Aviation carbon transfer and compensation of international routes in Africa from 2019 to 2021.

As an underdeveloped region, the aviation industry in Africa is developing rapidly, and its carbon emissions play an essential role in achieving carbon neutrality in the aviation industry in underdeveloped areas. However, the problem of carbon transfer caused by passenger flow on international routes has not been addressed, especially in Africa. This paper first calculates the CO2 emissions of African international routes from 2019 to 2021 based on the Modified Fuel Percentage Method (MFPM) and the ICAO standard methods. Then, we measure African routes' carbon transfer and carbon compensation. The most carbon transfer routes between African countries and from countries outside Africa to African countries are from Ethiopia to Kenya and from Honduras to Ghana. Relatively poor countries bear a significant amount of carbon transfer.

Environment and health co-benefits of vehicle emission control policy in Hubei, China

Vehicle exhaust has been important source of atmospheric pollution in China. In terms of the environmental effects of vehicle emission control policies (VECPs), changes in air pollutants and greenhouse gases (GHG) emissions are receiving increasing attention. Hubei has implemented many traffic controls to accelerate pollution abatement. However, few studies have reported how they would affect pollutant emissions in Hubei in the future, as most concentrate on assessments during COVID-19. Further, there has been little research on whether these controls bring observable health benefits. Thus, this study comprehensively evaluates the emission of major air pollutants (including NOx, CO, VOCs, PM2.5, PM10, and PMTSP) and GHGs (CO2, CH4, and N2O) from the transportation sector concerning different VECPs in Hubei during 2015–2050, together with health outcomes. It highlights that individual VECPs contribute differently to environmental and health benefits, encouraging innovation in mechanisms and technologies to mitigate atmospheric pollution while generating health benefits.

Reliable and efficient emergency rescue networks: A blockchain and fireworks algorithm-based approach.

In recent years, coronavirus disease 2019 (COVID-19) has been a severe issue the world faces. Emergency rescue networks concerning the distribution of relief materials have gained extensive attention to tackle COVID-19 and related emergency issues. However, it is challenging to establish reliable and efficient emergency rescue networks due to information asymmetry and lack of trust among different rescue stations. In this work, we propose blockchain-based emergency rescue networks to track every transaction of the relief materials reliably and make decisions to deliver relief materials efficiently. More specifically, we propose a hybrid blockchain architecture that employs on-chain data verification to authenticate data records and off-chain data storage to reduce storage overhead. Furthermore, we propose a fireworks algorithm to efficiently calculate the optimal allocation strategies for relief materials. The algorithm provides chaotic random screening and node request guarantee techniques with good convergence. The simulation results show that integrating blockchain technology and the fireworks algorithm can significantly improve relief materials' operation efficiency and distribution quality.

Global age-structured spatial modeling for emerging infectious diseases like COVID-19.

Modeling the global dynamics of emerging infectious diseases (EIDs) like COVID-19 can provide important guidance in the preparation and mitigation of pandemic threats. While age-structured transmission models are widely used to simulate the evolution of EIDs, most of these studies focus on the analysis of specific countries and fail to characterize the spatial spread of EIDs across the world. Here, we developed a global pandemic simulator that integrates age-structured disease transmission models across 3,157 cities and explored its usage under several scenarios. We found that without mitigations, EIDs like COVID-19 are highly likely to cause profound global impacts. For pandemics seeded in most cities, the impacts are equally severe by the end of the first year. The result highlights the urgent need for strengthening global infectious disease monitoring capacity to provide early warnings of future outbreaks. Additionally, we found that the global mitigation efforts could be easily hampered if developed countries or countries near the seed origin take no control. The result indicates that successful pandemic mitigations require collective efforts across countries. The role of developed countries is vitally important as their passive responses may significantly impact other countries.

Evaluating Prediction Models for Airport Passenger Throughput Using a Hybrid Method

This paper proposes a hybrid evaluation method to assess the prediction models for airport passenger throughput (APT). By analyzing two hundred three airports in China, five types of models are evaluated to study the applicability to different airports with various airport passenger throughput and developing conditions. The models were fitted using the historical data before 2014 and were verified by using the data from 2015–2019. The evaluating results show that the models employed for evaluating perform well in general except that there are insufficient historical data for modelling, or the APT of the airports changes abruptly owing to expansion, relocation or other kinds of external forces such as earthquakes. The more the APT of an airport is, the more suitable the models are for the airport. Particularly, there is no direct relation between the complexity and the predicting accuracy of the models. If the parameters of the models are properly set, time series models, causal models, market share methods and analogy-based methods can be utilized to predict the APT of 88% of studied airports effectively.

Why were some countries more successful than others in curbing early COVID-19 mortality impact? A cross-country configurational analysis.

Why was there considerable variation in initial COVID-19 mortality impact across countries? Through a configurational lens, this paper examines which configurations of five conditions-a delayed public-health response, past epidemic experience, proportion of elderly in population, population density, and national income per capita-influence early COVID-19 mortality impact measured by years of life lost (YLL). A fuzzy-set qualitative comparative analysis (fsQCA) of 80 countries identifies four distinctive pathways associated with high YLL rate and four other different pathways leading to low YLL rate. Results suggest that there is no singular "playbook"-a set of policies that countries can follow. Some countries failed differently, whereas others succeeded differently. Countries should take into account their situational contexts to adopt a holistic response strategy to combat any future public-health crisis. Regardless of the country's past epidemic experience and national income levels, a speedy public-health response always works well. For high-income countries with high population density or past epidemic experience, they need to take extra care to protect elderly populations who may otherwise overstretch healthcare capacity.

How to avoid a local epidemic becoming a global pandemic.

Here, we combine international air travel passenger data with a standard epidemiological model of the initial 3 mo of the COVID-19 pandemic (January through March 2020; toward the end of which the entire world locked down). Using the information available during this initial phase of the pandemic, our model accurately describes the main features of the actual global development of the pandemic demonstrated by the high degree of coherence between the model and global data. The validated model allows for an exploration of alternative policy efficacies (reducing air travel and/or introducing different degrees of compulsory immigration quarantine upon arrival to a country) in delaying the global spread of SARS-CoV-2 and thus is suggestive of similar efficacy in anticipating the spread of future global disease outbreaks. We show that a lesson from the recent pandemic is that reducing air travel globally is more effective in reducing the global spread than adopting immigration quarantine. Reducing air travel out of a source country has the most important effect regarding the spreading of the disease to the rest of the world. Based upon our results, we propose a digital twin as a further developed tool to inform future pandemic decision-making to inform measures intended to control the spread of disease agents of potential future pandemics. We discuss the design criteria for such a digital twin model as well as the feasibility of obtaining access to the necessary online data on international air travel.

Mechanisms controlling the transport and evaporation of human exhaled respiratory droplets containing the severe acute respiratory syndrome coronavirus: a review.

Transmission of the coronavirus disease 2019 is still ongoing despite mass vaccination, lockdowns, and other drastic measures to control the pandemic. This is due partly to our lack of understanding on the multiphase flow mechanics that control droplet transport and viral transmission dynamics. Various models of droplet evaporation have been reported, yet there is still limited knowledge about the influence of physicochemical parameters on the transport of respiratory droplets carrying the severe acute respiratory syndrome coronavirus 2. Here we review the effects of initial droplet size, environmental conditions, virus mutation, and non-volatile components on droplet evaporation and dispersion, and on virus stability. We present experimental and computational methods to analyze droplet transport, and factors controlling transport and evaporation. Methods include thermal manikins, flow techniques, aerosol-generating techniques, nucleic acid-based assays, antibody-based assays, polymerase chain reaction, loop-mediated isothermal amplification, field-effect transistor-based assay, and discrete and gas-phase modeling. Controlling factors include environmental conditions, turbulence, ventilation, ambient temperature, relative humidity, droplet size distribution, non-volatile components, evaporation and mutation. Current results show that medium-sized droplets, e.g., 50 µm, are sensitive to relative humidity. Medium-sized droplets experience delayed evaporation at high relative humidity, and increase airborne lifetime and travel distance. By contrast, at low relative humidity, medium-sized droplets quickly shrink to droplet nuclei and follow the cough jet. Virus inactivation within a few hours generally occurs at temperatures above 40 °C, and the presence of viral particles in aerosols impedes droplet evaporation.

What matters the most in curbing early COVID-19 mortality? A cross-country necessary condition analysis.

COVID-19 represents a turbulent problem: a volatile, uncertain, complex, and ambiguous crisis, in which bounded-rational policymakers may not be able to do everything right, but must do critical things right in order to reduce the death toll. This study conceptualizes these critical things as necessary conditions (NCs) that must be absent to prevent high early mortality from occurring. We articulate a policy-institution-demography framework that includes seven factors as NC candidates for high early COVID-19 mortality. Using necessary condition analysis (NCA), this study pinpoints high levels of a delayed first response, political decentralization, elderly populations, and urbanization as four NCs that have inflicted high early COVID-19 mortality across 110 countries. The results highlight the critical role of agility as a key dimension of robust governance solutions-a swift early public-health response as a malleable policy action-in curbing early COVID-19 deaths, particularly for politically decentralized and highly urbanized countries with aging populations.

A retrospective study of Pupingqinghua prescription versus Lianhuaqingwen in Chinese participants infected with SARS-CoV-2 Omicron variants.

Background: Coronavirus disease (COVID-19) seriously endangers global public health. Pupingqinghua prescription (PPQH) is an herbal formula from traditional Chinese medicine used for treatment of SARS-CoV-2 infection. This study aims to evaluate the clinical efficacy and safety of PPQH in Chinese participants infected with the SARS-CoV-2 Omicron variant. Methods: A total of 873 SARS-CoV-2 (Omicron)-infected patients were included. Among them, the patients were divided into the PPQH group (653 cases) and LHQW group (220 cases) according to different medications. The effectiveness indicators (hematological indicators, Ct values of novel Coronavirus nucleic acid tests, and viral load-shedding time) and safety indicators (liver and kidney function and adverse events) were analyzed. Results: There was no significant difference in baseline characteristics between the PPQH group and the LHQW group, except the gender; After the treatment, the levels of IL-5, IL-6, IL-10, NK cells, and INF-α of the patients in the PPQH group showed a downward trend (p < 0.05); The viral load shedding time was 5.0 (5.0, 7.0) in the PPQH group and 5.0 (4.0, 7.0) in the LHQW group; both PPQH and LHQW can shorten the duration of symptoms of fever, cough, and sore throat. The re-positive rate of COVID-19 test was 1.5 % in the PPQH group and 2.3 % in the LHQW group. In terms of safety, the levels of γ-GTT decreased significantly (p < 0.01); gastrointestinal reaction was the primary adverse reaction, and the reaction rate was 4.7 % in the PPQH group and 9.5 % in the LHQW group. Conclusion: PPQH can shorten the length of hospital stay and improve clinical symptoms of patients with SARS-COV-2 (Omicron), and it also has a good safety profile.

Development and validation of a nomogram to predict failure of 14-day negative nucleic acid conversion in adults with non-severe COVID-19 during the Omicron surge: a retrospective multicenter study.

BACKGROUND: With the variability in emerging data, guidance on the isolation duration for patients with coronavirus disease 2019 (COVID-19) due to the Omicron variant is controversial. This study aimed to determine the predictors of prolonged viral RNA shedding in patients with non-severe COVID-19 and construct a nomogram to predict patients at risk of 14-day PCR conversion failure. METHODS: Adult patients with non-severe COVID-19 were enrolled from three hospitals of eastern China in Spring 2022. Viral shedding time (VST) was defined as either the day of the first positive test or the day of symptom onset, whichever was earlier, to the date of the first of two consecutively negative PCR tests. Patients from one hospital (Cohort I, n = 2033) were randomly grouped into training and internal validation sets. Predictors of 14-day PCR conversion failure were identified and a nomogram was developed by multivariable logistic regression using the training dataset. Two hospitals (Cohort II, n = 1596) were used as an external validation set to measure the performance of this nomogram. RESULTS: Of the 2033 patients from Cohort I, the median VST was 13.0 (interquartile range: 10.0‒16.0) days; 716 (35.2%) lasted > 14 days. In the training set, increased age [per 10 years, odds ratio (OR) = 1.29, 95% confidence interval (CI): 1.15‒1.45, P < 0.001] and high Charlson comorbidity index (OR = 1.25, 95% CI: 1.08‒1.46, P = 0.004) were independent risk factors for VST > 14 days, whereas full or boosted vaccination (OR = 0.63, 95% CI: 0.42‒0.95, P = 0.028) and antiviral therapy (OR = 0.56, 95% CI: 0.31‒0.96, P = 0.040) were protective factors. These predictors were used to develop a nomogram to predict VST > 14 days, with an area under the ROC curve (AUC) of 0.73 in the training set (AUC, 0.74 in internal validation set; 0.76 in external validation set). CONCLUSIONS: Older age, increasing comorbidities, incomplete vaccinations, and lack of antiviral therapy are risk factors for persistent infection with Omicron variant for > 14 days. A nomogram based on these predictors could be used as a prediction tool to guide treatment and isolation strategies.

Enhance antibiotic resistance and human health risks in aerosols during the COVID-19 pandemic.

Aerosols are an important route for the transmission of antibiotic resistance genes (ARGs). Since the 2019 (COVID-19) pandemic, the large-scale use of disinfectants has effectively prevented the spread of environmental microorganisms, but studies regarding the antibiotic resistance of airborne bacteria remain limited. This study focused on four functional urban areas (commercial areas, educational areas, residential areas and wastewater treatment plant) to study the variations in ARG abundances, bacterial community structures and risks to human health during the COVID-19 pandemic in aerosol. The results indicated the abundance of ARGs during the COVID-19 period were up to approximately 13-fold greater than before the COVID-19 period. Large-scale disinfection resulted in a decrease in total bacterial abundance. However, chlorine-resistant bacteria tended to be survived. Among the four functional areas, the diversity and abundance of aerosol bacteria were highest in commercial aera. Antibiotic susceptibility assays suggested elevated resistance of isolated bacteria to several tested antibiotics due to disinfection exposure. The potential exposure risks of ARGs to human health were 2 times higher than before the COVID-19 pandemic, and respiratory intake was the main exposure route. The results highlighted the elevated antibiotic resistance of bacteria in aerosols that were exposed to disinfectants after the COVID-19 pandemic. This study provides theoretical guidance for the rational use of disinfectants and control of antimicrobial resistance.

Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter

The outbreak of the COVID-19 has brought about huge economic loss and civil aviation industries all over the world have suffered severe damage. An effective method is urgently needed to accurately predict air-transport demand under the influences of such accidental factors. This paper proposes a novel predicting framework for the air-transport demand considering the uncertainties caused by accidental factors including regional wars, climatic anomalies, and virus outbreaks. By employing a seasonal autoregressive integrated moving average (sARIMA) model as the basic model, a particle filter (PF)-based sARIMA-pf model is proposed. The applicability of adapting the high-order sARIMA model as the state transition model in a PF framework is shown and proven to be effective. The proposed method has the advantage of coping with short-term prediction with known uncertainties. By conducting case studies on the prediction of air passenger traffic volume in China, the sARIMA-pf model showed better performance than the sARIMA model and improved the accuracy by 49.29% and 44.96% under the conventional and pandemic scenarios, respectively, when using the root mean square error (RMSE) as the indicator.

Establishment and application of SYBR Green I fluorescence quantitative RT-PCR method for detection of Porcine epidemic diarrhea virus

To develop a real-time fluorescent quantitative PCR method for rapid, accurate, sensitive and quantitative detection of porcine epidemic diarrhea virus (PEDV), according to the highly conserved nucleotide sequence of S gene reported by GenBank, a pair of PEDV S gene specific primers were designed, and a fluorescent quantitative RT-PCR detection method using SYBR Green I as the dye was established. The clinical samples suspected of PEDV infection were tested and compared with the results of ordinary RT-PCR. The results showed that the established standard curve of the SYBR Green I fluorescence quantitative RT-PCR method had a good linear relationship. The linear correlation coefficient R2=1, its amplification efficiency E=2.03, and the melting curve was a sharp single peak. The amplification of transmissible gastroenteritis virus, porcine parvovirus, classical swine fever virus, porcine reproductive and respiratory syndrome virus, porcine deltacoronavirus and porcine rotavirus was negative and had strong specificity. The lowest detection concentration of 1 x 101 copies/L was 100 times more sensitive than that of the ordinary RT-PCR method. The coefficient of variation of intra- and inter-assay repeatability test were both less than 2%, with good repeatability and stability. Comparing the test results of 36 clinical samples, the total coincidence rate with ordinary RT-PCR was 88.89%. The results show that the established real-time fluorescent quantitative RT-PCR detection method has strong specificity, good reproducibility, and high sensitivity, which is of great significance for the rapid and quantitative detection of PEDV.

Human genetic basis of severe or critical illness in COVID-19.

Coronavirus Disease 2019 (COVID-19) caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to considerable morbidity and mortality worldwide. The clinical manifestation of COVID-19 ranges from asymptomatic or mild infection to severe or critical illness, such as respiratory failure, multi-organ dysfunction or even death. Large-scale genetic association studies have indicated that genetic variations affecting SARS-CoV-2 receptors (angiotensin-converting enzymes, transmembrane serine protease-2) and immune components (Interferons, Interleukins, Toll-like receptors and Human leukocyte antigen) are critical host determinants related to the severity of COVID-19. Genetic background, such as 3p21.31 and 9q34.2 loci were also identified to influence outcomes of COVID-19. In this review, we aimed to summarize the current literature focusing on human genetic factors that may contribute to the observed diversified severity of COVID-19. Enhanced understanding of host genetic factors and viral interactions of SARS-CoV-2 could provide scientific bases for personalized preventive measures and precision medicine strategies.

A retrospective study of Pupingqinghua prescription versus Lianhuaqingwen in Chinese participants infected with SARS-CoV-2 Omicron variants

Background: Coronavirus disease (COVID-19) seriously endangers global public health. Pupingqinghua prescription (PPQH) is an herbal formula from traditional Chinese medicine used for treatment of SARS-CoV-2 infection. This study aims to evaluate the clinical efficacy and safety of PPQH in Chinese participants infected with the SARS-CoV-2 Omicron variant. Methods: A total of 873 SARS-CoV-2 (Omicron)-infected patients were included. Among them, the patients were divided into the PPQH group (653 cases) and LHQW group (220 cases) according to different medications. The effectiveness indicators (hematological indicators, Ct values of novel Coronavirus nucleic acid tests, and viral load-shedding time) and safety indicators (liver and kidney function and adverse events) were analyzed. Results: There was no significant difference in baseline characteristics between the PPQH group and the LHQW group, except the gender;After the treatment, the levels of IL-5, IL-6, IL-10, NK cells, and INF-α of the patients in the PPQH group showed a downward trend (p < 0.05);The viral load shedding time was 5.0 (5.0, 7.0) in the PPQH group and 5.0 (4.0, 7.0) in the LHQW group;both PPQH and LHQW can shorten the duration of symptoms of fever, cough, and sore throat. The re-positive rate of COVID-19 test was 1.5 % in the PPQH group and 2.3 % in the LHQW group. In terms of safety, the levels of γ-GTT decreased significantly (p < 0.01);gastrointestinal reaction was the primary adverse reaction, and the reaction rate was 4.7 % in the PPQH group and 9.5 % in the LHQW group. Conclusion: PPQH can shorten the length of hospital stay and improve clinical symptoms of patients with SARS-COV-2 (Omicron), and it also has a good safety profile.

Nucleic acid amplification with specific signal filtration and magnification for ultrasensitive colorimetric detection.

Recently, sensitive, fast and low cost nucleic acid isothermal amplification technologies (such as loop-mediated isothermal amplification, LAMP) have attracted great attention in the urgent needs of point-of-care testing (POCT) and regular epidemic prevention and control. However, unlike PCR which usually employs TaqMan probe to report specific signals, specific-signal-output strategies in isothermal amplification are immature and visual detection even rare, which limits their popularity in POCT. We hypothesize to address this issue by designing a visual-signal-report system to both filtrate and magnify the target information in isothermal amplification. In this work, we developed a specific signal filtration and magnification colorimetric isothermal sensing platform (SFMC for short) for ultrasensitive detection of DNA and RNA. SFMC consists of two processes: an isothermal amplification with specific signal filtration and a self-replication catalyzed hairpin assembly (SRCHA) for rapid target-specific signal magnification and outputting. With these unique properties, this biosensing platform could detect target DNA as low as 5 copies per reaction and target RNA as low as 10 copies per reaction by naked eyes. Benefited from the excellent colorimetric detection performance, this biosensing platform has been successfully used for African swine fever virus (ASFV) and SARS-CoV-2 detection.