Is economic crisis an opportunity for realizing the low-carbon transition? A simulation study on the interaction between economic cycle and energy regulation policy

The COVID-19 and the resulting financial crisis have led researchers to focus on the impact of the exogenous shock on the economy and the effectiveness of energy policy for a low-carbon transition. However, measuring this impact sophistically is notoriously fraught with difficulties. In this research, we build a combined agent-based economy–energy model to capture the change in the effectiveness of energy policy in response to an economic crisis. Simulation results show that the government can achieve its low-carbon transition development target using the regulation in the energy market, such as the emissions trading scheme policy. However, this regulation in the energy market will negatively affect the economy, and this adverse effect becomes more severe with either higher energy consumption or a lower energy capacity. Nevertheless, introducing the policy with appropriate timing, typically in the recovery phase of an economic crisis, can effectively reduce the negative impact of government regulation. Finally, some policy implications are proposed for different situations of countries and to reduce the negative effects of energy regulation policy.

Different Transmission Dynamics of Coronavirus Disease 2019 (COVID-19) and Influenza Suggest the Relative Efficiency of Isolation/Quarantine and Social Distancing Against COVID-19 in China.

BACKGROUND: Nonpharmaceutical interventions (NPIs) against coronavirus disease 2019 (COVID-19) are vital to reducing transmission risks. However, the relative efficiency of social distancing against COVID-19 remains controversial, since social distancing and isolation/quarantine were implemented almost at the same time in China. METHODS: In this study, surveillance data of COVID-19 and seasonal influenza in 2018-2020 were used to quantify the relative efficiency of NPIs against COVID-19 in China, since isolation/quarantine was not used for the influenza epidemics. Given that the relative age-dependent susceptibility to influenza and COVID-19 may vary, an age-structured susceptible/infected/recovered model was built to explore the efficiency of social distancing against COVID-19 under different population susceptibility scenarios. RESULTS: The mean effective reproductive number, Rt, of COVID-19 before NPIs was 2.12 (95% confidence interval [CI], 2.02-2.21). By 11 March 2020, the overall reduction in Rt of COVID-19 was 66.1% (95% CI, 60.1-71.2%). In the epidemiological year 2019-20, influenza transmissibility was reduced by 34.6% (95% CI, 31.3-38.2%) compared with transmissibility in epidemiological year 2018-19. Under the observed contact pattern changes in China, social distancing had similar efficiency against COVID-19 in 3 different scenarios. By assuming the same efficiency of social distancing against seasonal influenza and COVID-19 transmission, isolation/quarantine and social distancing could lead to 48.1% (95% CI, 35.4-58.1%) and 34.6% (95% CI, 31.3-38.2%) reductions of the transmissibility of COVID-19, respectively. CONCLUSIONS: Though isolation/quarantine is more effective than social distancing, given that the typical basic reproductive number of COVID-19 is 2-3, isolation/quarantine alone could not contain the COVID-19 pandemic effectively in China.

The impact of health education videos on general public's mental health and behavior during COVID-19.

BACKGROUND: COVID-19 has seriously affected people's mental health and changed their behaviors. Previous studies for mental state and behavior promotion only targeted limited people or were not suitable for daily activity restrictions. Therefore, we decided to explore the effect of health education videos on people's mental state and health-related behaviors. METHODS: Based on WeChat, QQ, and other social media, we conducted an online survey by snowball sampling. Spearman's non-parametric method was used to analyze the correlation related to mental health problems and health-related behaviors. Besides, we used binary logistic regression analyses to examine mental health problems and health-related behaviors' predictors. We performed SPSS macro PROCESS (model 4 and model 6) to analyze mediation relationships between exposure to health education videos and depression/anxiety/health-related behaviors. These models were regarded as exploratory. RESULTS: Binary logistic regression analyses indicated that people who watched the health education videos were more likely to wear masks (OR 1.15, p < 0.001), disinfect (OR 1.26, p < 0.001), and take temperature (OR 1.37, p < 0.001). With higher level of posttraumatic growth (PTG) or perceived social support (PSS), people had lower percentage of depression (For PSS, OR 0.98, p < 0.001; For PTG, OR 0.98, p < 0.01) and anxiety (For PSS, OR 0.98, p < 0.001; For PTG, OR 0.98, p = 0.01) and better health behaviors. The serial multiple-mediation model supported the positive indirect effects of exposure to health education videos on the depression and three health-related behaviors through PSS and PTG (Depression: B[SE] = - 0.0046 [0.0021], 95% CI - 0.0098, - 0.0012; Mask-wearing: B[SE] = 0.0051 [0.0023], 95% CI 0.0015, 0.0010; Disinfection: B[SE] = 0.0059 [0.0024], 95% CI 0.0024, 0.0012; Temperature-taking: B[SE] = 0.0067 [0.0026], 95% CI 0.0023, 0.0013). CONCLUSION: Exposure to health education videos can improve people's self-perceived social support and inner growth and help them cope with the adverse impact of public health emergencies with better mental health and health-related behaviors.

Predicting COVID-19 epidemiological trend by applying population mobility data in two-stage modeling.

:To predict the epidemiological trend of coronavirus disease 2019 (COVID-19) by mathematical modeling based on the population mobility and the epidemic prevention and control measures. : As of February 8，2020，the information of 151 confirmed cases in Yueqing，Zhejiang province were obtained，including patients' infection process，population mobility between Yueqing and Wuhan，etc. To simulate and predict the development trend of COVID-19 in Yueqing, the study established two-stage mathematical models，integrating the population mobility data with the date of symptom appearance of confirmed cases and the transmission dynamics of imported and local cases. : It was found that in the early stage of the pandemic，the number of daily imported cases from Wuhan (using the date of symptom appearance) was positively associated with the number of population travelling from Wuhan to Yueqing on the same day and 6 and 9 days before that. The study predicted that the final outbreak size in Yueqing would be 170 according to the number of imported cases estimated by consulting the population number travelling from Wuhan to Yueqing and the susceptible-exposed-infectious-recovered (SEIR) model; while the number would be 165 if using the reported daily number of imported cases. These estimates were close to the 170，the actual monitoring number of cases in Yueqing as of April 27，2020. : The two-stage modeling approach used in this study can accurately predict COVID-19 epidemiological trend.

Impact of socioeconomic status，population mobility and control measures on COVID-10 development in major cities of China.

:To evaluate the impact of socioeconomic status，population mobility，prevention and control measures on the early-stage coronavirus disease 2019 (COVID-19) development in major cities of China. : The rate of daily new confirmed COVID-19 cases in the 51 cities with the largest number of cumulative confirmed cases as of February 19，2020 (except those in Hubei province) were collected and analyzed using the time series cluster analysis. It was then assessed according to three aspects，that is, socioeconomic status，population mobility，and control measures for the pandemic. : According to the analysis on the 51 cities，4 development patterns of COVID-19 were obtained，including a high-incidence pattern (in Xinyu)，a late high-incidence pattern (in Ganzi)，a moderate incidence pattern (in Wenzhou and other 12 cities)，and a low and stable incidence pattern (in Hangzhou and other 35 cities). Cities with different types and within the same type both had different scores on the three aspects. : There were relatively large difference on the COVID-19 development among different cities in China，possibly affected by socioeconomic status，population mobility and prevention and control measures that were taken. Therefore，a timely public health emergency response and travel restriction measures inside the city can interfere the development of the pandemic. Population flow from high risk area can largely affect the number of cumulative confirmed cases.

Impact of Wuhan lockdown on the spread of COVID-19 in China: a study based on the data of population mobility.

This study aimed to quantitatively assess the effectiveness of the Wuhan lockdown measure on controlling the spread of coronavirus diesase 2019 (COVID-19). : Firstly，estimate the daily new infection rate in Wuhan before January 23，2020 when the city went into lockdown by consulting the data of Wuhan population mobility and the number of cases imported from Wuhan in 217 cities of Mainland China. Then estimate what the daily new infection rate would have been in Wuhan from January 24 to January 30th if the lockdown measure had been delayed for 7 days，assuming that the daily new infection in Wuhan after January 23 increased in a high，moderate and low trend respectively (using exponential, linear and logarithm growth models). Based on that，calculate the number of infection cases imported from Wuhan during this period. Finally，predict the possible impact of 7-day delayed lockdown in Wuhan on the epidemic situation in China using the susceptible-exposed-infectious-removed (SEIR) model. : The daily new infection rate in Wuhan was estimated to be 0.021%，0.026%，0.029%，0.033% and 0.070% respectively from January 19 to January 23. And there were at least 20 066 infection cases in Wuhan by January 23，2020. If Wuhan lockdown measure had been delayed for 7 days，the daily new infection rate on January 30 would have been 0.335% in the exponential growth model，0.129% in the linear growth model，and 0.070% in the logarithm growth model. Correspondingly，there would have been 32 075，24 819 and 20 334 infection cases travelling from Wuhan to other areas of Mainland China，and the number of cumulative confirmed cases as of March 19 in Mainland China would have been 3.3-3.9 times of the officially reported number. Conclusions: Timely taking city-level lockdown measure in Wuhan in the early stage of COVID-19 outbreak is essential in containing the spread of the disease in China.

Perinatal depressive and anxiety symptoms of pregnant women during the coronavirus disease 2019 outbreak in China

BACKGROUND: On January 20, 2020, a new coronavirus epidemic with human-to-human transmission was officially declared by the Chinese government, which caused significant public panic in China. In light of the coronavirus disease 2019 outbreak, pregnant women may be particularly vulnerable and in special need for preventive mental health strategies. Thus far, no reports exist to investigate the mental health response of pregnant women to the coronavirus disease 2019 outbreak. OBJECTIVE: This study aimed to examine the impact of coronavirus disease 2019 outbreak on the prevalence of depressive and anxiety symptoms and the corresponding risk factors among pregnant women across China. STUDY DESIGN: A multicenter, cross-sectional study was initiated in early December 2019 to identify mental health concerns in pregnancy using the Edinburgh Postnatal Depression Scale. This study provided a unique opportunity to compare the mental status of pregnant women before and after the declaration of the coronavirus disease 2019 epidemic. A total of 4124 pregnant women during their third trimester from 25 hospitals in 10 provinces across China were examined in this cross-sectional study from January 1, 2020, to February 9, 2020. Of these women, 1285 were assessed after January 20, 2020, when the coronavirus epidemic was publicly declared and 2839 were assessed before this pivotal time point. The internationally recommended Edinburgh Postnatal Depression Scale was used to assess maternal depression and anxiety symptoms. Prevalence rates and risk factors were compared between the pre- and poststudy groups. RESULTS: Pregnant women assessed after the declaration of coronavirus disease 2019 epidemic had significantly higher rates of depressive symptoms (26.0% vs 29.6%, P=.02) than women assessed before the epidemic declaration. These women were also more likely to have thoughts of self-harm (P=.005). The depressive rates were positively associated with the number of newly confirmed cases of coronavirus disease 2019 (P=.003), suspected infections (P=.004), and deaths per day (P=.001). Pregnant women who were underweight before pregnancy, primiparous, younger than 35 years, employed full time, in middle income category, and had appropriate living space were at increased risk for developing depressive and anxiety symptoms during the outbreak. CONCLUSION: Major life-threatening public health events such as the coronavirus disease 2019 outbreak may increase the risk for mental illness among pregnant women, including thoughts of self-harm. Strategies targeting maternal stress and isolation such as effective risk communication and the provision of psychological first aid may be particularly useful to prevent negative outcomes for women and their fetuses.