Covid-19 Epidemiological Factor Analysis: Identifying Principal Factors with Machine Learning
Dolgikh, S.
Show abstract
Based on a subset of Covid-19 Wave 1 cases at a time point near TZ+3m (April, 2020), we perform an analysis of the influencing factors for the epidemics impacts with several different statistical methods. The consistent conclusion of the analysis with the available data is that apart from the policy and management quality, being the dominant factor, the most influential factors among the considered were current or recent universal BCG immunization and the prevalence of smoking.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Evaluating transmission heterogeneity and super-spreading event of COVID-19 in a metropolis of China 94%
- Quantifying the Effects of Social Distancing on the Spread of COVID-19 94%
- Predicting mortality, duration of treatment, pulmonary embolism and required ceiling of ventilatory support for COVID-19 inpatients: A Machine-Learning Approach 93%
Similar papers in this journal
- An Epidemic Model SIPHERD and its application for prediction of the spread of COVID-19 infection in India 94%
- An SEIARD epidemic model for COVID-19 in Mexico: mathematical analysis and state-level forecast 94%
- Clustering of Countries for COVID-19 Cases based on Disease Prevalence, Health Systems and Environmental Indicators 93%
Similar papers in this journal
- Isolation Considered Epidemiological Model for the Prediction of COVID-19 Trend in Tokyo, Japan 95%
- General Model for COVID-19 Spreading with Consideration of Intercity Migration, Insufficient Testing and Active Intervention: Application to Study of Pandemic Progression in Japan and USA 93%
- Serial interval, basic reproduction number and prediction of COVID-19 epidemic size in Jodhpur, India 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.