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The outbreak of information epidemic in crisis events, with the channel effect of social media, has brought severe challenges to global public health. Combining information, users and environment, understanding how emotional information spreads on social media plays a vital role in public opinion governance and affective comfort, preventing mass incidents and stabilizing the network order. Therefore, from the perspective of the information ecology and elaboration likelihood model (ELM), this study conducted a comparative analysis based on two large-scale datasets related to COVID-19 to explore the influence mechanism of sentiment on the forwarding volume, spreading depth and network influence of information dissemination. Based on machine learning and social network methods, topics, sentiments, and network variables are extracted from large-scale text data, and the dissemination characteristics and evolution rules of online public opinions in crisis events are further analyzed. The results show that negative sentiment positively affects the volume, depth, and influence compared with positive sentiment. In addition, information characteristics such as richness, authority, and topic influence moderate the relationship between sentiment and information dissemination. Therefore, the research can build a more comprehensive connection between the emotional reaction of network users and information dissemination and analyze the internal characteristics and evolution trend of online public opinion. Then it can help sentiment management and information release strategy when emergencies occur.
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The outbreak of information epidemic in crisis events, with the channel effect of social media, has brought severe challenges to global public health. Combining information, users and environment, understanding how emotional information spreads on social media plays a vital role in public opinion governance and affective comfort, preventing mass incidents and stabilizing the network order. Therefore, from the perspective of the information ecology and elaboration likelihood model (ELM), this study conducted a comparative analysis based on two large-scale datasets related to COVID-19 to explore the influence mechanism of sentiment on the forwarding volume, spreading depth and network influence of information dissemination. Based on machine learning and social network methods, topics, sentiments, and network variables are extracted from large-scale text data, and the dissemination characteristics and evolution rules of online public opinions in crisis events are further analyzed. The results show that negative sentiment positively affects the volume, depth, and influence compared with positive sentiment. In addition, information characteristics such as richness, authority, and topic influence moderate the relationship between sentiment and information dissemination. Therefore, the research can build a more comprehensive connection between the emotional reaction of network users and information dissemination and analyze the internal characteristics and evolution trend of online public opinion. Then it can help sentiment management and information release strategy when emergencies occur.
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The current epidemiological status of the new coronary pneumonia epidemic in China is being explored to prevent and control the localized dissemination of aggregated outbreaks. This study analyzed the characteristics of new outbreaks of coronavirus disease 2019 (COVID-19) at three stages of aggregated outbreaks in Jilin Province, China, to provide a reference for the prevention and control of aggregated outbreaks. Case information were collected from all patients in Jilin Province from January 12, 2020 to the present. The epidemic was divided into three stages according to the time of onset. The first stage comprised 97 cases reported from January 12, 2020 to February 19, 2020, during which 17 aggregated outbreaks occurred. The second comprised 43 cases reported from April 25, 2020 and May 23, 2020, involving one aggregated outbreak. The third comprised 435 cases reported on January 10, 2021 and February 9, 2021, involving one aggregated outbreak. The relationship between aggregated and non-aggregated cases in the first phase of the outbreak and the difference between imported and local cases during the aggregated outbreak were assess using statistical analysis, and the differences in the baseline information between the three phases were analyzed. The incubation periods of the three phases were 10 days, 8 days, and 5 days. The number of aggregated epidemic events in Jilin Province tended to increase and then decrease over time. The clustered events in Jilin Province were divided into four categories: household contact (14 times, 51 cases); household contact and public places (one time, three cases); household contact, public places, and gatherings (one time, six cases); and household contact, public places, gatherings, and work (three times, 495 cases). Clustered events occurred mainly between January 22, 2020, and February 4, 2020. Among all cases in the first phase of the outbreak, the method of detection and the time from diagnosis to discharge were longer in aggregated cases than in non-aggregated cases, and that the source of infection and renewal cases were more frequent and more likely to be detected in the outpatient clinics during aggregated outbreaks than the imported cases. The second phase of the epidemic showed significant spatial variability (Moran's I<0, P<0.05). The third stage of the epidemic occurred in a higher proportion of individuals aged 50-90 years and within a shorter incubation period compared with the first two stages. The current focus of prevention and control of the COVID-19 epidemic in Jilin Province is to strictly implement the restrictions on gatherings and to perform timely screening and isolation of close contacts of infectious sources while strengthening the supervision of the inflow of people from outside the region. Simultaneously, more targeted prevention and control measures can be implemented for different age groups and occupations.
Assuntos
COVID-19 , Epidemias , Humanos , COVID-19/epidemiologia , SARS-CoV-2 , China/epidemiologia , Surtos de Doenças/prevenção & controleRESUMO
The Unmanned Aerial Vehicle (UAV) has been used for the delivery of medical supplies in urban logistical distribution, due to its ability to reduce human contact during the global fight against COVID-19. However, due to the reliability of the UAV system and the complex and changeable operation scene and population distribution in the urban environment, a few ground-impact accidents have occurred and generated enormous risks to ground personnel. In order to reduce the risk of UAV ground-impact accidents in the urban logistical scene, failure causal factors, and failure modes were classified and summarized in the process of UAV operation based on the accumulated operation data of more than 20,000 flight hours. The risk assessment model based on the Bayesian network was built. According to the established network and the probability of failure causal factors, the probabilities of ground impact accidents and intermediate events under different working conditions were calculated, respectively. The posterior probability was carried out based on the network topology to deduce the main failure inducement of the accidents. Mitigation measures were established to achieve the equivalent safety level of manned aviation, aiming at the main causes of accidents. The results show that the safety risk of the UAV was reduced to 3.84 × 10−8 under the action of risk-mitigation measures.
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BACKGROUND: Based on differences in populations and prevention and control measures, the spread of new coronary pneumonia in different countries and regions also differs. This study aimed to calculate the transmissibility of coronavirus disease 2019 (COVID-19), and to evaluate the effectiveness of measures to control the disease in Jilin Province, China. METHODS: The data of reported COVID-19 cases were collected, including imported and local cases from Jilin Province as of March 14, 2019. A Susceptible-Exposed-Infectious-Asymptomatic-Recovered/Removed (SEIAR) model was developed to fit the data, and the effective reproduction number (Reff) was calculated at different stages in the province. Finally, the effectiveness of the measures was assessed. RESULTS: A total of 97 COVID-19 infections were reported in Jilin Province, among which 45 were imported infections (including one asymptomatic infection) and 52 were local infections (including three asymptomatic infections). The model fit the reported data well (R2 = 0.593, P < 0.001). The Reff of COVID-19 before and after February 1, 2020 was 1.64 and 0.05, respectively. Without the intervention taken on February 1, 2020, the predicted cases would have reached a peak of 177,011 on October 22, 2020 (284 days from the first case). The projected number of cases until the end of the outbreak (on October 9, 2021) would have been 17,129,367, with a total attack rate of 63.66%. Based on the comparison between the predicted incidence of the model and the actual incidence, the comprehensive intervention measures implemented in Jilin Province on February 1 reduced the incidence of cases by 99.99%. Therefore, according to the current measures and implementation efforts, Jilin Province can achieve good control of the virus's spread. CONCLUSIONS: COVID-19 has a moderate transmissibility in Jilin Province, China. The interventions implemented in the province had proven effective; increasing social distancing and a rapid response by the prevention and control system will help control the spread of the disease.