ArticleEnvironmental research2022
Impact analysis of environmental and social factors on early-stage COVID-19 transmission in China by machine learning.
Article in Environmental research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Confounding amplifies the effect of environmental factors on COVID-19.Infectious Disease Modelling · 2024Article
- Article
- High-resolution short-term prediction of the COVID-19 epidemic based on spatial-temporal model modified by historical meteorological data.Fundamental research · 2024Article
- Review
- Analysis of the influence of the stay-at-home order on the electricity consumption in Chinese university dormitory buildings during the COVID-19 pandemic.Energy and buildings · 2022Article
- The environment, epidemics, and human health.Environmental research · 2022Article
- Study of the Economic, Environmental, and Social Factors Affecting Chinese Residents' Health Based on Machine Learning.Frontiers in public health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a highly contagious disease, COVID-19 caused a worldwide pandemic and it is still ongoing. However, the infection in China has been successfully controlled although its initial transmission was also nationwide and has caused a serious public health crisis. The analysis on the early-stage COVID-19 transmission in China is worth investigating for its guiding significance on prevention to other countries and regions. In this study, we conducted the experiments from the perspectives of COVID-19 occurrence and intensity. We eliminated unimportant factors from 113 variables and applied four machine learning-based classification and regression models to predict COVID-19 occurrence and intensity, respectively. The influence of each important factor was analysed when applicable. Our optimal model on COVID-19 occurrence prediction presented an accuracy of 91.91% and the best R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.