Analysing the impact of global demographic characteristics over the
COVID-19 spread using class rule mining and pattern matching
- URL: http://arxiv.org/abs/2009.12923v3
- Date: Sun, 31 Jan 2021 11:07:45 GMT
- Title: Analysing the impact of global demographic characteristics over the
COVID-19 spread using class rule mining and pattern matching
- Authors: Wasiq Khan, Abir Hussain, Sohail Ahmed Khan, Mohammed Al-Jumailey,
Raheel Nawaz, Panos Liatsis
- Abstract summary: This study presents an intelligent approach to investigate the multi-dimensional associations between demographic attributes and COVID-19 global variations.
We gather multiple demographic attributes and COVID-19 infection data from reliable sources, which are then processed by intelligent algorithms to identify the significant associations and patterns within the data.
- Score: 8.025086113117291
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Since the coronavirus disease (COVID-19) outbreak in December 2019, studies
have been addressing diverse aspects in relation to COVID-19 and Variant of
Concern 202012/01 (VOC 202012/01) such as potential symptoms and predictive
tools. However, limited work has been performed towards the modelling of
complex associations between the combined demographic attributes and varying
nature of the COVID-19 infections across the globe. This study presents an
intelligent approach to investigate the multi-dimensional associations between
demographic attributes and COVID-19 global variations. We gather multiple
demographic attributes and COVID-19 infection data (by 8 January 2021) from
reliable sources, which are then processed by intelligent algorithms to
identify the significant associations and patterns within the data. Statistical
results and experts' reports indicate strong associations between COVID-19
severity levels across the globe and certain demographic attributes, e.g.
female smokers, when combined together with other attributes. The outcomes will
aid the understanding of the dynamics of disease spread and its progression,
which in turn may support policy makers, medical specialists and society, in
better understanding and effective management of the disease.
Related papers
- Bayesian Networks and Machine Learning for COVID-19 Severity Explanation and Demographic Symptom Classification [12.40025057417184]
We present a three-stage data-driven approach to distill the hidden information about COVID-19.
The first stage employs a Bayesian network structure learning method to identify the causal relationships among COVID-19 symptoms.
As a second stage, the output serves as a useful guide to train an unsupervised machine learning (ML) algorithm.
The final stage then leverages the labels obtained from clustering to train a demographic symptom identification model.
arXiv Detail & Related papers (2024-06-16T05:43:24Z) - A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds [49.34500499203579]
We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics.
We generate high-quality synthetic fMRI data based on user-supplied demographics.
arXiv Detail & Related papers (2024-05-13T17:49:20Z) - Correlations Between COVID-19 and Dengue [0.8164433158925593]
This paper shows how a neural network approach can incorporate Dengue and COVID-19 data as well as external factors.
We define a Correlation Model through which we show that the number of cases of COVID-19 and Dengue have very similar trends.
We then illustrate the relevance of our model by extending it to a Long short-term memory model (LSTM) that incorporates both diseases.
arXiv Detail & Related papers (2022-07-27T14:55:28Z) - Investigating the Relationship Between World Development Indicators and
the Occurrence of Disease Outbreaks in the 21st Century: A Case Study [0.0]
The timely identification of socio-economic sectors vulnerable to a disease outbreak presents an important challenge to the civic authorities.
We leverage data driven models to determine the relationship between the trends of World Development Indicators and occurrence of disease outbreaks.
arXiv Detail & Related papers (2021-09-20T06:31:03Z) - 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 Outbreak Prediction and Analysis using Self Reported Symptoms [12.864257751458712]
We use self-reported symptoms survey data to understand trends in the spread of COVID-19.
From our studies, we try to predict the likely % of the population that tested positive for COVID-19 based on self-reported symptoms.
We forecast that % of the population having COVID-19-like illness (CLI) and those tested positive as 0.15% and 1.14% absolute error respectively.
arXiv Detail & Related papers (2020-12-21T00:37:24Z) - Classification supporting COVID-19 diagnostics based on patient survey
data [82.41449972618423]
logistic regression and XGBoost classifiers, that allow for effective screening of patients for COVID-19 were generated.
The obtained classification models provided the basis for the DECODE service (decode.polsl.pl), which can serve as support in screening patients with COVID-19 disease.
This data set consists of more than 3,000 examples is based on questionnaires collected at a hospital in Poland.
arXiv Detail & Related papers (2020-11-24T17:44:01Z) - Understanding the temporal evolution of COVID-19 research through
machine learning and natural language processing [66.63200823918429]
The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world.
We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research.
Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
arXiv Detail & Related papers (2020-07-22T18:02:39Z) - Mapping the Landscape of Artificial Intelligence Applications against
COVID-19 [59.30734371401316]
COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization.
We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
arXiv Detail & Related papers (2020-03-25T12:30:33Z)
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.