The impacts of massive infectious and contagious diseases and its impacts on economy performance a case of wuhan, china

iew ed The Impact of Massive Infectious and Contagious Diseases and Its Impact on the Economic Performance: The Case of Wuhan, China Keywords: Economic Simulation, contagious diseases, China, Wuhan, Policy Modeling JEL Code: I15, I18 ev Corresponding First Author Mario Arturo Ruiz Estrada, Faculty of Economics and Administration (FEA) University of Malaya, 50603 Kuala Lumpur, MALAYSIA [E-mail] marioruiz@um.edu.my er r Second Author Donghyun PARK, Principal Economist, Asian Development Bank (ADB), ADB Avenue, Mandaluyong City, Metro Manila, Philippines 1550 [E-mail]: dpark@adb.org pe Third Author Evangelos Koutronas Social Security Research Centre (SSRC) Faculty of Economics and Administration (FEA) University of Malaya, 50603 Kuala Lumpur, MALAYSIA Email: evangel_gr@um.edu.my rin tn ot Fourth Author Alam KHAN, Faculty of Economics, Department of Economics, KUST, Kohat 26000, Khyber Pakhtunkhwa, Pakistan [E-mail] alamkhan@kust.edu.pk Fifth Author Muhammad TAHIR, Department of Management Sciences, Comsats Institute of Information Technology, Abbottabad, Pakistan [E-mail] tahirm@ciit.net.pk Abstract Pr ep This paper attempts to evaluate the impact of massive infectious and contagious diseases and its final impact on the economic performance anywhere and anytime We are considering to evaluate the case of Wuhan, China We are taking in consideration the case of Wuhan coronavirus to be evaluated under a domestic, national, and international level impact In this paper, we also propose a new simulator to evaluate the impact of massive infections and contagious diseases on the economic performance subsequently This simulator is entitled "The Integral Massive Infections and Contagious Diseases Economic Simulator (IMICDESimulator)." Hence, this simulator tries to show a macro and micro analysis with different possible scenarios simultaneously Finally, the IMICDE-Simulator was applied to the case of Wuhan-China respectively This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 iew ed 1.1 Introduction Pr ep rin tn ot pe er r ev In December 2019, an outbreak of respiratory illness is emerging caused by a novel (new) coronavirus (named “2019-nCoV”) that was first detected in Wuhan City, Hubei Province, China and which continues to expand Chinese health officials have reported tens of thousands of infections with 2019-nCoV in China, with the virus reportedly spreading from person-to-person in parts of that country Infections with 2019-nCoV, most of them associated with travel from Wuhan, also are being reported in a growing number of international locations At the time of this writing, Worldometer1 reported 28,726 confirmed 2019-nCoV incidents of which 3,826 are in critical condition, 565 died, and 1,170 recovered, affecting 28 countries and territories around the world (Worldometer, 2020) WHO is estimated that the novel coronavirus' case fatality rate has been estimated at around percent (WHO, 2020), substantially lower than Middle East Respiratory Syndrome MERS (34 percent) and Severe Acute Respiratory Syndrome SARS (10 percent)(Worldometer, 2020) The incubation period of the virus may appear in as few as days or as long as 14 (World Health Organization (WHO): 2-10 days; China’s National Health Commission (NHC): 2-14 days; The United States’ Centers for Disease Control and Prevention (CDC) and 10-14 days), during which the virus is contagious but the patient does not display any symptom (asymptomatic transmission) All population groups can be infected by the 2019-nCoV, however, seniors and people with pre-existing medical conditions (such as asthma, diabetes, heart disease) appear to be more vulnerable to becoming severely ill with the virus Beyond the public health impacts of regional or global emerging and endemic infectious disease events lay wider socioeconomic consequences that are often not considered in risk or impact assessments Endemic infectious deseases set in motion a complex chain of events in the economy They are rare and extreme events, highly diverse and volatile over time and across countries Estimating terrorism risk depends upon several factors that varied by the type of activity The idiosyncratic nature of endemic infectious deseases is based, among others, on the magnitude and duration of the event, the size and state of the local economy, the geographical locations affected, the population density and the time of the day they occurred If the calculation of costs associated with death loss, chronically ill cattle marketed prematurely at a discount, and treatment are are readily traceable the estimation of indirect costs such as reduced performance of the local labor force and/or the impact on the international travel and trade can be an onerous task This paper formulates an analytical framework for estimating the economic consequences of endemic infectious disease both in terms of immediate policy response in the aftermath of the desease and of medium-term policy implications for regulatory and fiscal policy The Integral Massive Infections and Contagious Diseases Economic Simulator (IMICDE-Simulator) – to evaluate an economy in times of massive infections and contagious diseases The IMICDE-Simulator is based on seven basic indicators - (i) the massive infections and diseases contagious spread intensity (cidc), (ii) the level of treatment and prevention level (ηtp); (iii) the massive infections and diseases infected causalities (-Lidc); (iv) the economic wear from massive infections and diseases contagious (Πidc); Our sources include the United Nations Population Division, World Health Organization (WHO), Food and Agriculture Organization (FAO), International Monetary Fund (IMF), and World Bank This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 ev iew ed (v) the level of the massive infections and diseases contagious multiplier (Midc); (vi) the total economic leaking from massive infections and diseases contagious (Lidc-total); and (vii) the economic desgrowth from massive infections and diseases contagious (-δidc) To illustrate and illuminate the IMICDE-Simulator, we apply the simulator to the case of Wuhan coronavirus The model investigates the uncertainty and behavioral change under a new perspective within the framework of a dynamic imbalanced state (DIS) (Ruiz Estrada & Yap, 2013) and the Omnia Mobilis assumption (Ruiz Estrada, 2011) The paper is organized as follows Section offers an overview of the massive infections and contagious diseases in China for the last twenty years Section describes Wuhan’s economy Section introduces the model Section sets a simulation framework and presents model findings for the Wuhan province Section concludes er r 1.2 A General Review of the Pandemics and Influenza Epidemics in China Pr ep rin tn ot pe The world and specially China have witnessed the pandemics and influenza epidemics from ancient time to now It affected millions of people in China and all over the world through different ways of emergence and its transmission One of them is the pandemic influenza, which is emerged and transmitted in various forms from centuries Human pandemics are produced by emergence of novel strains of influenza, which caused widespread death, illness and disruption The history showed there are five influenza pandemics occurred in the last hundred years (see Table 1) During this period, the improvement in medicine, epidemiology, and globalization process changed the way of these pandemics From the literature it is cleared that these pandemics are the outcomes of human development and due to the eruption of global landscaping according to Kuszewski and Brydak (2000) On the other hand, there are continuous improvements in the prevention, treatment and control of these infectious diseases Now with the technological advancement human beings are able to control these types of outbreaks, emergence and its transmission But if proper care is not taken, then due to globalization, free mobility, demographics and human behavior can increase spread of these pandemics easily from one place to other place and it can spread globally Therefore, it is necessary that proper planning must be present at any to avoid such types of pandemics and when it arises should not be transmitted to other areas and people There are two subtypes of Influenza virus characterized on the basis of antigenic properties of two surface glayco proteins, i.e hemagglutinin (H), and neuraminidase (N) There are 18 H subtypes and 11 N sub types identified by the US Centers for Disease Control and Prevention (Centers for Disease Control and Prevention, 2014) However, only three of them H1, H2, and H3 are causes transmission from human to human (Webby, 2003) Due to drift in Antigenic, causes changes in the encoding of genes H and N antigens This occurs continuously, and it shrinks the immune system, that causes the occurrence of seasonal influenza (Zambon (1999) Within the last hundred years there are five pandemics occurred due to the emergence of the novel influenza strain, for that human beings had no or weak immunity This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 iew ed Table Five Pandemics and Influenza Epidemics in China Spanish flu (H1N1), which occurred during 1918 to 1920 and now outbreak in China which caused approximately 40 to 50 million deaths This disaster in history is known as the greatest medical holocaust (Waring (1971) This pandemic has three different waves, the first was the spring (1918), and the second was fall (1918), while the third was winter (1918–1919) (Johnson and Mueller, 2002 and Humphries, 2013) The first and third was considered as mild, while second was considered globally disastrous, that caused about ten million deaths The number of deaths toll revised and told that the original deaths were more than the earlier declared The revised estimates in 1920s were about 21.5 million, while in 1991 it is recalculated and estimates were between 24.71 to 39.3 million (Jordan 1927 and Patterson and Pyle, 1991) Asian flu (1957–1958) Asian flu(H2N2), which occurred during 1957 to1958 due to (H2N2) strain that outbreak in China and caused one to two million deaths approximately In 1957, a new type of influenza strain was detected in the Chinese province (Yunnan) (Pyle, 1986) Human under the age of 65 years did not possess immunity to this type of strain From China this type of virus first spreads to Hong Kong, then to Taiwan, Singapore, Japan and then spread all over the world (Fukumi, 1959) This pandemic spread mainly through sea and land routes, while some of the proportion through air travel (Pyle, 1986) The global transmission mostly occurred through land routes from Russia to Scandinavian countries and then to Eastern Europe (Payne, 1958 and Langmuir, 1961) Hong Kong flu (H3N2) that occurred during the period 1968 to 1970 due to the H3N2 strain and it outbreak in China and caused deaths from 0.5 to million (Guan,et.al, 2010), (Reperant, Moesker and Osterhaus, 2016) The interesting things is that this type of pandemic is mostly spread through the air travel (Cockburn, Delon and Ferreira, 1969), (Longini, Fine and Thacker, 1986) Although this pandemic is highly transmissible, but this was milder than the earlier Asian flu tn ot Hong Kong Flu (1968–1970) pe er r ev Spanish Flu (1918–1920) rin Swine Flu (2009–2010) Pr ep Wuhan Coronavirus (2020) While the Swine flu (H1N1) that occurred over the period from 2009 to 2010 in Mexico and deaths toll reached to 575,000 (Guan,et.al, 2010) This influenza pandemic spread in 30 countries within weeks (Smith, et.al, 2009) and within four months it reached almost in 122 countries, while 134,000 cases were confirmed and 800 deaths recorded (Henderson, 2009) This type of virus detected currently in Wuhan (China) and more than 4,500 peoples are affected and spreading very rapidly to other areas and countries, so far more than 240 deaths have been recorded This type of virus causes pneumonia like illness with fever and coughing in many cases of infection With the fear to affect other people and areas, Chinese government did not allow the citizens of Wuhan to move freely to other regions, and many countries stopped travelling to China with the fear to spread virus This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 Pr ep rin tn ot pe er r ev iew ed 1.3 A General Overview of Wuhan and its Economy Wuhan is basically the capital city of Hubei province and is located in Central China The Wuhan city is comprising of three sub-parts Wuchang, Hankou and Hanyang The Wuhan city has a total physical area of 8,494 Km2 The total population is 10.60 million which makes Wuhan one of the most populous cities of central China (Gain Report, 2018) It is considered one of the main hubs for both industry and transport for the central China Cheng and Zhou (2015) highlighted the importance of Wuhan city and endorsed that it is playing a vital role in economic, transportation and educational sectors of the Chinese economy Cowley et al (2018) discussed the importance of Wuhan city in terms of transportation and commented that it has linked East with West and South with the North In recent times, Wuhan established itself as one of the largest hub of industry, commerce, culture and education (Bovenkamp and Fei, 2016) The city of Wuhan has a strong industrial base and has been considered an economic and industrial powerhouse of central China High technology industries such as Chip-making and biomedicines are playing a significant part in the economic growth process of the city (Wong et al 2019) The automobile industry is also playing a vital role in promoting the economic growth process Different economic and development zones were established in Wuhan by the government in order to grow the economy These zones include the Wuhan East Lake Hi-Tech Development Zone, Wuhan Economic and Technological Development Zone and Wuhan Wujiashan Economic and Technological Development Zone The Wuhan East Lake Hi-Tech Development Zone includes various important industries such as bio-medical, manufacturing, electronic information and energy related industries Similarly, the Wuhan Economic and Technological Development Zone is very popular for its automobile industry and it successfully created a hundred billion RMB industry in 2010 Similarly, the Wujiashan Economic and Technological Development Zone consists of food processing and high technology electronical products industries Some other important industries such as metallurgical, hydropower, shipbuilding are also located in Wuhan (Bovenkamp and Fei, 2016) Moreover, the economy of Wuhan has also attracted significant foreign direct inflows owing to the presence of low wages and increased propensity to consume (Miura, 2017) Both low wages and higher propensity of consumption are indeed the key driving forces of foreign direct investment Finally, Wuhan has also attracted investment from 230 Fortune Global 500 firms over the years (Wong et al 2019) The establishment of economic zones have helped the economy of Wuhan a great deal in subsequent years The establishment of development and high-technology zones have contributed to the industrialization process of the Wuhan economy significantly The report published by Hubei government in 2013 demonstrated that both development and high-technology zones promoted industrial growth of Wuhan city and the value of output from high-technology industry reached to more than 230 billion RMB Miura (2017) demonstrated that in 2015, the contribution of hightechnology industries in Wuhan’s GDP increased to 20.5 percent which is indeed a reflection of strong industrial capability of the Wuhan economy The official report of Hubei government of 2018 reflected that in 2017, the output value of three strategic industries such as IT, health and life and intelligent manufacturing has been increased by more than 17 percent which is indeed remarkable This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 Pr ep rin tn ot pe er r ev iew ed Lastly, the Wuhan is also famous for its tourist attractions and in 2014 it earned 28.9 billion dollars from tourism (Kemp, 2017) The economic performance of the Wuhan has been phenomenal indeed over the years According to the reports of the government of Hubei, the Wuhan economy achieved a growth rate of 7.8 percent in 2019 The economic growth of Wuhan economy is even higher than the national average growth of Chinese economy The contribution of high-technology sector and digital economy was estimated to be 24.5 and 40 percent of the GDP respectively Similarly, in 2018, the Wuhan economy grew at a remarkable growth of 10.7 percent and reached to 1484 billion RMB (Daxueconsulting, 2019) According to statistics, the GDP of Wuhan was 1090.56 billion RMB in 2015 and the growth rate of the economy was 8.8 percent which is indeed a significant improvement as compared to previous years The breakdown of GDP shows that the contribution of industrial sector is 45.7 percent in GDP followed by service sector 51 percent The share of agriculture sector in Wuhan GDP is marginal as its contribution is only 3.3 percent In 2013, Wuhan economy was the ninth largest urban economy in China as its GDP crossed 900 billion RMB (Ke and Wang, 2016) The policy makers set targets of achieving GDP worth 1900 billion RMB in 2020 with an ambitious growth rate of 11 percent (Gain Report, 2018) Overall, the growth of Wuhan economy is directly linked with the growth of Chinese economy Wuhan is considered the industrial, financial and transportation hub of Chinese economy and therefore, its growth is important for the rest of Chinese economy Important growth-promoting industries such as automotive, manufacturing, iron and steel, electronic and food processing are located in Wuhan The contribution of Wuhan economy in the overall growth of Chinese economy is quite substantial In 2019, the growth of Wuhan economy was higher than the average growth of Chinese economy The statistics of 2015 shows that the GDP growth of Wuhan was 8.8 percent which was highest in Central China and it secured 8th position among 100 major cities in China (Canada Trade Commissioner Report) Similarly, in 2018, alone the economy of Wuhan achieved a growth rate of 10.7 percent and its share in the GDP of China increased to 1.6 percent (Daxueconsulting, 2019) At the same time, it also contributed more than 60 percent to the GDP of Hubei province (Gain Report, 2018) Further, the statistics of 2018 also revealed that Wuhan’s economy was the 9th largest in mainland China in absolute terms Finally, Tan et al (2014) highlighted the economic performance of Wuhan economy and further documented that it has played a noticeable role in the development process of other Chinese cities To summarize, the economy of Wuhan has done well economically owing to the presence of sound industrial base Wuhan has developed and established well performing economic zones and at the same time have also attracted world leading firms owing to favorable business conditions The economic growth of Wuhan has been remarkable and it has contributed significantly to the overall growth of Chinese economy Therefore, the growth performance of Wuhan economy can affect the overall growth of Chinese economy This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 iew ed An Introduction to The Integral Massive Infections and Contagious Diseases Economic Simulator (IMICDE-Simulator) ep rin tn ot pe er r ev The primary objective of this paper is to set forth a simulator – The Integral Massive Infections and Contagious Diseases Economic Simulator (IMICDE-Simulator) – to evaluate an economy in times of massive infections and contagious diseases The IMICDE-Simulator is based on seven basic indicators (i) the massive infections and diseases contagious spread intensity (cidc), (ii) the level of treatment and prevention level (ηtp); (iii) the massive infections and diseases infected causalities (-Lidc); (iv) the economic wear from massive infections and diseases contagious (Πidc); (v) the level of the massive infections and diseases contagious multiplier (Midc); (vi) the total economic leaking from massive infections and diseases contagious (Lidc-total); and (vii) the economic desgrowth from massive infections and diseases contagious (-δidc) The methodology and approach used in the IMICDE-Simulator applies different elements from an alternative mathematical and graphical analytical framework To illustrate and illuminate the IMICDE-Simulator, we apply the simulator to the case of Wuhan coronavirus We believe that our research makes a significant contribution to a more systematic, analytical and accurate measurement of the economic impact of the massive infectious and diseases contagious anywhere and anytime An important value-added of the IMICDE-Simulator, in the context of contributing to a more precise understanding of any massive infectious and diseases contagious, is that it accounts for the uncertainty and behavioural change inherent in new infections and diseases or consolidation of old infections and diseases respectively The simulator does so within the theoretical framework of a Dynamic Imbalanced State (DIS) (Ruiz Estrada and Yap, 2013) and the Omnia Mobilis assumption (Ruiz Estrada, 2011) The idea is to move beyond classical economic models – e.g CGE modeling and any classic econometric modeling – to a new economic mathematical modeling and mapping of massive infections and diseases contagious - e.g ex-ante (before the massive infections and diseases contagious appear) versus ex-post (after the massive infections and diseases contagious appear) – by utilizing high resolution multidimensional graphs (Ruiz Estrada, 2017) and maps This alternative analytical framework can yield interesting and relevant insights which can improve and strengthen the measurement of the economic effects of any massive infections and diseases contagious In this section, we derive the IMICDE-Simulator presents firstly three basic indicators: (i) the massive infections and diseases contagious spread intensity (cidc); (ii) the level of treatment and prevention level (ηtp); (iii) the massive infections and diseases infected causalities (-Lidc) The IMICDE-Simulator uses three different groups of organizations The first group is the domestic health organizations –hospitals and agencies- (HDi; i= (1,2,…, ∞)) The second group is the regional health organizations (HRj; j= (1,2,…, ∞)) The last group is the large international health organizations such as the World Health Organization (WHO) (HLk; k= (1,2,…, ∞)) Pr i Initial Infection and Contagious Disease Stage The IMICDE-Simulator assumes that there are four root causes of the infection and contagious disease: (i) natural disasters (R1); (ii) humans’ disaster (R2); (iii) hybrid disasters – natural and humans’ disaster together- (R3); and (iv) unknown disasters –non-natural disasters or non-humans’ This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 iew ed disaster- (R4) These four factors directly affect “the massive infections and diseases contagious spread intensity (cidc)”, which is a function of four variables as in (1) cidc = ƒ(R1, R2, R3, R4) (1) So, the following measure is to compute the minimum and maximum level of the massive infections and diseases contagious spread intensity (cidc) through the application of the first derivative according to (2) and (3) ƒ’(cidc) = (∂cidc/∂R1) + (∂cidc/∂R2)+ (∂cidc/∂R3) + (∂cidc/∂R4) (2) ∆R1→0 ∆R2→0 ∆R3→0 (3) ev ƒ’(cidc) = ∑(lim ∆cidc/∆R1)+ (lim ∆cidc/∆R2)+ (lim ∆cidc/∆R3)+ (lim ∆cidc/∆R4) ∆R4→0 er r Moreover, the massive infections and diseases contagious spread intensity (cidc) applies a second derivative to find the inflection point according to Expression ƒ”(R1, R2, R3, R4)= (∂2cidc/∂R12) + (∂2cidc/∂R22) + (∂2cidc/∂R32)+ (∂2cidc/∂R42) (4) pe To probe the massive infections and diseases contagious spread intensity (cidc), we apply the Jacobian determinants under the first-order derivatives (see Expression 5) ∂cidc/∂R1 ∂cidc/∂R2 ’ |J |= ∂cidc/∂R3 ∂cidc/∂R4 (5) tn ot On the other hand, the application of the Jacobian determinants under the second-order derivatives can help to find the inflection point in the massive infections and diseases contagious spread intensity (cidc) between the two players: (i) the health organizations effectiveness (hospitals and agencies) (P1) and (ii) all sick patients from a massive infection and disease contagious under control (P2) see Expression ∂2cidc/∂R3 ∂2cidc/∂R42 (6) rin | J’’ | = ∂2cidc/∂R12 ∂2cidc/∂R22 Pr ep Consequently, the initial massive infections and diseases contagious stage is necessary to assume that any massive infections and diseases contagious spread intensity (cidc) (endogenous variable) is going to determine the level of treatment and prevention level (ηtp) (exogenous variable) in the form of interaction among the domestic health organizations –hospitals and agencies- (HDi; i= (1,2,…, ∞)), the regional health organizations (RHi; i= (1,2,…, ∞)), and the large international health organizations such as world health organization (WHO) (HLk; k= (1,2,…, ∞)) In this part of the IMICDE-Simulator if the massive infections and diseases contagious spread intensity (cidc) is escalating then the level of treatment and prevention level (ηtp) is going to be more intensive until all possibilities to eradicate less causalities and potential causalities are exhausted Hence, the level of This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 iew ed treatment and prevention level (ηtp) depends directly on the massive infections and diseases contagious spread intensity (cidc) in the short run ot pe er r ev Fig The Relationship between the massive infections and diseases contagious speed intensity (cidc) and the level of treatment and prevention level (ηtp) Source: Authors ep rin tn Figure shows the relationship between the massive infections and the diseases contagious spread intensity (cidc) and the level of treatment and prevention level (ηtp) The relationship is a logarithmic curve in the 2-dimensional Cartesian plane according to Expression The interaction of three organizations such as the domestic health organizations (DHO), the regional health organizations (RHO), and the large international health organizations such as world health organization (WHO) may play a crucial role in the level of treatment and prevention level (ηtp) If the diseases contagious spread intensity (cidc) rises, then the level of treatment and prevention level (ηtp) will play an important role in reducing number of causalities from any massive infections and diseases contagious efficiently according to figure cidc = xlog2(ηtp) => { ηtp/ηtp : R ∩ DHO, RHO, WHO} (7) Pr ii The Rapidly Infection and the Disease Contagious Spread Stage The rapidly infection and the disease contagious spread stage consists of two stages – (i) the national infection and disease spread stage and (ii) the worldwide infection and disease spread stage This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 P1(Rd) ≠ P2(Si) (8) iew ed ii.a The National Infection and Disease Spread Stage In the national infection and disease spread stage, it is necessary to assume that both players such as (i) the domestic health organizations effectiveness –hospitals and agencies- control a massive infection and disease contagious (P1) and (ii) all sick patients from a massive infection and disease contagious under control (P2) have different levels of Respond (Rd) and Safety (Si) [see (8)] P1(∆cidc) ≠ P2(∆cidc) ev Therefore, the massive infections and the diseases contagious spread intensity (cidc) for both players (P1, P2) have different proportions (∆) according to (9) (9) er r Nevertheless, the nine variables used by both players (P1, P2) show the different proportions (∆) P1(∆cidcrespond) ≠ P2(∆cidcsafety) (10) ot pe In the national infection and disease spread stage, both players fully exist different proportions of expansion to find its critical point and solve fully complete to cover fully the national infection and disease spread control This means that if the massive infections and the diseases contagious spread intensity (cidc) reaches its maximum limit then the level of treatment and prevention level (ηtp) success (see Expression, 11) cidcmax = ƒ’(ηtp) = ∂xlog2(cidc)/∂ηtp > (11) tn Accordingly, this part of the IMICDE-Simulator requires the application of a second derivative to observe the estimate the inflection point cidcmax = ƒ”(ηtp) = ∂2xlog2(cidc)/∂ηtp2 > (12) Pr ep rin ii.b The Worldwide Infection and Disease Spread Stage If a worldwide infection and disease spread starts now then the respond (Rd) and safety levels (Si) needs to take fast actions quickly, butt in different magnitudes [P1 (∆Rd) ≠ P2 (∆Si)] The diseases contagious spread intensity (cidc) is going to define the level of treatment and prevention level (ηtp) worldwide respectively The massive infections and diseases infected causalities (-Lidc) is calculated using nine main variables These nine variables are based on: (i) the late mass media information systems to the general public (k1); (ii) the limited hospital emergencies access (k2); (iii) the limited medicine diversity access (k3); (iv) the limited social platform protections access (k4); (v) the higher water pollution levels (k5); (vi) the higher air pollution (k6); (vii) a poor healthiness measures (k7); (viii) the limited international health cooperation (k8); and (ix) a basic knowledge of health education (k9) see Expression 13 The IMICDE-Simulator also assumes that in the long run a high diseases 10 This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 ’ | J (∆K)| = iew ed contagious spread intensity (cidc) is going to define the massive infections and diseases infected causalities (-Lidc) directly Hence, an uncontrolled national massive infection and disease contagious spread albeit to different places worldwide dramatically ∂k1(t+1)/∂k1(t-1) ∂k2(t+1)/∂k2(t-1) ∂k3(t+1)/∂k3(t-1) ∂k4(t+1)/∂k4(t-1) ∂k5(t+1)/∂k5(t-1) ∂k6(t+1)/∂k6(t-1) ∂k7(t+1)/∂k7(t-1) ∂k8(t+1)/∂k8(t-1) ∂k9(t+1)/∂k9(t-1) -Lidc = / | J’ (∆K) | (14) ev The final calculation is shown in (14) (13) er r Therefore, the economic wear from the massive infections and diseases contagious (Πidc) depends on the changes of the diseases contagious spread intensity (cidc) and the massive infections and diseases infected causalities (-Lidc) according to expression 15 Π = ƒ(cidc, -Lidc) (15) pe The final step is to calculate the economic wear from the economic wear from the massive infections and diseases contagious (Πidc) according to expression 16 Πidc = [∫∫01 (-Lidc) [∫01 (cidc) dt] dt] (16) tn ot The next step is to specify the limits of each variable involved in the calculation of the economic wear from massive infections and diseases contagious (Πidc) – i.e ensure that the limit is between and Πidc = [∫01 -Lidc(cidc)-ntdt = lim -Lidc(cidc)-ntdt] (17) Y ->1 ep rin To find the present value of the economic wear from massive infections and diseases contagious (Πidc) under a uniform rate of the diseases contagious spread intensity (cidc) and the patients’ massive infections and diseases causalities (-Lidc) per year, we assume a continuous discount rate of –n Since we simply take the limit of a proper integral in evaluating an improper integral, the final result is represented in the expression 18 Πidc = [-Lidc∫01 (cidc)-nt dt = [-1/n (cidc)-nt ]01] (18) Y ->1 Pr We estimate the massive infections and diseases contagious (Πidc) by first-order derivatives (see Expression 19) At the same time, we apply the second-order derivative on the economic wear from the massive infections and diseases contagious (Πidc) to find the inflection point see expression 20 11 This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 iew ed Πidc‘ = ∂Πidc(t)/∂Πidc(t+1) (19) Πidc” = ∂2Πidc(t)/∂Πidc2(t+1) (20) Hence, the boundary conditions for the economic wear from the massive infections and diseases contagious (Πidc) are equal to the expression 21 Πidc' = ∂Πidc’0/∂T│t=0 = 0, ∂Πidc’1/∂T│t=1 = 1, ∂Πidc’2/∂T│ t=2 = 2, …, ∂ Πidc’∞/∂T│ t=∞ = ∞ (21) rin tn ot pe er r ev iii Post Massive Infection and Disease Contagious Recovery Initially, all sick patients from a massive infection and disease contagious (P2) show a considerable deceleration respectively Hence, we can calculate the final amount of the massive infections and diseases causalities (-Lidc) and the economic wear from the massive infections and diseases contagious (Πidc) The IMICDE-Simulator assumes that all organizations such as national, regional, and the large international health organizations such as world health organization (WHO) need to unified efforts will find it difficult to respond from the post massive infection and disease contagious recovery The recovery of the economic wear from the massive infections and diseases contagious (Πidc) from the post massive infection and disease contagious recovery will levy huge burden to its own economy which will slow down the domestic and global economy Intuitively, recovery from the economic wear from the massive infections and diseases contagious (Πidc) needs a considerable period of time until the infection and disease has a stronger and effective medication and a massive systematic control of quarantine To improve the economic wear from the massive infections and diseases contagious (Πidc) requires a multilateral reconstruction plan, international assistance, and institutional and society re-organizing in order to rebuild any economy In the long run the recovery of all sick patients from a massive infection and disease contagious can experience different magnitudes (∆) At the same time, this recovery depends highly on the reduction of the massive infections and diseases causalities (-Lidc) Additionally, the recovery of all sick patients from a massive infection and disease contagious highly depend on their integral health system, civil society cooperation, military and emergency forces, and political support until the massive infections and diseases causalities (-Lidc) is equal or close to zero -Lidc = (22) Pr ep iv The Level of the Massive Infections and Diseases Contagious Multiplier (Midc) The level of the massive infections and diseases contagious multiplier (Midc) calculation is equal to one divided by the final result from the annual population growth rate (∆Pidc-annual) minus the annual the post massive infection and disease causalities growth rate (∆-Lidc-annual) Subsequently, we can observe how any massive infection and disease contagious magnitude allows us to elaborate more elaborated policies using the formula below (see Expression 23): Midc = / ((∆Pidc-annual) – (∆-Lidc-annual)) (23) 12 This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 Economic Desgrowth from Infections and Diseases Contagious (-δidc) In this section, we discuss the concept of the economic desgrowth from massive diseases contagious (-δidc) (Ruiz Estrada, Yap, and Park, 2014), which plays an essential role in the construction of the IMICDE-Simulator The main objective of inclusion of “economic desgrowth from massive infections and diseases contagious (-δidc)” is to create a health-socio-economic indicator that can help us to analyze how controlled and non-controlled massive diseases contagious can adversely affect GDP in the short run The economic desgrowth from massive diseases contagious diseases contagious (-δidc) is delineated as “an indicator that can show the impact of any massive infections and diseases contagious leakage, originated from non-controlled infections and diseases that can bear on the execution of the final GDP formation into a period of one year” Additionally, the economic desgrowth from infections and diseases contagious (-δidc) assumes that there are irregular oscillations in different periods by applying the simple rule of irregular series The IMICDE-Simulator assumes that any infections and diseases contagious is perpetually in a province of constant chaos and subject to different degrees of infections and diseases contagious ratio coverages The economic desgrowth from massive infections and diseases contagious (-δidc) applies different random intervals, which builds its potential to analyze unexpected shocks from different non-controlled massive infections and diseases contagious These are the massive infections and diseases contagious that cannot be anticipated and monitored easily by traditional methods of linear and non-linear mathematical modelling In addition, the IMICDE-Simulator assumes that economic desgrowth from massive infections and diseases contagious (-δidc) has a substantial connection of total economic leaking from massive infections and diseases contagious (Lidc-total) The total economic leaking from massive infections and diseases contagious (Lidc-total) is based on nine variables: (i) α11 is equal to V1 (food consumption) to the power of ε1 (speed of consumption growth rate); (ii) α12 is equal to V2 (exports) to the power of ε2 (exports volume dynamicity growth rate); (iii) α13 is equal to β3 (imports) to the power of ε3 (imports volume dynamicity growth rate); (iv) α14 is equal to V4 (airways and tourism) to the power of ε4 (arrives to the country growth rate); (v) α21 is equal to V5 (exchange rate) to the power of ε5 (depreciation growth rate); (vi) α22 is equal to V6 (government spending) to the power of ε6 (public health spending growth rate); (vii) α23 is equal to V7 (sells online) to the power of ε7 (customers respond growth rate); (viii) α24 is equal to by V8 (financial service) to the power of ε8 (stock market performance growth rate); (ix) α31 is equal to V9 (public services –electricity, water, education) to the power of ε9 (public services demand growth rate) The final measurement of total economic leaking from massive infections and diseases contagious (Lidc-total) is derived by applying a large number of multidimensional partial derivatives on each variable (9 variables) to evaluate the changes of each variable (9 variables) based on the first derivative (between the present year (t+1) and the previous year (t-1) (see Expression 24) ΔVi = ∑∂Viε (t+1)/∂Viε (t-1) ≥ R+ ≤ (24) ep rin tn ot pe er r ev iew ed v Pr Next step is to convert from ΔViε to ∆Vi-ε (see Expression 25) [0 ≤ 1/∂Viε ≥ 1] = [0 ≤ ∂Vi-ε ≥ 1] (25) 13 This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 iew ed Where the exponent –ε can be replaced by any of the eight different exponents in the expression 26 Vi-ε = (-ε1, -ε2, -ε3, -ε4, -ε5, -ε6, -ε7, -ε8, -ε9) (26) Initial conditions ex-ante (see Expression 27) and final conditions ex-post (see Expression 28) ε1│t-1=0 = 0, ε2│t-1=0 = 0, -ε3│t-1=0 = 0, ε4│t-1=0 = 0, ε5│t-1=0 = ε6│t-1=0 = 0, ε7│t-1=0 = 0, ε8│t-1=0 = 0, ε9│t-1=0 = (27) ev ε1│t+1= ∞ = ∞, ε2│t+1= ∞ = ∞, ε3│t+1= ∞ = ∞, ε4│t+1=∞ = ∞, ε5│t+1= ∞ = ∞, ε6│t+1= ∞ = ∞, ε7│t+1= ∞ = ∞, ε8 │t+1= ∞ = ∞, ε9 (28) er r Next step in this part of the IMICDE-Simulator need to run nine first partial derivatives simultaneously to evaluate all possible changes in each economic leaking from massive infections and diseases contagious (Lidc-total) in a fixed period of time (one year) according to all expressions (29), (30), (31), (32), (33), (34), (35), (36), (37) pe α’11 = [0 ≥ ∂V1ε1 (t+1)/∂V1ε1 (t-1) ≤ 1] (29); α’12 = [0 ≥ ∂V2ε2(t+1)/∂V2ε2(t-1) ≤ 1] (30); α’13 = [0 ≥ ∂V3ε3 (t+1)/∂V3ε3(t-1) ≤ 1] (31); α’21 = [0 ≥ ∂V5ε5(t+1)/∂V5ε5(t-1) ≤ 1] (32); α’22 = [0 ≥ ∂V6ε6 (t+1)/∂V6ε6(t-1) ≤ 1] (33); α’23 = [0 ≥ ∂V7ε7(t+1)/∂V7ε7(t-1) ≤ 1] (34); α’31 = [0 ≥ ∂V9ε9(t+1)/∂V9ε9 (t-1) ≤ 1] (35); α’32 = [0 ≥ ∂V10ε10(t+1)/∂V10ε10 (t-1) ≤ 1] (36); α’33 = [0 ≥ ∂V11ε11(t+1)/∂V11ε11(t-1) ≤ 1] (37) tn ot The next step in the calculation of total economic leaking from massive infections and diseases contagious (Lidc-total) is to calculate the denominator by applying the Jacobian determinant under the first-order derivatives At the same time, we apply an inverse matrix according to the expression 38 rin j-1 = -1 α’11 α’12 α’13 α’21 α’22 α’23 α’31 α’32 α’33 (38) ep The final step is to determine the total economic leaking from massive infections and diseases contagious (Lidc) by dividing by the inverse matrix from expression 46 to the power of refer to the expression 39 Lidc = 1/(j-1)2 (39) Pr Lastly, it is possible to calculate economic desgrowth from diseases contagious (-δidc) as in the expression 40 14 This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 iew ed (40) ev The computation of the economic desgrowth from massive infections and diseases contagious (-δidc) is based on the final GDP in real prices (GDPreal) and the total economic leaking from massive infections and diseases contagious (Lidc-total) from the expression 40 This part of the IMICDESimulator reminds us that total economic leaking from massive infections and diseases contagious (Lidc-total) always affects economic desgrowth from massive infections and diseases contagious (-δidc) behavior according to figure tn ot pe er r Fig The relationship between the total economic leaking from massive infections and disease contagious (Lidc) and the economic desgrowth from massive infections and disease contagious (δidc) Source: Authors rin Boundary conditions for economic desgrowth from massive infections and disease contagious (-δidc) is equal to the expression 41 -δ’dc = ∂-δ’idc0/∂T│t=0 = 0, ∂-δ’idc1/∂T│t=1 = 1, ∂-δ’idc2/∂T│ t=2 = 2, …, ∂-δ’idc∞/∂T│ t=∞ = ∞ (41) Pr ep On the other hand, the full potential GDP (GDPreal) calculation is shown in the expressions 42 and 43 Ξ = (-δidc + ∆GDPreal) *-1 (42) GDPreal = ([1+ Ξ]*∆GDPreal)*100% (43) 15 This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 expression 44 GDPPot = [(GDPreal) - (-δidc)]/100% iew ed Therefore, it is possible to assess full potential GDPPot in real prices (GDPPot) by using the (44) The economic desgrowth from massive infections and diseases contagious (-δidc) is based on the application of the Omnia Mobilis assumption of Ruiz Estrada and Park (2018) to generate the relaxation of the total economic leaking (Lidc-total) calculation (non-controlled and controlled events) and the full potential GDP (GDPPot) (see Expression 44) Pr ep rin tn ot pe er r ev The Application of IMICDE-Simulator on the Case of Wuhan, China: According to the IMICDE-Simulator, it is possible to observe that the massive infections and diseases contagious speed intensity (cidc) between SARS in year 2003/2004 (Hong Kong (cidc) = 0.53 with a probability of contagious is equal to P=3/10,000 people) and Coronavirus in year 2020 (Wuhan (cidc) = 0.77 with a probability of contagious is equal to P=10/10,000 people) This calculation is based on the number of cases daily in a period of 12 days On another hand, the massive infections and diseases contagious speed intensity (cidc) in the case of SARS between domestic expansion (0.37/1) and global expansion (0.49/1) However, the massive infections and diseases contagious speed intensity (cidc) in the case of Coronavirus between domestic expansion (0.77/1) and global (0.35/1), it is mean that SARS shows a fast expansion globally more than locally and vice versa Therefore, we can confirm that the Coronavirus is more deadly than SARS domestically, we can confirm from now anytime can appear a new virus mutation with more strong defences and a high difficulty to fight and control in areas with high population concentration In the case of the level of treatment and prevention level (ηtp) between SARS in year 2003/2004 (Hong Kong (ηtp) = 0.82 with a capability to attend cases of beds/for each 1,000 people) and Coronavirus in year 2020 (Wuhan (ηtp) = 0.39 with a capability to attend cases of beds/for each 10,000 people) We can observe that main land China is not prepared for an immediately massive infections and diseases contagious action plan and infrastructure Only, recently the Chinese government is building a mega hospital in few days at Wuhan to attend more cases with Coronavirus Hence, the patients’ massive infections and diseases infected causalities (-Lidc) between SARS in year 2003/2004 (Hong Kong (-Lidc) = 0.43 with a probability of SARS causalities is equal to P=1 causality/100,000 people) and Coronavirus in year 2020 (Wuhan (-Lidc) = 0.73 with a probability of Coronavirus causalities is equal to P=3 causalities/10,000 people) respectively The economic wear from massive infections and diseases contagious (Πidc) between SARS in year 2003/2004 (Hong Kong (Πidc) = 0.24 and Coronavirus year 2020 (Wuhan (Πidc) = 0.64 We can observe that the impact of Coronavirus in year 2020 is going to have times more negative impact on the Chinese economy than SARS in year 2003/2004 according to our results Subsequently, the level of the massive infections and diseases contagious multiplier (Midc) between SARS in year 2003/2004 (Hong Kong (Midc) = 0.35 and Coronavirus year 2020 (Wuhan (Midc) = 0.75 These results 16 This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 er r ev iew ed can show us the magnitude of any massive infections and diseases contagious multiplier effect and its impact on the short run anywhere and anytime In fact, the total economic leaking from massive infections and diseases contagious (Lidc-total) show that between SARS in year 2003/2004 (Hong Kong (Lidc-total) = -0.15 and Coronavirus in year 2020 (Wuhan (Lidc-total) = -0.45 It is mean that by each one percent of the GDPfull-potential growth rate of China in the present year, China can lose easily approximately -0.45 per a unit of growth rate Additionally, we can observe the next results using the nine sub-variables of (Lidc-total): (i) food consumption = -0.39; (ii) exports = -0.35; (iii) imports = +0.35; (iv) airways and tourism = -0.75; (v) exchange rate = -0.35; (vi) government spending = +0.45; sells online = -0.37; (viii) financial service = -0.55; (ix) public services = -0.35 Finally, the economic desgrowth from massive infections and diseases contagious (-δidc) between SARS in year 2003/2004 (Hong Kong (-δidc) = -0.17 and Coronavirus year 2020 (Wuhan (-δidc) = -0.45) According to our calculations, China economy can drop its GDP (year 2019) = US$ 14.30 trillion dollars (GDPreal-price = 6.2%) to GDP (year 2020) = US$ 10.00 trillion dollars (GDPreal-price = 4.3%) (see Figure 4) We predict that China can lose from its GDPrealprice between 1.9% to 2% Pr ep rin tn ot pe Conclusions The Wuhan Coronavirus is a major infections and diseases contagious in Asia with outsized economic repercussions for the Chinese economy The Chinese economy is the second biggest economy within the European Union (EU) and U.S., a global trade and manufacturing center The uncontrolled Wuhan Coronavirus is still unclear at the time of this writing Assessment of the potential economic effects of Wuhan Coronavirus is unpredictable and inconsistent to calculate the final impact on the Chinese economy and globally More recently, in line with China emergence as a globally significant economic power, China has become a major trade partner of the world economy, which are semi-open and highly integrated into the global economy The central objective of this paper is to empirically assess the effect of Wuhan Coronavirus on the Chinese trade and financial markets To so, we develop a new simulator – the IMICDESimulator (The Integral Massive Infections and Contagious Diseases Economic Simulator) The simulator is based on seven main indicators, namely (i) the massive infections and diseases contagious speed intensity (cidc), (ii) the level of treatment and prevention level (ηtp); (iii) the patients’ massive infections and diseases infected causalities (-Lidc); (iv) the economic wear from massive infections and diseases contagious (Πidc); (v) the level of the massive infections and diseases contagious multiplier (Midc); (vi) the total economic leaking from massive infections and diseases contagious (Lidc-total); and (vii) the economic desgrowth from massive infections and diseases contagious (-δidc) To assess the impact of Wuhan Coronavirus on the Chinese economy, we use the IMICDE-Simulator to analyze and compare pre-massive infections and diseases contagious spread versus post- massive infections and diseases contagious spread The comparative analysis indicates that Wuhan Coronavirus will have a deep negative economic effect on the Chinese economy More precisely, our simulation results indicate that the Chinese GDPreal-prices falls from (GDP (year 2019) = US$ 14.30 trillion dollars and GDPreal-prices = 6.2%) to (GDP (year 2020) = US$ 10.00 trillion dollars and GDPreal-prices = 4.3%) (See Figure 4) In addition, the Wuhan Coronavirus will affect the economic 17 This preprint research paper has not been peer reviewed Electronic copy available at: https://ssrn.com/abstract=3527330 ev iew ed growth of East Asia and Southeast Asia considerably Finally, it is important to note that the IMICDE-Simulator represents a useful new analytical tool which can help policymakers and researchers evaluate the effect of massive infections and diseases contagious on the economy, international trade and financial transactions domestically and globally The small amount of economic studies about the impact of massive infections and diseases contagious and its impact on the economic performance in the short run, however, important in that they highlight the areas that are disproportionately prone to be evaluated deeply, such as the role of relationship between health prevention programs and healthiness systems because of the uncertainty of appearance of any massive infections and diseases contagious anytime and anywhere To engage civil society, government, and private sector to planning and coordinate dynamic and suitable programs to monitoring massive infections and diseases contagious just at time should be implemented rin tn ot pe er r Fig the Visualization of the Chinese GDP after Coronavirus effect between year 2019 and year 2020 Source: (Ruiz Estrada, 2017) ep References Asian Development Bank (2020) Database Online available at: https://www.adb.org/ Accessed on 05/02/2020 Bovenkamp, J & Fei, Y (2016) Economic Overview of Hubei Province Netherland Enterprise Agency Online available at: https://www.rvo.nl/sites/default/files/2016/08/Economic-overview-Hubeiprovince-China.pdf Accessed on 31/01/2020 Pr Canadian Trade Commissioner Service (2016) Economic Profile of China’s Hubei Province Online available at: 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and diseases contagious leakage, originated from non-controlled infections and diseases that can bear on the execution of the