Estimating and modelling the transmissibility of Middle East Respiratory Syndrome CoronaVirus during the 2015 outbreak in the Republic of Korea

Abstract

BackgroundEmerging respiratory infections represent a significant public health threat. Because of their novelty, there are limited measures available to control their early spread. Learning from past outbreaks is important for future preparation. The Middle Eastern Respiratory Syndrome CoronaVirus (MERS‐CoV ) 2015 outbreak in the Republic of Korea (ROK) provides one such opportunity.ObjectivesWe demonstrated through quantitative methodologies how to estimate MERS‐CoV's transmissibility and identified the effective countermeasures that stopped its spread.MethodsUsing the outbreak data, statistical methods were employed to estimate the basic reproductive number R 0, the average number of secondary cases produced by a typical primary case during its entire infectious period in a fully susceptible population. A transmission dynamics model was also proposed to estimate R 0 and to identify the most effective countermeasures. The consistency between results will provide cross‐validation of the approaches.Results R 0 ranged from 2.5 with 95% confidence interval (CI): [1.7, 3.1] (using the sequential Bayesian method) to 7.2 with 95% CI: [5.3, 9.4] (using the Nowcasting method). Estimates from transmission model were higher but overlapped with these. Personal protection and rapid confirmation of cases were identified as the most important countermeasures.ConclusionsOur estimates were in agreement with others from the ROK outbreak, albeit significantly higher than estimates based on other small outbreaks and sporadic cases of MERS‐CoV. The large‐scale outbreak in the ROK was jointly due to the high transmissibility in the healthcare‐associated setting and the Korean culture‐associated contact behaviour. Limiting such behaviour by rapidly identifying and isolating cases and avoiding high‐risk contacts effectively stopped further transmission.

A mathematical model for simulating the phase-based transmissibility of a novel coronavirus

“…The reported value of the R 0 of MERS was about 0.8-1.3 [31], with the inter-human transmissibility of the disease was about 0.6 or 0.9 in Middle East countries [32]. However, MERS had a high transmissibility in the outbreak in the Republic of Korea with the R 0 of 2.5-7.2 [33,34]. Therefore, the transmissibility of SARS-CoV-2 might be higher than MERS in the Middle East countries, similar to SARS, but lower than MERS transmitted in the Republic of Korea.…”

Estimating human-to-human transmissibility of hepatitis A virus in an outbreak at an elementary school in China, 2011

“…In general, for any outbreak of infectious disease that originated from one index case and was self-limited or stopped under control, its overall reproductive number throughout the entire outbreak course has a mean equal to ( n -1)/ n for an outbreak of size n , and its variance can vary from ( n -1)/ n 2 to ( n -1) 3 / n 2 , depending on the structure of the transmission tree (c.f. [ 28 , 29 ]). For the 2011 outbreak in Anhui, the overall mean of reproductive number through the whole outbreak is 0.969, with a standard deviation of 0.106.…”

“…What we are keen to estimate and is relevant to response plan for policymakers is the transmissibility under somewhat natural conditions (e.g., without changes in humans’ behavior and active control strategies, with all people being susceptible). This simple method of considering growth reduction during the outbreaks can also be applied to the analyses for other outbreaks of other infectious agents [ 29 ].…”

The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison

“…the SAR in SARIMA) which have frequently been used for prediction of infectious disease time series [20,[58][59][60][61]. What is less clear is how to incorporate structural information about outbreak data, such as the typical Gaussian incidence curve that is seen in both empirical data [62] and theoretical transmission dynamic models [63], or the known impact of changing herd immunity levels [64]. This may also help predict atypical outbreaks, such as the 2009 influenza pandemic which occurred in the Northern Hemisphere's summer [65], and for which extrapolating approaches like LASSO may break down.…”