Farmer Households' Livelihood Resilience in Ethnic Tourism Villages: A Case Study of the Wuling Mountain Area, China

Farmer households in tourist villages have been severely impacted by the COVID-19 pandemic, and the recovery of livelihood is proving difficult. In order to improve farmer households' ability to cope with external shocks, we have applied the theoretical framework of resilience to study farmer households' livelihood in ethnic tourism villages. Based on the survey data of 480 farmer households from 10 ethnic tourism villages in the Wuling Mountain area, this study constructs a livelihood resilience evaluation index system from three of the following dimensions: buffer capacity, adaptive capacity, and transformation capacity. These households are classified into three types: government-led, company-led, and community-led. In addition, the livelihood resilience and its influencing factors of each type is quantitatively assessed. The results show that the livelihood resilience of farmer households administered by the government, companies, and communities is 0.2984, 0.3250, and 0.2442, respectively. Government-led farmer households have the greatest transformation capacity, company-led farmer households have the largest buffer capacity and adaptive capacity, and community-led farmer households have the least capacity across the board. The results indicated that the company-led management of tourism development is currently the most appropriate mode of management for the local area. Four factors, namely, the number of family members engaged in tourism, the training opportunities for the development of professional skills, the education level of core family members, and the type of assistance subsidy available to a family, are the dominant obstacle factors with respect to the livelihood resilience of different types of farmer households. Finally, some recommendations are made to improve the farmer households' livelihood resilience in ethnic tourism villages based on two aspects of organization management and farmer households' behavior. The findings of this study can be used as a theoretical foundation for future research on farmer households' resilience to poverty in underdeveloped ethnic tourism villages. © 2022 by the authors.

Complex Systems of Disaster Response: The Case of COVID-19

At present, disasters frequently occur throughout the world. Due to different cultural backgrounds and organisational structures, most countries adopt network governance, hierarchical organization, and centralised management. However, the effect of management is often not satisfactory. Therefore, this paper takes the outbreak of COVID-19 in 2019 as a case to explore whether complex systems management can provide ideas to disaster response. The study demonstrates the need for complex systems in disaster response by conducting an in-depth analysis of response data in China and Australia, using the case study of the 2019 pandemic outbreak. © 2022 IEEE.

Robust SARS-CoV-2-specific and heterologous immune responses in vaccine-naive residents of long-term care facilities who survive natural infection

We studied humoral and cellular immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 152 long-term care facility staff and 124 residents over a prospective 4-month period shortly after the first wave of infection in England. We show that residents of long-term care facilities developed high and stable levels of antibodies against spike protein and receptor-binding domain. Nucleocapsid-specific responses were also elevated but waned over time. Antibodies showed stable and equivalent levels of functional inhibition against spike-angiotensin-converting enzyme 2 binding in all age groups with comparable activity against viral variants of concern. SARS-CoV-2 seropositive donors showed high levels of antibodies to other beta-coronaviruses but serostatus did not impact humoral immunity to influenza or other respiratory syncytial viruses. SARS-CoV-2-specific cellular responses were similar across all ages but virus-specific populations showed elevated levels of activation in older donors. Thus, survivors of SARS-CoV-2 infection show a robust and stable immunity against the virus that does not negatively impact responses to other seasonal viruses. This study shows that during the first wave of SARS-CoV-2 infection in England, residents of long-term care facilities who survived infection developed a robust and stable immunity against the virus that did not negatively impact responses to other seasonal viruses.

Stacking based prediction of COVID-19 Pandemic by integrating infectious disease dynamics model and traditional machine learning

Accurate prediction of 2019 novel coronavirus diseases (COVID-19) has been playing an important role in making more effective prevention and control policies during pandemic crises. The aim of this paper was to develop an innovative stacking based prediction of COVID-19 pandemic cumulative confirmed cases (StackCPPred) by integrating infectious disease dynamics model and traditional machine learning. Based on population migration characteristics, five feature indicators were first extracted from the population flow data in the early stage of this epidemic, which were collected from the National Health Commission of the People's Republic of China. Then, stacking based ensemble learning (SEL) model was established for COVID-19 prediction using traditional machine learning, including the multiple linear regression (MLR) and the tree regression model (XGBoost and LightGBM). By introducing the variable "death state", an improved Susceptible-Infected-Recovered (ISIR) model was established. Finally, a hybrid model, StackCPPred was proposed by incorporating the ISIR model outputs and the five feature indicators into the SEL model. Real data on population movements and daily cumulative number of newly confirmed cases across the country from January 23 to February 6 were used to validate our model. The results positively proved that the proposed StackCPPred model outperformed the existing models for COVID-19 prediction, as quantified by the root mean square error (RMSE), the root mean square logarithmic error (RMSLE) and the coefficient of determination (R2) (g1/41841 persons, g1/40.1 and >0.9, respectively). Furthermore, this study confirms the validity and usefulness of the StackCPPred model for COVID-19 prediction. © 2022 ACM.

The SARS-CoV-2 rS1-E-PLGA nanovaccine and evaluation of its immune effect in BALB/c mice.

OBJECTIVE: Vaccination is an important method for preventing COVID-19 infection. However, certain vaccines do not meet the current needs. To improve the vaccine effect, discard ineffective antigens, and focus on high-quality antigenic clusters, S1-E bivalent antigens were designed. MATERIALS AND METHODS: Vaccine delivery is performed using poly (lactic-co-glycolic acid) (PLGA). Here, the recombinant S1-E (rS1-E) was covered on PLGA and injected intramuscularly into mice. In total, 48 BALB/c mice were randomly divided into six groups with 8 mice in each group. The mice received intramuscular injections. Prior to vaccination, the hydrophobicity of the rS1-E and the antigenic site of the E protein were both analysed. The morphology, zeta potential, and particle size distribution of rS1-E-PLGA were examined. Anti-S1 and anti-E antibodies were detected in mouse serum by ELISA. Neutralising an-tibodies were detected by co-incubating the pseudovirus with the obtained serum. IL-2 and TNF-α levels were also measured. RESULTS: The designed recombinant S1-E protein was successfully coated on PLGA nanoparticles. rS1-E-PLGA nanovaccine has suitable size, shape, good stability, sustained release and other characteristics. Importantly, mice were stimulated with rS1-E-PLGA nanovaccines to produce high-titre antibodies and a good cellular immune response. CONCLUSIONS: Our results indicate that rS1-E-PLGA nanovaccine may provide a good protective effect, and the vaccine should be further investigated in human clinical trials for use in vaccination or as a booster.

Real-Time Measurement of Alcohol Vapours Released from Alcohol-based Hand Disinfectants and User Habits Study of Hong Kong Residents in the Pandemic of COVID-19

Based on the information suggested by World Health Organization (WHO) and Hong Kong Special Administrative Region (HKSAR) government, wearing a mask and sterilizing hands with alcohol-based hand disinfectants are effective ways to maintain good personal hygiene to prevent viral infections. This study focused on the real-time concentrations of alcohol vapor in the air associated with five alcohol-based hand disinfectants. The results indicated that the alcohol concentrations increased dramatically (max. ~46,000 ppb/g sample) in the hand-rubbing process. Hong Kong residents' survey on habits of using such disinfectants showed that 65% of people use them daily and 34% of people use them ≥ 5 times per day, indicating a high frequency of usage. About 79% of respondents claimed to have skin problems, and 18% got eyes discomfort when using these disinfectants. Despite the potential health risks of using alcohol disinfectants remaining unclear, such a large amount and frequent usage should be aware of potential health problems in the long term. © 2022 by the authors.

CLL-VR: A UK study of vaccine responses in patients with B-cell chronic lymphocytic leukaemia

B-cell chronic lymphocytic leukaemia (CLL) is associated with immune suppression and patients are at increased risk following SARS-CoV-2 infection. The Chronic Lymphocytic Leukaemia-Vaccine Response (CLL-VR) study was designed to assess immune responses following the introduction of Covid-19 vaccination in UK. Five hundred patients with CLL were recruited nationally through NHS and charity communications. Phlebotomy blood samples were taken from local patients ( n = 100) and dried blood spot samples were collected via post from participants across the UK ( n = 400). Ninety-six age-matched control subjects were also recruited locally. Samples were taken at 2-3 weeks following the first, second and third primary vaccine doses. Antibody and cellular responses against spike protein, and neutralising antibody titre to delta and omicron variant, were measured. Total serum immunoglobulin level was also determined. Responses were analysed according to clinical history, serum immunoglobulin level and vaccine type received. Donors with a clinical or serological history of prior natural infection were excluded from the analysis. Twenty percent (70/353) of participants developed a measurable antibody response after the first vaccination and this increased to 67% (323/486) following the second dose and 80% (202/254) after a third dose. The response rate in healthy controls plateaued at 100% after only two doses. The magnitude of the antibody response was also 3.7-fold lower following the second vaccine compared to controls ( n = 244;490 vs. 1821 U/ml, p < 0.0001) but increased markedly to 3114 U/ ml after third dose ( n = 51). No difference was observed in relation to the initial vaccine platform received. Multivariate analysis on 486 participants showed that BTKi therapy, history of recurrent infection and low serum antibody levels of IgA or IgM were independent prognostic markers for poor antibody response. Among participants with a detectable antibody response, a marked reduction in the ability to neutralise the delta and omicron variants of concern was noted compared to healthy controls following both the second and third dose of vaccine. Cellular responses were assessed following the second vaccine by IFN-g ELISPOT ( n = 91). Patients who had received the ChAdOx1 vaccine had similar levels to controls ( p = 0.39), while those who had received BNT162b2 had lower levels ( p < 0.0001). Five patients with poor spike-specific antibody responses to vaccination subsequently developed breakthrough infection with SARS-CoV-2 delta variant. Antibody responses and neutralisation remained poor following recovery from infection although T-cell responses were strong and only one patient required hospital admission. CLL-VR is the largest vaccine study conducted in patients with CLL and reveals diminished but comparable antibody responses to both the ChAdOx1 and BNT162b2 vaccines with some improvement following third primary dose of mRNA vaccine. In contrast T-cell responses following second dose are greater in those who received ChAdOx1 platform. Low neutralising activity against the delta and omicron variants highlights an ongoing risk for this vulnerable population despite repeated vaccination and reveals the need for alternative approaches to protection including prophylactic monoclonal antibody therapy..

Coronavirus Epidemic (COVID-19) Prediction and Trend Analysis Based on Time Series

In the global fight against the novel corona-virus pneumonia epidemic (COVID-19), a reasonable prediction of the spread of the epidemic has important reference significance for epidemic prevention and control. In order to solve the problem of time series prediction and analysis of the epidemic with limited sample data, nonlinear and high-dimensional features, this study applies the Nonlinear Auto-Regressive neural network (NAR) model for machine learning. The paper predicts the development of the epidemic in the two dimensions of the number of confirmed cases and the number of deaths in major countries in the world, and compares NAR with the traditional Logistic Regression (LR), the classic time series model ARIMA and the SEIR infectious disease dynamic model. This research provides rapid decision-making and new ideas for countries to respond to the 'post-epidemic era'. © 2021 IEEE.

Query and Clustering of Spatio-Temporal Trajectory Big Data under the Background of COVID-19

With the continuous development of sensor technology, computer technology, artificial intelligence and other advanced technologies, there are more and more researches on trajectory tracking and detection technology, which have been widely used in urban planning, traffic management, safety control and other aspects. Trajectory tracking and detection has always been the focus of research by experts and scholars. The purpose of this study is to track and detect the spatial trajectory of the infected person under the current new crown virus epidemic, to timely and accurately understand the itinerary of the new crown virus infected person and to find out all the suspected contacts that the infected person may come into contact with. The current epidemic situation in various countries has made a certain contribution. © 2021 ACM.

Antibody responses after first and second Covid-19 vaccination in patients with chronic lymphocytic leukaemia.

B-cell chronic lymphocytic leukaemia (CLL) is associated with immunosuppression and patients are at increased clinical risk following SARS-CoV-2 infection. Covid-19 vaccines offer the potential for protection against severe infection but relatively little is known regarding the profile of the antibody response following first or second vaccination. We studied spike-specific antibody responses following first and/or second Covid-19 vaccination in 299 patients with CLL compared with healthy donors. 286 patients underwent extended interval (10-12 week) vaccination. 154 patients received the BNT162b2 mRNA vaccine and 145 patients received ChAdOx1. Blood samples were taken either by venepuncture or as dried blood spots on filter paper. Spike-specific antibody responses were detectable in 34% of patients with CLL after one vaccine (n = 267) compared to 94% in healthy donors with antibody titres 104-fold lower in the patient group. Antibody responses increased to 75% after second vaccine (n = 55), compared to 100% in healthy donors, although titres remained lower. Multivariate analysis showed that current treatment with BTK inhibitors or IgA deficiency were independently associated with failure to generate an antibody response after the second vaccine. This work supports the need for optimisation of vaccination strategy in patients with CLL including the potential utility of booster vaccines.

A Comprehensive Predictive Evaluation Model Based on T-S Fuzzy Neural Network and Regression Fitting Cross Analysis

With the outbreak of COVID-19 at the end of 2019, under the requirement of in-depth study and implementation of the overall national security concept, people's health level has become the focus of people's attention, and it is also the most basic and fundamental important indicator to reflect people's livelihood. Taking Shenzhen, a city with strong comprehensive economic level, as an example, this paper uses data processing to select six major influencing factors, such as medical treatment and environment, and uses the method of regression and fitting crossover analysis to establish the fitting curve between factors and people's health level for prediction, and obtains the regression equation. On this basis, T-S Fuzzy Neural Network (T-S FNN) is used to divide the evaluation grade of regression model, make an effective evaluation of multiple factors of people's physical health level, establish a comprehensive prediction evaluation model, and obtain the gradient grade of factors affecting people's physical health correlation and their own direct factors. © 2020 IEEE.