The transmission dynamics of Middle East Respiratory Syndrome Coronavirus in dromedary camels: implications for vaccination
File(s)
Author(s)
Dighe, Amy
Type
Thesis
Abstract
Middle East Respiratory Syndrome Coronavirus (MERS-CoV) causes severe disease in humans –
with a reported case fatality ratio of 35% – and is capable of causing explosive nosocomial
outbreaks. Sporadic outbreaks in the Arabian Peninsula are driven by recurring zoonotic
spillover from dromedary camels (Camelus dromedarius), leading to demand for a dromedary vaccine to avert human cases. With two vaccine candidates shown to reduce viral shedding, there is an urgent need to assess the potential impact of dromedary vaccination. This is hindered, however, by poor understanding of the transmission of MERS-CoV in dromedaries
and a lack of mathematical models of transmission dynamics.
In this thesis, I use mathematical models to better characterise the dynamics of MERS-CoV in dromedary populations and to simulate the potential impact of vaccination. In Chapter 2,
published measures of MERS-CoV infection prevalence and seroprevalence in dromedaries are systematically reviewed. In Chapter 3, age-stratified seroprevalence data from dromedary
populations across Africa, the Middle East and South Asia are used to fit catalytic models of
seroconversion, producing estimates of transmissibility and the rate of waning of maternally acquired antibodies. In Chapter 4, a stochastic, age-structured, dynamic transmission model of MERS-CoV in dromedaries is developed. The model is used to estimate key, epidemiological quantities including R0 and the critical community size, that give an insight into how controllable MERS-CoV is in different dromedary populations. Seasonal calving is also explored as a potential driver of peaks of infection. Finally, in Chapter 5, the dynamic model is extended to simulate vaccination under different efficacy scenarios. The potential impact of vaccination on transmission in dromedaries is evaluated, as well as the optimal age for vaccination.
Alongside empirical studies, insights from dynamic models such as those developed in this thesis could contribute to informing an effective response to the zoonotic transmission of MERS-CoV.
with a reported case fatality ratio of 35% – and is capable of causing explosive nosocomial
outbreaks. Sporadic outbreaks in the Arabian Peninsula are driven by recurring zoonotic
spillover from dromedary camels (Camelus dromedarius), leading to demand for a dromedary vaccine to avert human cases. With two vaccine candidates shown to reduce viral shedding, there is an urgent need to assess the potential impact of dromedary vaccination. This is hindered, however, by poor understanding of the transmission of MERS-CoV in dromedaries
and a lack of mathematical models of transmission dynamics.
In this thesis, I use mathematical models to better characterise the dynamics of MERS-CoV in dromedary populations and to simulate the potential impact of vaccination. In Chapter 2,
published measures of MERS-CoV infection prevalence and seroprevalence in dromedaries are systematically reviewed. In Chapter 3, age-stratified seroprevalence data from dromedary
populations across Africa, the Middle East and South Asia are used to fit catalytic models of
seroconversion, producing estimates of transmissibility and the rate of waning of maternally acquired antibodies. In Chapter 4, a stochastic, age-structured, dynamic transmission model of MERS-CoV in dromedaries is developed. The model is used to estimate key, epidemiological quantities including R0 and the critical community size, that give an insight into how controllable MERS-CoV is in different dromedary populations. Seasonal calving is also explored as a potential driver of peaks of infection. Finally, in Chapter 5, the dynamic model is extended to simulate vaccination under different efficacy scenarios. The potential impact of vaccination on transmission in dromedaries is evaluated, as well as the optimal age for vaccination.
Alongside empirical studies, insights from dynamic models such as those developed in this thesis could contribute to informing an effective response to the zoonotic transmission of MERS-CoV.
Version
Open Access
Date Issued
2022-04
Date Awarded
2022-07
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Ferguson, Neil
Jombart, Thibaut
Sponsor
Wellcome Trust (London, England)
Grant Number
203871/Z/16/Z
Publisher Department
Infectious Disease Epidemiology
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)