Background: During the coronavirus disease 2019 (COVID-19) outbreak, every public health system faced the potential challenge of medical capacity shortages. Infections without timely diagnosis or treatment may facilitate the stealth transmission and spread of the virus. Important as the influence of capacity shortages on the epidemic, it is still unclear how they could intensify the spread of the epidemic qualitatively under different circumstances. Our study aims to throw light on this influence.
Methods: Using infection and medical capacity information reported in Wuhan in China, New York State in the United States, and Italy, we developed a dynamic susceptible–exposed–infected–recovered (SEIR) model to estimate the impact of medical capacity shortages during the COVID-19 outbreak at the city, state, and country levels.
Results: The proposed model can fit data well (R-square > 0.9). Through sensitivity analysis, we found that doubled capacity would lead to a 39% lower peak infected number in Wuhan. Italy and New York State have similar results.
Conclusions: The less shortages in medical capacity, the faster decline in the daily infection numbers and the fewer deaths, and more shortage would lead to steepen infection curve. This study provides a method for estimating potential shortages and explains how they may dynamically facilitate disease spreading during future pandemics such as COVID-19. Based on this, policy makers may figure out some way to explore more medical capacity and control the epidemic better.