Effects of operational decisions on the diffusion of epidemic disease: A system dynamics modeling of the MERS-CoV outbreak in South Korea

Abstract

We evaluated the nosocomial outbreak of Middle East Respiratory Syndrome (MERS) Coronavirus (CoV) in the Republic of Korea, 2015, from a healthcare operations management perspective. Establishment of healthcare policy in South Korea provides patients' freedom to select and visit multiple hospitals. Current policy enforces hospitals preference for multi-patient rooms to single-patient rooms, to lower financial burden. Existing healthcare systems tragically contributed to 186 MERS outbreak cases, starting from single "index patient" into three generations of secondary infections. By developing a macro-level health system dynamics model, we provide empirical knowledge to examining the case from both operational and financial perspectives. In our simulation, under base infectivity scenario, high emergency room occupancy circumstance contributed to an estimated average of 101 (917%) more infected patients, compared to when in low occupancy circumstance. Economic patient room design showed an estimated 702% increase in the number of infected patients, despite the overall 98% savings in total expected costs compared to optimal room design. This study provides first time, system dynamics model, performance measurements from an operational perspective. Importantly, the intent of this study was to provide evidence to motivate public, private, and government healthcare administrators' recognition of current shortcomings, to optimize performance as a whole system, rather than mere individual aspects.

Keywords: Health care operations planning; Korean MERS outbreak; Operational decision-based modeling; Patient-care performance; System dynamics.

Fig. 1

Proliferation of MERS virus within 20 days in Korea.

Fig. 2

Schematic representation of ER elements, processes, and pathways considered for system dynamics model.

Fig. 3

General diagram of the model.

Fig. 4

First secondary infection model for Patient 1 and Patient 14.

Fig. 5

System dynamics model of emergency room.

Fig. 6

Potential number of infected patients (y-axis) and number of days (x-axis).

Fig. 7

System dynamics model of general ward.

Fig. 8

Numbers of potentially infected patients (y-axis) by patient-room design (x-axis).