Igor Burstyn

Drexel University

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3 Article

A Bayesian approach to improving spatial estimates of prevalence of COVID-19 after accounting for misclassification bias in surveillance data in Philadelphia, PA
Goldstein, N. D., Wheeler, D. C., Gustafson, P. & Burstyn, I., 1 Feb 2021, In: Spatial and Spatio-temporal Epidemiology. 36
Research output: Contribution to journal › Article › peer-review

Open Access
It can be dangerous to take epidemic curves of COVID-19 at face value
Burstyn, I., Goldstein, N. D. & Gustafson, P., 1 Jun 2020, In: Canadian Journal of Public Health. 111, 3
Research output: Contribution to journal › Article › peer-review

Open Access
1 Scopus citations
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
Burstyn, I., Burstyn, I., Goldstein, N. D. & Gustafson, P., 6 Jun 2020, In: BMC Medical Research Methodology. 20, 1
Research output: Contribution to journal › Article › peer-review

Open Access
1 Scopus citations

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
Burstyn, I. (Contributor), Goldstein, N. D. (Contributor) & Gustafson, P. (Contributor), Figshare - Springer, 1 Jan 2020
DOI: 10.6084/m9.figshare.c.5010965.v1, https://springernature.figshare.com/collections/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/5010965/1
Dataset

Unanswered Questions; What we still need to find out about the coronavirus and its effects.
Igor Burstyn & Reynold A. Panettieri
28/04/20
2 items of Media coverage
Press/Media
What we still don’t know: Here are some of the big coronavirus questions scientists are racing to answer
Igor Burstyn & Reynold A. Panettieri
27/04/20
3 items of Media coverage
Press/Media

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

It can be dangerous to take epidemic curves of COVID-19 at face value

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

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

Unanswered Questions; What we still need to find out about the coronavirus and its effects.

What we still don’t know: Here are some of the big coronavirus questions scientists are racing to answer