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

Abstract

Background: Emerging 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.

Objectives: We demonstrated through quantitative methodologies how to estimate MERS-CoV's transmissibility and identified the effective countermeasures that stopped its spread.

Methods: Using the outbreak data, statistical methods were employed to estimate the basic reproductive number R₀ , 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 R₀ and to identify the most effective countermeasures. The consistency between results will provide cross-validation of the approaches.

Results: R₀ 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.

Conclusions: Our 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.

Keywords: Middle Eastern Respiratory Syndrome CoronaVirus; South Korean outbreak; mathematical modelling; parameter estimation; statistical analysis; transmissibility.

Figure 1

The timeline of intervention measures along with the exposure dates of cases. Here, exposure dates of cases are assumed to be uniformly distributed over the recorded potential exposure windows. The index case is exclusive with his exposure window from 29 April to 2 May 2015

Figure 2

Transmission dynamics model fitting to the confirmed, symptomatic and exposed cases data under model assuming the breaking point at 28th May in both contact and diagnosis rates. Red filled circles are the cases data, thick blue lines represent the median predictions from transmission dynamics model, and the thin blue lines represent 95% credible intervals

Figure 3

The effective reproductive number obtained by epiestim package. The estimates are obtained over a gap of 13 days. The symptom‐onset data are used for model fitting. Solid line represents the mean and dashed the upper and lower levels of 95% CIs. The horizontal dotted line represents the threshold value R = 1. The estimates show that R _t reduces to below 1.0 from 14th June (day 37)

Figure 4

Transmission tree reconstruction and estimation of effective reproductive number. (A) Effective reproductive number (R _t) estimated by the method2; (B) R _t by method.7 (C) A sample transmission tree reconstructed by method.7 In panels (A) and (B), filled circles represent means and triangles the lower and upper levels of 95% CIs. Notice the huge variation in Figure 4B, especially on day 11 (21st May), the R _t has mean 27.8 and 95% CI ranging from 0 to 85. (The 97.5% level point 85 is not shown in the Figure 4B.) In the transmission tree that describes who acquired infection from whom among 185 cases, 162 cases (black circles except index case) know their unique infectors and the infectors of other 23 cases (red triangles) were reconstructed by method7