A Bayesian approach to improving spatial estimates of prevalence of COVID-19 after accounting for misclassification bias in surveillance data in Philadelphia, PA

相似書籍

• Improving Spatial Estimates for COVID-19 Using Surveillance Data in Philadelphia
  由: Goldstein, N.D., et al.
  出版: (2020)
• Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: case study of COVID-19 in Alberta, Canada and Philadelphia, USA
  由: Igor Burstyn, et al.
  出版: (2020-06-01)
• Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: case study of COVID-19 in Alberta, Canada and Philadelphia, USA
  由: Burstyn, Igor, et al.
  出版: (2020)
• Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: Case study of COVID-19 in Alberta, Canada and Philadelphia, USA
  由: Burstyn, Igor, et al.
  出版: (2020)
• Adjustment for misclassification bias is necessary in survey-based arthritis surveillance to produce credible estimates of prevalence
  由: Jafarzadeh, S. Reza, et al.
  出版: (2018)