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Prediction of the final size for COVID-19 epidemic using machine learning: A case study of Egypt.

ABSTRACT: COVID-19 is spreading within the sort of an enormous epidemic for the globe. This epidemic infects a lot of individuals in Egypt. The World Health Organization states that COVID-19 could be spread from one person to another at a very fast speed through contact and respiratory spray. On these days, Egypt and all countries worldwide should rise to an effective step to investigate this disease and eliminate the effects of this epidemic. In this paper displayed, the real database of COVID-19 for Egypt has been analysed from February 15, 2020, to June 15, 2020, and predicted with the number of patients that will be infected with COVID-19, and estimated the epidemic final size. Several regression analysis models have been applied for data analysis of COVID-19 of Egypt. In this study, we've been applied seven regression analysis-based models that are exponential polynomial, quadratic, third-degree, fourth-degree, fifth-degree, sixth-degree, and logit growth respectively for the COVID-19 dataset. Thus, the exponential, fourth-degree, fifth-degree, and sixth-degree polynomial regression models are excellent models specially fourth-degree model that will help the government preparing their procedures for one month. In addition, we have applied the well-known logit growth regression model and we obtained the following epidemiological insights: Firstly, the epidemic peak could possibly reach at 22-June 2020 and final time of epidemic at 8-September 2020. Secondly, the final total size for cases 1.6676E+05 cases. The action from government of interevent over a relatively long interval is necessary to minimize the final epidemic size.

SUBMITTER: Amar LA

PROVIDER: S-EPMC7446670 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Prediction of the final size for COVID-19 epidemic using machine learning: A case study of Egypt.

Amar Lamiaa A LA Taha Ashraf A AA Mohamed Marwa Y MY

Infectious Disease Modelling 20200825

COVID-19 is spreading within the sort of an enormous epidemic for the globe. This epidemic infects a lot of individuals in Egypt. The World Health Organization states that COVID-19 could be spread from one person to another at a very fast speed through contact and respiratory spray. On these days, Egypt and all countries worldwide should rise to an effective step to investigate this disease and eliminate the effects of this epidemic. In this paper displayed, the real database of COVID-19 for Egy ...[more]

PMID: 32864516

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Project description:Background Since the beginning of coronavirus disease 2019 (COVID-19), the development of predictive models has sparked relevant interest due to the initial lack of knowledge about diagnosis, treatment, and prognosis. The present study aimed at developing a model, through a machine learning approach, to predict intensive care unit (ICU) mortality in COVID-19 patients based on predefined clinical parameters. Results Observational multicenter cohort study. All COVID-19 adult patients admitted to 25 ICUs belonging to the VENETO ICU network (February 28th 2020-april 4th 2021) were enrolled. Patients admitted to the ICUs before 4th March 2021 were used for model training (“training set”), while patients admitted after the 5th of March 2021 were used for external validation (“test set 1”). A further group of patients (“test set 2”), admitted to the ICU of IRCCS Ca’ Granda Ospedale Maggiore Policlinico of Milan, was used for external validation. A SuperLearner machine learning algorithm was applied for model development, and both internal and external validation was performed. Clinical variables available for the model were (i) age, gender, sequential organ failure assessment score, Charlson Comorbidity Index score (not adjusted for age), Palliative Performance Score; (ii) need of invasive mechanical ventilation, non-invasive mechanical ventilation, O2 therapy, vasoactive agents, extracorporeal membrane oxygenation, continuous venous-venous hemofiltration, tracheostomy, re-intubation, prone position during ICU stay; and (iii) re-admission in ICU. One thousand two hundred ninety-three (80%) patients were included in the “training set”, while 124 (8%) and 199 (12%) patients were included in the “test set 1” and “test set 2,” respectively. Three different predictive models were developed. Each model included different sets of clinical variables. The three models showed similar predictive performances, with a training balanced accuracy that ranged between 0.72 and 0.90, while the cross-validation performance ranged from 0.75 to 0.85. Age was the leading predictor for all the considered models. Conclusions Our study provides a useful and reliable tool, through a machine learning approach, for predicting ICU mortality in COVID-19 patients. In all the estimated models, age was the variable showing the most important impact on mortality. Supplementary Information The online version contains supplementary material available at 10.1186/s44158-021-00002-x.

Project description:Expression of CCR5 and its cognate ligands have been implicated in COVID-19 pathogenesis, consequently therapeutics directed against CCR5 are being investigated. Here, we explored the role of CCR5 and its ligands across the immunologic spectrum of COVID-19. We used a bioinformatics approach to predict and model the immunologic phases of COVID so that effective treatment strategies can be devised and monitored. We investigated 224 individuals including healthy controls and patients spanning the COVID-19 disease continuum. We assessed the plasma and isolated peripheral blood mononuclear cells (PBMCs) from 29 healthy controls, 26 Mild-Moderate COVID-19 individuals, 48 Severe COVID-19 individuals, and 121 individuals with post-acute sequelae of COVID-19 (PASC) symptoms. Immune subset profiling and a 14-plex cytokine panel were run on all patients from each group. B-cells were significantly elevated compared to healthy control individuals (P<0.001) as was the CD14+, CD16+, CCR5+ monocytic subset (P<0.001). CD4 and CD8 positive T-cells expressing PD-1 as well as T-regulatory cells were significantly lower than healthy controls (P<0.001 and P=0.01 respectively). CCL5/RANTES, IL-2, IL-4, CCL3, IL-6, IL-10, IFN-γ, and VEGF were all significantly elevated compared to healthy controls (all P<0.001). Conversely GM-CSF and CCL4 were in significantly lower levels than healthy controls (P=0.01). Data were further analyzed and the classes were balanced using SMOTE. With a balanced working dataset, we constructed 3 random forest classifiers: a multi-class predictor, a Severe disease group binary classifier and a PASC binary classifier. Models were also analyzed for feature importance to identify relevant cytokines to generate a disease score. Multi-class models generated a score specific for the PASC patients and defined as S1 = (IFN-γ + IL-2)/CCL4-MIP-1β. Second, a score for the Severe COVID-19 patients was defined as S2 = (IL-6+sCD40L/1000 + VEGF/10 + 10*IL-10)/(IL-2 + IL-8). Severe COVID-19 patients are characterized by excessive inflammation and dysregulated T cell activation, recruitment, and counteracting activities. While PASC patients are characterized by a profile able to induce the activation of effector T cells with pro-inflammatory properties and the capacity of generating an effective immune response to eliminate the virus but without the proper recruitment signals to attract activated T cells.

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Prediction of the final size for COVID-19 epidemic using machine learning: A case study of Egypt.

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Prediction of the final size for COVID-19 epidemic using machine learning: A case study of Egypt.

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