC intervention 2 averts 1852 more HCV infections, increases QALYS by 3095, costs $21.6 million more and has an ICER of $6975/QALY. For SF, intervention 2 averts 36 473 more HCV infections, increases QALYs by 7893, costs $872 million more and has an ICER of $11 044/QALY. The cost-effectiveness of intervention 2 was robust to several sensitivity analysis.ConclusionsHepatitis C screening and treatment for people who inject drugs, combined with medication-assisted treatment and syringe-service programs, is a cost-effective strategy for reducing hepatitis C burden in the United States.

Project description:BackgroundIn the United States, COVID-19 is a nationally notifiable disease, meaning cases and hospitalizations are reported by states to the Centers for Disease Control and Prevention (CDC). Identifying and reporting every case from every facility in the United States may not be feasible in the long term. Creating sustainable methods for estimating the burden of COVID-19 from established sentinel surveillance systems is becoming more important.ObjectiveWe aimed to provide a method leveraging surveillance data to create a long-term solution to estimate monthly rates of hospitalizations for COVID-19.MethodsWe estimated monthly hospitalization rates for COVID-19 from May 2020 through April 2021 for the 50 states using surveillance data from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) and a Bayesian hierarchical model for extrapolation. Hospitalization rates were calculated from patients hospitalized with a lab-confirmed SARS-CoV-2 test during or within 14 days before admission. We created a model for 6 age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) separately. We identified covariates from multiple data sources that varied by age, state, and month and performed covariate selection for each age group based on 2 methods, Least Absolute Shrinkage and Selection Operator (LASSO) and spike and slab selection methods. We validated our method by checking the sensitivity of model estimates to covariate selection and model extrapolation as well as comparing our results to external data.ResultsWe estimated 3,583,100 (90% credible interval [CrI] 3,250,500-3,945,400) hospitalizations for a cumulative incidence of 1093.9 (992.4-1204.6) hospitalizations per 100,000 population with COVID-19 in the United States from May 2020 through April 2021. Cumulative incidence varied from 359 to 1856 per 100,000 between states. The age group with the highest cumulative incidence was those aged ≥85 years (5575.6; 90% CrI 5066.4-6133.7). The monthly hospitalization rate was highest in December (183.7; 90% CrI 154.3-217.4). Our monthly estimates by state showed variations in magnitudes of peak rates, number of peaks, and timing of peaks between states.ConclusionsOur novel approach to estimate hospitalizations for COVID-19 has potential to provide sustainable estimates for monitoring COVID-19 burden as well as a flexible framework leveraging surveillance data.

Dataset Information

A bayesian approach for modeling cattle movements in the United States: scaling up a partially observed network.

Publications

A bayesian approach for modeling cattle movements in the United States: scaling up a partially observed network.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets