[Dynamic basic reproduction number based evaluation for current prevention and control of COVID-19 outbreak in China].

Objective: To evaluate the current status of the prevention and control of coronavirus disease (COVID-19) outbreak in China, establish a predictive model to evaluate the effects of the current prevention and control strategies, and provide scientific information for decision-making departments. Methods: Based on the epidemic data of COVID-19 openly accessed from national health authorities, we estimated the dynamic basic reproduction number R(0)(t) to evaluate the effects of the current COVID-19 prevention and control strategies in all the provinces (municipalities and autonomous regions) as well as in Wuhan and the changes in infectivity of COVID-19 over time. Results: For the stability of the results, 24 provinces (municipality) with more than 100 confirmed COVID-19 cases were included in the analysis. At the beginning of the outbreak, the R(0)(t) showed unstable trend with big variances. As the strengthening of the prevention and control strategies, R(0)(t) began to show a downward trend in late January, and became stable in February. By the time of data analysis, 18 provinces (municipality) (75%) had the R(0)(t)s less than 1. The results could be used for the decision making to free population floating conditionally. Conclusions: Dynamic R(0)(t) is useful in the evaluation of the change in infectivity of COVID-19, the prevention and control strategies for the COVID-19 outbreak have shown preliminary effects, if continues, it is expected to control the COVID-19 outbreak in China in near future.

[Inference of start time of resurgent COVID-19 epidemic in Beijing with SEIR dynamics model and evaluation of control measure effect].

Objective: To infer the start time of the resurgent COVID-19 epidemic in Xinfadi wholesale market in Beijing in June 2020 and evaluate the effect of comprehensive prevention and control measures in this epidemic. Methods: SEIR dynamics model was used to fit daily onset infections to search the start date of this resurgent COVID-19 epidemic in Beijing. The number of cumulative infections from June 12 to July 1 in Beijing were fitted considering different levels of control strength. Results: The current reemerged COVID-19 epidemic in Beijing probably started between May 22 and May 28 (cumulative probability: 95%), with the highest probability on May 25 (23%). The R(0) of the current reemerged COVID-19 epidemic was 4.22 (95%CI: 2.88-7.02). Dynamic model fitting suggested that by June 11, the cumulative number of COVID-19 cases would reached 99 (95%CI: 77-121), which was in line with the actual situation, and without control, by July 1, the cumulative number of COVID-19 cases would reach 65 090 (95%CI: 39 068-105 037). Since June 12, comprehensive prevention and control measures have been implemented in Beijing, as of July 1, compared with uncontrolled situation, the number of infections had been reduced by 99%, similar to the fitting result of a 95% reduction of the transmission rate. The sensitivity analysis showed consistent results. Conclusions: For the emergent outbreak of COVID-19, the dynamics model can be used to infer the start time of the transmission and help tracing the source of epidemic. The comprehensive prevention and control measures taken in Beijing have quickly blocked over 95% of the transmission routes and reduced 99% of the infections, containing the sudden epidemic timely and effectively, which have value in guiding the prevention and control of the epidemic in the future.

[Risk assessment of global COVID-19 imported cases into China].

Objective: To assess the risk of COVID-19 foreign imports cases to China. Methods: We collected epidemic data (cumulative daily confirmed cases in each country, cumulative confirmed imported cases), demographic data (population density, population) and information on potential source groups of tourists (the daily estimated number of overseas Chinese, overseas Chinese students, overseas workers, foreign students coming to China and flight passengers) and the global health security index (GHS) to assess and predict risk of imported cases for recent (February 1(st) to April 25(th)) and future (after April 26(th)). Results: Strong positive correlation was found among variables including the number of imported cases, cumulative confirmed cases, attack rate, number of overseas Chinese, number of overseas Chinese students, number of foreign students coming to China, number of flight passengers and GHS. In the recent risk analysis, imported cases of Russian were the highest, followed by United Kingdom, United States, France and Spain. In the future risk prediction, 44 countries including United States and Singapore are evaluated as potential high-risk countries in the future through the attack rate index of each country and the estimated average number of daily passengers. Conclusion: The risk assessment of COVID-19 imported cases can be used to identify high-risk areas in recent and future, and might be helpful to strengthen the prevention and control of the epidemic and ultimately overcome the epidemic.

[Fitting and forecasting the trend of COVID-19 by SEIR(+CAQ) dynamic model].

Objectives: Fitting and forecasting the trend of COVID-19 epidemics. Methods: Based on SEIR dynamic model, considering the COVID-19 transmission mechanism, infection spectrum and prevention and control procedures, we developed SEIR(+CAQ) dynamic model to fit the frequencies of laboratory confirmed cases obtained from the government official websites. The data from January 20, 2020 to February 7, 2020 were used to fit the model, while the left data between February 8-12 were used to evaluate the quality of forecasting. Results: According to the cumulative number of confirmed cases between January 29 to February 7, the fitting bias of SEIR(+CAQ) model for overall China (except for cases of Hubei province), Hubei province (except for cases of Wuhan city) and Wuhan city was less than 5%. For the data of subsequent 5 days between February 8 to 12, which were not included in the model fitting, the prediction biases were less than 10%. Regardless of the cases diagnosed by clinical examines, the numbers of daily emerging cases of China (Hubei province not included), Hubei Province (Wuhan city not included) and Wuhan city reached the peak in the early February. Under the current strength of prevention and control, the total number of laboratory-confirmed cases in overall China will reach 80 417 till February 29, 2020, respectively. Conclusions: The proposed SEIR(+CAQ) dynamic model fits and forecasts the trend of novel coronavirus pneumonia well and provides evidence for decision making.

[Using Markov Chain Monte Carlo methods to estimate the age-specific case fatality rate of COVID-19].

Objectives: The COVID-19 epidemic has swept all over the world. Estimates of its case fatality rate were influenced by the existing confirmed cases and the time distribution of onset to death, and the conclusions were still unclear. This study was aimed to estimate the age-specific case fatality rate of COVID-19. Methods: Data on COVID-19 epidemic were collected from the National Health Commission and China CDC. The Gamma distribution was used to fit the time from onset to death. The Markov Chain Monte Carlo simulation was used to estimate age-specific case fatality rate. Results: The median time from onset to death of COVID-19 was M=13.77 (P(25)-P(75): 9.03-21.02) d. The overall case fatality rate of COVID-19 was 4.1% (95%CI: 3.7%-4.4%) and the age-specific case fatality rate were 0.1%, 0.4%, 0.4%, 0.4%,0.8%, 2.3%, 6.4%, 14.0 and 25.8% for 0-, 10-, 20-, 30-, 40-, 50-, 60-, 70- and ≥80 years group, respectively. Conclusions: The Markov Chain Monte Carlo simulation method adjusting censored is suitable for case fatality rate estimation during the epidemic of a new infectious disease. Early identification of the COVID-19 case fatality rate is helpful to the prevention and control of the epidemic.

Magnetoelectric coupling in the paramagnetic state of a metal-organic framework.

Although the magnetoelectric effects - the mutual control of electric polarization by magnetic fields and magnetism by electric fields, have been intensively studied in a large number of inorganic compounds and heterostructures, they have been rarely observed in organic materials. Here we demonstrate magnetoelectric coupling in a metal-organic framework [(CH3)2NH2]Mn(HCOO)3 which exhibits an order-disorder type of ferroelectricity below 185 K. The magnetic susceptibility starts to deviate from the Curie-Weiss law at the paraelectric-ferroelectric transition temperature, suggesting an enhancement of short-range magnetic correlation in the ferroelectric state. Electron spin resonance study further confirms that the magnetic state indeed changes following the ferroelectric phase transition. Inversely, the ferroelectric polarization can be improved by applying high magnetic fields. We interpret the magnetoelectric coupling in the paramagnetic state in the metal-organic framework as a consequence of the magnetoelastic effect that modifies both the superexchange interaction and the hydrogen bonding.