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Predicting the international spread of Middle East respiratory syndrome (MERS)
Kyeongah Nah1,2,3, Shiori Otsuki2,4, Gerardo Chowell5,6
1Bolyai Institute, University of Szeged, Aradi vértanúk tere 1, Szeged, H-6720, Hungary.
Background:
The Middle East respiratory syndrome (MERS) associated coronavirus has been imported via travelers into multiple countries around the world. In order to support risk assessment practice, the present study aimed to devise a novel statistical model to quantify the country-level risk of experiencing an importation of MERS case.
Methods:
We analyzed the arrival time of each reported MERS importation around the world, i.e., the date on which imported cases entered a specific country, which was modeled as a dependent variable in our analysis. We also used openly accessible data including the airline transportation network to parameterize a hazard-based risk prediction model. The hazard was assumed to follow an inverse function of the effective distance (i.e., the minimum effective length of a path from origin to destination), which was calculated from the airline transportation data, from Saudi Arabia to each country. Both country-specific religion and the incidence data of MERS in Saudi Arabia were used to improve our model prediction.
Results:
Our estimates of the risk of MERS importation appeared to be right skewed, which facilitated the visual identification of countries at highest risk of MERS importations in the right tail of the distribution. The simplest model that relied solely on the effective distance yielded the best predictive performance (Area under the curve (AUC) = 0.943) with 100 % sensitivity and 79.6 % specificity. Out of the 30 countries estimated to be at highest risk of MERS case importation, 17 countries (56.7 %) have already reported at least one importation of MERS. Although model fit measured by Akaike Information Criterion (AIC) was improved by including country-specific religion (i.e. Muslim majority country), the predictive performance as measured by AUC was not improved after accounting for this covariate.
Conclusions:
Our relatively simple statistical model based on the effective distance derived from the airline transportation network data was found to help predicting the risk of importing MERS at the country level. The successful application of the effective distance model to predict MERS importations, particularly when computationally intensive large-scale transmission models may not be immediately applicable could have been benefited from the particularly low transmissibility of the MERS coronavirus.
Insights
A new statistical model accurately predicts Middle East respiratory syndrome (MERS) importation risk using airline travel data. This model helps identify countries most vulnerable to MERS cases, aiding global health risk assessments.
Area of Science:
- Epidemiology
- Public Health
- Statistical Modeling
Background:
- Middle East respiratory syndrome (MERS) coronavirus poses a global health risk due to international travel.
- Importation of MERS cases into multiple countries necessitates robust risk assessment tools.
Purpose of the Study:
- To develop a novel statistical model for quantifying country-level risk of MERS case importation.
- To support global risk assessment practices for MERS outbreaks.
Main Methods:
- Utilized arrival times of reported MERS importations worldwide as the dependent variable.
- Developed a hazard-based risk prediction model using airline transportation network data to calculate effective distance from Saudi Arabia.
- Incorporated country-specific religion and MERS incidence data from Saudi Arabia to refine predictions.
Main Results:
- The effective distance model demonstrated high predictive performance (AUC=0.943), identifying countries at highest risk of MERS importation.
- 17 out of 30 highest-risk countries (56.7%) had already reported MERS importations.
- While including religion improved model fit (AIC), it did not enhance predictive performance (AUC).
Conclusions:
- A straightforward statistical model using airline network effective distance effectively predicts country-level MERS importation risk.
- This model offers a practical tool for risk prediction, especially when complex transmission models are not feasible.
- The model's success may be partly attributed to the MERS coronavirus's low transmissibility.
