A comparative study for predictive monitoring of COVID-19 pandemic

Fatimah, Binish; Aggarwal, Priya; Singh, Pushpendra; Gupta, Anubha

doi:10.1016/j.asoc.2022.108806

Cited by 10 publications

(5 citation statements)

References 49 publications

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“…is actively continuing. Similar work continues in 2022 [11][12][13][14]. Our latest work [15] presents the results of a statistical analysis of daily, total by day of the week and monthly data on officially registered deaths from the new coronavirus COVID-19 in the countries of the South Caucasus (Armenia, Azerbaijan, Georgia).…”

Section: Introductionmentioning

confidence: 90%

Results of a Study on the Impact of Surface Ozone Concentration on the Spread of COVID-19 in Tbilisi

Amiranashvili,

Japaridze,

Kharchilava

et al. 2023

GGS

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The results of a study of the influence of diurnal values of surface ozone concentration (SOC) on the infection positivity rate with coronavirus COVID-19 (IR) of the population of Tbilisi from October 8, 2020 to May 31, 2021 are presented. It was found that IR values are inversely correlated with SOC. For example, at ozone concentrations from 0 to 20 mcg/m3 values of COVID-19 Infection Rate on average was 18.5 %, whereas with SOC values from 80 to 100 mcg/m3 – only 2.3%. Connection daily values of IR with SOC has an inverse linear form. IR = -0.2307·SOC + 20.543 for individual cases; IR = -0.2113·SOC + 19.756 for averaged values of IR in different ranges of SOC values.

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Section: Introductionmentioning

confidence: 90%

Results of a Study on the Impact of Surface Ozone Concentration on the Spread of COVID-19 in Tbilisi

Amiranashvili,

Japaridze,

Kharchilava

et al. 2023

GGS

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“…Since the emergence of machine learning (ML)-based solutions specific to COVID-19 disease, a number of methods have been proposed in the literature for the identification and severity prediction of COVID-19 that may help medical practitioners in early COVID-19 treatment [ 4 - 24 ]. In this study, we propose a novel hierarchical artificial intelligence (AI) model, named hereby as COVID-19 severity predictor (CoSP), that is a multi-stage or hierarchical classification model for four-class COVID-19 severity prediction, developed on a large dataset of more than 7,000 patients, covering two waves of COVID-19 in India.…”

Section: Introductionmentioning

confidence: 99%

“…Since COVID-19 created a huge personal, emotional, and financial loss at multiple levels, research on COVID-19 paced up rapidly in the recent past. Researchers invested themselves in different facets of this disease, including the modeling of trends of COVID-19 cases and the prediction of cases [3][4][5], diagnosing COVID-19 using chest X-rays and computed tomography scans [6,7], and studying heart-related disorders in post-COVID-19 patients [8,9]. So far, it has been observed that this disease with mild symptoms is self-limiting in the majority of patients [6].…”

Section: Introductionmentioning

confidence: 99%

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Clinical Features Predicting COVID-19 Severity Risk at the Time of Hospitalization

Sagar,

Dwivedi,

Gupta

et al. 2024

Cureus

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The global spread of COVID-19 has led to significant mortality and morbidity worldwide. Early identification of COVID-19 patients who are at high risk of developing severe disease can help in improved patient management, care, and treatment, as well as in the effective allocation of hospital resources. The severity prediction at the time of hospitalization can be extremely helpful in deciding the treatment of COVID-19 patients. To this end, this study presents an interpretable artificial intelligence (AI) model, named COVID-19 severity predictor (CoSP) that predicts COVID-19 severity using the clinical features at the time of hospital admission. We utilized a dataset comprising 64 demographic and laboratory features of 7,416 confirmed COVID-19 patients that were collected at the time of hospital admission. The proposed hierarchical CoSP model performs four-class COVID severity risk prediction into asymptomatic, mild, moderate, and severe categories. CoSP yielded better performance with good interpretability, as observed via Shapley analysis on COVID severity prediction compared to the other popular ML methods, with an area under the received operating characteristic curve (AUC-ROC) of 0.95, an area under the precision-recall curve (AUPRC) of 0.91, and a weighted F1-score of 0.83. Out of 64 initial features, 19 features were inferred as predictive of the severity of COVID-19 disease by the CoSP model. Therefore, an AI model predicting COVID-19 severity may be helpful for early intervention, optimizing resource allocation, and guiding personalized treatments, potentially enabling healthcare professionals to save lives and allocate resources effectively in the fight against the pandemic.

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“…In the time series forecasting literature, two techniques compete: forecast model selection [1] and forecast model combination [2] . The traditional approach to forecasting seasonal and non-seasonal time series is to select a single best model from a pool of candidate models based on certain criteria or employing a given technique [3] , potentially neglecting model risk. The ensemble prediction method is widely considered a promising strategy and it has been used with considerable success in research and industry thanks to the availability of a wide variety of individual models.…”

Section: Introductionmentioning

confidence: 99%

An ensemble learning strategy for panel time series forecasting of excess mortality during the COVID-19 pandemic

Ashofteh

Bravo²,

Ayuso³

2022

Applied Soft Computing

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A comparative study for predictive monitoring of COVID-19 pandemic

Cited by 10 publications

References 49 publications

Results of a Study on the Impact of Surface Ozone Concentration on the Spread of COVID-19 in Tbilisi

Results of a Study on the Impact of Surface Ozone Concentration on the Spread of COVID-19 in Tbilisi

Clinical Features Predicting COVID-19 Severity Risk at the Time of Hospitalization

An ensemble learning strategy for panel time series forecasting of excess mortality during the COVID-19 pandemic

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