Quantifying spatiotemporal heterogeneity of MERS-CoV transmission in the Middle East region: A combined modelling approach

Abstract

MERS coronavirus cases notified in the Middle East region since the identification of the virus in 2012 have displayed variations in time and across geography. Through a combined modelling approach, we estimate the rates of generation of cases along the zoonotic and human-to-human transmission routes and assess their spatiotemporal heterogeneity. We consider all cases notified to WHO from March 2012 to mid-September 2014. We use a stochastic modelling of the time series of case incidence in the Middle East region to estimate time- and space-dependent zoonotic and human-to-human transmission parameters. The model also accounts for possible lack of identification of secondary transmissions among notified cases. This approach is combined with the analysis of imported cases out of the region to assess the rate of underreporting of cases. Out of a total of 32 possible models, based on different parameterisation and scenario considered, the best-fit model is characterised by a large heterogeneity in time and across space for both zoonotic and human-to-human transmission. The variation in time that occurred during Spring 2014 led to a 17-fold and 3-fold increase in the two transmissions, respectively, bringing the reproductive rate to values above 1 during that period for all regions under study. The model suggests that 75% of MERS-CoV cases are secondary cases (human-to-human transmission), which is substantially higher than the 34% of reported cases with an epidemiological link to another case. Overall, estimated reporting rate is 0.26. Our findings show a higher level of spatial heterogeneity in zoonotic transmission compared to human-to-human, highlighting the strong environmental component of the epidemic. Since sporadic introductions are predicted to be a small proportion of notified cases and are responsible for triggering secondary transmissions, a more comprehensive understanding of zoonotic source and path of transmission could be critical to limit the epidemic spread.

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

“…These estimates well overlap with the statistical estimates. Although being significantly higher than the estimates based on the previous small outbreaks, our estimates are compatible with others on the ROK outbreak and also the estimate on the 2014 outbreak in the KSA . In view of these analyses, R 0 of MERS‐CoV before the effective intervention can be much higher than the threshold level in the healthcare‐associated environment.…”

A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation

“…This implies, MERS-CoV transmission triggered from the hospital setting (see Table 2B). These estimates agree with some previous values reported in the literature [4,[17][18], while being a result well supported by other R 0 estimates [29][30][31]33]. However, previous modeling attempts to model Saudi Arabia MERS-CoV clinical incidence used only a 1-strain model with BL transmission [4].…”

“…https://doi.org/10.1371/journal.pntd.0008065.g008 (S1 Fig) is well supported by some previous estimates in literature [28][29][30]32]. Lower contribution of community transmission in R 0 (See Table 2B) in Riyadh and Macca suggests that MERS-CoV transmission is triggered from hospital settings in those provinces.…”

“…They found that the estimate of R 0 in Jeddah and Riyadh are in the ranges (3.5-6.7) and (2.0-2.8), respectively. A stochastic epidemiological model of MERS-CoV with zoonotic and human-to-human transmission was considered by Poletto et al [29] to quantify the rates of generation of cases from those two transmission routes. They found that spring 2014 cases led to the increase in transmission rates, which brings R 0 to values above unity.…”

Risk of MERS importation and onward transmission: a systematic review and analysis of cases reported to WHO

“…Joint analysis of incidence data in the Middle East region with international travel flows allowed estimating the expected number of MERS introductions in countries outside the affected area [9,10], for example on coming back from pilgrimage in Mecca [9] or distinguishing between residents and visitors [10]. In the latter, predictions were based on cumulative attack rates per country in the affected area [10], thus disregarding the strong temporal nature of MERS epidemic [11].…”