An Evaluation of Open Source Tools to Estimate the Reproduction Number of the 2009 Influenza A/H1N1 Pandemic in the USA Open Access

Kumbale, Carla (Spring 2018)

Permanent URL: https://etd.library.emory.edu/concern/etds/fb494843x?locale=en
Published

Abstract

Introduction: The reproduction number (R), which is a key epidemiological parameter to be estimated during emerging outbreaks such as the 2009 influenza pandemic, is computed using the R0 R-package. In addition, another R-package, EpiEstim, is also utilized to compute the instantaneous reproduction numbers (R(t)). Estimates are compared with the literature in order to further validate the use of these R-packages so that institutions such as the Centers for Disease Control and Prevention can implement these tools in order to rapidly compute these parameters during an emergency.

 

Methods: In the R0 package we use the Maximum Likelihood (ML), Exponential Growth (EG), Time-Dependent (TD), and Sequential Bayesian (SB) methods to compute four different estimates of R. In the EpiEstim package, we use the Bayesian Statistical technique implemented in this R-package to estimate R(t).  A serial interval of 3.6 days with a standard deviation of 1.6 days is assumed for all of the estimates. These reproduction numbers are then compared to the literature.

 

Results: Several estimates are computed through the use of the R0 package. For the ML method, we estimate R values that vary between 1.41 to 3.54 depending on the selected time period of incidence cases. For the TD, EG, and SB methods, an R value of 1.88, 1.91, and 1.24 are computed respectively. Finally, the R(t) values, computed through the use of the EpiEstim package vary between 1.04-3.37 (weekly time window) and 1.24 – 3.33 (10-day time window).  These values are subsequently compared with the results found in the literature.

 

Conclusion: This study has both demonstrated the use of and further validated the two computer packages that are now available for general use by non-modelers.  Although the use of these packages still requires a certain minimum knowledge of statistical methods, the availability of these packages vastly improves the tools now at the disposal of public health practitioners during an epidemic/pandemic.

 

Table of Contents

INTRODUCTION ......................................................................................................... 1

 

Purpose ................................................................................................................... 5

 

Specific Aims ......................................................................................................... 6

 

METHODS .................................................................................................................... 7

 

Data ....................................................................................................................... 7

 

R0 Package ........................................................................................................... 9

 

EpiEstim Package ................................................................................................. 9

 

Description of Mathematical Methods ............................................................... 10

 

   R0 - Maximum Likelihood Estimator(ML) ........................................... 10

 

R0 - Exponential Growth Method (EG) ................................................ 13

 

       R0 - Sequential Bayesian Method (SB) ................................................ 16

 

       R0 – Time Dependent Method (TD) ..................................................... 17

 

       EpiEstim - Bayesian Statistical Inference.............................................. 18

 

Parameter Inputs: R0 Package .......................................................................... 22

 

Parameter Inputs: EpiEstim Package ................................................................ 23

 

RESULTS ................................................................................................................... 24

 

Aim 1 Results ....................................................................................................... 25

 

Aim 2 Results ....................................................................................................... 25

 

Aim 3 Results ....................................................................................................... 26

 

DISCUSSION ........................................................................................................... 28

 

Limitations and Observation ................................................................................ 33

 

Conclusion ............................................................................................................ 34

 

WORKS CITED ....................................................................................................... 35

 

FIGURES ............................................................................................................... 37 

 

TABLES .................................................................................................................. 40

 

APPENDICES ......................................................................................................... 47

 

APPENDIX A ..................................................................................................... 47

 

APPENDIX B ..................................................................................................... 48

 

About this Master's Thesis

Rights statement
  • Permission granted by the author to include this thesis or dissertation in this repository. All rights reserved by the author. Please contact the author for information regarding the reproduction and use of this thesis or dissertation.
School
Department
Subfield / Discipline
Degree
Submission
Language
  • English
Research Field
Keyword
Committee Chair / Thesis Advisor
Partnering Agencies
Last modified

Primary PDF

Supplemental Files