Respiratory Infectious Diseases Research Articles

Technologies and main functionalities of the telemonitoring application reCOVeryaID.

The COVID-19 pandemic has highlighted the need to take advantage of specific and effective patient telemonitoring platforms, with specific reference to the constant monitoring of vital parameters of patients most at risk. Among the various applications developed in Italy, certainly there is reCOVeryaID, a web application aimed at remotely monitoring patients potentially, currently or no longer infected with COVID-19. Therefore, in this paper we present a system model, consisting of a multi-platform intelligent telemonitoring application, that enables remote monitoring and provision of integrated home care to both patients symptomatic, asymptomatic and pre-symptomatic with severe acute respiratory infectious disease or syndrome caused by viruses belonging to the Coronavirus family, as well as simply to people with respiratory problems and/or related diseases (chronic obstructive pulmonary disease or asthma). In fact, in this paper we focus on exposing the technologies and various functionalities offered by the system, which constitute the practical implementation of the theoretical framework described in detail in another paper. Specifically, the reCOVeryaID telemonitoring application is a stand-alone, knowledge base-supported application that can promptly react and inform physicians if dangerous trends in a patient's short- and long-term vital signs are detected, thus enabling them to be monitored continuously, both in the hospital and at home. The paper also reports an evaluation of user satisfaction, carried out by actual patients and medical doctors.

Evaluation of airborne transmission of respiratory contaminants in a passenger car cabin with window opening

Exhaled aerosols can be suspended in the air for long periods, facilitating the transmission of highly infectious respiratory diseases. The risk of exposure to occupants varies due to different usage conditions and driving scenarios of moving vehicles. Therefore, the present study investigates the airborne transmission of contaminants generated by coughing in a moving vehicle and explores the relationship between aerosol dispersion and fluid exchange between the interior and exterior domains of the car under the influence of window opening configurations, seating layouts and driving speeds. The results show that the cross-opening configurations provide a superior contaminant removal efficiency. The effect of seating layout on removal efficiency is significant, although it has a slight effect on air change rate (ACH). The best removal efficiency can be obtained when the passenger sits in the front seat with the right front window opening. Furthermore, the in-cabin ACH is almost linearly correlated with the driving speed. Higher speeds reduce contaminant concentrations faster. However, the risk of driver exposure does not depend solely on ACH, the peak cumulative contaminant concentration and residence time also need to be considered. This study offers data for the prevention and control of respiratory infectious diseases.

Effectiveness of Bairui granules in the treatment of respiratory tract infections: A systematic review and meta-analysis.

Respiratory tract infections (RTIs) are characterized by a high mortality rate and clinical incidence. Bairui granules (BG), which employ a method of heat elimination and detoxification, have demonstrated benefits in the treatment of infectious respiratory diseases. A computerized search of 6 databases was conducted to identify randomized controlled trials (RCTs) relevant to the treatment of RTIs with BG up to November 30, 2023. Two researchers independently conducted data extraction, risk of bias assessment, and grading analysis. To evaluate the stability of the results, trial sequential analysis was employed. This meta-analysis included 31 RCTs with a total of 4073 patients and demonstrated that the use of BG in the treatment of RTIs was associated with enhanced treatment efficacy (relative risk = 1.19, 95% credible interval: 1.16-1.22, P < .001). It also indicated a faster resolution of symptoms including pulmonary rales, cough, and fever, as well as a reduction in serological index factors, compared to the use of Western medicine treatment (WT) alone. Additionally, the duration of hospitalization for patients was significantly reduced (relative risk = -1.36, 95% credible interval: -1.55 to -1.17, P < .001). Trial sequential analysis confirmed the stability and conclusive evidence of the study results. The efficacy of treating RTIs with BG, either alone or in combination with WT, was found to be superior to WT alone. However, further high-quality RCTs are necessary to validate these outcomes. The effectiveness of treating RTIs using BG alone or in combination with WT was determined to be superior to using WT alone, with no serious adverse effects observed. However, additional RCTs are essential to further confirm the findings of this study.

Housing and respiratory health among Indigenous peoples in Canada

Indigenous (First Nations, Metis and Inuit) peoples bear a disproportionate burden of infectious and noninfectious respiratory disease in rural and urban communities in Canada. Biologic and behavioral determinants exist and have relevance, but the primary determinants are socioeconomic, environmental and political. Although Canada has recently declared a national housing crisis, crowded and poor-quality housing, or no housing at all, has been experienced by generations of Indigenous peoples. Crowding in homes or shelters increases risk of exposure and dose to infectious agents. Indoor air pollutants are due to housing defects, sources of heat and smoke, and include mold, endotoxin, mite allergens, gases and particulate matter, which in turn are associated with respiratory irritation and disease. Environmental radon is a noted risk factor for lung cancer in communities located in high risk regions. Studies have demonstrated that interventions in Indigenous communities on both the social and biomedical fronts, to improve housing and health care, result in improved well-being, of which respiratory health is only one component.

Genetically Predicted Peripheral Immune Cells Mediate the Effect of Gut Microbiota on Influenza Susceptibility.

The communication mechanism of the gut-lung axis has received increasing attention in recent years, particularly in acute respiratory infectious diseases such as influenza. The peripheral immune system serves as a crucial bridge between the gut and the lungs, two organs that are not in close proximity to each other. However, the specific communication mechanism involving gut microbiota, immune cells, and their anti-influenza effects in the lung remains to be further elucidated. In this study, the effects of 731 species of peripheral immune cells and 211 different gut microbiota on influenza outcomes were analyzed using a two-sample Mendelian randomization analysis. After identifying specific species of gut microbiota and peripheral immune cells associated with influenza outcomes, mediation analyses were conducted to determine the mediating effects of specific immune cells in the protective or injurious effects of influenza mediated by gut microbiota. 19 species of gut microbiota and 75 types of peripheral immune cells were identified as being associated with influenza susceptibility. After rigorous screening, 12 combinations were analyzed for mediated effects. Notably, the down-regulation of CD64 on CD14- CD16- cells mediated 21.10% and 18.55% of the protective effect of Alcaligenaceae and Dorea against influenza, respectively. In conclusion, focusing on influenza, this study genetically inferred different types of gut microbiota and peripheral immune cells to determine their protective or risk factors. Furthermore, mediation analysis was used to determine the proportion of mediating effects of peripheral immune cells in gut microbiota-mediated susceptibility to influenza. This helps elucidate the gut-lung axis mechanism by which gut microbiota affects influenza susceptibility from the perspective of regulation of peripheral immune cells.

Two-Stream Convolutional Neural Networks for Breathing Pattern Classification: Real-Time Monitoring of Respiratory Disease Patients.

A two-stream convolutional neural network (TCNN) for breathing pattern classification has been devised for the continuous monitoring of patients with infectious respiratory diseases. The TCNN consists of a convolutional neural network (CNN)-based autoencoder and classifier. The encoder of the autoencoder generates deep compressed feature maps, which contain the most important information constituting data. These maps are concatenated with feature maps generated by the classifier to classify breathing patterns. The TCNN, single-stream CNN (SCNN), and state-of-the-art classification models were applied to classify four breathing patterns: normal, slow, rapid, and breath holding. The input data consisted of chest tissue hemodynamic responses measured using a wearable near-infrared spectroscopy device on 14 healthy adult participants. Among the classification models evaluated, random forest had the lowest classification accuracy at 88.49%, while the TCNN achieved the highest classification accuracy at 94.63%. In addition, the proposed TCNN performed 2.6% better in terms of classification accuracy than an SCNN (without an autoencoder). Moreover, the TCNN mitigates the issue of declining learning performance with increasing network depth, as observed in the SCNN model. These results prove the robustness of the TCNN in classifying breathing patterns despite using a significantly smaller number of parameters and computations compared to state-of-the-art classification models.

Short-term effects of PM2.5 components on the respiratory infectious disease: a global perspective.

Although previous research has reached agreement on the significant impact of particulate matter (PM2.5) on respiratory infectious diseases, PM2.5 acts as an aggregation of miscellaneous pollutants and the individual effect of each component has not been examined. Here, we investigate the effects of PM2.5 components, including black carbon (BC), organic carbon (OC), sulfate ion (SO4), dust, and sea salt (SS), on the morbidity and mortality of the recent respiratory disease, i.e. COVID-19. The daily data of 236 countries and provinces/states (e.g., in the United States and China) worldwide during 2020-2022 are utilized. To derive the pollutant-specific causal effects, optimal instrumental variables for each pollutant are selected from a large set of atmospheric variables. We find that one µg/m3 increase in OC increases the number of cases and death by about 3% to 6% from the mean worldwide during a lag of one day up to three days. Our findings remain consistent and robust when we change control variables such as the flight index and weather proxies, and also when applying a sine transformation to the positivity and death rate. When analyzing health effects among different areas, we find stronger impact in China, for its higher local OC concentration, as opposed to the impact in the United States. Health benefits from PM2.5 pollution reduction are comparatively high for developed regions, yet decreases in cases and deaths number are rather overt in less developing regions. Our research provides inspiration and reference for dealing with other respiratory diseases in the post-pandemic era.

Integrating socio-psychological factors in the SEIR model optimized by a genetic algorithm for COVID-19 trend analysis

The global spread of COVID-19 has profoundly affected health and economies, highlighting the need for precise epidemic trend predictions for effective interventions. In this study, we used infectious disease models to simulate and predict the trajectory of COVID-19. An SEIR (susceptible, exposed, infected, removed) model was established using Wuhan data to reflect the pandemic. We then trained a genetic algorithm-based SEIR (GA-SEIR) model using data from a specific U.S. region and focused on individual susceptibility and infection dynamics. By integrating socio-psychological factors, we achieved a significant enhancement to the GA-SEIR model, leading to the development of an optimized version. This refined GA-SEIR model significantly improved our ability to simulate the spread and control of the epidemic and to effectively track trends. Remarkably, it successfully predicted the resurgence of COVID-19 in mainland China in April 2023, demonstrating its robustness and reliability. The refined GA-SEIR model provides crucial insights for public health authorities, enabling them to design and implement proactive strategies for outbreak containment and mitigation. Its substantial contributions to epidemic modelling and public health planning are invaluable, particularly in managing and controlling respiratory infectious diseases such as COVID-19.

Effect of oxidative stress on antigen productivity in B. Pertussis cultures

Whooping cough, also known as pertussis, is an infectious respiratory disease caused by Bordetella pertussis, a type of gram-negative, aerobic, pathogenic bacteria with a coccobacillus shape and an outer capsule. The acellular vaccine for this disease contains a combination of pertussis toxin, fimbriae, filamentous hemagglutinin, and pertactin. The main constraint in the manufacturing of the vaccine relates to pertactin productivity due to its low abundance in the fermentation broth. Being the most abundant nutrient in the media, variability in initial glutamate concentration is hypothesized to be a major cause of process variability. This study proposes the combined use of cytometry and chromatography based separation to study the impact of glutamate-induced oxidative stress on the growth and productivity of pertactin antigen. Surface expression of pertactin was observed using fluorescence microscopy and flow cytometry while extracellular concentration was quantified using affinity chromatography. Oxidative stress levels and secretion of NADPH, a crucial reactant in antioxidant reactions, were monitored in flask and bioreactor experiments. The results established clear correlations between oxidative stress with growth and productivity and differentiate between the reduction in productivity to growth and to synthesis rate of pertactin. These findings are industrially relevant for improving productivity and reducing process variability.

Long-term exposure to road traffic noise and acute lower respiratory infections in the Danish Nurse Cohort

BackgroundLong-term exposure to road traffic noise is associated with cardiovascular disease, but the evidence on respiratory diseases is just emerging. We aimed to examine the association between long-term exposure to road traffic noise and the incidence of acute lower respiratory infections (ALRIs) in adults. MethodsWe followed 23,141 female nurses (age ≥ 44 years) from the Danish Nurse Cohort from baseline (1993 or 1999) to their first hospital contact (inpatient, outpatient, or emergency room) for ALRI, death, emigration or the end of 2015. The residential annual mean levels of road traffic noise (Lden) during the follow-up were estimated using the Nord2000 model. We applied time-varying Cox models to estimate the association of 3-year mean exposure to Lden with ALRIs incidence and piecewise analysis to estimate the threshold of Lden. We examined the robustness of the results by adjusting for residential exposure to air pollution, and the effect modification by attained age, socioeconomic status (SES), comorbidity, and lifestyle. ResultsDuring 18.5 years of follow-up, 2,004 nurses developed ALRIs. In a linear model, we detected a statistically significant positive association between Lden and ALRI, with a hazard ratio (HR) of 1.11 (95 % confidence interval (CI): 1.04, 1.17) per 9.2 dB (interquartile range, IQR). We observed non-linear association with a threshold at 57 dB, above which the HR was 1.25 (95 % CI: 1.09, 1.43) per IQR. Further adjustment for PM2.5 reduced the HRs slightly to 1.21 (95 % CI: 1.04, 1.40). The associations were stronger for nurses with asthma, and in those with lowest SES. ConclusionWe present novel findings in support of the association between long-term exposure to road traffic noise and ALRIs, independent of air pollution, suggesting noise as a risk factor for infectious respiratory diseases.

Chinese Medicine for Treatment of COVID-19: A Review of Potential Pharmacological Components and Mechanisms.

Coronavirus disease 2019 (COVID-19) is an acute infectious respiratory disease that has been prevalent since December 2019. Chinese medicine (CM) has demonstrated its unique advantages in the fight against COVID-19 in the areas of disease prevention, improvement of clinical symptoms, and control of disease progression. This review summarized the relevant material components of CM in the treatment of COVID-19 by searching the relevant literature and reports on CM in the treatment of COVID-19 and combining with the physiological and pathological characteristics of the novel coronavirus. On the basis of sorting out experimental methods in vivo and in vitro, the mechanism of herb action was further clarified in terms of inhibiting virus invasion and replication and improving related complications. The aim of the article is to explore the strengths and characteristics of CM in the treatment of COVID-19, and to provide a basis for the research and scientific, standardized treatment of COVID-19 with CM.

Exploring the use of exhaled breath as a diagnostic tool for pulmonary TB.

Despite its historical decline, TB remains a significant cause of infectious disease-related global deaths. The lack of reliable diagnostic tests for vulnerable groups, such as children and immunocompromised patients, remains a challenge for TB control. For decades, it has been recognised that exhaled breath has great potential as a non-invasive and universally accessible clinical alternative to sputum and invasive sampling methods. Although translation into clinical practice has not yet occurred, there has been significant progress with promising results in various applications, including diagnosis, estimation of infectiousness, and monitoring of treatment response. More recently, the COVID-19 pandemic reignited global interest in this field and technological advances have further accelerated its development. In the coming decade, breath sampling will enhance our understanding of respiratory infectious diseases and host-immune responses, which may lead to clinical applications. Here we discuss the diagnostic landscape of TB and the current state of the art of breath sampling.

Pediatric respiratory infectious emergencies: clinical profiles and outcomes.

Infectious diseases are among the most frequent causes of hospital admission and a substantial contributor to morbidity and mortality. These diseases pose a persistent concern, particularly within the pediatric population, where delays or inappropriate management can result in serious harm. Additionally, infectious diseases contribute to overcrowding in pediatric emergency departments (EDs). This study aimed to explore the epidemiology, clinical presentation, diagnostics, outcome, and social and behavioral impacts of infectious diseases on child health. We conducted a retrospective, single-hospital study at a tertiary care center that is publicly funded and owned to serve the entire community. Pediatric patients with at least one or more chief complaints related to the respiratory system or infectious etiology were included. Data analysis was performed using SPSS to assess relationships between variables. A total of 15,106 patients were included, with a mean age of 3.80 years. The largest age group was toddlers (42.8%). Most cases (71.9%) were classified as urgent (Priority 3). Regarding patient outcomes, 76.1% were discharged after receiving appropriate treatment in the ED, and 22.9% required admission for further management. Nearly 38% of patients presented with a single complaint. Fever was the most frequent complaint across all groups. In summary, this study provides insights into the presentation of pediatric respiratory infectious diseases in the emergency room. The study revealed that toddlers were the most affected age group, with fever and cough being the common symptoms.

The relationship between occupant behaviour and indoor air quality in Malaysian hospital outpatient departments: A multistage cross-sectional study

IntroductionPoor indoor air quality (IAQ) in healthcare settings may adversely impact occupants’ well-being and promote transmission of infectious respiratory disease. However, evidence on its potentially modifiable determinants, including occupant behaviour, remains scarce. This study aims to determine the relationship between occupant behaviour and IAQ in Malaysian hospital outpatient departments (OPDs). MethodsA multistage cross-sectional study of six randomly selected Malaysian public hospital OPDs was conducted. In stage one, IAQ parameters, including temperature, relative humidity (RH), air velocity (AV), carbon dioxide (CO2), total bacterial count (TBC), and total fungal count (TFC) were measured. In stage two, an observation form based on the Korsavi and Montazami tool for measuring adaptive behaviour was used to examine occupant density, activities, and operation of building envelopes and appliances. Simple correlation, partial correlation, and linear regression analyses were performed to examine the relationship between occupant behaviour and IAQ parameters. ResultsThe IAQ of selected hospital OPDs complied with established standards, except for temperature and AV. Occupant density was positively correlated with temperature and CO2. Meanwhile, occupants' activities including slow walking and brisk walking were positively correlated with temperature, AV, CO2, TBC and TFC. Conversely, occupants' opening of windows and doors were positively correlated with temperature and AV but negatively correlated with CO2, TBC and TFC. Finally, turning on fans was positively correlated with AV but negatively correlated with TBC, whereas turning on air conditioner was positively correlated with CO2. Among occupants’ behaviour, opening of windows and doors contributed the most to variation in IAQ parameters. ConclusionsThe study findings suggest that IAQ in hospital OPDs are influenced by occupant density, activities, and operation of doors, windows, and appliances. Prospective hospital IAQ guidelines should incorporate policies and measures targeting these factors to ensure occupants’ best practices in maintaining healthy hospital indoor air environments.

Professional Quality of Life among Healthcare Workers: A Case Study Conducted during the COVID-19 Pandemic

Coronavirus is an infectious respiratory disease. Following its outbreak in China, the virus spread rapidly throughout the world. Consequently, the WHO declared it as a serious international emergency concern. The healthcare workers (HCWs) fighting as active and critical frontline warriors during the COVID-19 pandemic were at a high risk of exhibiting psychosocial stress and mental health symptoms. The aim of this study was to evaluate the professional quality of life of healthcare workers during the COVID-19 pandemic. The population of this research consisted of 238 HCWs. The data was collected through online Google forms and participants were assessed by using professional quality of life tool (ProQOL-5). The results showed that the majority (84.2%) of HCWs had average compassion satisfaction (CS), while only 14.6% had high CS scores. For burnout, the majority had average scores whereas only 8.3% had a low count of burnout. Similarly, the majority of the participants had average secondary traumatic (ST) stress (88.3%), while 9.2% had low ST stress, and 2.5% had high ST stress. This study concluded that the healthcare workers (HCWs) had average CS, burnout, and ST stress

Biosecurity perceptions among Ontario horse owners during the COVID-19 pandemic.

Disease outbreaks present a significant challenge to horse health and welfare and the economic stability of horse industries internationally. This is a particular concern in Ontario, Canada, where there have been frequent outbreaks of respiratory infectious diseases among horses. Despite these risks, there has been limited research on whether Ontario horse owners engage in biosecurity measures sufficient to mitigate risk of equine diseases, and whether current events such as the COVID-19 pandemic influence attitudes towards equine biosecurity practices. To explore Ontario horse owners' perceptions, attitudes and experiences relating to on-farm biosecurity during the COVID-19 pandemic. Qualitative study using virtual semi-structured interviews. Participants (horse owners, frequent horse riders and part boarders) were recruited using social media snowball sampling where advertisements were shared by equine and veterinary organisations. Interviews were conducted virtually between June and September 2022 and were analysed using reflexive thematic analysis. Three key themes relating to biosecurity perceptions among the 14 participants were identified. Participants relied on minimal preventative measures (such as vaccines) where perceived risk of disease was low, but implemented additional measures including quarantine and handwashing when perceived risk of disease was high. Participants' choice of biosecurity practices often mirrored those recommended by the barn manager. Moreover, participants felt that responsibility for biosecurity was not shared equally across horse owners, with more emphasis placed on those engaging in high-risk situations for disease spread. Despite experiencing biosecurity during the COVID-19 pandemic, horse owners were not consistently applying these practices to their horse care routines. The perspectives reported here are from a small sample of horse owners and may not be generalisable to all populations. Our findings indicate that horse owners need improved access to and engagement with educational initiatives that emphasise the importance and purpose of all biosecurity measures.

Detection of COVID-19 by quantitative analysis of carbonyl compounds in exhaled breath

COVID-19 has caused a worldwide pandemic, creating an urgent need for early detection methods. Breath analysis has shown great potential as a non-invasive and rapid means for COVID-19 detection. The objective of this study is to detect patients infected with SARS-CoV-2 and even the possibility to screen between different SARS-CoV-2 variants by analysis of carbonyl compounds in breath. Carbonyl compounds in exhaled breath are metabolites related to inflammation and oxidative stress induced by diseases. This study included a cohort of COVID-19 positive and negative subjects confirmed by reverse transcription polymerase chain reaction between March and December 2021. Carbonyl compounds in exhaled breath were captured using a microfabricated silicon microreactor and analyzed by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). A total of 321 subjects were enrolled in this study. Of these, 141 (85 males, 60.3%) (mean ± SD age: 52 ± 15 years) were COVID-19 (55 during the alpha wave and 86 during the delta wave) positive and 180 (90 males, 50%) (mean ± SD age: 45 ± 15 years) were negative. Panels of a total of 34 ketones and aldehydes in all breath samples were identified for detection of COVID-19 positive patients. Logistic regression models indicated high accuracy/sensitivity/specificity for alpha wave (98.4%/96.4%/100%), for delta wave (88.3%/93.0%/84.6%) and for all COVID-19 positive patients (94.7%/90.1%/98.3%). The results indicate that COVID-19 positive patients can be detected by analysis of carbonyl compounds in exhaled breath. The technology for analysis of carbonyl compounds in exhaled breath has great potential for rapid screening and detection of COVID-19 and for other infectious respiratory diseases in future pandemics.

Exclusive breastfeeding in Colombia: analysis of data on respiratory infections and acute diarrhoeal diseases

Introduction: Despite the fact that Colombia is the fourth country in the world with the highest concession of maternity leave days, it still does not cover what is defined as essential to reduce Respiratory Tract Infection (RTI) and Acute Diarrhoeal Disease (ADD) in infants. Objective: The objective of this research was to analyze indicators of respiratory infections and acute diarrheal diseases during exclusive breastfeeding (EBF) in Colombia. Methodology: The management of solid research data at the institutional level used as instrument the LERU roadmap for health indicators in institutions attached to the Secretariat of Health of Bogota, based on the case study and matched controls 1: 2, from publications of the SaluData observatory, the Ten-Year Breastfeeding and Complementary Feeding Plan- PDLMAC 2021-2030, as well as the registry book of infants between 0 to 5 months of age, taking as a focused sample the Hospital San Juan de Dios in Colombia, the detection of the occurrence of events that allow formulating a hypothesis in accordance with the variables: breastfeeding and maternity leave; respiratory infections and acute diarrheal diseases. Results: The findings show that there is an association between exclusive breastfeeding during maternity leave to reduce respiratory infections and acute diarrheal diseases in the hospitals attached to the Health Secretariat of Bogota. One in six children does not receive exclusive breastfeeding during maternity leave in Colombia. Conclusion: As implications, the public health strategies must be reconsidered to favor EBF and extend maternity leave times, complying with the 6-month goal defined by the World Health Organization.

Safety Assessment of Concurrent Vaccination with the HPV Vaccine and the COVID-19 Vaccine in Fujian Province, China: A Retrospective Study.

During acute respiratory infections, women may concurrently receive human papillomavirus (HPV) and respiratory vaccines, as observed during the coronavirus disease 2019 (COVID-19) pandemic in China. However, few studies have assessed the safety of such concurrent administration, which could impact HPV vaccination schedules. This study analyzes the safety and optimal sequence of concurrent HPV and COVID-19 vaccinations. For this purpose, we surveyed women with both vaccines from January to October 2023 in Fujian Province, China. During this process, we collected vaccination history and adverse event (AE) data via telephone or interviews. Participants were grouped as Before, Concurrent, or After based on their vaccination sequence. A Chi-squared test, exact Fisher tests, and logistic regression were used to analyze the incidence of AEs and factors influencing vaccine safety. Overall, 1416 eligible participants were included. Although overall AE risk with the HPV vaccine was unaffected by vaccination sequence, individual AEs varied statistically between groups, including pain at the vaccination site (p < 0.001) and prolonged menstruation duration (p = 0.003). Based on the results, the optimal sequence would be to receive the HPV vaccine after the COVID-19 vaccine (After group). This insight may guide future emergency vaccination sequences for HPV and other respiratory infectious diseases.

Detection of Covid‐19 disease by using privacy‐aware artificial intelligence system

AbstractCovid‐19 is a highly infectious respiratory disease that spreads quickly between individuals and has been recognized as a pandemic by the World Health Organization (WHO). Chest x‐ray images, lung computed tomography images, and polymerase chain reaction tests are generally used to diagnose this disease by the doctors. Nevertheless, manual diagnosis of Covid‐19 disease is laborious and requires highly experienced professionals. Therefore, automated systems are always needed to assist doctors in their diagnostic decisions. In the field of medicine and healthcare, artificial intelligence and deep learning currently offer incredibly effective and rapid automatic decision‐support systems. Since sensitive data is used to diagnose Covid‐19, privacy has become a major concern in research that uses artificial intelligence and deep learning. In order to eliminate these issues, this paper proposes a novel deep learning model that privately detects Covid‐19 disease. The proposed model utilizes differential privacy technique to provide data privacy and convolutional neural network to diagnose Covid‐19 disease. The performance of the proposed model was evaluated through experiments conducted on five different datasets, resulting a maximum accuracy rate of 97%.