Stochastic Modeling of Intra- and Inter-Hospital Transmission in Middle East Respiratory Syndrome Outbreak

Middle East Respiratory Syndrome (MERS) is an endemic disease that presents a significant global health challenge characterized by a high risk of transmission within healthcare settings. Understanding both intra- and inter-hospital spread of MERS is crucial for effective disease control and prevention. This study utilized stochastic modeling simulations to capture inherent randomness and unpredictability in disease transmission. This approach provides a comprehensive understanding of potential future MERS outbreaks under various scenarios in Korea.

Our simulation results revealed a broad distribution of case number, with a mean of 70 and a credible interval of [0, 315]. Additionally, we assessed the risks associated with delayed outbreak detection and investigated the preventive impact of mask mandates within hospitals. Our findings emphasized the critical role of early detection and the implementation of preventive measures in curbing the spread of infectious diseases. Specifically, even under the worst-case scenario of late detection, if mask mandates achieve a reduction effect exceeding 55%, the peak number of isolated cases would remain below 50.

The findings derived from this study offer valuable guidance for policy decisions and healthcare practices, ultimately contributing to the mitigation of future outbreaks. Our research underscores the critical role of mathematical modeling in comprehending and predicting disease dynamics, thereby enhancing ongoing efforts to prepare for and respond to MERS or other comparable infectious diseases.

Author summary

In our study, we used stochastic modeling simulations to understand the potential future outbreaks of Middle East Respiratory Syndrome (MERS) in Korea. Our simulations revealed a wide range of possible case numbers, emphasizing the unpredictable nature of disease transmission. We found that early detection of an outbreak and the implementation of preventive measures, such as mask mandates in hospitals, play a critical role in controlling the spread of infectious diseases. Even in the worst-case scenario of late detection, if masks are mandated and achieve a reduction effect exceeding 55%, the peak number of isolated cases would remain below 50. Our research highlights the importance of mathematical modeling in understanding and predicting disease dynamics, providing valuable insights for policy decisions and healthcare practices. This work contributes to the ongoing efforts to prepare for and respond to MERS or other similar infectious diseases.