Worldwide Spread Of COVID-19 Research Articles

Acil Abdominal Cerrahi Yapılan COVID-19 Hastalarında Postoperatif Komplikasyonların Öngördürücüleri

Amaç: COVID-19 dünya çapında hızla yayılması ve pandemi haline gelmesi, cerrahi hastalarda postoperatif komplikasyonların insidansında da artışa neden oldu. Acil ameliyata alınan hastalarda COVID-19 pozitifliğinin ameliyat sonrası komplikasyonları etkileyebileceği açıktır. Bu çalışmada acil abdominal cerrahi uygulanan COVID-19 hastalarında postoperatif komplikasyonlar için öngördürücü faktörlerin değerlendirilmesi amaçlanmıştır.
 Gereç ve Yöntemler: Bu çalışmada acil abdominal cerrahi uygulanan ve perioperatif 72 saat içerisinde COVID-19 Polimeraz Zincir Reaksiyonu (PCR) testi pozitif sonuçlanan 52 hasta retrospektif olarak değerlendirildi.
 Bulgular: Çalışmaya dahil edilen hastaların medyan yaşı 50.5’di (aralık: 18-83). 30 günlük mortalite oranı %15.4 olarak saptandı. Postoperatif komplikasyon oranı %23.1’di. Postoperatif komplikasyon durumu ile yaş (p=0.003) ve preoperatif hemoglobin değeri (p=0.001) arasında anlamlı bir ilişki saptandı. Yaş ve hemoglobinin birlikte değerlendirilmesinin postoperatif komplikasyonlar için sensivitesinin %91.7, spesifitesinin %87.5 olduğu görüldü (p

Exploring and measuring the health resilience of urban buildings against the pandemic: A case study of Hong Kong public housing during COVID-19

The worldwide spread of COVID-19 raises considerable concern about occupant health and pandemic prevention. The building plays a significant role in preventing and controlling the pandemic as most of the time people stay within the built environment. Even urban resilience is widely used to cope with urban changes, the concept of resilience has seldom been adopted at the building level for occupants' health during the pandemic. There is a lack of suitable measurement methods specially designed for health resilience. Consequently, this study designs the framework of “building health resilience (BHR)” to reveal buildings’ ability to resist the pandemic spread within occupants, and develops the BHR model to measure the building performance in health resilience. The case study of Hong Kong public housing under COVID-19 reveals that implementing BHR framework and measurement approach are effective for single buildings and building groups within certain areas or during different periods. This study contributes to proposing BHR and developing BHR measurement approach by considering the building scale and external pandemic situation in nearby local area of the building. The BHR model is quite flexible to adapt to diverse disturbance scenarios to quantify the BHR performance, provides valuable results to find out weak buildings during the pandemic spread, and takes targeted actions toward healthy and resilient buildings.

Standardization of an antimicrobial resistance surveillance network through data management.

The rapid spread of COVID-19 worldwide within 2 months demonstrated the vulnerability of the world's population to infectious diseases. In 2015, the Global Antimicrobial Resistance and Use Surveillance System (GLASS) was launched to combat antimicrobial resistance (AMR). However, there has been no comprehensive assessment of the decade-long global battle against AMR based on GLASS data. South Korea established Kor-GLASS (Korean-GLASS) to proactively monitor data quality and enable international collaborations. A unique feature of Kor-GLASS is the quality control center (QCC), which uses network hubs and ensures standardized, high-quality data through interlaboratory proficiency testing (IPT) and external quality assessment (EQA). In addition, the QCC multifaceted endeavors for integrated data quality management. Since 2020, high-quality AMR data have indicated fluctuating antibiotic resistance rates in South Korea. This trend does not align with the decrease in antibiotic usage seen in humans but coincides with non-human antibiotic sales, indicating a need for greater monitoring of non-human antibiotic resistance. Comprehensive and robust management taking account of the intricate interplay among humans, animals, and the environment is essential. Kor-GLASS has been expanded into a "One Health" multiagency collaborative initiative. Although a standardized solution is not suitable for all countries, it must align with the local context and international standards. A centralized top-down management structure such as that of the QCC is essential to ensure continuous data quality coordination. Sustained efforts and surveillance systems are crucial for monitoring and managing AMR and safeguarding human health.

Crafts in the Time of Coronavirus

Crafts in the Time of Coronavirus

Voluntary blood donation camp- A challenge during COVID-19 pandemic

Voluntary blood donors are the mainstay of any blood donation camp, whether in a hospital or in a blood bank. Selection of such donors becomes an important element in any blood donation program. However, the spread of COVID-19 worldwide had a crippling impact on blood donations/ blood safety and had posed a huge challenge. Hence, an insight into conduction of a blood donation camp during the pandemic and analysis of reasons for donor deferral becomes highly important.This is an observational study about a voluntary blood donation camp held for general public during the second wave of COVID-19 in a school at Kottur, Karnataka. Donor details were taken with the help of questionnaires. Procedures and safety protocols put in place during donation was noted and reasons for deferral obtained from donor questionnaires and by examination of donors was analyzed.The blood donation camp was conducted with proper COVID-19 guidelines. A total of 105 individuals came for donation of which 37 were deferred. 6 deferrals were due to COVID-related cause, 31 deferrals were non-COVID related and overall the most common cause of deferral was hypertension. With the help of proper safety measures, blood donation camps can be successfully conducted during pandemic like COVID-19. And the crisis for blood and its products can be met by adopting appropriate guidelines.

Measuring the worldwide spread of COVID-19 using a comprehensive modeling method

BackgroundWith the global spread of COVID-19, detecting high-risk countries/regions timely and dynamically is essential; therefore, we sought to develop automatic, quantitative and scalable analysis methods to observe and estimate COVID-19 spread worldwide and further generate reliable and timely decision-making support for public health management using a comprehensive modeling method based on multiple mathematical models.MethodsWe collected global COVID-19 epidemic data reported from January 23 to September 30, 2020, to observe and estimate its possible spread trends. Countries were divided into three outbreak levels: high, middle, and low. Trends analysis was performed by calculating the growth rate, and then country grouping was implemented using group-based trajectory modeling on the three levels. Individual countries from each group were also chosen to further disclose the outbreak situations using two predicting models: the logistic growth model and the SEIR model.ResultsAll 187 observed countries' trajectory subgroups were identified using two grouping strategies: with and without population consideration. By measuring epidemic trends and predicting the epidemic size and peak of individual countries, our study found that the logistic growth model generally estimated a smaller epidemic size than the SEIR model. According to SEIR modeling, confirmed cases in each country would take an average of 9–12 months to reach the outbreak peak from the day the first case occurred. Additionally, the average number of cases at the peak time will reach approximately 10–20% of the countries’ populations, and the countries with high trends and a high predicted size must pay special attention and implement public health interventions in a timely manner.ConclusionsWe demonstrated comprehensive observations and predictions of the COVID-19 outbreak in 187 countries using a comprehensive modeling method. The methods proposed in this study can measure COVID-19 development from multiple perspectives and are generalizable to other epidemic diseases. Furthermore, the methods also provide reliable and timely decision-making support for public health management.

Well-being of university students during the Covid-19 pandemic: An Indonesian case study

The spread of COVID-19 worldwide challenged the foundations of many people's lives. This research aimed to investigate the impact of the pandemic and its associated lockdown restrictions on the physical activity behaviours and attitudes of a small group of sports science students in a provincial state university in Indonesia. The study results showed that most of the respondents (93%) believed in the importance of maintaining their physical activity during lockdown. However, the actual activity levels practised failed to match the international guidelines considered necessary for this age group. Only 23% practised daily physical activity, and 40% were active for three days or less. The intensity of the physical activity was only classified as light in over 80% of the cases, and 26% reported that their activity was less than 30 minutes. A further concern about mental health was identified in the finding that over 73% of the participants reported suffering significant boredom during the lockdown conditions imposed. Students reported carrying out exercise and a range of other activities aimed to reduce boredom and avoid more potentially dangerous feelings of stress and depression. Recommendations include maintaining and improving fitness through physical activity and participating in moderate activity between 70 - 80% with a maximum pulse rate of 150 - 300 minutes per week to high intensity between 80 - 90% maximum pulse with a frequency of at least 3 days or more per week, 75-150 minutes per week. All physical activities can be done gradually over time with the principle of progressive exercise. Meanwhile, to reduce boredom, apart from doing physical activity in the form of sports and exercise, other active daily physical activities such as washing vehicles, gardening, cleaning the house and environment, and pursuing hobbies may be considered.

TGRA-P: Task-driven model predicts 90-day mortality from ICU clinical notes on mechanical ventilation

BackgroundWith the outbreak and spread of COVID-19 worldwide, limited ventilators fail to meet the surging demand for mechanical ventilation in the ICU. Clinical models based on structured data that have been proposed to rationalize ventilator allocation often suffer from poor ductility due to fixed fields and laborious normalization processes. The advent of pre-trained models and downstream fine-tuning methods allows for learning large amounts of unstructured clinical text for different tasks. But the hardware requirements of large-scale pre-trained models and purposeless networks downstream have led to a lack of promotion in the clinical domain. ObjectiveIn this study, an innovative architecture of a task-driven predictive model is proposed and a Task-driven Gated Recurrent Attention Pool model (TGRA-P) is developed based on the architecture. TGRA-P predicts early mortality risk from patients' clinical notes on mechanical ventilation in the ICU, which is used to assist clinicians in diagnosis and decision-making. MethodsSpecifically, a Task-Specific Embedding Module is proposed to fine-tune the embedding with task labels and save it as static files for downstream calls. It serves the task better and prevents GPU overload. The Gated Recurrent Attention Unit (GRA) is proposed to further enhance the dependency of the information preceding and following the text sequence with fewer parameters. In addition, we propose a Residual Max Pool (RMP) to avoid ignoring words in common text classification tasks by incorporating all word-level features of the notes for prediction. Finally, we use a fully connected decoding network as a classifier to predict the mortality risk. ResultThe proposed model shows very promising results with an AUROC of 0.8245±0.0096, an AUPRC of 0.7532±0.0115, an accuracy of 0.7422±0.0028 and F1-score of 0.6612±0.0059 for 90-day mortality prediction using clinical notes of ICU mechanically ventilated patients on the MIMIC-III dataset, all of which are better than previous studies. Moreover, the superiority of the proposed model in comparison with other baseline models is also statistically validated through the calculated Cohen's d effect sizes. ConclusionThe experimental results show that TGRA-P based on the innovative task-driven prognostic architecture obtains state-of-the-art performance. In future work, we will build upon the provided code and investigate its applicability to different datasets. The model balances performance and efficiency, not only reducing the cost of early mortality risk prediction but also assisting physicians in making timely clinical interventions and decisions. By incorporating textual records that are challenging for clinicians to utilize, the model serves as a valuable complement to physicians' judgment, enhancing their decision-making process.

Efficacy and safety of proxalutamide (GT0918) in severe or critically ill patients with COVID-19: study protocol for a prospective, open-label, single-arm, single-center exploratory trial

BackgroundThe rapid worldwide spread of COVID-19 has caused a global health challenge with high mortality of severe or critically ill patients with COVID-19. To date, there is no specific efficient therapeutics for severe or critically ill patients with COVID-19. It has been reported that androgen is related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Proxalutamide as an androgen receptor antagonist has shown potential treatment effects on COVID-19 patients. Thus, this trial is designed to investigate the efficacy and safety of proxalutamide in severe or critically ill patients with COVID-19.MethodsThis single-arm, open-label, single-center prospective exploratory trial is planned to recruit 64 severe or critically ill patients with COVID-19 in China. Recruitment started on 16 May 2022 and is foreseen to end on 16 May 2023. Patients will be followed-up until 60 days or death, whichever comes first. The primary outcome is the 30-day all-cause mortality. Secondary endpoints included 60-day all-cause mortality, rate of clinical deterioration within 30 days after administration, time to sustain clinical recovery (determined using an 8-point ordinal scale), mean change in the Acute Physiology and Chronic Health Evaluation II scores, change in oxygenation index, changes in chest CT scan, percentage of patients confirmed negative for SARS-CoV-2 by nasopharyngeal swab, change in Ct values of SARS-CoV-2 and safety. Visits will be performed on days 1 (baseline), 15 or 30, 22, and 60.DiscussionThe trial is the first to investigate the efficacy and safety of proxalutamide in severe or critically ill patients with COVID-19. The findings of this study might lead to the development of better treatment for COVID-19 and provide convincing evidence regarding the efficacy and safety of proxalutamide.Trial registrationThis study was registered on 18 June 2022 at the Chinese Clinical Trial Registry (ChiCTR2200061250).

Existential insecurity and deference to authority: the pandemic as a natural experiment

IntroductionThe global coronavirus pandemic offers a quasi-experimental setting for understanding the impact of sudden exposure to heightened existential risk upon both individual and societal values.MethodsWe examined the effect of the pandemic on political attitudes by comparing data from eight countries surveyed before and after the worldwide spread of COVID-19 in March 2020 with continuous weekly polling tracker data from the United Kingdom from 2019 to 2021. Multilevel models were used to explore the drivers of change, and the results indicated that reported emotions of fear and stress were positively associated with institutional approval during periods of greater pathogen risk.ResultsOur findings revealed that support for political and technocratic authority, as well as satisfaction with political institutions, rose significantly above long-term historical baselines during the pandemic.DiscussionThe results support the hypothesis that exposure to existential risk results in greater support for authority and that individual feelings of insecurity may be linked to less critical citizen orientations.

Does Digital Transformation Accelerate the Acceptance of Social CRM on the Small and Medium Enterprises?

Due to the rapid spread of COVID-19 worldwide, businesses that have been impacted by the pandemic are relying more on digital technologies to adapt to the current situation, where contactless interactions are becoming more common. With the decrease in face-to-face interactions, customers are using social media more frequently, and companies are utilizing social CRM(sCRM) to maintain relationships with customers through social media. Thus, This study aims to examine the intention of employees to use sCRM in a pandemic situation among small and medium-sized enterprises(SMEs) by utilizing the Technology Acceptance Model(TAM), which includes perceived ease of use and perceived usefulness, as well as four additional factors: cost reduction, compatibility, awareness of COVID-19, and digital transformation. Data were collected from employees who had experience in sCRM, and analyzed using structural equation models. As a result of the study, the four factors positively impacted the perceived usefulness and ease of use of sCRM, which significantly influenced employees’ intention to use sCRM. This finding is particularly relevant in the current COVID-19 era, where there is a preference for non-face-to-face communication and digital transformation is rapidly accelerating.

The Ability of Airborne Microalgae and Cyanobacteria to Survive and Transfer the Carcinogenic Benzo(a)pyrene in Coastal Regions.

Air pollution has been a significant problem threatening human health for years. One commonly reported air pollutant is benzo(a)pyrene, a dangerous compound with carcinogenic properties. Values which exceed normative values for benzo(a)pyrene concentration in the air are often noted in many regions of the world. Studies on the worldwide spread of COVID-19 since 2020, as well as avian flu, measles, and SARS, have proven that viruses and bacteria are more dangerous to human health when they occur in polluted air. Regarding cyanobacteria and microalgae, little is known about their relationship with benzo(a)pyrene. The question is whether these microorganisms can pose a threat when present in poor quality air. We initially assessed whether cyanobacteria and microalgae isolated from the atmosphere are sensitive to changes in PAH concentrations and whether they can accumulate or degrade PAHs. The presence of B(a)P has significantly affected both the quantity of cyanobacteria and microalgae cells as well as their chlorophyll a (chl a) content and their ability to fluorescence. For many cyanobacteria and microalgae, an increase in cell numbers was observed after the addition of B(a)P. Therefore, even slight air pollution with benzo(a)pyrene is likely to facilitate the growth of airborne cyanobacteria and microalgae. The results provided an assessment of the organisms that are most susceptible to cellular stress following exposure to benzo(a)pyrene, as well as the potential consequences for the environment. Additionally, the results indicated that green algae have the greatest potential for degrading PAHs, making their use a promising bioremediation approach. Kirchneriella sp. demonstrated the highest average degradation of B(a)P, with the above-mentioned research indicating it can even degrade up to 80% of B(a)P. The other studied green algae exhibited a lower, yet still significant, B(a)P degradation rate exceeding 50% when compared to cyanobacteria and diatoms.

Totally-green cellulosic fiber with prominent sustained antibacterial and antiviral properties for potential use in spunlaced non-woven fabric production

The worldwide spread of COVID-19 has put a higher requirement for personal medical protective clothing, developing protective clothing with sustained antibacterial and antiviral performance is the priority for safe and sustaining application. For this purpose, we develop a novel cellulose based material with sustained antibacterial and antiviral properties. In the proposed method, the chitosan oligosaccharide (COS) was subjected to a guanylation reaction with dicyandiamide in the presence of Scandium (III) triflate; because of the relatively lower molecular weight and water solubility of the COS, GCOS (guanylated chitosan oligosaccharide) with high substitution degree (DS) could be successfully synthetized without acid application. In this instance, the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the GCOS were only 1/8 and 1/4 of that of COS. The introduction of GCOS onto the fiber endowed the fiber with extremely high antibacterial and antiviral performance, showing 100% bacteriostatic rate against Staphylococcus aureus and Escherichia coli and 99.48% virus load reduction of bacteriophage MS2. More importantly, the GCOS modified cellulosic fibers (GCOS-CFs) exhibit excellent sustained antibacterial and antiviral properties; namely, 30 washing cycles had negligible effect on the bacteriostatic rate (100%) and inhibition rate of bacteriophage MS2 (99.0%). Moreover, the paper prepared from the GCOS-CFs still exhibited prominent antibacterial and antiviral activity; inferring that the sheeting forming, press, and drying process have almost no effect on the antibacterial and antiviral performances. The insensitive of antibacterial and antiviral activity to water washing (spunlace) and heat (drying) make the GCOS-CFs a potential material applicable in the spunlaced non-woven fabric production.

Explaining the Factors Involved in Psychological Effects of Covid-19 on Healthcare Workers by Interpretive Structural Modeling

Introduction: Spread of COVID-19 worldwide created anxiety, fear, and stress. In the meantime, healthcare workers have been suffering from significant psychological distress due to providing direct care to the patient, side injuries and quarantine. Examining the effects of this disease based on structural models can help develop a more appropriate program. Therefore, the present research aims to investigate and identify the factors involved in reduction of psychological effects of COVID-19 by interpretive structural modeling.
 Methods:This study was a mixed study (quantitative and qualitative). The study population included doctors, supervisors and nurses of the clinical departments of Khatam-ul-AnbiyaAbarkoh Hospital in Yazd. The participants were selected using the snowball sampling method. Semi-structured interviews were used to collect data. In the quantitative part of the research, the structural self-interaction matrix was used based on our pairwise comparisons between the research components in the form of a square matrix to receive experts' votes and opinions. . The final analysis was performed by MATLAB2018.
 Result:The results show that the greatest effects are related to "appropriate measures to reduce the disease such as environmental ventilation" and "financial support of personnel" and "sufficient provision of personal protective equipment" and "dedicated sufficient manpower to the involved departments" (with a power of 8). And the lowest influence is related to "continuous training of stress management and constant presence of a psychologist" (with an influence of influence 5)
 Conclusion:In a situation where the medical staff have a serious responsibility, it is necessary to maintain their mental health, because the main role in fighting this virus is definitely the responsibility of these people. The basic factors in the research model include continuous stress management training, the presence of a psychologist, and inspection of infected areas. by the management team and providing adequate personal protective equipment. As a result, the factors involved in reducing the psychological burden and the prevailing conditions should originate from the mentioned variables and be extended to other variables.

Potent Therapeutic Strategies for COVID-19 with Single-Domain Antibody Immunoliposomes Neutralizing SARS-CoV-2 and Lip/cGAMP Enhancing Protective Immunity.

The worldwide spread of COVID-19 continues to impact our lives and has led to unprecedented damage to global health and the economy. This highlights the need for an efficient approach to rapidly develop therapeutics and prophylactics against SARS-CoV-2. We modified a single-domain antibody, SARS-CoV-2 VHH, to the surface of the liposomes. These immunoliposomes demonstrated a good neutralizing ability, but could also carry therapeutic compounds. Furthermore, we used the 2019-nCoV RBD-SD1 protein as an antigen with Lip/cGAMP as the adjuvant to immunize mice. Lip/cGAMP enhanced the immunity well. It was demonstrated that the combination of RBD-SD1 and Lip/cGAMP was an effective preventive vaccine. This work presented potent therapeutic anti-SARS-CoV-2 drugs and an effective vaccine to prevent the spread of COVID-19.

Spatio-temporal model to investigate COVID-19 spread accounting for the mobility amongst municipalities

The rapid spread of COVID-19 worldwide led to the implementation of various non-pharmaceutical interventions to limit transmission and hence reduce the number of infections. Using telecom-operator-based mobility data and a spatio-temporal dynamic model, the impact of mobility on the evolution of the pandemic at the level of the 581 Belgian municipalities is investigated. By decomposing incidence, particularly into within- and between-municipality components, we noted that the global epidemic component is relatively more important in larger municipalities (e.g., cities), while the local component is more relevant in smaller (rural) municipalities. Investigation of the effect of mobility on the pandemic spread showed that reduction of mobility has a significant impact in reducing the number of new infections.

Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location.

With the rapid spread of COVID-19 worldwide, viral genomic data are available in the order of millions of sequences on public databases such as GISAID. This Big Data creates a unique opportunity for analysis toward the research of effective vaccine development for current pandemics, and avoiding or mitigating future pandemics. One piece of information that comes with every such viral sequence is the geographical location where it was collected-the patterns found between viral variants and geographical location surely being an important part of this analysis. One major challenge that researchers face is processing such huge, highly dimensional data to obtain useful insights as quickly as possible. Most of the existing methods face scalability issues when dealing with the magnitude of such data. In this article, we propose an approach that first computes a numerical representation of the spike protein sequence of SARS-CoV-2 using k-mers (substrings) and then uses several machine learning models to classify the sequences based on geographical location. We show that our proposed model significantly outperforms the baselines. We also show the importance of different amino acids in the spike sequences by computing the information gain corresponding to the true class labels.

The Contribution of Learner Characteristics and Perceived Learning to Students’ Satisfaction and Academic Performance during COVID-19

With the rapid spread of COVID-19 worldwide, governments of all countries declared the closure of educational institutions to control its transmission. As a result, institutions were under pressure to offer online education opportunities so that students could continue their education without interruption. The unintended, hasty and unknown duration of the strategy encountered challenges at all pedagogical levels, especially for students who felt stressed out by this abrupt shift, resulting in the decline of their academic performance. Hence, it is necessary to comprehend the approach that might improve students’ involvement and performance in online learning. In this context, the current study used four models to understand the phenomenon: the Task Technology Fit (TTF), the DeLone and McLean Model of Information Systems Success (DMISM), the Technology-to-Performance Chain model (TPC) and the Technology Acceptance Model (TAM). The data for this study were obtained from 404 university students from the top ten universities of Pakistan. The results analyzed using structural equation modeling (SEM) show that learner characteristics positively predict performance through user satisfaction and task technology fit mediating function. Moreover, learner characteristics were also observed to have a significant positive influence on the academic performance of the students, with the mediating functions of user satisfaction and actual usage of the system. Likewise, perceived learning moderated the relationship between learner characteristics and user satisfaction. This research work provides policymakers with a profound framework that emphasizes how employing online learning technologies can strengthen the academic potential of students.

Health safety perception of hotel guests in the post-Covid-19 stage

The rapid spread of Covid-19 worldwide generated a paralysis in all sectors, with tourism being the most severely affected, representing a challenge for all territories that depend to a greater or lesser extent on tourism. In this context, all tourism systems have the obligation to include security aspects due to the growing need of tourists and service providers to perceive confidence in the environment so that they can carry out the activities arranged for this purpose. The objective of the present research is to evaluate the perception of security of clients staying in hotels in the Pesquero tourist pole in the post Covid-19 stage. This is a quantitative and descriptive research. By means of a survey applied to a sample of 159 clients, it was found that there is a positive perception regarding health safety in the destination's hotels. The factors cleanliness and hygiene, medical assistance and hotel staff were the most highly valued, while prevention and mitigation, technology and communication scored lower. Finally, it can be inferred that the hotels in the Pesquero pole of the Holguín tourist destination, in the post-pandemic stage, are safe hotels.

Aspect of COVID-19 Pandemic: Case for Mass Spectrometry

Fast spread of COVID-19 worldwide causing unprecedented number of coronavirus infections, Scientist community are in race to control the disease by the development of both novel effective diagnostics strategies and effective therapies. Ultimately an efficient vaccine. Research efforts has focused on recognition of the pathogen entry mechanisms to the host's cells, simultaneously characterize the host’s cellular defence mechanisms in response to the pathogen attack. Early genome sequencing enlighten us on the origin of the virus responsible for the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) pandemic. PCR tests first implemented to recognize the presence of viral RNA. PCR tests for SARS-CoV-2 has no gold standard reference, which limits the accuracy of results and increases the percentages of false positives, besides PCR test are unable to predict disease status (progression). Alternative techniques where tested and evaluated, such as immunological based tests. Mass spectrometric methods. Which was suggested as a complementary method to the structural information gathered by molecular level test? Here, we highlight on the contributions of mass spectrometry to several areas of SARS-CoV-2 infectious disease research understanding the host’s cell proteome changes including diagnostics techniques, vaccines and therapies.