ArticleSpatial and spatio-temporal epidemiology2022
Spatio-temporal modeling of COVID-19 prevalence and mortality using artificial neural network algorithms.
Article in Spatial and spatio-temporal epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses 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
33 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Spatial and spatiotemporal machine learning models for COVID-19 dynamics: a review of methodology and reporting practices.Epidemiologic reviews · 2025Pooled it
- Spatiotemporal evolution characteristics and influencing factors of hepatitis B incidence rate in China.Scientific reports · 2026Article
- Examining the association between spatial accessibility to emergency cardiac care centers and cardiovascular mortality in Georgia, United States.BMC cardiovascular disorders · 2026Article
- Geospatial and machine learning analyses of cardiovascular disease mortality across the continental United States: Identifying associated variables using Shapley values.BMC public health · 2026Article
- Spatiotemporal drivers of foodborne disease seasonality pattern in a coastal city of China.BMC public health · 2025Article
- Bayesian spatio-temporal modeling of COVID-19 incidence in Algerian provinces using integrated nested Laplace approximations.Scientific reports · 2025Article
- Developing predictive models for COVID-19 positive tests based on the XGBoost and random forest algorithms with internet search data.BMC public health · 2025Article
- Spatial bayesian modeling of diabetes mellitus (DM) risk in the United States.BMC public health · 2025Article
- Modeling mumps incidence in China: spatiotemporal clusters and evolving risk factors (2005-2020).BMC public health · 2025Article
- Spatiotemporal analysis and risk prediction of foodborne diseases based on meteorological risk factors: a case study of Wuxi city, China.BMC infectious diseases · 2025Article
- Epidemiological characteristics and spatio-temporal analysis of influenza in Sichuan Province from 2015 to 2024.BMC infectious diseases · 2025Article
- COVID-19 risk stratification among older adults: a machine learning approach to identify personal and health-related risk factors.BMC public health · 2025Article
- An investigation into the impact of temporality on COVID-19 infection and mortality predictions: new perspective based on Shapley Values.BMC medical research methodology · 2025Article
- Geospatial modelling of COVID19 mortality in Oman using geographically weighted Poisson regression GWPR.Scientific reports · 2025Article
- Validation study for assessing COVID-19 pneumonia treatments.Scientific reports · 2024Article
- Prolonged exposure to air pollution and risk of acute kidney injury and related mortality: a prospective cohort study based on hospitalized AKI cases and general population controls from the UK Biobank.BMC public health · 2024Article
- Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic.Life (Basel, Switzerland) · 2024Review
- A Bayesian spatio-temporal model of COVID-19 spread in England.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The outbreak of coronavirus disease (COVID-19) has become one of the most challenging global concerns in recent years. Due to inadequate worldwide studies on spatio-temporal modeling of COVID-19, this research aims to examine the relative significance of potential explanatory variables (n = 75) concerning COVID-19 prevalence and mortality using multilayer perceptron artificial neural network topology. We utilized ten variable importance analysis methods to identify the relative importance of the explanatory variables. The main findings indicated that several variables were persistently among the most influential variables in all periods. Regarding COVID-19 prevalence, unemployment and population density were among the most influential variables with the highest importance scores. While for COVID-19 mortality, health-related variables such as diabetes prevalence and number of hospital beds were among the most significant variables. The obtained findings from this study might provide general insights for public health policymakers to monitor the spread of disease and support decision-making.
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.