Analyzing the Effect of Vaccination Over COVID Cases and Deaths in Asian Countries Using Machine Learning Models

Rustagi, Vanshika; Bajaj, Monika; Tanvi,; Singh, Priya; Aggarwal, Rajiv; Alajmi, Mohamed F.; Hussain, Afzal; Hassan, Md. Imtaiyaz; Singh, Archana; Singh, Ishwar

doi:10.3389/fcimb.2021.806265

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…The underlying reasons for the performance differences among these severity scores could be multifactorial. Patient characteristics ( 46 ), vaccination status ( 47 ), healthcare system ( 48 ), and the variables included in each of the score all contribute to the performance differences. Table 3 shows the different variables used in each scoring systems.…”

Section: Discussionmentioning

confidence: 99%

Outcome prediction in hospitalized COVID-19 patients: Comparison of the performance of five severity scores

Chung

Tang²,

Chen³

et al. 2023

Front. Med.

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BackgroundThe aim of our study was to externally validate the predictive capability of five developed coronavirus disease 2019 (COVID-19)-specific prognostic tools, including the COVID-19 Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC), Shang COVID severity score, COVID-intubation risk score-neutrophil/lymphocyte ratio (IRS-NLR), inflammation-based score, and ventilation in COVID estimator (VICE) score.MethodsThe medical records of all patients hospitalized for a laboratory-confirmed COVID-19 diagnosis between May 2021 and June 2021 were retrospectively analyzed. Data were extracted within the first 24 h of admission, and five different scores were calculated. The primary and secondary outcomes were 30-day mortality and mechanical ventilation, respectively.ResultsA total of 285 patients were enrolled in our cohort. Sixty-five patients (22.8%) were intubated with ventilator support, and the 30-day mortality rate was 8.8%. The Shang COVID severity score had the highest numerical area under the receiver operator characteristic (AUC-ROC) (AUC 0.836) curve to predict 30-day mortality, followed by the SEIMC score (AUC 0.807) and VICE score (AUC 0.804). For intubation, both the VICE and COVID-IRS-NLR scores had the highest AUC (AUC 0.82) compared to the inflammation-based score (AUC 0.69). The 30-day mortality increased steadily according to higher Shang COVID severity scores and SEIMC scores. The intubation rate exceeded 50% in the patients stratified by higher VICE scores and COVID-IRS-NLR score quintiles.ConclusionThe discriminative performances of the SEIMC score and Shang COVID severity score are good for predicting the 30-day mortality of hospitalized COVID-19 patients. The COVID-IRS-NLR and VICE showed good performance for predicting invasive mechanical ventilation (IMV).

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Section: Discussionmentioning

confidence: 99%

Outcome prediction in hospitalized COVID-19 patients: Comparison of the performance of five severity scores

Chung

Tang²,

Chen³

et al. 2023

Front. Med.

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“…Approval of multiple vaccines [54][55][56][57] and widespread availability through mass vaccination campaigns [58] has significantly reduced the global health burden of COVID-19 [59][60][61]. However, vaccine hesitancy has slowed down the scale of vaccination [62], and vaccination rates have decreased with approval of each additional booster [63].…”

Section: Discussionmentioning

confidence: 99%

Self-Reported Use of COVID-19 Immunologic Test Results to Inform Decisions About Daily Activities and COVID-19 Vaccination

Jiang

Akers

Gill

et al. 2022

Preprint

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Importance: Despite widespread use of clinical diagnostic tests to assess prior exposure to SARS-CoV-2, limited evidence exists regarding how test results affect patient behaviors and decision-making. Objective: To understand the rationale behind ordering diagnostic T-cell receptor (TCR) immunosequencing for assessment of prior SARS-CoV-2 infection and evaluate how test results affect patient behaviors, including day-to-day activities and decisions about vaccination. Design: Mandatory demographic information and clinical characteristics were collected for all individuals ordering T-DetectTM COVID. Study participants completed a one-time survey that included additional questions about demographics and clinical characteristics, relevant interactions with healthcare providers, reasons for ordering diagnostic TCR immunosequencing, and the utility of test results. Setting: US participants ordering T-Detect COVID between February 2021 and March 2022. Participants: Of the 806 individuals who underwent diagnostic TCR immunosequencing, provided informed consent, and were sent the email survey, 718 completed the survey (response rate, 89.1%). At the time of receiving the test report, 25.5% of participants had been vaccinated against COVID-19, 29.7% reported a previous COVID-19 infection, and 25.6% were immunocompromised. Main Outcome(s) and Measure(s): Patient demographics and clinical characteristics were reported using descriptive statistics. Additional analyses explored trends in reported data over time and evaluated reasons for ordering diagnostic TCR immunosequencing and behaviors among participant subgroups (vaccinated or unvaccinated individuals and those with positive or negative test results). Logistic regression analysis evaluated factors that increased the likelihood of post-test vaccination. Results: Study participants ordered diagnostic TCR immunosequencing to understand their health status (55.0%) and to inform decision-making about daily activities (43.6%) and vaccination (38.3%). Most participants (92.1%) ordered diagnostic TCR immunosequencing for themselves without consulting their physician. Testing negative for prior SARS-CoV-2 infection was associated with increased likelihood of subsequent COVID-19 vaccination (31.0% vs 6.9%; median time to vaccination, 17.0 days vs 47.5 days), which was confirmed by logistic regression analysis. Conclusions and Relevance: This report presents patient-reported clinical utility of a commercial COVID-19 assay based on an immune response readout. Our findings suggest that participants used diagnostic TCR immunosequencing results to inform decisions about daily activities and COVID-19 vaccination. Trial Registration: Not applicable.

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“…O banco de dados de COVID-19 de 'Our World in Data' entre o 24 de fevereiro de 2020 a 26 de setembro de 2021, usado por Rustagi e colegas [14] para fazer previsões sobre os casos positivos de COVID-19 e a taxa de mortalidade. Foi realizada uma análise de regressão linear para investigar os fatores dos dados da vacina para indivíduos vacinados com a primeira e segunda dose e positivos para COVID-19, que influenciam as flutuações da taxa de mortalidade por COVID-19.…”

Section: Trabalhos Relacionadosunclassified

Análise de Desfechos de COVID-19 no RJ através de Técnicas de Aprendizado de Máquina

Zavaleta,

Silva,

Oliveira

et al. 2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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Given the rapid spread of COVID-19, having tools to screen patients and reduce the risk of death is crucial. This study focuses on the outcomes (cures and deaths) of confirmed COVID-19 cases in Rio de Janeiro State for both vaccinated and unvaccinated patients. Machine Learning (ML) algorithms were used to classify outcomes based on symptom, comorbidity, and age data obtained from the State Health Secretariat of Rio de Janeiro. After cleaning the dataset and selecting relevant attributes, the final model achieved an accuracy of 87,3% and a precision of 86,6% in predicting outcomes for unvaccinated patients. Similarly, the final model for vaccinated patients achieved an accuracy of 86,3% and a precision of 83,1% in predicting outcomes. In addition, the attributes of patients that stand out with and without the vaccine were evaluated. Overall, these results demonstrate the potential benefits of using machine learning methods to improve patient screening and reduce the risk of COVID-19-related deaths.

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Analyzing the Effect of Vaccination Over COVID Cases and Deaths in Asian Countries Using Machine Learning Models

Cited by 10 publications

References 37 publications

Outcome prediction in hospitalized COVID-19 patients: Comparison of the performance of five severity scores

Outcome prediction in hospitalized COVID-19 patients: Comparison of the performance of five severity scores

Self-Reported Use of COVID-19 Immunologic Test Results to Inform Decisions About Daily Activities and COVID-19 Vaccination

Análise de Desfechos de COVID-19 no RJ através de Técnicas de Aprendizado de Máquina

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