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Qiyang Ge
Researcher at Fudan University
Publications - 9
Citations - 396
Qiyang Ge is an academic researcher from Fudan University. The author has contributed to research in topics: Public health & Vaccination. The author has an hindex of 5, co-authored 9 publications receiving 269 citations.
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Artificial Intelligence Forecasting of Covid-19 in China
TL;DR: If the data are reliable and there are no second transmissions, the AI-inspired methods can accurately forecast the transmission dynamics of the Covid-19 across the provinces/cities in China, which is a powerful tool for helping public health planning and policymaking.
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Artificial Intelligence Forecasting of Covid-19 in China
TL;DR: Wang et al. as discussed by the authors developed a modified stacked auto-encoder for modeling the transmission dynamics of the epidemics and applied this model to real-time forecasting the confirmed cases of Covid-19 across China.
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Forecasting and Evaluating Multiple Interventions for COVID-19 Worldwide
TL;DR: Artificial intelligence-inspired methods for modeling the transmission dynamics of the epidemics and evaluating interventions to curb the spread and impact of COVID-19 observed that delaying intervention for 1 month caused the maximum number of cumulative cases reduce by −166, and the number of deaths increased from 53,560 to 8,938,725.
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Forecasting and evaluating intervention of Covid-19 in the World
TL;DR: To develop the artificial intelligence (AI) inspired methods for real-time forecasting and evaluating intervention strategies to curb the spread of Covid-19 in the World, a modified auto-encoder for modeling the transmission dynamics of the epidemics is developed and applied.
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Evaluating the effect of public health intervention on the global-wide spread trajectory of Covid-19
TL;DR: Modified auto-encoders (MAE) method was developed to forecast spread trajectory of Covid-19 of countries affected, under different levels and timing of intervention strategies and showed public health interventions should be executed as soon as possible.