CTC: COVID-19 Tweet Classification using CT-BERT

CTC is my submitted work to the Information Retrieval from Microblogs during Disasters (IRMiDis) Track at the Forum for Information Retrieval Evaluation (FIRE) 2022. Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus experience a mild to moderate respiratory illness and recover without requiring special treatment. However, some become seriously ill and require medical attention. Vaccines against coronavirus and prompt reporting of symptoms saved many lives during the pandemic. The analysis of COVID-19-related tweets can provide valuable insights regarding the stance of people toward the new vaccine. It can also help the authorities to plan their strategies based on people's opinions about the vaccine and ensure the effectiveness of vaccination campaigns. Tweets describing symptoms can also aid in identifying high-alert zones and determining quarantine regulations. The IRMiDis track focuses on these COVID-19-related tweets that flooded Twitter. I developed an effective classifier for both Tasks 1 and 2. The evaluation score of my submitted run is reported in terms of accuracy and macro-F1 score. I achieved an accuracy of 0.770, a macro-F1 score of 0.773 in Task 1, and an accuracy of 0.820, a macro-F1 score of 0.746 in Task 2. I enjoyed the first rank among other submissions in both the tasks. © 2022 Copyright for this paper by its authors.

Epigenetic regulation of the COVID-19 pathogenesis: its impact on the host immune response and disease progression

Coronavirus disease 2019 (COVID-19) is highly infectious and may induce epigenetic alteration of the host immune system. Understanding the role of epigenetic mechanisms in COVID-19 infection is a clinical need to minimize critical illness and widespread transmission. The susceptibility to infection and progression of COVID-19 varies from person to person;pathophysiology substantially depends on epigenetic changes in the immune system and preexisting health conditions. Recent experimental and epidemiological studies have revealed the method of transmission and clinical presentation related to COVID-19 pathogenesis, however, the underlying pathology of variation in the severity of infection remains questionable. Epigenetic changes may also be responsible factors for multisystem association and deadly systemic complications of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infected patients. Commonly, epigenetic changes are evoked by alteration of the host's immune response, stress, preexisting condition, oxidative stress response, external behavioral or environmental factors, and age. In addition, the viral infection itself might manipulate the host immune responses associated with inflammation by reprogramming epigenetic processes which are the susceptible factor for disease severity and death. As a result, epigenetic events such as histone modification and DNA methylation are implicated in regulating pro-inflammatory cytokines production by remodeling macrophage and T-cell activity towards inflammation, consequently, may also affect tissue repair and injury resolution process. This review aims to discuss the comprehensive understanding of the epigenetic landscape of COVID-19 disease progression that varies from person to person with supporting interdisciplinary prognosis protocol to overcome systemic impairment.

All That Coughs Is Not COVID.

We report the case of a woman from the Bronx, New York, who presented to the emergency department (ED) in June 2020 with a febrile respiratory illness resembling coronavirus disease 2019 (COVID-19) but was ultimately diagnosed with Legionnaires' disease (LD). New York City (NYC) rapidly became an epicenter of the global COVID-19 pandemic in 2020. In the years since the pandemic started, variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have recurred in multiple waves and remain an important cause of viral respiratory illness. The bacteria Legionella pneumophila is often under-recognized as a cause of community-acquired pneumonia, yet it recurs each year in clusters, outbreaks, or as sporadic infections. Pneumonia caused by SARS-CoV-2 and Legionella can present similarly and may not be readily distinguished in the absence of diagnostic testing.

Fungal Pathogens as Causes of Acute Respiratory Illness in Hospitalized Veterans: Frequency of Fungal Positive Test Results Using Rapid Immunodiagnostic Assays.

Fungal respiratory illnesses caused by endemic mycoses can be nonspecific and are often mistaken for viral or bacterial infections. We performed fungal testing on serum specimens from patients hospitalized with acute respiratory illness (ARI) to assess the possible role of endemic fungi as etiologic agents. Patients hospitalized with ARI at a Veterans Affairs hospital in Houston, Texas, during November 2016-August 2017 were enrolled. Epidemiologic and clinical data, nasopharyngeal and oropharyngeal samples for viral testing (PCR), and serum specimens were collected at admission. We retrospectively tested remnant sera from a subset of patients with negative initial viral testing using immunoassays for the detection of Coccidioides and Histoplasma antibodies (Ab) and Cryptococcus, Aspergillus, and Histoplasma antigens (Ag). Of 224 patient serum specimens tested, 49 (22%) had positive results for fungal pathogens, including 30 (13%) by Coccidioides immunodiagnostic assays, 19 (8%) by Histoplasma immunodiagnostic assays, 2 (1%) by Aspergillus Ag, and none by Cryptococcus Ag testing. A high proportion of veterans hospitalized with ARI had positive serological results for fungal pathogens, primarily endemic mycoses, which cause fungal pneumonia. The high proportion of Coccidioides positivity is unexpected as this fungus is not thought to be common in southeastern Texas or metropolitan Houston, though is known to be endemic in southwestern Texas. Although serological testing suffers from low specificity, these results suggest that these fungi may be more common causes of ARI in southeast Texas than commonly appreciated and more increased clinical evaluation may be warranted.

Knowledge, Concept on severe Acute Respiratory Syndrome Coronavirus-2(SARS-CoV-2). A Review of the Literature and Future perspective

Severe acute respiratory corona virus-2 (SARS-CoV-2) is a newly recognized pathogen and may cause severe respiratory illness among virus-infected people. The virus in the open market of Wuhan city, China was identified. The virus causative agent for the COVID-19 disease and became pandemic in December 2019 to now with no proper disease management protocols. So, the authors felt a need to bring awareness to the disease and its causative agent among worldwide.The current review article is trying to bringglance information on SARS-CoV-2 on various aspects of disease condition as Common characteristics, history, and mode of transmissions of the virus. The virus can be detected through investigations, Identified clinical manifestations for the virus, and available management used to treat the virus-infected patient. Here discussed possible preventive measures for SARS-CoV-2;to control the spread of the disease among the communities. This article information maybea help people to have an awareness of the disease.Health professional are trying hard for providing effective care to the virus affected people with minimal disease preventive protocols. People should understand the effectiveness of the vaccine and undergoing vaccination process which helps the spread of virus among the healthy people. Every individual should take initiation for the control of the disease spreads by following controlling measures.Copyright © RJPT All right reserved.

Using Dynamic Models to Showcase Pandemic Prevention Empirical Covid-19

Coronavirus disease (COVID-19) is a viral contagious disease caused by a newly discovered coronavirus. The COVID-19 virus primarily spreads from an infected person through droplets of saliva or nasal discharge when the person coughs or sneezes, and most people who have been infected with the virus usually experience mild to severe respiratory illness, and they recover with minimal or no treatment. COVID-19 causes mild illness in the majority of patients although it can be fatal in rare cases. Our project focuses on using an SPO2 level monitor and thermal scanning to monitor patient health and take precautions to avoid constant transmission, as well as providing support to patients by assisting them with basic needs with the help of food delivery agencies and non-governmental organizations (NGOs) and assisting with prevention. We use an enhanced version of the SIR epidemic model, which is further explained in this work as an IoT-based system which is being used for automated health monitoring and surveillance, this work aims to reveal certain facts about the current situation that are not presented by data, as well as predict and forecast future situations. AI-assisted sensors can be of major help to foresee whether or not someone is tested positive for the virus supported on indicators like body temperature, coughing patterns, and blood oxygen levels. The ability to track people's locations is another helpful function. All these problems collectively checked will make an efficient model to curb the virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Arduino based Anti-Face-Touching Device and prediction model

Due of the fast pace at which COVID-19 may spread through respiratory illness, the terrible condition it was in heightened public tension. The WHO's primary recommendations advised against often touching your face in order to avoid the transmission of viruses through your lips, eyes, and nose. According to research, the typical person was discovered to touch their face about 20 times each hour since it is everyone's unconscious behavior. In order to cope with this, the study suggests a hardware model that recognizes hand motions that are made in the direction of the user's face and alerts them to such movements using both aural and visual sensory feedback modalities. In order to create a model for the prediction of facial touch motions, the study analyses deep learning architectures in more detail. The FaceGuard device, which is a deep learning-based prediction model used to determine whether or not a hand movement would result in face contact, is compared to the accuracy of the suggested hardware model in the paper 'FaceGuard: A Wearable System To Avoid Face Touching1.' It alerts the user through vibrotactile, aural, and visual sensory modalities. After investigation, it was discovered that the hardware model had less accuracy than the deep learning model and required shorter time to respond to vibro tactile sensory data. © 2022 IEEE.

kNN-SVM with Deep Features for COVID-19 Pneumonia Detection from Chest X-ray

Most attention has been paid to chest Computed Tomography (CT) in this burgeoning crisis because many cases of COVID-19 demonstrate respiratory illness clinically resembling viral pneumonia which persists in prominent visual signatures on high-resolution CT befitting of viruses that damage lungs. However, CT is very expensive, time-consuming, and inaccessible in remote hospitals. As an important complement, this research proposes a novel kNN-regularized Support Vector Machine (kNN-SVM) algorithm for identifying COVID-induced pneumonia from inexpensive and simple frontal chest X-ray (CXR). To compute the deep features, we used transfer learning on the standard VGG16 model. Then the autoencoder algorithm is used for dimensionality reduction. Finally, a novel kNN-regularized Support Vector Machine algorithm is developed and implemented which can successfully classify the three classes: Normal, Pneumonia, and COVID-19 on a benchmark chest X-ray dataset. kNN-SVM combines the properties of two well-known formalisms: k-Nearest Neighbors (kNN) and Support Vector Machines (SVMs). Our approach extends the total-margin SVM, which considers the distance of all points from the margin;each point is weighted based on its k nearest neighbors. The intuition is that examples that are mostly surrounded by similar neighbors, i.e., of their own class, are given more priority to minimize the influence of drastic outliers and improve generalization and robustness. Thus, our approach combines the local sensitivity of kNN with the global stability of the total-margin SVM. Extensive experimental results demonstrate that the proposed kNN-SVM can detect COVID-19-induced pneumonia from chest X-ray with greater or comparable accuracy relative to human radiologists. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Convolutional Neural Network-based Covid-19 Analysis with Internet of Things

The very hazardous respiratory illness known as COVID-2 (SARS-CoV-2), which is the root cause of the even more serious illness known as COVID-19, was caused by the COVID-2 virus. The COVID-19 virus was identified in Wuhan City, China, in the month of December in 2019. It began in China and then spread to other parts of the world before it was officially classified as a pandemic. It has had a significant impact on day-To-day life, the welfare of people in general, and the economy of the whole globe. It is of the utmost importance, particularly in the beginning stages of treatment, to pinpoint the constructive experiences that are useful at the proper time. The identification of this virus involves a substantial number of tests, each of which takes a certain amount of time;nevertheless, there are currently no other automated tool kits that can be used in their place. X-ray photos of the chest that are obtained via the use of radiology imaging methods may provide significant insight into the COVID-19 infection if they are analysed carefully. An accurate diagnosis of the infection may be obtained via the application of deep learning techniques, which are applied to radiological images and make use of cutting-edge technology such as artificial intelligence. Patients who reside in distant places, where it may not be feasible for them to have rapid access to medical facilities, may benefit from this kind of analysis throughout the course of their therapy. One of the deep learning strategies that are used in the creation of the model that has been proposed is the use of convolutional neural networks. The images of chest X-rays are analysed by these networks to detect whether a person has a positive or negative result for the Covid gene. © 2022 IEEE.

Antiviral Potential of the Genus Panax: An updated review on their effects and underlying mechanism of action.

Viral infections are known as one of the major factors causing death. Ginseng is a medicinal plant that demonstrated a wide range of antiviral potential, and saponins are the major bioactive ingredients in the genus Panax with vast therapeutic potential. Studies focusing on the antiviral activity of the genus Panax plant-derived agents (extracts and saponins) and their mechanisms were identified and summarized, including contributions mainly from January 2016 until January 2022. P. ginseng, P. notoginseng, and P. quinquefolius were included in the review as valuable medicinal herbs against infections with 14 types of viruses. Reports from 9 extracts and 12 bioactive saponins were included, with 6 types of protopanaxadiol (PPD) ginsenosides and 6 types of protopanaxatriol (PPT) ginsenosides. The mechanisms mainly involved the inhibition of viral attachment and replication, the modulation of immune response by regulating signaling pathways, including the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway, cystathionine γ-lyase (CSE)/hydrogen sulfide (H2S) pathway, phosphoinositide-dependent kinase-1 (PDK1)/ protein kinase B (Akt) signaling pathway, c-Jun N-terminal kinase (JNK)/activator protein-1 (AP-1) pathway, and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway. This review includes detailed information about the mentioned antiviral effects of the genus Panax extracts and saponins in vitro and in vivo, and in human clinical trials, which provides a scientific basis for ginseng as an adjunctive therapeutic drug or nutraceutical.

Healthcare-seeking behavior for respiratory illnesses in Kenya: implications for burden of disease estimation.

BACKGROUND: Understanding healthcare-seeking patterns for respiratory illness can help improve estimation of disease burden and target public health interventions to control acute respiratory disease in Kenya. METHODS: We conducted a cross-sectional survey to determine healthcare utilization patterns for acute respiratory illness (ARI) and severe pneumonia in four diverse counties representing urban, peri-urban, rural mixed farmers, and rural pastoralist communities in Kenya using a two-stage (sub-locations then households) cluster sampling procedure. Healthcare seeking behavior for ARI episodes in the last 14 days, and severe pneumonia in the last 12 months was evaluated. Severe pneumonia was defined as reported cough and difficulty breathing for > 2 days and report of hospitalization or recommendation for hospitalization, or a danger sign (unable to breastfeed/drink, vomiting everything, convulsions, unconscious) for children < 5 years, or report of inability to perform routine chores. RESULTS: From August through September 2018, we interviewed 28,072 individuals from 5,407 households. Of those surveyed, 9.2% (95% Confidence Interval [CI] 7.9-10.7) reported an episode of ARI, and 4.2% (95% CI 3.8-4.6) reported an episode of severe pneumonia. Of the reported ARI cases, 40.0% (95% CI 36.8-43.3) sought care at a health facility. Of the74.2% (95% CI 70.2-77.9) who reported severe pneumonia and visited a medical health facility, 28.9% (95% CI 25.6-32.6) were hospitalized and 7.0% (95% CI 5.4-9.1) were referred by a clinician to the hospital but not hospitalized. 21% (95% CI 18.2-23.6) of self-reported severe pneumonias were hospitalized. Children aged < 5 years and persons in households with a higher socio-economic status were more likely to seek care for respiratory illness at a health facility. CONCLUSION: Our findings suggest that hospital-based surveillance captures less than one quarter of severe pneumonia in the community. Multipliers from community household surveys can account for underutilization of healthcare resources and under-ascertainment of severe pneumonia at hospitals.

Hospital Variation in Management and Outcomes of Acute Respiratory Distress Syndrome Due to COVID-19.

OBJECTIVES: To describe hospital variation in use of "guideline-based care" for acute respiratory distress syndrome (ARDS) due to COVID-19. DESIGN: Retrospective, observational study. SETTING: The Society of Critical Care Medicine's Discovery Viral Infection and RESPIRATORY ILLNESS UNIVERSAL STUDY COVID-19 REGISTRY. PATIENTS: Adult patients with ARDS due to COVID-19 between February 15, 2020, and April 12, 2021. INTERVENTIONS: Hospital-level use of "guideline-based care" for ARDS including low-tidal-volume ventilation, plateau pressure less than 30 cm H2O, and prone ventilation for a Pao2/Fio2 ratio less than 100. MEASUREMENTS AND MAIN RESULTS: Among 1,495 adults with COVID-19 ARDS receiving care across 42 hospitals, 50.4% ever received care consistent with ARDS clinical practice guidelines. After adjusting for patient demographics and severity of illness, hospital characteristics, and pandemic timing, hospital of admission contributed to 14% of the risk-adjusted variation in "guideline-based care." A patient treated at a randomly selected hospital with higher use of guideline-based care had a median odds ratio of 2.0 (95% CI, 1.1-3.4) for receipt of "guideline-based care" compared with a patient receiving treatment at a randomly selected hospital with low use of recommended therapies. Median-adjusted inhospital mortality was 53% (interquartile range, 47-62%), with a nonsignificantly decreased risk of mortality for patients admitted to hospitals in the highest use "guideline-based care" quartile (49%) compared with the lowest use quartile (60%) (odds ratio, 0.7; 95% CI, 0.3-1.9; p = 0.49). CONCLUSIONS: During the first year of the COVID-19 pandemic, only half of patients received "guideline-based care" for ARDS management, with wide practice variation across hospitals. Strategies that improve adherence to recommended ARDS management strategies are needed.

Diagnostic Efficacy of RealStar SARS-CoV-2 Reverse Transcription-Polymerase Chain Reaction (RT-PCR) in Comparison to GeneXpert System for the Detection of COVID-19.

Background and objective The coronavirus disease 2019 (COVID-19) pandemic has become a major health concern due to the rapid transmission of the virus that causes it: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To address the growing demand on healthcare systems to control this pandemic, more effective diagnostic methods need to be applied. In this study, we aimed to compare the efficacy of RealStar® SARS-CoV-2 reverse transcription-polymerase chain reaction (RT-PCR) versus the GeneXpert® system. Methods A retrospective cross-sectional study was conducted in the central lab of King Abdulaziz Medical City (KAMC) in Riyadh, Saudi Arabia. Data from all nasopharyngeal swabs (NPS) (150,000) submitted for SARS-CoV-2 analysis from July 2020 to July 2021 were reviewed retrospectively. Furthermore, all NPS (n=384) that were analyzed on both the RealStar® SARS-CoV-2 RT-PCR and GeneXpert® systems for confirmatory purposes were included in the study. Acute respiratory illness (ARI) screening forms of the selected samples were reviewed from the electronic database (BestCare system), and they were analyzed and compared at one point in time; therefore, a cross-sectional study was found to be the best suitable study design. Using the statistical analysis software, the receiver operating characteristic (ROC) curve was obtained to compare the sensitivity (Sn), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV). The test was considered significant if the area under the curve (AUC) value was >0.5. Results The diagnostic performance of the RealStar® and GeneXpert® assays in detecting SARS-CoV-2 was evaluated using ROC curve analysis, which showed AUCs of 0.597 and 0.637, respectively. In addition, 35% of the total results fell into a substantial agreement of 0.76 (95% CI: 0.6626-0.8732). The majority of the NPS were reported negative by both RealStar® (246, 80.66%) and GeneXpert® (226, 74.10%). Most samples (210, 68.85%) were obtained from asymptomatic patients, scoring less than 4 (ARI <4) based on the ARI screening form. Conclusion Based on the AUC of ROC, there is no significant difference in the performance characteristics between the RealStar® RT-PCR and GeneXpert® in detecting COVID-19.

Mathematical Model for Anisotropic diffusion Filter and GLRLM Feature Extraction to Detect Covid-19 from Chest X-Ray Images

In December 2019, the SARS-CoV-2 virus, often referred to as COVID-19, was discovered in Wuhan, China. It is very virulent and has spread very quickly throughout the world. With COVID-19, people have described a wide variety of symptoms, from little discomfort to life-threatening respiratory illness. In this study, chest X-ray scan images are preprocessed using an anisotropic diffusion filter and three classifiers, and the Covid-19 cases are classified from the chest X-ray images using the GLRLM feature extraction approach. Common metrics like sensitivity, selectivity, and accuracy are utilized to compare the performance of the classifiers. When compared to other classifiers in this study, the Gaussian Mixture Model had the best accuracy of 91.07%. © 2022 IEEE.

The Effect of Harmful Algal Blooms on COVID-19 Patient Outcomes in Florida

Harmful Algal Blooms (HABs) in coastal and inland waterbodies release toxins which are known to have negative effects on local ecology and public health. Toxins released by Karenia Brevis and other phytoplankton are known to cause fatigue, muscle aches, neurological and respiratory illness in humans after exposure, which match those of COVID-19. A relationship between HABs and COVID virality could help explain the seasonality and unique symptoms in COVID-infections. COVID infection, hospitalization, and ICU usage data in the state of Florida were compared with instances of K. brevis blooms on a state and county basis. Results of correlation analysis indicate that blooms potentially correlated with increased hospitalizations compared to infections on a state-level. County level analysis was inconclusive. Due to broadness and complexity of subject, further investigation is necessary to fully understand how HABs and coastal ecology affect public health and virality of infectious disease. © 2022 IEEE.

Clinical and Economic Impact of COVID-19 on Agricultural Workers, Guatemala1.

We evaluated clinical and socioeconomic burdens of respiratory disease in banana farm workers in Guatemala. We offered all eligible workers enrollment during June 15-December 30, 2020, and annually, then tracked them for influenza-like illnesses (ILI) through self-reporting to study nurses, sentinel surveillance at health posts, and absenteeism. Workers who had ILI submitted nasopharyngeal swab specimens for testing for influenza virus, respiratory syncytial virus, and SARS-CoV-2, then completed surveys at days 0, 7, and 28. Through October 10, 2021, a total of 1,833 workers reported 169 ILIs (12.0 cases/100 person-years), and 43 (25.4%) were laboratory-confirmed infections with SARS-CoV-2 (3.1 cases/100 person-years). Workers who had SARS-CoV-2‒positive ILIs reported more frequent anosmia, dysgeusia, difficulty concentrating, and irritability and worse clinical and well-being severity scores than workers who had test result‒negative ILIs. Workers who had positive results also had greater absenteeism and lost income. These results support prioritization of farm workers in Guatemala for COVID-19 vaccination.

Simulating and Optimizing Nasopharyngeal Swab Insertion Paths for use in Robotics

The nasopharyngeal swab is the standardized method of collecting specimens for diagnosing COVID-19, among numerous other respiratory illnesses. While there has been interest from the robotics community in the design of robots and manipulators for performing swab collections, detailed simulation and planning for swab insertion trajectories through the nasal cavity is less studied. In this work, we propose a simulation environment with the swab modelled as an Euler-Bernoulli beam, subject to linear elastic collisions coming from the nasal cavity. We evaluate the impact of inserting the swab with different amounts of force. We also leverage the simulation environment to pose an optimization problem that finds trajectories that minimize strain on the swab during the insertion. We find that the optimized trajectories adhere to qualitative clinical advice. © 2022 IEEE.

Probing Micron-sized Objects with Photoacoustic Sensing: Theory and Applications

The covid-19 respiratory illness caused by the rapid spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) resulted in a worldwide pandemic over the past two years. Despite vaccinations and preventative screening methods, covid-19 remains a global issue in part due to high contagiousness and airborne transmission via droplet aerosolization. Current screening methods lack sensitivity or have a slow response time. In this study, we investigate an air-based photoacoustic spectroscopy method to rapidly detect viral RNA within aerosolized droplets. © 2022 IEEE.

Association between ABO blood groups and the risk of infection with SARS-CoV-2 in Iraq.

OBJECTIVE: The primary goals of this research were to analyze the relationship between ABO blood types and the severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and investigate the effect of vaccination in Iraq. METHODS: Data and outcomes were gathered from the medical records of 200 patients. Patients were categorized by blood group and vaccination status in the analysis. RESULTS: In total, 200 hospitalized patients (125 men and 75 women) with confirmed SARS-CoV-2 infection and blood group (ABO) and clinical data were enrolled. Of the 200 patients, 155 (77.5%) were vaccinated against SARS-CoV-2. The results illustrated that 25 patients died, which might have been attributable to a lack of vaccination or older age. Our analysis revealed that blood group O individuals were much less likely to be infected by SARS-CoV-2 than non-O subjects, whereas blood group A individuals carried a higher risk of infection. CONCLUSIONS: Our findings illustrated that immunization significantly reduces COVID-19 risk across all age groups, but there has been an increase in the number of cases because of decreased vaccine efficacy in older patients and persons with comorbidities. However, 45% vaccination coverage lowered the outbreak's peak.

Effect of Vitamin C Supplements on Respiratory Tract Infections: A Systematic Review and Meta-Analysis.

BACKGROUND: Respiratory tract infections are a primary cause of illness and mortality over the world. OBJECTIVE: This study was aimed to investigate the effectiveness of vitamin C supplementation in preventing and treating respiratory tract infections. METHODS: We used the Cochrane, PubMed, and MEDLINE Ovid databases to conduct our search. The inclusion criteria were placebo-controlled trials. Random effects meta-analyses were performed to measure the pooled effects of vitamin C supplementation on the incidence, severity, and duration of respiratory illness. RESULTS: We found ten studies that met our inclusion criteria out of a total of 2758. The pooled risk ratio (RR) of developing respiratory illness when taking vitamin C regularly across the study period was 0.94 (with a 95% confidence interval of 0.87 to 1.01) which found that supplementing with vitamin C lowers the occurrence of illness. This effect, however, was statistically insignificant (P= 0.09). This study showed that vitamin C supplementation had no consistent effect on the severity of respiratory illness (SMD 0.14, 95% CI -0.02 to 0.30: I2 = 22%, P=0.09). However, our study revealed that vitamin C group had a considerably shorter duration of respiratory infection (SMD -0.36, 95% CI -0.62 to -0.09, P = 0.01). CONCLUSION: Benefits of normal vitamin C supplementation for reducing the duration of respiratory tract illness were supported by our meta-analysis findings. Since few trials have examined the effects of therapeutic supplementation, further research is needed in this area.