Improving resiliency of healthcare systems in response to epidemics

• Main Author: Low, Alan Yu Hao
• Other Authors: Chen Songlin
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Mathematics and analysis; Engineering::Mechanical engineering
• Online Access: https://hdl.handle.net/10356/150784
• Institution: Nanyang Technological University

With the outbreak of COVID- 19 at the start of 2020, healthcare systems all over the world have been overwhelmed. As the dust settles, questions have been raised with regards to the efficacy and resiliency of these healthcare systems. COVID- 19 isn’t the first epidemic to have befallen the world; it won’t be the last either. There is hence a need to better prepare healthcare systems for the next outbreak. By modelling a healthcare system as a dynamic feedback model in times of an epidemic, we can analyse how much the system can take before it implodes. Input to the system is the number of infected patients, and output is those who have recovered or succumbed to the disease. Using a stock and flow diagram coupled with system dynamics, we can also find out the feedback loops that reinforce this cycle. Such feedback loops are essentially the factors that cause an epidemic to spiral beyond the capacity of a healthcare system. Quantitative aspects of this study include predictive modelling that helps hospitals develop foresight when it comes to capacity planning, and also its inventory management. A case study of Singapore – which has performed exceptionally well in COVID- 19 – will also be explored. The conclusion shows that a resilient healthcare system requires more than just intrinsic factors on the hospital’s part – macro factors at play needs to be managed effectively as well. This research paper aims to address the gaping holes that have been revealed in today’s healthcare systems by COVID- 19, and it illuminates the fact that much more can indeed be done to prepare such systems for the next infectious disease outbreak.