Abstract
Summary IntroductionEpidemiological investigations are used to identify outbreaks, collect data, and implement control measures, playing a crucial role in disease control and prevention. Mathematical and statistical approaches enhance these investigations by incorporating data analysis to understand disease characteristics and provide insights.MethodsTo estimate the transmissibility of a disease, we introduce a process to derive the likelihood function using individual patient information from epidemiological investigation. This method was applied to the case of Pyeongtaek St. Mary’s Hospital during the 2015 Middle East Respiratory Syndrome outbreak in Korea. A stochastic model was developed, and scenario analysis reflecting actual outbreak progress, risk factors, and mask mandates was conducted.ResultsWe applied transmission rate estimation to the Pyeongtaek St. Mary’s Hospital case, showing a high patient-to-patient transmission rate. The superspreader was observed to have approximately 25 times higher transmissibility than other patients. Given these conditions, if hospital transmission period is prolonged, number of cases could be three times higher than the actual incidence. The effect of mask-wearing in hospital was investigated based on the type of mask and the intensity of the intervention. It was found that the scale of epidemic could be reduced by a maximum of 77% and a minimum of 17%.ConclusionsThrough the application of mathematical and statistical methodologies in epidemiological investigations, this study identified and quantified risk factors. Methodology of this study can be easily adapted and applied to other diseases and is expected to help in establishing effective strategies to fight against emerging infectious diseases.
Publisher
Cold Spring Harbor Laboratory