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Dynamic modelling of hepatitis C transmission among people who inject drugs: A tool to support WHO elimination targets
Stockholm University, Faculty of Science, Department of Mathematics.ORCID iD: 0000-0002-0526-1061
Stockholm University, Faculty of Science, Department of Mathematics.ORCID iD: 0000-0002-9228-7357
(English)Manuscript (preprint) (Other academic)
Abstract [en]

To reach the WHO goal of hepatitis C elimination, it is essential to identify the number of people unaware of their hepatitis C virus (HCV) infection and to investigate the effect of interventions on the disease transmission dynamics. In developed countries, one of the primary routes of HCV transmission is via contaminated needles shared by people who inject drugs (PWIDs). However, substantial underreporting combined with high uncertainty regarding the size of this difficult to reach population, makes it challenging to estimate the core indicators recommended by the WHO. To help enable countries to monitor their progress towards the elimination goal, we present a novel multi-layered dynamic transmission model for HCV transmission within a PWIDs population. The model explicitly accounts for disease stage (acute and chronic), injection drug use status (active and former PWIDs), status of diagnosis (diagnosed and undiagnosed) and country of disease acquisition (domestic or abroad). First, based on this model, and using routine surveillance data, we estimate the number of undiagnosed PWIDs, the true incidence, the average time until diagnosis, the reproduction numbers and associated uncertainties. Second, we examine the impact of two interventions on disease dynamics: 1) direct-acting antiviral drug treatment, and 2) needle exchange programs. To make the model accessible to relevant users and to support communication of our results to public health decision makers, the model and its output are made available through a Shiny app. As a proof of concept, we illustrate our results for a specific data set; however, through the app our model can be easily adapted to other high-income countries with similar transmission patterns among PWIDs where the disease is endemic.

Keywords [en]
dynamic modelling, hepatitis C virus, treatment, needle exchange programs, surveillance data, inference, Shiny app
National Category
Mathematics
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:su:diva-161486OAI: oai:DiVA.org:su-161486DiVA, id: diva2:1259166
Funder
Swedish Research Council, 2015_05182_VRSwedish Research Council, 2015_05015_VRAvailable from: 2018-10-28 Created: 2018-10-28 Last updated: 2018-10-31Bibliographically approved
In thesis
1. Stochastic dynamic modelling and statistical analysis of infectious disease spread and cancer treatment
Open this publication in new window or tab >>Stochastic dynamic modelling and statistical analysis of infectious disease spread and cancer treatment
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Mathematical models have proven valuable for public health decision makers as they can provide insights into the understanding, control and, ultimately, the prevention of diseases. This thesis contains four manuscripts dealing with stochastic dynamic modelling and statistical analysis of infectious disease spread and optimization of cancer treatment.

Paper I is concerned with deriving a patient- and organ-specific measure for the estimated negative side effects of radiotherapy using a stochastic logistic birth-death process. Our analysis shows that the region of a maximum tolerable radiation dose can be related to the solution of a logistic differential equation; we illustrate our results for brachytherapy for prostate cancer.

Paper II and III deal with inference for stochastic epidemic models. Parameter estimation for this model class can be challenging as disease spread is usually only partially observed, e.g. in the form of accumulated reported incidences within specified time periods. To perform inference for these types of models, a useful method for maximum likelihood estimation is iterated filtering which takes advantage of the fact that it is relatively easy to generate samples from the underlying transmission process while the likelihood function for the given data is intractable.

Paper II is an application-oriented introduction to iterated filtering via the R package pomp (King et al., 2016) which contains a wide collection of simulation-based inference methods for partially observed Markov processes. We review the theoretical background of the method and discuss by two examples its performance and some associated practical difficulties.

Paper III is concerned with model selection for partially observed epidemic models that differ with respect to the amount of variability they allow for and parameter estimation of those models from routinely collected surveillance data. We illustrate the model selection and inference framework via the R package pomp for rotavirus transmission in Germany, however, the method can be easily adapted to other diseases.

In Paper IV we develop a transmission model for hepatitis C virus (HCV) infection among people who inject drugs (PWIDs) to enable countries to monitor their progress towards HCV elimination. In the scope of the WHO’s commitment to viral hepatitis elimination, this topic is highly relevant to public health since injection drug use is the main route of transmission in many countries. From the model and using surveillance data, we derive estimates of four key HCV-indicators. Furthermore, the model can be used to investigate the impact of two interventions, direct-acting antiviral drug treatment and needle exchange programs, on the disease dynamics. In order to make the model and its output accessible to relevant users, it is made available through a Shiny app.

Place, publisher, year, edition, pages
Stockholm: Department of Mathematics, Stockholm University, 2018
Keywords
Mathematical modelling, infectious disease spread, cancer treatment, statistical inference, population dynamics, birth-death process, partially observed Markov process
National Category
Mathematics
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:su:diva-161485 (URN)978-91-7797-484-0 (ISBN)978-91-7797-485-7 (ISBN)
Public defence
2018-12-13, sal 14, hus 5, Kräftriket, Roslagsvägen 101, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council, 2015_05182_VR
Available from: 2018-11-20 Created: 2018-10-29 Last updated: 2020-05-11Bibliographically approved

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