Respiratory Infectious Diseases Research Articles

Transmission of respiratory diseases in high-metabolic environments: A case study of gym

Outbreaks of respiratory infectious diseases have often been reported in fitness centers, likely attributed to high population density, extensive shared surfaces, and elevated metabolic equivalent (MET) levels. This study analyzed the behaviors of 30 gym attendees to establish a connection between exercise intensity and virus exposure. Close interactions among participants were tracked using self-developed wearable devices that utilized computer vision technologies, while surface-contact behaviors were recorded using video cameras. A multi-route transmission model for respiratory infectious diseases was subsequently created, integrating the observed behaviors. The Omicron variant of COVID-19 served as a case study to evaluate infection risk via various transmission routes and to assess the efficacy of interventions. The METs during physical activity were about 3.5 times higher than those recorded at rest. The average interpersonal distance during close interactions in the gym was measured at 0.82 m, with 36.7 % of interactions occurring face-to-face. On average, the participants made contact with surfaces 770.3 times per hour, with 517.5 of these contacts involving public surfaces. The hourly infection rate was calculated at 18.5 %, with long-range airborne transmission and close contact accounting for 70.1 % and 28.5 % of the cases, respectively. To mitigate transmission risk, several intervention scenarios were modeled. These included (1) 100 % mask-wearing with N95 masks and occupancy reduced to 62 % (25 m2/person); (2) 100 % mask-wearing with surgical masks and occupancy reduced to 26 % (59.6 m2/person); (3) no mask-wearing, with occupancy reduced to 18 % (86.1 m2/person). All scenarios fulfilled the criteria for achieving an Rt below 1, indicating that under these conditions, gyms could be reopened safely.

Screening potential biomarkers for acute respiratory infectious diseases from antipyretics, antiviral, and antibiotics in wastewater

Since the onset of COVID-19, respiratory diseases have emerged as a focal concern within the field of public health. This study aims to reveal the prevalence of acute respiratory infectious diseases by screening antipyretic, antiviral, and antibiotic biomarkers through wastewater analysis. Samples were collected over a seven-day period each year in 2022, 2023, and 2024 from a northern city in China, assessing the concentrations of two antipyretics (paracetamol and ibuprofen), one antiviral drug (oseltamivir), eleven antibiotics, and three pathogens (influenza A, influenza B, and Mycoplasma pneumoniae). The usage of most antipyretics and antibiotics was higher in 2023 and 2024, primarily due to the outbreak of COVID-19 in 2023 and the prevalence of influenza A, influenza B, and Mycoplasma pneumoniae in 2024. The prevalence assessed using antipyretics (2.68 %) and pathogens (2.70 %) demonstrated a high degree of consistency, whereas the prevalence estimated using antibiotics and antiviral drugs was only 0.53 % and 0.36 %, respectively. Antibiotics are generally used to treat a broad spectrum of bacterial infections rather than targeting a specific pathogen, so their presence in wastewater may not directly reflect the prevalence of a particular disease. In contrast, antipyretics and specific pathogens exhibit a stronger correlation, suggesting that they may serve as more reliable biomarkers than antiviral and antibiotic drugs. The research findings offer alternative biomarkers, such as antipyretics, aside from pathogens, for the assessment of acute respiratory infectious diseases.

B-152 Analysis of carbonyl compounds in exhaled breath for detection of COVID-19

Abstract Background COVID-19 pandemic has caused enormous loss of lives and economic disruption. The global crisis has resulted in more than 700 million infections and 7 million deaths worldwide. Rapid screening and detection of highly infectious respiratory diseases is a key for preventing and curbing future epidemic and pandemic. The objective of this work is to develop a novel breath analysis technique for detection of COVID-19 using chemoselective capture of carbonyl compounds in exhaled breath for analysis by gas chromatography-mass spectrometry (GC-MS). Respiratory viral infection causes oxidative stress and inflammation in its host body which leads to production of higher levels of some carbonyl compounds through oxidation of lipids and could be detected in exhaled breath. This breath analysis technique can be rapidly adapted for screening infectious respiratory diseases for future epidemic and potential pandemic. Methods A total of 237 subjects were enrolled in this study. Of these, 142 were COVID-19 positive and 95 were negative. One liter exhaled breath in Tedlar bags from both COVID-19 positive and negative subjects confirmed by reverse transcriptase polymerase chain reaction between April 2022 and September 2023. The collected one liter breath samples were completely evacuated through a cartridge packed with silica particles in 2-3 minutes. Silica particles were coated with a reactive aminooxy agent for capture of carbonyl compounds in the breath samples through oximation reactions. The captured carbonyl compounds were eluted by methanol and the eluted solutions were analyzed by GC-MS for calculation of each compound concentration in the exhaled breath samples. Biostatistics was used to identify significant carbonyl compounds (defined as p value < 0.05) as markers of CPOVID-19 infection. A machine learning algorithm was implemented with the captured carbonyl compounds as input features for differentiation of COVID-19 positive from negative subjects. Results A total of more than 30 carbonyl compounds were detected in exhaled breath samples of all subjects. 10 features including 3 individual carbonyl compounds and 7 compound ratios show significant differences (p value <0.05) between COVID-19 positive and negative groups. The developed machine learning and artificial intelligence (AI) algorithms successfully separated COVOD-19 positive from negative with an accuracy of 95.4%, sensitivity of 96.5%, specificity of 93.7%, and AUROC=0.974. Conclusions The analysis of carbonyl compounds in exhaled breath by GC-MS has great potential for non-invasive detection of COVID-19. The method could be rapidly adapted for detection of other respiratory infectious diseases with a small sample size and data set for training of the machine learning and AI algorithms.

Impact of Indoor Mobility Behavior on the Respiratory Infectious Diseases Transmission Trends

Impact of Indoor Mobility Behavior on the Respiratory Infectious Diseases Transmission Trends

Smoking and COVID-19 search engine trends: An explanatory study

ABSTRACT Background: Smokers are generally more susceptible to infectious respiratory diseases and are at higher risk of developing severe complications from the infections. SARS-Cov-2 is a new virus, which was first identified in December 2019, and has rapidly spread into a global pandemic of primarily respiratory illness designated as Coronavirus disease 2019 (COVID-19). Smokers are at greater risk for respiratory infections and were expected to be at increased risk for COVID-19 as well. Conflicting reports exist regarding the impact of smoking on the risk of COVID-19. Aim and Objectives: To conduct the exploratory analysis of Internet search volumes of smoking and COVID-19 using Google trends. Methodology: The aim of this research is to see if there is a correlation between global search patterns and smoking and COVID. The time frame can range from November 2019 to March 2021, which can be divided into months or days. Seven different search terms have been explored and compared based on the highest related search volume and comma related search volume to obtain the highest search traffic. Results: The search volume measured over time noted “smoking and COVID” to be the most searched term in South Africa and least in France. The years of the search terms are high when there is a surge in the COVID cases. Conclusion: The result from the present study offers an insight into a competent tool that can analyse and compare oral diseases over time. This tool can be utilised in forecasting, modulating marketing strategies and planning disability limitation techniques.

Educational needs for virtual reality-based simulation training in the nursing care of adult respiratory patients: an explanatory sequential mixed-methods study.

We aimed to ascertain the current state of and needs for virtual reality (VR)-based simulation training in the care of adult respiratory patients. Learning to care for patients with infectious respiratory disease is crucial in nurses' practical education. Thus, studies on the state of VR-based simulation-training are required. We used an explanatory sequential mixed-methods design. The sample included 122 new nurses at two advanced general hospitals and five educators at a nursing college. The educational needs of virtual reality-based simulation education were measured by the tool developed in this study. Research data were collected from August 31 to September 26, 2022. Quantitative data were subjected to a descriptive analysis, while qualitative data were subjected to content analysis. The new nurses in this study had a mean age of 24.51 years and total clinical experience of 11.18 months; 82.8% of the participants had experience in adult respiratory patient care, and 94.3% were aware of the need for this training. In the qualitative data analysis of six new nurses, all participants had experience with simulation training and were aware of its need. Moreover, all participants wanted repeated VR-based simulation training using real clinical cases. Regarding the qualitative data analysis of five nursing college educators, most of them had experience providing and receiving simulation training, and they were aware of its need in caring for respiratory patients. They reported the need for VR learning spaces that reflect the clinical environment, alongside sufficient duration and frequency of learning. Our findings could help develop simulation training programs for nursing students to assist adult respiratory patients. We provide a basis for developing VR-based simulation training for respiratory care by studying the current state of and demand for nurses' VR-based simulation training. The results can contribute to improving the quality of nursing for adult respiratory patients.

Individual Characteristics Associated with Fears and Prevention Behaviors Related to Respiratory Infectious Disease among South Korean Adults Using Complex Sample Design.

This study aimed to identify the relationships among individual characteristics, COVID-19-related fears, and infection-prevention behaviors using data from 228,932 adults (weighted to 43,583,798.71) aged 19 and older who participated in the 2021 Community Health Survey conducted by the Korea Disease Control and Prevention Agency. The complex sample design analysis using SPSS 20.0 revealed the following. Women had statistically significantly higher COVID-19-related fears and a higher level of preventive behaviors compared to men. During the COVID-19 pandemic, the level of physical activity among men was higher than that of women. Compared to the period before COVID-19, the frequency of smoking and drinking decreased for both men and women, with the reduction being more pronounced in men than in women. The high-exercise group had a lower level of COVID-19-related fears. Self-perceived health status was inversely and significantly related to COVID-19-related fears. The low-exercise group had a lower score for self-perceived health status than the high-exercise group. This study demonstrated that maintaining sufficient physical activity positively influences self-perceived health and reduces anxieties related to COVID-19 infection and fatalities. The results highlight the importance of physical activity and the need to identify strategies that effectively maintain regular exercise while adhering to infection-prevention measures during pandemics.

Epidemiological Characteristics and Spatiotemporal Clustering of Pulmonary Tuberculosis Among Students in Southwest China From 2016 to 2022: Analysis of Population-Based Surveillance Data.

Pulmonary tuberculosis (PTB), as a respiratory infectious disease, poses significant risks of covert transmission and dissemination. The high aggregation and close contact among students in Chinese schools exacerbate the transmission risk of PTB outbreaks. This study investigated the epidemiological characteristics, geographic distribution, and spatiotemporal evolution of student PTB in Chongqing, Southwest China, aiming to delineate the incidence risks and clustering patterns of PTB among students. PTB case data from students monitored and reported in the Tuberculosis Information Management System within the China Information System for Disease Control and Prevention were used for this study. Descriptive analyses were conducted to characterize the epidemiological features of student PTB. Spatial trend surface analysis, global and local spatial autocorrelation analyses, and disease rate mapping were performed using ArcGIS 10.3. SaTScan 9.6 software was used to identify spatiotemporal clusters of PTB cases. From 2016 to 2022, a total of 9920 student TB cases were reported in Chongqing, Southwest China, with an average incidence rate of 24.89/100,000. The incidence of student TB showed an initial increase followed by a decline, yet it remained relatively high. High school students (age: 13-18 years; 6649/9920, 67.03%) and college students (age: ≥19 years; 2921/9920, 29.45%) accounted for the majority of student PTB cases. Patient identification primarily relied on passive detection, with a high proportion of delayed diagnosis and positive etiological results. COVID-19 prevention measures have had some impact on reducing incidence levels, but the primary factor appears to be the implementation of screening measures, which facilitated earlier case detection. Global spatial autocorrelation analysis indicated Moran I values of >0 for all years except 2018, ranging from 0.1908 to 0.4645 (all P values were <.05), suggesting strong positive spatial clustering of student PTB cases across Chongqing. Local spatial autocorrelation identified 7 high-high clusters, 13 low-low clusters, 5 high-low clusters, and 4 low-high clusters. High-high clusters were predominantly located in the southeast and northeast parts of Chongqing, consistent with spatial trend surface analysis and spatiotemporal clustering results. Spatiotemporal scan analysis revealed 4 statistically significant spatiotemporal clusters, with the most likely cluster in the southeast (relative risk [RR]=2.87, log likelihood ratio [LLR]=574.29, P<.001) and a secondary cluster in the northeast (RR=1.99, LLR=234.67, P<.001), indicating higher reported student TB cases and elevated risks of epidemic spread within these regions. Future efforts should comprehensively enhance prevention and control measures in high-risk areas of PTB in Chongqing to mitigate the incidence risk among students. Additionally, implementing proactive screening strategies and enhancing screening measures are crucial for early identification of student patients to prevent PTB outbreaks in schools.

Exploring the Impact and Prevention of Epidemics Based on Inter-Animal Transmission from an Environmental Perspective

Respiratory infectious diseases are more likely to occur in indoor environments. Therefore, the probability of transmission when sharing the same indoor space with an infected individual for a certain period of time has an impact on the response measures to influenza outbreaks. The experimental methods for studying indoor transmission risks present significant operational challenges. Hence, to accurately predict the process of virus transmission in human living environments, it is crucial to use animal experiments in controlled environments. This study extensively reviews classical documents, taking into account exposure methods as well as environmental factors such as temperature, humidity, viral release intensity, and ventilation frequency. Based on the reference to animal experiments, the analogy law between the animal experiment environment and the human living environment is put forward. For human society, a dynamic respiratory infectious disease model that takes environmental factors into account is developed. The incidence probability of susceptible populations and the law of respiratory virus transmission at a certain time and space are explored. Ultimately, the statistical analysis revealed that temperature and susceptible people, followed by humidity and ventilation frequency, are the most sensitive factors influencing disease outbreak. In conclusion, this research provides a new reference model for predicting the spread of respiratory infectious diseases. It expands the application scope of animal experiments and offers guidance for setting environmental factors in animal virus transmission experiments, assessing the likelihood of infection in human living environments, guiding human behavior, and preparing for future virus outbreaks.

Integration of Smart Sensor Technology in Wearable Masks for Real-Time Monitoring of Respiratory Health and Infectious Diseases

Integration of Smart Sensor Technology in Wearable Masks for Real-Time Monitoring of Respiratory Health and Infectious Diseases

Human behavior-based COVID-19 transmission in two dining spaces

Since December 2019, the COVID-19 pandemic has rapidly disseminated globally, posing significant threats to the world. The dining spaces are high-risk indoor environments for the transmission of SARS-CoV-2, posing challenges for intervention and control. This study, based on surveillance videos from two COVID-19 outbreak cases in restaurants, obtained real data on human behaviors of close contact and surface touch. A respiratory infectious disease transmission model was developed, incorporating four transmission routes: short-range airborne, long-range airborne, fomite and large droplet. The results indicate that diners and staff spent 21.9 %−28.7 % and 17.5 %−27.8 % of their time on speaking, respectively, while spending 85.9 %−90.7 % and 83.4 %−87.6 % of their time on surface touching. The primary transmission routes were short-range (contributing 5.8 %−70.9 %) and long-range airborne (contributing 28.4 %−93.0 %), with fomite and large droplet routes contributing less than 12.0 %. Staff-only mask wearing reduced infection risk by 12.8 %−31.8 %. It is recommended that mandatory mask wearing for staff is necessary, while diners should wear masks as much as possible, and that the equivalent ventilation rate of clean fresh air is suggested to 30.0 m3/ (h·person). This study provides a scientific support to make non-pharmaceutical interventions in dinning spaces.

Effect of vaccination against Mycoplasma hyopneumoniae on divergent pig genetic groups

The bacterium Mycoplasma hyopneumoniae (Mhp) causes a chronic infectious respiratory disease in pigs, leading to important economic losses. This study aimed to compare the immune response of the local Piau breed and a commercial line to Mhp vaccination. For this, two phases were carried out. In the first, gene expression of toll-like receptors (TLR2, TLR4, TLR6, and TLR10) and cytokines (IL2, IL6, IL8, IL10, IL12, IL13, TNFα, and TGFβ) was assessed in porcine blood mononuclear cells (PBMC) from the two genetic groups before and after vaccination. In the second experiment, nitric oxide production, specific antibodies, and gene expression of toll-like receptors and cytokines were evaluated in bronchoalveolar lavage fluid (BALF) cells of vaccinated and unvaccinated pigs. After vaccination against Mhp, TLR2, TLR4, TLR6, TLR10, IL6, TNFα, and TGFβ expression levels were elevated in PBMC from commercial animals, and TLR6, TLR10, and TGFβ expression levels were elevated in PBMC from the Piau group. Vaccination also increased the production of Mhp-specific IgG antibodies in BALF cells in the Piau breed. Comparison of the two genetic groups revealed differences in TNFα and IL10 expression in BALF cells. These results show that Piau pigs have different immune responses to vaccination compared with commercial animals. It is worth noting that these genetic differences between both genetic groups may be related to phenotypic differences in Mhp resistance or susceptibility.

Mapping the Characteristics of Respiratory Infectious Disease Epidemics in China Based on the Baidu Index from November 2022 to January 2023.

Infectious diseases pose a significant global health and economic burden, underscoring the critical need for precise predictive models. The Baidu index provides enhanced real-time surveillance capabilities that augment traditional systems. Baidu search engine data on the keyword "fever" were extracted from 255 cities in China from November 2022 to January 2023. Onset and peak dates for influenza epidemics were identified by testing various criteria that combined thresholds and consecutive days. The most effective scenario for indicating epidemic commencement involved a 90th percentile threshold exceeded for seven consecutive days, minimizing false starts. Peak detection was optimized using a 7-day moving average, balancing stability and precision. The use of internet search data, such as the Baidu index, significantly improves the timeliness and accuracy of disease surveillance models. This innovative approach supports faster public health interventions and demonstrates its potential for enhancing epidemic monitoring and response efforts.

Assessing the impact of COVID-19 non-pharmaceutical interventions and relaxation policies on Class B respiratory infectious diseases transmission in China

This study investigates the incidence of Class B respiratory infectious diseases (RIDs) in China under the Coronavirus disease 2019 (COVID-19) epidemic and examines variations post-epidemic, following the relaxation of non-pharmaceutical interventions (NPIs). Two-stage evaluation was used in our study. In the first stage evaluation, we established counterfactual models for the pre-COVID-19 period to estimate expected incidences of Class B RIDs without the onset of the epidemic. In the second stage evaluation, we constructed seasonal autoregressive integrated moving average intervention (SARIMA-Intervention) models to evaluate the impact on the Class B RIDs after NPIs aimed at COVID-19 pandemic were relaxed. The counterfactual model in the first stage evaluation suggested average annual increases of 10.015%, 78.019%, 70.439%, and 67.799% for tuberculosis, scarlet fever, measles, and pertussis respectively, had the epidemic not occurred. In the second stage evaluation, the total relative reduction in 2023 of tuberculosis, scarlet fever, measles and pertussis were − 35.209%, − 59.184%, − 4.481%, and − 9.943% respectively. The actual incidence declined significantly in the first stage evaluation. However, the results of the second stage evaluation indicated that a rebound occurred in four Class B RIDs after the relaxation of NPIs; all of these showed a negative total relative reduction rate.

Highly-Efficient Selection of Aptamers for Quantitative Fluorescence Detecting Multiple IAV Subtypes.

Influenza A virus (IAV) can cause infectious respiratory diseases in humans and animals. IAVs mutate rapidly through antigenic drift and shift, resulting in the emergence of numerous IAV subtypes and significant challenges for IAV detection. Therefore, achieving the simultaneous detection of multiple IAVs is crucial. In this work, three specific aptamers targeting the hemagglutination (HA) protein of the influenza A H5N1, H7N9, and H9N2 viruses were screened using a multichannel magnetic microfluidic chip. The aptamers exhibit nanomolar affinity and excellent specificity for the HA protein of H5N1, H7N9, and H9N2 viruses. Furthermore, three specific aptamers were truncated and labeled with different fluorescence markers to realize fluorescence quantitative detection of influenza A H5N1, H7N9, and H9N2 viruses through an aptamer sandwich assay in 1 h. The limit of detection (LOD) of the developed method is 0.38 TCID50/mL for the H5N1 virus, 0.75 TCID50/mL for the H7N9 virus, and 1.14 TCID50/mL for the H9N2 virus. The detection method has excellent specificity, strong anti-interference ability, and good reproducibility. This work provides a sensitive quantitative detection method for the H5N1, H7N9, and H9N2 viruses, enabling quantitative fluorescence detection for multiple IAV subtypes.

New progress in laboratory detection of respiratory infectious diseases in children

Respiratory infectious disease has become ahead of all the children's diseases, with the trend of continuously increasing global incidence, antimicrobial resistance and simultaneous infection with multiple pathogens. Diagnosis of this disease is mainly based on clinical symptoms and pathogenic detection. However, there are some differences in clinical manifestations, progression and prognosis between pediatric patients and adults, which prompting clinical diagnosis mainly depending on clinical laboratory test. Therefore, fast, convenient and accurate methods are urgently needed to clarify the type of infectious pathogen and carry out differentiated treatment, and reduce the burden on families and public health-care systems in schools. This article aims to elaborate the laboratory methods of children's respiratory infectious diseases and explore the opportunities and challenges, which can provide ideas for prevention, early screening and diagnosis, prognosis and treatment monitoring.

Evaluation Of Raftlin And 8-Iso Prostaglandin Levels On The Clinical Severity Of Cases With Covid-19 In Turkey : A Cross-Sectional Study

COVID-19 is an infectious respiratory disease caused by the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). Raftlin is a large lipid raft protein found in cell membranes and plays a crucial role in inducing autoimmune and vascular inflammatory responses. Although there have been few studies on 8-iso-prostaglandin F2α (8-iso-PGF2α) as a marker of oxidative stress in COVID-19, the levels of raftlin in the disease have not been previously investigated. The objective of our study was to evaluate the levels of Raftlin and 8-iso-prostaglandin F2α (8-iso-PGF2α) in COVID patients. We analyzed clinical findings from three groups: healthy controls, patients with a mild course of COVID, and patients with a severe course of COVID. We measured the levels of Raftlin and 8-iso-PGF2α in serum samples using ELISA. Our results showed that the levels of Raftlin and 8-iso-PGF2α were higher in patients with severe COVID compared to the control and mild course groups. Furthermore, a significant positive correlation was found between Raftlin and 8-iso-PGF2α in the severe COVID group. Raftlin was identified as a potentially important biomarker in COVID-19 patients. This study is valuable as it is the first to investigate changes in Raftlin and 8-iso-PGF2α levels in COVID-19 disease.

Critical measures for the prevention and control of respiratory infectious diseases in the workplace: a South African facilities management perspective

PurposeThis study aims to comprehensively examine the respiratory infection prevention and control measures used in South African workplaces, particularly in the context of facilities management (FM), during the 2020–2022 pandemic.Design/methodology/approachThe present research involved pre-crafted semi-structured interviews conducted by University of the Witwatersrand students with FM heads in Johannesburg, South Africa. Recurrent themes were generated using NVIVO software and analysed using frequencies, word counts and word clouds.FindingsThis study identified 119 measures and placed these into two broad categories. Twelve critical measures were found. These measures were further validated by five FM experts, separate from FM heads, who cross-referenced them with WHO guidelines, enhancing the credibility of the findings. Subsequently, challenges with implementing these measures were explored.Practical implicationsThe findings have direct relevance to the work of FM professionals, as they suggest that applying timely and comprehensive infection prevention and control measures can help employees to safely continue to work or return to the workplace during any future epidemic outbreaks.Originality/valueThus far, limited studies have explored critical measures used to prevent and control COVID-19 in the workplaces of developing countries during the 2020–2022 pandemic.

Study on the impact of meteorological factors on influenza in different periods and prediction based on artificial intelligence RF-Bi-LSTM algorithm: to compare the COVID-19 period with the non-COVID-19 period

ObjectiveAt different times, public health faces various challenges and the degree of intervention measures varies. The research on the impact and prediction of meteorology factors on influenza is increasing gradually, however, there is currently no evidence on whether its research results are affected by different periods. This study aims to provide limited evidence to reveal this issue.MethodsDaily data on influencing factors and influenza in Xiamen were divided into three parts: overall period (phase AB), non-COVID-19 epidemic period (phase A), and COVID-19 epidemic period (phase B). The association between influencing factors and influenza was analysed using generalized additive models (GAMs). The excess risk (ER) was used to represent the percentage change in influenza as the interquartile interval (IQR) of meteorology factors increases. The 7-day average daily influenza cases were predicted using the combination of bi-directional long short memory (Bi-LSTM) and random forest (RF) through multi-step rolling input of the daily multifactor values of the previous 7-day.ResultsIn periods A and AB, air temperature below 22 °C was a risk factor for influenza. However, in phase B, temperature showed a U-shaped effect on it. Relative humidity had a more significant cumulative effect on influenza in phase AB than in phase A (peak: accumulate 14d, AB: ER = 281.54, 95% CI = 245.47 ~ 321.37; A: ER = 120.48, 95% CI = 100.37 ~ 142.60). Compared to other age groups, children aged 4–12 were more affected by pressure, precipitation, sunshine, and day light, while those aged ≥ 13 were more affected by the accumulation of humidity over multiple days. The accuracy of predicting influenza was highest in phase A and lowest in phase B.ConclusionsThe varying degrees of intervention measures adopted during different phases led to significant differences in the impact of meteorology factors on influenza and in the influenza prediction. In association studies of respiratory infectious diseases, especially influenza, and environmental factors, it is advisable to exclude periods with more external interventions to reduce interference with environmental factors and influenza related research, or to refine the model to accommodate the alterations brought about by intervention measures. In addition, the RF-Bi-LSTM model has good predictive performance for influenza.

Exploring Varicella Vaccine Coverage and Influencing Factors in Rural and Pastoral Children of Qinghai Province: A Cross-Sectional Catch-Up Vaccination Study

&amp;lt;i&amp;gt;Background: &amp;lt;/i&amp;gt;Varicella is a respiratory infectious disease caused by varicella-zoster virus (VZV) infection. Varicella vaccine has been shown to be highly effective in preventing varicella disease, however it is not included in Qinghai Province’s local immunization planning program and must be paid for by families. Its use in local areas is options instead of compulsory, so high coverage is difficult to guarantee. Starting in October 2021, one dose of live attenuated varicella vaccine was recommended at lest for 3-17-year-old children in Qinghai. In 2022, it was conducted that an investigation of varicella vaccine coverage and factors influencing coverage among children in rural rural and pastoral areas to determine the impact of this VarV catch-up policy. &amp;lt;i&amp;gt;Objective: &amp;lt;/i&amp;gt; To explore varicella vaccine coverage and factors influencing caverage among 3-17-year-old children in rural and pastoral areas of Qinghai province. &amp;lt;i&amp;gt;Methods:&amp;lt;/i&amp;gt; A stratified cluster sampling method was used to select children aged 3-17 years from kindergartens and primary /secondary schools in rural and pastoral areas of Qinghai province for a questionnaire-based survey of their guardians. Coverage levels of one and two doses of VarV (VarV&amp;lt;sub&amp;gt;1&amp;lt;/sub&amp;gt; and VarV&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;) before and after a catch-up vaccination activity initiated in October 2021, and identified factors influenceing VarV&amp;lt;sub&amp;gt;1&amp;lt;/sub&amp;gt; coverage. &amp;lt;i&amp;gt;Results:&amp;lt;/i&amp;gt; VarV&amp;lt;sub&amp;gt;1&amp;lt;/sub&amp;gt; and VarV&amp;lt;sub&amp;gt;2 &amp;lt;/sub&amp;gt;coverage levels after the catch-up activity were 79.06% (676/855) and 43.79% (363/829), respectively, and increased by 34.38 and 24.13 percentage points compared with before the catch-up activity. Multivariate logistic regression showed that VarV&amp;lt;sub&amp;gt;1 &amp;lt;/sub&amp;gt;coverage was higher in rural areas than in pastoral areas (OR=4.63, 95%&amp;lt;I&amp;gt;CI&amp;lt;/I&amp;gt;: 2.91-7.39), and higher among children whose guardians scored 4-6 or 7-10 points on knowledge about varicella and VarV than among children whose guardians scored 0-3 points (OR=8.61, 95%&amp;lt;I&amp;gt;CI&amp;lt;/I&amp;gt;: 4.73-15.69, OR=2.86, 95%&amp;lt;I&amp;gt;CI&amp;lt;/I&amp;gt;: 1.69-4.84). the main reasons for non-vaccination were guardians’ lack of understanding of VarV (48.6%, 87 children), guardians’ unawarness of the need for VarV vaccination (43.6%, 78 children), and unavailability of VarV at vaccination centers (31.3%, 56 children). &amp;lt;i&amp;gt;Conclusions: &amp;lt;/i&amp;gt;The catch-up activity significantly increased VarV coverage among children in the surveyed areas. It should been strengthened that health education on knowledge about varicella and VarV among guardians of children in Qinghai, especially in pastoral areas, to promote VarV vaccination of age-eligible children.