A Scenario-Based Evaluation of COVID-19-Related Essential Clinical Resource Demands in China

Abstract

The coronavirus disease 2019 (COVID-19) pandemic is a global crisis, and medical systems in many countries are overwhelmed with supply shortages and increasing demands to treat patients due to the surge in cases and severe illnesses. This study aimed to assess COVID-19-related essential clinical resource demands in China, based on different scenarios involving COVID-19 spreads and interventions. We used a susceptible-exposed-infectious-hospitalized/isolated-removed (SEIHR) transmission dynamics model to estimate the number of COVID-19 infections and hospitalizations with corresponding essential healthcare resources needed. We found that, under strict non-pharmaceutical interventions (NPIs) or mass vaccination of the population, China would be able to contain community transmission and local outbreaks rapidly. However, under scenarios involving a low intensity of implemented NPIs and a small proportion of the population vaccinated, the use of a peacetime-wartime transition model would be needed for medical source stockpiles and preparations to ensure a normal functioning healthcare system. The implementation of COVID-19 vaccines and NPIs in different periods can influence the transmission of COVID-19 and subsequently affect the demand for clinical diagnosis and treatment. An increased proportion of asymptomatic infections in simulations will not reduce the demand for medical resources; however, attention must be paid to the increasing difficulty in containing COVID-19 transmission due to asymptomatic cases. This study provides evidence for emergency preparations and the adjustment of prevention and control strategies during the COVID-19 pandemic. It also provides guidance for essential healthcare investment and resource allocation.

Keywords: COVID-19; Clinical resource demands; Transmission dynamics model; Vaccination.

Fig. 1

Data flow chart. PPE includes protective coveralls and surgical masks.

Fig. 2

An illustration of the extended SEIHR transmission dynamics model. v: the proportion of immunized population (vaccinated population x vaccine effectiveness); β: the rate per unit of time at which the susceptible become infected; σ_a: rate of a latent infection progressing to a symptomatic infection; σ_b: rate of a latent infection progressing to an asymptomatic infection; γ_a: rate of a symptomatic infection progressing to inpatient admission; γ_b: rate of an asymptomatic infection progressing to inpatient admission; m_a: rate of a symptomatic inpatient progressing to recovery or death; and m_b: rate of an asymptomatic inpatient progressing to recovery or death.

Fig. 3

The reliability of the model was tested through comparing evaluation data with real-world results in Italy. The pink shadow indicates the gap between the model evaluation and the actual number of reported cases in Italy. The smaller the gap, the more reliable the model parameters and the more accurate the results of this study, with errors in the evaluation results regarded as acceptable.

Fig. 4

The number of infections and inpatients under six scenarios. Panels (a–f) correspond to the number of asymptomatic and symptomatic infections and inpatients that coincided with the duration of the epidemic under scenarios 1–6.

Fig. 5

Healthcare worker demand under the six scenarios, represented as different colored panels: (a) doctors; (b) nurses; and (c) nursing workers.

Fig. 6

Equipment demand (a) hospital beds, (b) isolation beds, (c) ICU beds, (d) ventilators, and (e) oxygen machines under six scenarios, represented as different colored panels.

Fig. 7

The six scenarios of epidemiological resource demand. Panels (a–f) refer to scenarios 1–6.