ArticleFundamental research2024
Development of the second version of Global Prediction System for Epidemiological Pandemic.
Article in Fundamental research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- The urban infection susceptibility index and its application in cities of China.Biosafety and health · 2026Article
- Development of two-dimension epidemic prediction model.Infectious Disease Modelling · 2025Article
- Traffic-driven spatio-temporal prediction for fine-scale epidemic outbreaks in China.BMC infectious diseases · 2025Article
- High-resolution short-term prediction of the COVID-19 epidemic based on spatial-temporal model modified by historical meteorological data.Fundamental research · 2024Article
- Monitoring and prediction of pathogenic microorganisms in the atmosphere.Fundamental research · 2024Article
- Heat waves accelerate the spread of infectious diseases.Environmental research · 2023Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease 2019 (COVID-19) is a severe global public health emergency that has caused a major crisis in the safety of human life, health, global economy, and social order. Moreover, COVID-19 poses significant challenges to healthcare systems worldwide. The prediction and early warning of infectious diseases on a global scale are the premise and basis for countries to jointly fight epidemics. However, because of the complexity of epidemics, predicting infectious diseases on a global scale faces significant challenges. In this study, we developed the second version of Global Prediction System for Epidemiological Pandemic (GPEP-2), which combines statistical methods with a modified epidemiological model. The GPEP-2 introduces various parameterization schemes for both impacts of natural factors (seasonal variations in weather and environmental impacts) and human social behaviors (government control and isolation, personnel gathered, indoor propagation, virus mutation, and vaccination). The GPEP-2 successfully predicted the COVID-19 pandemic in over 180 countries with an average accuracy rate of 82.7%. It also provided prediction and decision-making bases for several regional-scale COVID-19 pandemic outbreaks in China, with an average accuracy rate of 89.3%. Results showed that both anthropogenic and natural factors can affect virus spread and control measures in the early stages of an epidemic can effectively control the spread. The predicted results could serve as a reference for public health planning and policymaking.
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Registered trials
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