BackgroundChinese government has taken strong measures in response to the epidemic of new coronavirus (2019-nCoV) from Jan.23, 2020. The number of confirmed infected individuals are still increasing rapidly. Estimating the accurate infected population and the future trend of epidemic spreading under control measures is significant and urgent. There have been reports external icon of spread from an infected patient with no symptoms to a close contact, which means the incubation individuals may has the possibility of infectiousness. However, the traditional transmission model, Susceptible-Exposed-Infectious-Recovered (SEIR) model, assumes that the exposed individual is being infected but without infectiousness. Thus, the estimating infected populations based on SEIR model from the existing literatures seems too far more than the official reported data.
MethodsHere, we inferred that the epidemic could be spread by exposed (incubation) individuals. Then, we provide a new Exposed-identified-Recovered (EIR) model, and simulated the epidemic spreading processes from free propagation phase to extremely control phase. Then, we estimate of the size of the epidemic and forecast the future development of the epidemics under strong prevention interventions. According to the spread characters of 2019-nCov, we construct a novel EIR compartment system dynamics model. This model integrates two phases of the epidemic spreading: before intervention and after intervention. We assume that 2019-nCov is firstly spread without intervention then the government started to take strong quarantine measures. Use the latest reported data from National Health Commission of the People's Republic of China, we estimate the basic parameters of the model and the basic reproduction number of 2019-nCov. Then, based on this model, we simulate the future spread of the epidemics. Both the infected population and the spreading trend of 2019-nCov under different prevention policy scenarios are estimated. The epidemic spreading trends under different quarantine rate and action starting date of prevention policy are simulated and compared.
FindingsIn our baseline scenario, the government has taken strict prevention actions, and the estimate numbers fit the official numbers very well. Simulation results tells that, if the prevention measures are relaxed or the action starting date of prevention measures is later than Jan. 23, 2020, the peak of identified individuals would be greatly increased, and the elimination date also would be delayed. We estimate the reproductive number for 2019-nCoV was 2.7. And simulation of the baseline scenario tells that, the peak infected individuals will be 49093 at Feb.16, 2020 and the epidemic spreading will be eliminated at the end of March 2020. The simulation results also tell that the quarantine rate and the starting date of intervention action policy have great effect on the epidemic spreading. Specifically, if the quarantine rate is reduced from 100% to less than 63%, which is the threshold of the quarantine rate to contr...
With heavy air pollution and the highest CO2 emissions in the world, China is in urgent need of technology innovation to improve the energy efficiency and control the pollution emission. This study empirically investigates the impact of environmental regulation intensity, political connections, and business connections on green technology innovation in China’s firms. The authors employ a panel data regression analysis on a dataset that comprises 884 observations for A-share listed companies from 2016 to 2019, owing to the availability of data. The results show: (1) Environmental regulation intensity (ERI) has a U-shaped effect on green technology innovation (GTI), which means GTI is inhibited by ERI in the early stage but gets promoted in the long run; (2) Political connections positively moderate the relationship between ERI and GTI mainly because of crowding-out effect and resource effect; (3) Business connections have a negative impact on the relationship between ERI and GTI, resulting from knowledge acquisition and lock-in; (4) Business connections have a greater moderating effect than political connections probably because political ties lack an effective mechanism to ensure long-term cooperation with the enterprises; (5) However, with regard to those firms in the non-heavily polluting industry, both connections moderate the relationship between ERI and GTI in an opposite direction to the main effect. The research results help policy makers formulate relevant policies, based on the impact of environmental regulation and social connections on green technology innovation.
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