Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning

• URL: http://arxiv.org/abs/2303.07600v1
• Date: Tue, 14 Mar 2023 02:46:42 GMT
• Title: Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning
• Authors: Leila Ismail, Huned Materwala, Alain Hennebelle
• Abstract summary: We develop time series models for the Gulf Cooperation Council (GCC) countries using the public COVID-19 dataset from Johns Hopkins. Our experimental results show that the developed models can forecast COVID-19 infections with high precision.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: COVID-19 has infected more than 68 million people worldwide since it was first detected about a year ago. Machine learning time series models have been implemented to forecast COVID-19 infections. In this paper, we develop time series models for the Gulf Cooperation Council (GCC) countries using the public COVID-19 dataset from Johns Hopkins. The dataset set includes the one-year cumulative COVID-19 cases between 22/01/2020 to 22/01/2021. We developed different models for the countries under study based on the spatial distribution of the infection data. Our experimental results show that the developed models can forecast COVID-19 infections with high precision.

Related papers

• Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Images [3.829821362301428]
  This paper evaluates the applicability of the recent top ten state-of-the-art Deep Convolutional Neural Networks (CNNs) for automatically detecting COVID-19 infection using chest X-ray images. Our trained models MobileNet, EfficentNet, and InceptionV3 achieved a classification average accuracy of 95%, 95%, and 94%, respectively.
  arXiv  Detail & Related papers  (2022-07-01T02:23:23Z)
• A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the United States [2.9822184411723645]
  We present COVID-LSTM, a data-driven model based on a Long Short-term memory architecture for forecasting COVID-19 incidence at the county-level in the US. We use the weekly number of new cases as temporal input, and hand-engineered spatial features from Facebook to capture the spread of the disease in time and space. Over the 4-week forecast horizon, our model is on average 50 cases per county more accurate than the COVIDhub-ensemble.
  arXiv  Detail & Related papers  (2021-09-24T17:40:08Z)
• The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations [59.48245489413308]
  We developed and tested a COVID-19 rapid prescreening model using the eye-region images captured in China and Spain with cellphone cameras. The performance was measured using area under receiver-operating-characteristic curve (AUC), sensitivity, specificity, accuracy, and F1.
  arXiv  Detail & Related papers  (2021-09-18T02:28:01Z)
• COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction [29.919578191688274]
  This paper proposes a method named COURAGE to generate a short-term prediction of 2-week-ahead COVID-19 related deaths for each county in the United States. Our model fully utilizes publicly available information of COVID-19 related confirmed cases, deaths, community mobility trends and demographic information, and can produce state-level prediction as an aggregation of the corresponding county-level predictions.
  arXiv  Detail & Related papers  (2021-05-03T04:00:59Z)
• COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics [116.6248556979572]
  COVIDx-US is an open-access benchmark dataset of COVID-19 related ultrasound imaging data. It consists of 93 lung ultrasound videos and 10,774 processed images of patients infected with SARS-CoV-2 pneumonia, non-SARS-CoV-2 pneumonia, as well as healthy control cases.
  arXiv  Detail & Related papers  (2021-03-18T03:31:33Z)
• Deep learning-based COVID-19 pneumonia classification using chest CT images: model generalizability [54.86482395312936]
  Deep learning (DL) classification models were trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries. We trained nine identical DL-based classification models by using combinations of the datasets with a 72% train, 8% validation, and 20% test data split. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better.
  arXiv  Detail & Related papers  (2021-02-18T21:14:52Z)
• COVID-19 Pandemic Outbreak in the Subcontinent: A data-driven analysis [0.8057708414390126]
  COVID-19 virus emerged in late December 2019 in Wuhan city, Hubei, China. Numerous studies claim that the subcontinent could remain in the worst affected region by the COVID-19. This paper uses publicly available epidemiological data of Bangladesh, India, and Pakistan to estimate the reproduction numbers.
  arXiv  Detail & Related papers  (2020-08-22T10:40:17Z)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)
• Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia [46.521323145636906]
  We develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT) In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%.
  arXiv  Detail & Related papers  (2020-05-06T09:56:51Z)
• CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images [0.0]
  CoroNet is a Deep Conceptional Neural Network model to automatically detect COVID-19 infection from chest X-ray images. The proposed model achieved an overall accuracy of 89.6% and the precision and recall rate for COVID-19 cases are 93% and 98.2%.
  arXiv  Detail & Related papers  (2020-04-10T07:46:07Z)
• A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models [53.900779250589814]
  Our method is able to produce stable and accurate forecasts 2 days ahead of current time. Our model's predictive power outperforms a collection of baseline models in 27 out of the 32 Chinese provinces.
  arXiv  Detail & Related papers  (2020-04-08T14:39:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.