EnbinYang/tb_prediction_files
A multivariate multi-step LSTM forecasting model for tuberculosis incidence with model explanation
This project helps public health officials and epidemiologists predict future tuberculosis (TB) incidence. It takes in historical data on TB cases, meteorological conditions, and socioeconomic factors, and outputs forecasts for how TB incidence might change over multiple steps into the future. Public health researchers and policymakers in regions like Liaoning Province, China, could use this to anticipate outbreaks and plan interventions.
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Use this if you need to forecast tuberculosis incidence based on a variety of contributing factors and want to understand which factors are most influential.
Not ideal if your primary goal is real-time, short-term disease surveillance or if you are working with diseases that do not have clear environmental or socioeconomic drivers.
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Language
Python
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Last pushed
May 24, 2022
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