Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging zoonotic pathogen first reported in 2012. The purpose of this thesis was to explore the dynamics of MERS-CoV in the reservoir dromedary camel population using available data sources and different methodological approaches. First, a scoping review was conducted to summarize the empirical evidence for MERS-CoV in animals in order to map knowledge gaps and to extract data for modeling disease transmission in dromedary camels. Meteorological risks of zoonotic infections were explored using a dataset that approximated MERS cases most likely to represent spillover transmission from camels. A case-crossover study identified associations between primary human MERS cases and preceding weather conditions. Infection dynamics in the reservoir dromedary camel population were explored utilizing field data from a serological survey in Kenya. The force of infection was estimated using age-stratified serological data in a generalized linear model framework. Transmission parameters were explored under different demographic scenarios using an agent-based model. The scoping review identified a number of field studies that estimated MERS-CoV prevalence, as well as laboratory experiments that shed light on the natural history of MERS-CoV infection in dromedary camels. It further identified critical research gaps such as longitudinal studies to improve the understanding of long-term dynamics of MERS-CoV. Some of these gaps were addressed in Chapters three through five of this thesis. The case-crossover analysis demonstrated a statistical association between temperature, humidity, wind speed, and ground visibility, and primary human cases of MERS. Force of infection, an important metric in understanding infection dynamics, was estimated to be higher for camels raised in a pastoralist setting, than for camels raised in a ranching system, possibly reflecting the differences in management and disease risk. Using field seroprevalence estimates, various parameter combinations such as probability of infection, contact rate, and duration of immunity were found to be biologically plausible given the current state of knowledge. Using data from a variety of sources and different analytical methods, this thesis describes insights that can be gained from limited information, while at the same revealing how important field data are for understanding infection dynamics.