key: cord-0973488-5wiywpy2
authors: Chen, Xingtian; Gong, Wei; Wu, Xiaoxu; Zhao, Wenwu
title: 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
date: 2021-11-09
journal: Int J Environ Res Public Health
DOI: 10.3390/ijerph182211753
sha: 77d3b5feb52c366434222feede82d93e1ac34f9a
doc_id: 973488
cord_uid: 5wiywpy2

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.

COVID-19, which shows high infectiousness and causes symptoms such as fever, cough, and shortness of breath, was first reported from Wuhan, Hubei, in mid-December 2019 [1] [2] [3] [4] . Travel restrictions [5] and control measures [6] implemented by the Chinese government curbed the spread of the COVID-19 epidemic in Wuhan [7, 8] . Stringent nonpharmacological interventions [9, 10] , including social distancing [11, 12] , masking [13] , tracing, and quarantine of patients and close contacts [14] [15] [16] , stopped the COVID-19 epidemic in China [17] . However, the control measures have inevitably caused a heavy economic burden and weakened economic vitality. Controlling the COVID-19 epidemic becomes a balancing issue, since we want to protect public health (less patients) with the least amount of economic loss [18, 19] .

In the first half of 2020, the GDP of the Hubei Province decreased by 19 .3% year-onyear [20] . The whole year GDP in 2020 of the Hubei Province decreased by 5.0% more than in 2019 [21] . However, China's annual GDP (gross domestic product) in 2020 increased by 2.3% over the past year, and the completion of the major objectives and tasks of socioeconomic development was better than expected [22] . If the Chinese government did not implement such strict control measures, as the COVID-19 epidemic spread, we would have suffered a more serious health crisis and a heavier economic burden. However, too strict of and showed that when the infections in the total population exceed 1%, the economic burden (staffing shortages) of the epidemic over an 18-month period will reach 10% [36] . Alvarez et al. (2020) [37] used the SIR model to study the optimal lockdown policy, and showed that the absence of testing would increase the economic cost of the lockdown and delay the duration of the lockdown. A multi-group SIR model was built to study targeted lockdown, and suggested that reducing social interactions between different groups and increasing the screening and isolation can minimize economic losses and deaths [38] .

In summary, there are many studies about COVID-19 control measures. For example, the full transmission dynamic of COVID-19 in Wuhan across five periods [8] was reconstructed with an extended SEIR model [7] . Multiple studies have estimated economic losses caused by the COVID-19 epidemic. For example, the economic cost of COVID-19 in China was estimated by health-care costs (diagnosis and treatment), non-health-care costs (compulsory quarantine), and productivity losses (lost work time) [32] . However, few studies estimate the economic losses caused by the COVID-19 epidemic under different control measures. Therefore, how to quantify the relationship between COVID-19 control measures and economic losses caused by the COVID-19 epidemic is still a key research question. This study aims to develop a model to parameterize the control measures and quantify economic losses caused by both the COVID-19 epidemic and the adopted control measures.

The paper is organized as follows: Section 1 introduces the literature review about economic losses caused by the COVID-19 epidemic, the research gap, and the research objective of this study. Section 2 introduces the analysis methodology of this study, including the infectious disease model (see Section 2.1), the economic losses model (see Section 2.2) , and their parameters (see Tables 1-3) . Section 3 introduces the infectious disease model verification (see Section 3.1), and the influence of social distancing (r) and tracking/quarantining ability (g) on public health (see Section 3.2) and economic losses (see Section 3.3). Section 4 compares our findings with previous studies, then summarizes the improvements and limitations of this study (see Section 4.1), and also proposes some suggestions on how to effectively control COVID-19 spread with less economic losses (see Section 4.2). Section 5 introduces the conclusion of this study.

This study focuses on parameterizing the control measures into two key factors, and then quantitatively evaluates economic losses caused by both the COVID-19 epidemic and the adopted control measures. The analysis of this study mainly includes the following steps. First, the infectious disease model was revised to parameterize control measures into two factors (see Section 2.1.1): (1) the level of social distancing, represented by the effective number of daily contacts (parameter r); (2) the level of tracing and quarantining the infected patients and their close contacts, represented by the average waiting time for quarantined patients (parameter g). Second, the revised model was used to simulate the spread process of the COVID-19 epidemic in Wuhan to further validate its accuracy (see Section 2.1.2). Third, we quantitatively analyzed the effect of COVID-19 on both human health and economic losses. The estimated economic losses of COVID- 19 Figure 1 explains the framework of the infectious disease model in this study. The classical Susceptible-Exposed-Infected-Recovered (SEIR) model can simulate the epidemic's dynamic transmission mechanism with differential equations and describes the flow between four main compartments: susceptible (S), exposed (E), infected (I), and recovered (R) [39] . Based on the work of Hao et al. (2020) [7] , which extended the classical SEIR model with three additional compartments: presymptomatic (P), asymptomatic (A), and hospitalized (H), we developed the model by adding a compartment of quarantined (Q) (see Figure 1 ). N The total population in Wuhan 10,000,000 10,000,000 10,000,000 10,000,000 10,000,000 [7, 40] n The daily domestic inbound and outbound travelers in Wuhan 500,000 800,000 0 0 0 [7, 40] The order of parameters was sorted according to Equation (1).

Moreover, considering the degree of self-protection (including social distancing, masking, and disinfecting), we parameterized susceptible peoples' risk of being infected. On the other hand, considering the effectiveness of quarantine (including medical resources and emergency response), we parameterized the capacity of isolating infected patients. Therefore, the transmission dynamics of COVID-19 can be described with the first-order differential equations, as shown in Equation (1) .

where r is the effective number of daily contacts; β is the SARS-CoV-2 infection rate; θ is the degree of self-protection; p is the ascertainment rate; α is the ratio of spread rate for unconfirmed over confirmed cases; D p is the presymptomatic infectious period; D e is the latent period; D i is the symptomatic infectious period; g is the average waiting time for quarantined patients; D h is the average hospitalization period; N is the total population in Wuhan; n is the daily domestic inbound and outbound travelers in Wuhan. 

Secondly, the first-order differential equations (Equation (1)) were divided into five stages. In the following full text, we prefer to call the infectious disease model in this study "the five-stage model" or "the developed model". Based on an earlier study of Pan et al. (2020) [8] , the early period of the COVID-19 outbreak in Wuhan was divided into five stages: 1-9 January (before Chinese Spring Festival Transportation), 10-22 January (Chinese Spring Festival Transportation), 23 January-1 February (during lockdown of Wuhan), 2-16 February (centralized isolation and quarantine), and 17 February-8 March (community screening and testing).

Next, the parameter of the five-stage model was introduced in detail. On one hand, most of the parameter's values of the five stages referred to the study by Hao et al. (2020) [7] and other original studies, which are listed in Table 1 . For example, the total population in Wuhan (N) is set to be 10,000,000 people [7, 40] . The daily domestic inbound and outbound travelers in Wuhan (n) in the five stages is set to be 500,000; 800,000; 0; 0; 0 people, respectively [7, 40] . The ascertainment rate (p) in the five stages is estimated to be 0.15, 0.15, 0.14, 0.10, and 0.16, respectively [7] . The ratio of the spread rate for unconfirmed (including patients with pre-symptoms) over confirmed cases (α) is estimated to be 0.55 [41] , which is lower than the infection rate of confirmed cases. The incubation period of confirmed cases is 5.2 days [3] . The transmission rate during the presymptomatic stage is 44% [42] . Consequently, the presymptomatic infectious period (D p ) is 2.3 (5.2 × 0.44 = 2.3) days [7, 42] , and the latent period (D e ) is 2.9 (5.2 − 2.3 = 2.9) days [3, 7] . Assuming that the infectivity of presymptomatic cases is consistent with the infectivity of symptomatic cases [14] , the symptomatic infectious period (D i ) is also 2.9 days [7, 14] , and the average hospitalization period (D h ) is 10 days [4] .

The effective number of daily contacts (r) and the average waiting time for quarantined patients (g) in the five stages were estimated. The transmission rate of confirmed cases (b) [7] was mainly depended on three key variables (b = r × β × θ): the effective number of daily contacts (r), the SARS-CoV-2 infection rate (β), and the degree of selfprotection (θ). The SARS-CoV-2 infection rate (β) was assumed to be stable in the early COVID-19 outbreak, and was 0.395 in the five stages [5] . The degree of self-protection (θ) among susceptible people was assumed to be time-varying in the five stages: 0.6, 0.6, 0.7, 0.8, and 0.9, respectively. Therefore, the effective number of daily contacts r = b βθ in the five stages was estimated to be 8.29, 8.29, 3.37, 2.15, and 2.53 people, respectively. Then, by fitting with COVID-19 confirmed cases in Wuhan, the average waiting time for quarantined patients (g) was estimated to be 3.5, 1.7, 2.0, 1.7, and 1.2 days, respectively.

The economic losses caused by COVID-19 can be categorized into four parts: (1) treatment costs of infected patients (see Section 2.2.1), which are proportional to the number of infected patients; (2) quarantine-related expenditures (see Section 2.2.2), which are proportional to the number of quarantined patients and their close contacts; (3) inoperable losses (see Section 2.2.3), which are due to the absence of work (patients at hospitals, patients staying at home but unable to work, and patients and close contacts quarantined); (4) social distancing losses (see Section 2.2.4), which are due to reducing the activity of social interactions (causing the supply shock and the demand shock to the industry sectors).

Among the above four parts of economic losses, the first three can be assumed to have a linear relationship with the number of patients [32] . Social distancing measures caused the reduction in contact activity that related to the fall in labor supply and consumer demand, which led to the decrease in GDP [24, 28] . The supply shock and demand shock are different for different sectors [24] . Thus, the last one, social distancing losses, was assumed to have a non-linear relationship with the level of contact activity [32, 43] (represented by the effective number of daily contacts).

Firstly, treatment costs were mainly related to the number of infected patients. We simulated the cumulative number of infected patients under various control measures (I i ). According to the different clinical severity of COVID-19 hospitalized patients [4] and their corresponding treatment costs [44] , we respectively estimated the treatment costs of infected patients with no complications (C 1 (1 − P 1 )I i ), with complications (C 2 P 1 (1 − P 2 )I i ), and with severe complications (C 3 P 1 P 2 I i ). Hence, the total treatment costs of infected patients under various control measures (r and g) were estimated with Equation (2). The parameters are listed in Table 2 .

where C 1 (1 − P 1 )I i is the treatment cost of COVID-19 infected patients with no complications; C 1 is the average treatment cost of a COVID-19 infected patient with no complications; P 1 is the probability of a COVID-19 infected patient being admitted to the Intensive Care Unit (ICU); I i is the cumulative number of infected patients under various control measures (r and g); C 2 P 1 (1 − P 2 )I i is the treatment cost of COVID-19 infected patients with complications; C 2 is the average treatment cost of a COVID-19 infected patient with complications; P 2 is the probability of Acute Respiratory Distress Syndrome (ARDS) after a COVID-19 infected patient is admitted to the Intensive Care Unit (ICU); C 3 P 1 P 2 I i is the treatment cost of COVID-19 infected patients with severe complications; C 3 is the average treatment cost of a COVID-19 infected patient with severe complications. 

Secondly, quarantine-related expenditures were mainly related to the number of quarantined patients. We simulated the cumulative number of quarantined patients during the COVID-19 outbreak in Wuhan (Q basic ) with Equation (1). We assumed that expenditures for disaster prevention and emergency management in the Hubei province in the first quarter of 2020 (S) were mainly spent in Wuhan. Since the number of close contacts can be estimated by multiplying the effective number of daily contacts, the number of days before quarantine, and the number of quarantined patients, the total quarantine-related expenditures were assumed to be proportional to the cumulative number of quarantined patients (Q i ). Hence, quarantine-related expenditures under various control measures (r and g) were estimated with Equation (3). The parameters are listed in Table 2 .

where S is the total expenditures for disaster prevention and emergency management in the Hubei province in the first quarter of 2020, which is assumed to be approximately proportional to the number of quarantined patients; Q basic is the cumulative number of quarantined patients during the COVID-19 outbreak in Wuhan; Q i is the cumulative number of quarantined patients under various control measures (r and g). (5) and (6) α

The degree of nonlinearity of each industry sector's response to the reduction in contact activity Equation (8) C 2019

The cumulative output value of each industry sector in the Hubei province in the first quarter of 2020 [45] GDP p The actual growth rate of the industry sector in the Hubei province in the first quarter of 2020 [45] GDP p The expected growth rate of the industry sector in the Hubei province in the first quarter of 2020

Estimated with the ARIMA model [48] Loss distancing (Social distancing losses) 

Thirdly, inoperable losses were mainly estimated by two parts. One part was that infected patients who were unable to work due to receiving treatment in the hospital (the time from first symptom to ARDS was eight days [4] and the hospital stay was ten days [4] ) and self-isolation at home (14 days) GDP per T I I i . The other part was that quarantined patients who were unable to work due to receiving quarantine (14 days) and self-isolation at home (14 days) GDP per T Q Q i . Hence, the total inoperable losses under various control measures (r and g) were estimated with Equation (4). The parameters are listed in Table 2 .

where GDP per T I I i is the infected patients who were unable to work due to receiving treatment in the hospital and self-isolation at home; GDP per is the per capita daily GDP in 2019; T I is the average time that infected patients received treatment (8 + 10 = 18 days) and self-isolation (14 days); GDP per T Q Q i is the quarantined patients who were unable to work due to receiving quarantine and self-isolation at home; T Q is the average time that quarantined patients received quarantine (14 days) and self-isolation (14 days).

Lastly, social distancing losses are caused by reducing the activity of social interactions (represented by the effective number of daily contacts (r)). Unlike other sources of losses, social distancing losses have a strong nonlinear relationship with the level of contact activity [32, 43] , which is represented by the effective number of daily contacts (r). The parameters are listed in Table 3 .

The effective number of daily contacts before the COVID-19 outbreak (r 0 ) was estimated by the contact's number matrix with different age structures in China (C i ) [47] and the proportion of the age structure in China in 2019 (P i ) [46] , which is shown in Equation (5).

where r 0 is the effective number of daily contacts before the COVID-19 outbreak; C i is the contact's number matrix with intervals of five years of age in China; P i is the proportion of the age structure with intervals of five years of age in China in 2019; i is the index of the five years of age interval. The weighted value of the effective number of daily contacts during the COVID-19 outbreak (r) was then estimated by the effective number of daily contacts in each stage of the COVID-19 epidemic in Wuhan (r i ) and the proportion of the duration in each stage of the COVID-19 epidemic in Wuhan (D i ) [7] , which is shown in Equation (6).

where r is the weighted value of the effective number of daily contacts during the COVID-19 outbreak; r i is the effective number of daily contacts in each stage of the COVID-19 epidemic in Wuhan (see Table 1 ), which is 8.29, 8.29, 3.37, 2.15, and 2.53 people, respectively; D i is the proportion of the duration in each stage of the COVID-19 epidemic in Wuhan (see Table 1 ), which is 13.24%, 19.12%, 14.71%, 22.06%, and 30.88%, respectively; i is the index of stage of the COVID-19 epidemic in Wuhan. Moreover, the basis of the GDP value, which is assumed to be the expected seasonal GDP if COVID-19 did not break out, is estimated with the Autoregressive Integrated Moving Average model (ARIMA) [48] . To facilitate future calculation, only the GDP growth rate is considered.

Social distancing losses caused by reducing the effective number of daily contacts from a normal level are assumed to be proportional to r r 0 α , where r r 0 is the reduction in contact activity (31.73%) caused by the COVID-19 outbreak in Wuhan, and α is the degree of nonlinearity in each industry sector's response to the reduction in contact activity r r 0 . The value of the nonlinearity index α of each industry sector (see Figure 5 ) can be estimated with Equations (7) and (8), where C 2019 is the cumulative output value of each industry sector in the Hubei province in the first quarter of 2020 [45] ; GDP p is the actual growth rate of each industry sector in the Hubei province in the first quarter of 2020 [45] ; GDP p is the expected growth rate of the industry sector in the Hubei province in the first quarter of 2020, which was simulated with the ARIMA model [48] .

Given each industry sector's nonlinearity index to the COVID-19 epidemic in Wuhan (Equation (7)), the expected growth rate of each industry sector under the different level of contact activity (GDP p r ) was estimated by Equation (9) . The social distancing losses of each industry sector are assumed to be equal to the difference between the actual growth rate and the expected growth rate under the different level of contact activity r r 0 . Therefore, the social distancing losses of each industry sector in Wuhan that suffered from the COVID-19 epidemic in a different parameter r (the effective number of daily contacts) can be estimated with Equation (10).

where r is the effective number of daily contacts, ranging from 0 to r 0 people; r r 0 is the different level of contact activity; GDP p r is the expected growth rate of each industry sector under the different level of contact activity; P is the percentage of the output value in 2019 Wuhan to the output value in the 2019 Hubei province [45] ; GDP p r − GDP p is the total losses rate of each industry sector that suffered from the COVID-19 impact with the different level of contact activity. Figure 2 introduces the framework for estimating the total economic losses under various control measures. The treatment costs (Loss treatment ), quarantine-related expenditures (Loss quarantine ), and inoperable losses (Loss inoperable ) were estimated under various control measures (r and g). The social distancing losses (Loss distancing ) under the different levels of contact activity (r) were estimated. Consequently, the total economic losses (Loss(r, g)) under various control measures (r and g) were estimated with Equation (11) .

Loss(r, g) = Loss treatment + Loss quarantine + Loss inoperable + Loss distancing (11) Figure 2 . The framework of estimating total economic losses under various control measures.

The parameters of the infectious disease model in five stages are presented in Table 1 . The parameters of estimating economic losses are presented in Tables 2 and 3 . Moreover, the COVID-19 confirmed cases data in Wuhan comes from The Chinese Centers for Disease Control and Prevention (Available online: http://2019ncov.chinacdc.cn/2019-nCoV/) (accessed on 16 September 2021). The economy-related monthly data of each industry sector is provided by the Hubei Provincial Statistics Bureau (Available online: http: //tjj.hubei.gov.cn/tjsj/sjkscx/tjyb/tjyb2021/) (accessed on 16 September 2021).

With the calibrated parameters in the five stages (Table 1) , simulated cases from the five-stage model were verified with the COVID-19 confirmed cases in Wuhan (Figure 3 ). The simulated cases can fit the reported cases sufficiently, except in the third stage (from 23 January to 2 February), where a significant deviation can be observed. There are two factors causing this. The first is the peak number of reported cases (the peak point in Figure 3 ). The COVID-19 epidemic peaked in the third stage, while the five-stage model did not capture the peak point and kept the trend upwards. The second is the medical resources. At this stage, due to limited medical resources, the capacity of hospitalization and quarantine was weakened. The simulated results showed that as the effective number of daily contacts (r) increased, the COVID-19 epidemic in Wuhan became hard to be contained. In the mid-term of the COVID-19 spread, when the parameter r ≈ 14, the daily increase in the number of infected patients accounts for about 1.1% of the total population (see Figure S1 in Supplementary Materials), and the daily increase in the number of quarantined patients accounts for about 0.8% of the total population (see Figure S2 in Supplementary Materials). Moreover, with the spread of COVID-19, the number of confirmed cases exceeded the affordability of local medical resources, which forced the government to carry out more stringent control measures to slow down the spread of COVID-19. The degree of strictness of control measures can be parameterized into two factors: the effective number of daily contacts (r), which represents the degree of social distancing, and the average waiting time for quarantined patients (g), which represents the ability of tracing and quarantining infected patients. Therefore, the effectiveness of multiple control measures represented by various combinations of gridded r and g values can be evaluated comprehensively, as addressed in the following sections.

The cumulative number of infected patients was calculated based on multiple scenarios of the parameter r and g, and its contour map is shown in Figure 4 . All other parameters of the five-stage model were set to the values of stage-2 (see Table 1 ). As shown in Figure 4 , the cumulative number of COVID-19 infected patients increases with the parameter r, but is almost parallel to x-axis if g is large. Clearly, to reduce the cumulative number of infected patients, simultaneously reducing r and g is preferred. The effectiveness of r on COVID-19 spreading is more significant, especially when g is large; if g is small, both r and g are effective in stopping the spread of COVID-19. 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. 

Firstly, treatment costs (see Figure S3 in Supplementary Materials), quarantine-related expenditures (see Figure S4 in Supplementary Materials), and inoperable losses (see Figure  S5 in Supplementary Materials) under various control measures (r and g) were estimated with Equations 2, 3, and 4, respectively. We confirmed that the treatment costs were proportional to the number of infected patients, and also that the quarantine-related expenditures were proportional to the number of quarantined patients.

Based on each industry sector's response pattern to the COVID-19 impact (see Section 2.2.4 for details), we estimated each industry sector's response to different contact activity levels, represented by the parameter r. The industry sector's response patterns to the COVID-19 impact can be divided into three types ( Figure 5 ): (1) The nonlinearity index α is less than 1. The level of contact activity represented by the parameter r has a slight effect on some industry sectors, because these sectors do not strongly rely on a lot of manpower or face-to-face communication. For example, the index α of chemical products is only 0.674, because operating modern automatic chemical equipment only requires a few people.

(2) The nonlinearity index α approximately equals to 1. In this case, the value of parameter r linearly affects some industries, such as the non-metallic mineral manufacturing industry sector, the equipment manufacturing industry sector, and the public management industry sector. (3) The nonlinearity index α is larger than 1. In this case, the value of parameter r significantly affects the productivity of some industries, such as agriculture, mining, construction, and most tertiary industries. For example, the α of building and finance is 4.387 and 2.841, respectively. (5) and (6)); α is the degree of nonlinearity of each industry sector's response to the reduction in contact activity r r 0 (see Equation (8)). When the contact activity level reaches 100% (r = r 0 ≈ 13.96), it means returning to the level before the COVID-19 outbreak in Wuhan.

Finally, the total economic losses in Wuhan under various gridded control measures (r and g) were estimated ( Figure 6 ). Generally, economic losses are sensitive to parameter r. A "central valley" around r = 5 ∼ 6 can be observed, indicating that there is an optimal region to reduce economic losses. If r is large, the treatment costs and inoperable losses become very large; if r is small, the social distancing losses are large. If the parameter g is large, the contour lines are nearly parallel to the x-axis, indicating that reducing g is effective if g is small enough, but useless if g is large. Therefore, it is necessary to track and quarantine infected patients on time. 

Firstly, the parameter r has a significant impact on the time required to reach the peak of the COVID-19 infection curves (see Figure S1 ). It is believed that restrictions on social activities have delayed the outbreak of COVID-19 [49, 50] . On the other hand, the parameter g can influence the scale of the COVID-19 infection curves (see Figure 4) . A larger parameter g, which means a lower capacity for tracing and quarantining potential patients, leads to the more serious spread of COVID-19. This result verifies that high-intensity isolation measures are very important to stopping the spread of COVID-19 [51] .

Secondly, the cumulative number of infected patients and quarantined patients can be used as indicators of the effectiveness of control measures against the COVID-19 spread. As shown in Figure 1 , the number of infected patients increases towards the direction of larger parameters r and g (lax control measures), and its growth rate is accelerating. This finding confirms that, in the early stage of the COVID-19 spread, a lacking in non-pharmaceutical interventions facilitates the spread of COVID-19 [52] . When the parameter g is small, as the parameter r increases, there is still a risk of the COVID-19 outbreak (see Figure 4 ). This finding confirms that, when implementing non-pharmaceutical interventions with strict control capabilities, relaxing the restriction on social activities increases the risk of COVID-19 recurrence [53, 54] . Moreover, when the parameter r is less than about six people, the COVID-19 outbreak is stopped, indicating that non-pharmaceutical interventions are effective in stopping the spread of COVID-19 (see Figure 4) . Therefore, if unknown infectious factors or ways increase the transmission rate of COVID-19 [55] , it is necessary to carry out stringent control measures to control the epidemic. This conclusion agrees well with a previous study [7] .

Thirdly, some industry sectors' response to the reduction in contact activity is insensitive (see Figure 5 ), such as working online or manufacturing that have little social interactions, which provide vitality for economic recovery. This finding confirms that with the popularity of social distancing and remote working, the digital economy can help mitigate the economic losses from COVID-19, for example, in Kenya [56] . Besides, by emphasizing the need for cooperation with the government, civil society, and private individuals, the case study in Vietnam offers valuable lessons for responding to a public health crisis [57] .

Finally, a "central valley" around r = 5~6 can be observed, indicating that there is an optimal region to reduce economic losses (see Figure 6 ). Among the economic losses estimated with Equation (11) in our study, the minimum value was CNY 188.81 billion, which was equivalent to 11.63% of Wuhan's GDP and to 0.19% of China's GDP. You et al. [31] estimated that monthly total economic losses (including health burden, mental health, direct losses, and indirect losses) reached CNY 177.04 billion, and made up 11.06% of Wuhan's GDP. Moreover, Jin et al. [32] estimated that the total societal cost (mainly referred to productivity loss) of COVID-19 in China was CNY 2646.70 billion, and was equivalent to 2.70% of China's GDP. The total cost of the pandemic is estimated to be approximately 90% of the annual GDP of the United States [58] . Moreover, the impact of factors (such as demography, contact patterns, disease severity, and health care capacity) on the COVID-19 suppression strategies in low-and middle-income countries [59] , and the sensitivity of patients (such as demographic and socioeconomic-cognitive) to health costs [60] , both need to be considered.

On one hand, the stringent control measures help to reduce costs and losses caused by the COVID-19 epidemic. On the other hand, the early establishment of an emergency response strategy helps to mitigate the economic burden caused by COVID-19. Consequently, to control the COVID-19 outbreak and mitigate the economic impact caused by the COVID-19 epidemic, we should develop a prompt and strict emergency strategy to stop the spread of COVID-19. This could simultaneously realize stopping the COVID-19 spread, minimizing the economic shock, and providing vitality for economic recovery.

This study presents three novel contributions, as follows. First, a novel infectious disease model has been developed based on coupling the infectious disease model and the economic model to quantify economic losses caused by COVID-19 in Wuhan. Second, the control measures were parameterized into two important parameters: the effective number of daily contacts (r), and the average waiting time for quarantined patients (g). Third, a new method for analyzing the nonlinear response relationship of the industry sector to the impact of COVID-19 and the degree of control measures was proposed. The responses of total infected patients and total economic losses to the two parameters were simulated and visualized as contour maps (see Figure 6 ). The quantitative assessment method in this study can apply to other areas/countries that are still fighting COVID-19, or to related studies on other infectious diseases.

However, some limitations should be noted in this study. First, the imbalance between the domestic inbound and outbound population size in Wuhan is not considered in this study, which is a further-up work for us in the future. Second, the close contacts of quarantined patients were also isolated, but we do not have the accurate data. So, we did not consider this in our method. Third, the applicability of the five-stage model to a larger spatial scale needs validation with reported cases in larger areas. Lastly, the impact of factors, such as socio-economic conditions and healthcare conditions, should be studied in future work.

This study developed a new infectious disease model by coupling the infectious diseases model and the economic losses model. Based on the developed model, we quantitatively evaluated the total economic losses under various control measures for combatting COVID-19. The main conclusions are as follows: (1) strict social distancing (less than five daily contacts, represented by parameter r) can drastically reduce the total economic losses caused by the COVID-19 epidemic, and can stop the COVID-19 spread in a short time. Although it may cost short-term economic recession, social distancing is indeed effective to protect public health. (2) The "optimal" control measure lies in the "central valley", where the effective number of daily contacts (r) is 5~6 people, and the average waiting time for quarantined patients (g) should be as short as possible. With the increasing level of emergency responses (the lower parameter g), the minimum value of total economic losses becomes smaller; the parameter r that can minimize the total economic losses increases with the lower parameter g. Therefore, we suggest implementing strict control measures as early as possible. This not only maintains economic vitality for recovery, but also minimizes the economic burden.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/ijerph182211753/s1, Figure S1 : The five-stage model simulates infected patients in different scenarios of the effective numbers of daily contacts (r), where I is the number of infected patients; N is the total population, Figure S2 : The five-stage model simulates quarantined patients in different scenarios of the effective numbers of daily contacts (r), where Q is the number of quarantined patients; N is the total population, Figure S3 : Treatment costs under various control measures, where r is the effective number of daily contacts; g is the average waiting time for quarantined patients, Figure S4 : Quarantine-related expenditures under various control measures, where r is the effective number of daily contacts; g is the average waiting time for quarantined patients, Figure S5 : Inoperable losses under various control measures, where r is the effective number of daily contacts; g is the average waiting time for quarantined patients.

Author Contributions: Conceptualization, X.C. and W.G.; methodology, X.C. and W.G.; writingoriginal draft preparation, X.C. and W.G.; writing-review and editing, W.G., X.W. and W.Z.; funding acquisition, W.G. and X.W. All authors have read and agreed to the published version of the manuscript. 

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Epidemiological and Clinical Characteristics of 99 Cases of 2019 Novel Coronavirus Pneumonia in Wuhan, China: A Descriptive Study

Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia

Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China

Early Dynamics of Transmission and Control of COVID-19: A Mathematical Modelling Study

An Investigation of Transmission Control Measures during the First 50 Days of the COVID-19 Epidemic in China

Reconstruction of the Full Transmission Dynamics of COVID-19 in Wuhan

Association of Public Health Interventions With the Epidemiology of the COVID-19 Outbreak in Wuhan, China

Effect of Non-Pharmaceutical Interventions to Contain COVID-19 in China

The Impact of Non-Pharmaceutical Interventions, Demographic, Social, and Climatic Factors on the Initial Growth Rate of COVID-19: A Cross-Country Study

Natural and Human Environment Interactively Drive Spread Pattern of COVID-19: A City-Level Modeling Study in China

Effects of Human Mobility Restrictions on the Spread of COVID-19 in Shenzhen, China: A Modelling Study Using Mobile Phone Data

Disposable Face Masks and Reusable Face Coverings as Non-Pharmaceutical Interventions (NPIs) to Prevent Transmission of SARS-CoV-2 Variants That Cause Coronavirus Disease (COVID-19): Role of New Sustainable NPI Design Innovations and Predictive Mathematical Modelling

Quantifying SARS-CoV-2 Transmission Suggests Epidemic Control with Digital Contact Tracing

COVID-19: Challenges to GIS with Big Data

Big Data Assimilation to Improve the Predictability of COVID-19

A Systematic Approach Is Needed to Contain COVID-19 Globally

Deciphering the COVID-19 Health Economic Dilemma (HED): A Scoping Review

New Structural Economic Analysis of Anti-COVID-19 Pandemic Model of BEST Region

Hubei's Economic Performance in the First Half of 2020

Hubei's Economic Performance in 2020

Stable Recovery of the National Economy in 2020

A Literature Review of the Economics of COVID-19

Economics in the Time of COVID-19

COVID-19 and the Macroeconomic Effects of Costly Disasters; Working Paper 26987

The Socio-Economic Implications of the Coronavirus Pandemic (COVID-19): A Review

The Coronavirus and the Great Influenza Pandemic: Lessons from the "Spanish Flu" for the Coronavirus's Potential Effects on Mortality and Economic Activity; Working Paper 26866

World Economic Outlook: Managing Divergent Recoveries

Global Supply-Chain Effects of COVID-19 Control Measures

Economic Footprint of California Wildfires

Assessment of Monthly Economic Losses in Wuhan under the Lockdown against COVID-19

Economic Burden of COVID-19, China

Modelling the Economic Impact and Ripple Effects of Disease Outbreaks

COVID-19 risk assessment driven by urban spatiotemporal big data: A case study of Guangdong-Hong Kong-Macao Greater Bay Area

The Effect of Large-Scale Anti-Contagion Policies on the COVID-19 Pandemic

What Will Be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios; Working Paper 26867

Simple Planning Problem for COVID-19 Lockdown; Working Paper 26981

Optimal Targeted Lockdowns in a Multi-Group SIR Model; Working Paper 27102

A Contribution to the Mathematical Theory of Epidemics

Nowcasting and Forecasting the Potential Domestic and International Spread of the 2019-NCoV Outbreak Originating in Wuhan, China: A Modelling Study

Substantial Undocumented Infection Facilitates the Rapid Dissemination of Novel Coronavirus (SARS-CoV-2)

Sectoral Effects of Social Distancing. AEA Pap. Proc. 2021

Temporal Dynamics in Viral Shedding and Transmissibility of COVID-19

Medical Costs of Keeping the US Economy Open during COVID-19

Hubei Statistical Yearbook 2020

China Statistical Yearbook

Projecting Social Contact Matrices in 152 Countries Using Contact Surveys and Demographic Data

Time Series Analysis

The Effect of Control Strategies to Reduce Social Mixing on Outcomes of the COVID-19 Epidemic in Wuhan, China: A Modelling Study

Association between Mobility Patterns and COVID-19 Transmission in the USA: A Mathematical Modelling Study

Feasibility of Controlling COVID-19 Outbreaks by Isolation of Cases and Contacts

Estimating the Effects of Non-Pharmaceutical Interventions on COVID-19 in Europe

First-Wave COVID-19 Transmissibility and Severity in China Outside Hubei after Control Measures, and Second-Wave Scenario Planning: A Modelling Impact Assessment

The End of Social Confinement and COVID-19 Re-Emergence Risk

The Engines of SARS-CoV-2 Spread

Can the Digital Economy Help Mitigate the Economic Losses from COVID-19 in Kenya

Policy Response, Social Media and Science Journalism for the Sustainability of the Public Health System Amid the COVID-19 Outbreak: The Vietnam Lessons

The COVID-19 Pandemic and the $16 Trillion Virus

The Impact of COVID-19 and Strategies for Mitigation and Suppression in Low-and Middle-Income Countries

Healthcare Consumers' Sensitivity to Costs: A Reflection on Behavioural Economics from an Emerging Market

Acknowledgments: This work was funded by the National Key Research and Development Program of China (2012CB955501), and the Fundamental Research Funds for the Central Universities. Furthermore, we would like to thank our colleague, Yanxu Liu from the Beijing Normal University.

The authors declare no conflict of interest.