Pandemic transmission forecasting
Insights from Covid-19 modeling involving environmental factors
Bibliographic Data
Numerous mathematical models use various predictors and modeling approaches to forecast pandemics; however, given the diversity of these models, it remains unclear which type offers the most advantage. This study aimed to systematically evaluate the accuracy of models that incorporate natural, social, and pollution-related environmental factors to predict COVID-19 transmission, and to explore the potential role of predictive models in managing future large-scale epidemics. The results revealed that, although only six studies used mathematical models with both natural and social environmental predictors, these models outperformed those with only natural or pollution predictors. Currently, most models focus primarily on temperature and humidity, with limited emphasis on other environmental factors, such as wind speed and pollution. In future pandemic responses, utilizing more multidimensional and diverse predictors can improve the accuracy and clinical applicability of the model and reduce the detrimental effects of disease transmission and spread
Computational model · Coronavirus disease 2019 (COVID-19 · Environmental pollution · Mathematical model · Pandemic · Predictive modelling · Transmission (telecommunications · COVID-19 diagnosis using AI · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts
Reduction in mobility and Covid-19 transmission
Pollution and health
Factors determining the diffusion of Covid-19 and suggested strategy to prevent future accelerated viral infectivity similar to Covid
Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning
The effects of atmospheric stability with low wind speed and of air pollution on the accelerated transmission dynamics of Covid-19
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