Machine Learning Applied to COVID-19: A Review of the Initial Pandemic Period
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ABSTRACT:
SUBMITTER: Mano L
PROVIDER: S-EPMC10163989 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:HypothesisIncreased social distancing was associated with a lower incidence of extremely preterm live births (EPLB) during the initial COVID-19 pandemic period.Study designProspective study at the NICHD Neonatal Research Network sites comparing EPLB (220/7-286/7 weeks) and extremely preterm intrapartum stillbirths (EPIS) rates during the pandemic period (March-July, weeks 9-30 of 2020) with the reference period (same weeks in 2018 and 2019), correlating with state-specific social distancing index (SDI).ResultsEPLB and EPIS percentages did not significantly decrease (1.58-1.45%, p = 0.07, and 0.08-0.06%, p = 0.14, respectively). SDI was not significantly correlated with percent change of EPLB (CC = 0.29, 95% CI = -0.12, 0.71) or EPIS (CC = -0.23, 95% CI = -0.65, 0.18). Percent change in mean gestational age was positively correlated with SDI (CC = 0.49, 95% CI = 0.07, 0.91).ConclusionsIncreased social distancing was not associated with change in incidence of EPLB but was associated with a higher gestational age of extremely preterm births.ClinicaltrialsGov idGeneric Database: NCT00063063.
| S-EPMC11226398 | biostudies-literature
Project description:Next-generation T-cell-directed vaccines for COVID-19 focus on establishing lasting T-cell immunity against current and emerging SARS-CoV-2 variants. Precise identification of conserved T-cell epitopes is critical for designing effective vaccines. Here we introduce a comprehensive computational framework incorporating a machine learning algorithm-MHCvalidator-to enhance mass spectrometry-based immunopeptidomics sensitivity. MHCvalidator identifies unique T-cell epitopes presented by the B7 supertype, including an epitope from a + 1-frameshift in a truncated Spike antigen, supported by ribosome profiling. Analysis of 100,512 COVID-19 patient proteomes shows Spike antigen truncation in 0.85% of cases, revealing frameshifted viral antigens at the population level. Our EpiTrack pipeline tracks global mutations of MHCvalidator-identified CD8 + T-cell epitopes from the BNT162b4 vaccine. While most vaccine epitopes remain globally conserved, an immunodominant A*01-associated epitope mutates in Delta and Omicron variants. This work highlights SARS-CoV-2 antigenic features and emphasizes the importance of continuous adaptation in T-cell vaccine development.
| S-EPMC11604954 | biostudies-literature
Project description:Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.
| S-EPMC8303306 | biostudies-literature
Project description:Coronavirus disease (COVID-19) is an inflammation disease from a new virus. The disease causes respiratory ailment (like influenza) with manifestations, for example, cold, cough and fever, and in progressively serious cases, the problem in breathing. COVID-2019 has been perceived as a worldwide pandemic and a few examinations are being led utilizing different numerical models to anticipate the likely advancement of this pestilence. These numerical models dependent on different factors and investigations are dependent upon potential inclination. Here, we presented a model that could be useful to predict the spread of COVID-2019. We have performed linear regression, Multilayer perceptron and Vector autoregression method for desire on the COVID-19 Kaggle data to anticipate the epidemiological example of the ailment and pace of COVID-2019 cases in India. Anticipated the potential patterns of COVID-19 effects in India dependent on data gathered from Kaggle. With the common data about confirmed, death and recovered cases across India for over the time length helps in anticipating and estimating the not so distant future. For extra assessment or future perspective, case definition and data combination must be kept up persistently.
| S-EPMC7261047 | biostudies-literature
Project description:BackgroundThe rapid spread of COVID-19 means that government and health services providers have little time to plan and design effective response policies. It is therefore important to quickly provide accurate predictions of how vulnerable geographic regions such as counties are to the spread of this virus.ObjectiveThe aim of this study is to develop county-level prediction around near future disease movement for COVID-19 occurrences using publicly available data.MethodsWe estimated county-level COVID-19 occurrences for the period March 14 to 31, 2020, based on data fused from multiple publicly available sources inclusive of health statistics, demographics, and geographical features. We developed a three-stage model using XGBoost, a machine learning algorithm, to quantify the probability of COVID-19 occurrence and estimate the number of potential occurrences for unaffected counties. Finally, these results were combined to predict the county-level risk. This risk was then used as an estimated after-five-day-vulnerability of the county.ResultsThe model predictions showed a sensitivity over 71% and specificity over 94% for models built using data from March 14 to 31, 2020. We found that population, population density, percentage of people aged >70 years, and prevalence of comorbidities play an important role in predicting COVID-19 occurrences. We observed a positive association at the county level between urbanicity and vulnerability to COVID-19.ConclusionsThe developed model can be used for identification of vulnerable counties and potential data discrepancies. Limited testing facilities and delayed results introduce significant variation in reported cases, which produces a bias in the model.
| S-EPMC7490002 | biostudies-literature
Project description:At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution-individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.
| S-EPMC9381137 | biostudies-literature
Project description:Background:The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. Main body:While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. Conclusion:This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume.
| S-EPMC7347420 | biostudies-literature
Project description:BackgroundIt is important to measure the public response to the COVID-19 pandemic. Twitter is an important data source for infodemiology studies involving public response monitoring.ObjectiveThe objective of this study is to examine COVID-19-related discussions, concerns, and sentiments using tweets posted by Twitter users.MethodsWe analyzed 4 million Twitter messages related to the COVID-19 pandemic using a list of 20 hashtags (eg, "coronavirus," "COVID-19," "quarantine") from March 7 to April 21, 2020. We used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigrams and bigrams, salient topics and themes, and sentiments in the collected tweets.ResultsPopular unigrams included "virus," "lockdown," and "quarantine." Popular bigrams included "COVID-19," "stay home," "corona virus," "social distancing," and "new cases." We identified 13 discussion topics and categorized them into 5 different themes: (1) public health measures to slow the spread of COVID-19, (2) social stigma associated with COVID-19, (3) COVID-19 news, cases, and deaths, (4) COVID-19 in the United States, and (5) COVID-19 in the rest of the world. Across all identified topics, the dominant sentiments for the spread of COVID-19 were anticipation that measures can be taken, followed by mixed feelings of trust, anger, and fear related to different topics. The public tweets revealed a significant feeling of fear when people discussed new COVID-19 cases and deaths compared to other topics.ConclusionsThis study showed that Twitter data and machine learning approaches can be leveraged for an infodemiology study, enabling research into evolving public discussions and sentiments during the COVID-19 pandemic. As the situation rapidly evolves, several topics are consistently dominant on Twitter, such as confirmed cases and death rates, preventive measures, health authorities and government policies, COVID-19 stigma, and negative psychological reactions (eg, fear). Real-time monitoring and assessment of Twitter discussions and concerns could provide useful data for public health emergency responses and planning. Pandemic-related fear, stigma, and mental health concerns are already evident and may continue to influence public trust when a second wave of COVID-19 occurs or there is a new surge of the current pandemic.
| S-EPMC7690968 | biostudies-literature
Project description:IntroductionThe extraordinary impact of COVID-19 pandemic on Spanish hospitals has led to a redistribution of resources for the treatment of these patients, with a decreased capacity of care for other common diseases. The aim of the present study is to analyse how this situation has affected the treatment of cholecystitis and cholelythiasis.MethodsIt is a descriptive national study after online voluntary distribution of a specific questionnaire with Google Drive™ to members of the Spanish Association of Surgeons (AEC).ResultsWe received 153 answers (one per hospital). Elective cholecystectomies have been cancelled in 96.7% of centres. Conservative treatment for acute cholecystitis has been selected in 90% (previously 18%), and if operated, 95% have been performed laparoscopically. Globally, only 49% perform preoperative diagnostic tests for SARS-CoV-2, and 58.5% recognize there have been cases confirmed postoperatively after other surgeries, with worse surgical outcomes in 54%.ConclusionsThis survey shows that most of the Spanish centers are following the surgical societies suggestions during the pandemic. However, some data requires to be taken into account for the next phase of the pandemic.
| S-EPMC8088215 | biostudies-literature
Project description: Not available
| S-BSST1055 | biostudies-other