ArticleEpidemiology and infection2021
Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality.
Article in Epidemiology and infection, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- Dynamic forecasting of China's carbon market prices by the coupling of macroeconomic indicators and LSTM model.Scientific reports · 2025Article
- Risk factors associated with food consumption and food-handling habits for sporadic listeriosis: a case-control study in China from 2013 to 2022.Emerging microbes & infections · 2024Article
- The intervention of local public authorities and the impact of the COVID-19 pandemic in Romania: a subnational analysis.Frontiers in public health · 2024Article
- Mapping the long-term associations between air pollutants and COVID-19 risks and the attributable burdens in the continental United States.Environmental pollution (Barking, Essex : 1987) · 2023Article
- Change in the air due to the coronavirus outbreak in four major cities of India: What do the statistics say?Journal of hazardous materials advances · 2023Article
- Air pollution rebound and different recovery modes during the period of easing COVID-19 restrictions.The Science of the total environment · 2022Article
- A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality.Epidemiology and infection · 2022Article
- What does the EKC theory leave behind? A state-of-the-art review and assessment of export diversification-augmented models.Environmental monitoring and assessment · 2022Review
- Mathematical Modeling to Determine the Fifth Wave of COVID-19 in South Africa.BioMed research international · 2022Article
- Convergence behaviours of energy series and GDP nexus hypothesis: A non-parametric Bayesian application.PloS one · 2022Article
- The Impact of China's Paired Assistance Policy on the COVID-19 Crisis-An Empirical Case Study of Hubei Province.Frontiers in public health · 2022Article
- Covid19/IT the digital side of Covid19: A picture from Italy with clustering and taxonomy.PloS one · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper demonstrates how the combustion of fossil fuels for transport purpose might cause health implications. Based on an original case study [i.e. the Hubei province in China, the epicentre of the coronavirus disease-2019 (COVID-19) pandemic], we collected data on atmospheric pollutants (PM2.5, PM10 and CO2) and economic growth (GDP), along with daily series on COVID-19 indicators (cases, resuscitations and deaths). Then, we adopted an innovative Machine Learning approach, applying a new image Neural Networks model to investigate the causal relationships among economic, atmospheric and COVID-19 indicators. Empirical findings emphasise that any change in economic activity is found to substantially affect the dynamic levels of PM2.5, PM10 and CO2 which, in turn, generates significant variations in the spread of the COVID-19 epidemic and its associated lethality. As a robustness check, the conduction of an optimisation algorithm further corroborates previous results.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.