Analysis of the Effectiveness of Face-Coverings on the Death Rate of
COVID-19 Using Machine Learning
- URL: http://arxiv.org/abs/2102.04419v1
- Date: Mon, 8 Feb 2021 18:26:30 GMT
- Title: Analysis of the Effectiveness of Face-Coverings on the Death Rate of
COVID-19 Using Machine Learning
- Authors: Ali Lafzi, Miad Boodaghi, Siavash Zamani, and Niyousha Mohammadshafie
- Abstract summary: Mask mandate (MM) order issued by states' governors to prevent spread of COVID-19 virus.
In this work, we quantify people's abidance to the MM order using survey data provided by New York Times.
Using different machine learning classification algorithms we investigated how the decrease or increase in death ratio for the counties in the US West Coast correlates with the input parameters.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The recent outbreak of the COVID-19 shocked humanity leading to the death of
millions of people worldwide. To stave off the spread of the virus, the
authorities in the US, employed different strategies including the mask mandate
(MM) order issued by the states' governors. Although most of the previous
studies pointed in the direction that MM can be effective in hindering the
spread of viral infections, the effectiveness of MM in reducing the degree of
exposure to the virus and, consequently, death rates remains indeterminate.
Indeed, the extent to which the degree of exposure to COVID-19 takes part in
the lethality of the virus remains unclear. In the current work, we defined a
parameter called the average death ratio as the monthly average of the ratio of
the number of daily deaths to the total number of daily cases. We utilized
survey data provided by New York Times to quantify people's abidance to the MM
order. Additionally, we implicitly addressed the extent to which people abide
by the MM order that may depend on some parameters like population, income, and
political inclination. Using different machine learning classification
algorithms we investigated how the decrease or increase in death ratio for the
counties in the US West Coast correlates with the input parameters. Our results
showed a promising score as high as 0.94 with algorithms like XGBoost, Random
Forest, and Naive Bayes. To verify the model, the best performing algorithms
were then utilized to analyze other states (Arizona, New Jersey, New York and
Texas) as test cases. The findings show an acceptable trend, further confirming
usability of the chosen features for prediction of similar cases.
Related papers
- Coronavirus disease situation analysis and prediction using machine
learning: a study on Bangladeshi population [1.7188280334580195]
In recent months, the number of death and infected rates has increased more distinguished than before in Bangladesh.
This study distinguishes machine learning models and creates a prediction system to anticipate the infected and death rate for the coming days.
arXiv Detail & Related papers (2022-07-12T09:48:41Z) - Computing the Death Rate of COVID-19 [0.34376560669160383]
The Infection Fatality Rate (IFR) of COVID-19 is difficult to estimate because the number of infections is unknown.
We introduce a new approach for estimating the IFR by first estimating the entire sequence of daily infections.
arXiv Detail & Related papers (2021-09-09T19:38:50Z) - Factors affecting the COVID-19 risk in the US counties: an innovative
approach by combining unsupervised and supervised learning [0.0]
factors that could affect the risk of COVID-19 infection and mortality were analyzed in county level.
Results showed that mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, population density, wind speed, longitude, and percent of uninsured people were the most significant attributes.
arXiv Detail & Related papers (2021-06-24T04:29:00Z) - Comparative Analysis of Machine Learning Approaches to Analyze and
Predict the Covid-19 Outbreak [10.307715136465056]
We present a comparative analysis of various machine learning (ML) approaches in predicting the COVID-19 outbreak in the epidemiological domain.
The results reveal the advantages of ML algorithms for supporting decision making of evolving short term policies.
arXiv Detail & Related papers (2021-02-11T11:57:33Z) - Timely Tracking of Infection Status of Individuals in a Population [70.21702849459986]
We consider real-time timely tracking of infection status of individuals in a population.
In this work, a health care provider wants to detect infected people as well as people who recovered from the disease.
arXiv Detail & Related papers (2020-12-24T18:49:22Z) - Classification of COVID-19 in CT Scans using Multi-Source Transfer
Learning [91.3755431537592]
We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans.
With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet.
Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
arXiv Detail & Related papers (2020-09-22T11:53:06Z) - Tracking disease outbreaks from sparse data with Bayesian inference [55.82986443159948]
The COVID-19 pandemic provides new motivation for estimating the empirical rate of transmission during an outbreak.
Standard methods struggle to accommodate the partial observability and sparse data common at finer scales.
We propose a Bayesian framework which accommodates partial observability in a principled manner.
arXiv Detail & Related papers (2020-09-12T20:37:33Z) - A self-supervised neural-analytic method to predict the evolution of
COVID-19 in Romania [10.760851506126105]
We use a recently published improved version of SEIR, which is the classic, established model for infectious diseases.
We propose a self-supervised approach to train a deep convolutional network to guess the correct set of ModifiedSEIR model parameters.
We find an optimistic result in the case fatality rate for Romania which may be around 0.3% and we also demonstrate that our model is able to correctly predict the number of daily fatalities for up to three weeks in the future.
arXiv Detail & Related papers (2020-06-23T12:00:04Z) - Effectiveness and Compliance to Social Distancing During COVID-19 [72.94965109944707]
We use a detailed set of mobility data to evaluate the impact that stay-at-home orders had on the spread of COVID-19 in the US.
We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks.
arXiv Detail & Related papers (2020-06-23T03:36:19Z) - 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) - A Deep Q-learning/genetic Algorithms Based Novel Methodology For
Optimizing Covid-19 Pandemic Government Actions [63.669642197519934]
We use the SEIR epidemiological model to represent the evolution of the virus COVID-19 over time in the population.
The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system.
We prove that our methodology is a valid tool to discover actions governments can take to reduce the negative effects of a pandemic in both senses.
arXiv Detail & Related papers (2020-05-15T17:17:45Z)
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.