Estimating Economic Losses Caused by COVID-19 under Multiple Control Measure Scenarios with a Coupled Infectious Disease-Economic Model: A Case Study in Wuhan, China

Abstract

Background: The outbreak of the COVID-19 epidemic has caused an unprecedented public health crisis and drastically impacted the economy. The relationship between different control measures and economic losses becomes a research hotspot.

Methods: In this study, the SEIR infectious disease model was revised and coupled with an economic model to quantify this nonlinear relationship in Wuhan. The control measures were parameterized into two factors: the effective number of daily contacts (people) (r); the average waiting time for quarantined patients (day) (g).

Results: The parameter r has a threshold value that if r is less than 5 (people), the number of COVID-19 infected patients is very close to 0. A "central valley" around r = 5~6 can be observed, indicating an optimal control measure to reduce economic losses. A lower value of parameter g is beneficial to stop COVID-19 spread with a lower economic cost.

Conclusion: The simulation results demonstrate that implementing strict control measures as early as possible can stop the spread of COVID-19 with a minimal economic impact. The quantitative assessment method in this study can be applied in other COVID-19 pandemic areas or countries.

Keywords: COVID-19; control measures; economic losses; infectious diseases model.

Figure 1

The framework of the infectious disease model. The black solid line indicates the transmission dynamics of the COVID-19 epidemic in each compartment.

Figure 2

The framework of estimating total economic losses under various control measures.

Figure 3

Simulated cases from the five-stage model were verified with the COVID-19 confirmed cases in Wuhan. The confirmed case data comes from The Chinese Centers for Disease Control and Prevention. The fitting technique of the five-stage model was introduced in Section 2.1.2.

Figure 4

The contour map of the cumulative number of COVID-19 infected patients in Wuhan, where $$r$$ is the effective number of daily contacts; $$g$$ is the average waiting time for quarantined patients; $$I_{cumulative}$$ is the cumulative number of infected patients; $$N$$ is the total population.

Figure 5

Reduction percentage of each industry sector under the different level of contact activity in Wuhan. The weighted level in Wuhan is the reduction in contact activity caused by the COVID-19 outbreak in Wuhan $$\left(\frac{\overline{r}}{r_0}\right)$$, is 31.73% (see Equations (5) and (6)); $$\alpha$$ is the degree of nonlinearity of each industry sector’s response to the reduction in contact activity $$\left(\frac{\overline{r}}{r_0}\right)$$ (see Equation (8)). When the contact activity level reaches 100% ($$r$$ = $$r_0$$ $$\approx$$ 13.96), it means returning to the level before the COVID-19 outbreak in Wuhan.

Figure 6

The contour map of total economic losses in Wuhan under various gridded control measures ($$r$$ and $$g$$), where $$r$$ is the effective number of daily contacts; $$g$$ is the average waiting time for quarantined patients. Total economic losses ranged from CNY 188.81 to 618.70 billion.