Particle filtering for infectious disease inference using wastewater data Restricted; Files Only

Abstract

Wastewater is increasingly being recognized as an important data stream that can contribute to infectious disease surveillance. With this recognition, an increasing number of statistical methods have been piloted to provide quantitative insights from these data. However, few have allowed for systematic integration of data streams for inference purposes, for example by combining case incidence data with wastewater data. Here, we develop the use of Markovian state-space models for inference of the time-varying effective reproduction number Rt using wastewater virus concentration data. To do so, we first describe the structure of our underlying disease transmission model and how Rt can be estimated by interfacing this model with either case incidence data or wastewater virus concentration data using a particle filter. We then show how inference can be expanded and improved upon when combining both of these data streams. Throughout, we use a simulated (mock) dataset to develop and test our statistical approach. We hope that the statistical approach we present will lead to greater reliance on wastewater data for disease surveillance that will aid public health practitioners in responding to infectious disease threats.

Table of Contents

Introduction ................................................................................................................................... 1

Methods ......................................................................................................................................... 2

The state-space model............................................................................................................ 2

Generation of the mock datasets.............................................................................................. 3

Statistical inference using case incidence and/or wastewater virus concentration data................. 5

Results ........................................................................................................................................... 6

Rt Estimation from Mock Case Data......................................................................................... 6

Rt Estimation from Mock Wastewater Data.............................................................................. 11

Rt Estimation from Both Case Data and Wastewater Data......................................................... 15

Discussion ................................................................................................................................... 18

References ................................................................................................................................... 19

About this Honors Thesis

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School	Emory College
Department	Biology
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Biology, General
Keyword	particle filtering effective reproduction number wastewater epidemiology
Committee Chair / Thesis Advisor	Katia Koelle, Emory University
Committee Members	Yuke Wang, Emory University Daniel Weissman, Emory University

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