Dengue — Machine learning prediction models using time-series weather data.

INTRODUCTION

GETTING THE DATA

  • Environmental data collected by various U.S. Federal Government agencies — from the Centers for Disease Control and Prevention to the National Oceanic
  • Atmospheric Administration in the U.S. Department of Commerce.

Our goal

Visualise the weekly reported cases in San Juan

San Juan Dengue Fever Total cases per week
  • We can see that the time series has seasonality. Seasonality refers to a periodic pattern within years related to the calendar day, month, quarter, etc…
  • We can see that the time series does not appear to have a trend. There is no long-run upward or downward direction in the series.

PREPROCESSING

Distribution of Data before and after Standardization
Correlation between our target variable and the others
  • We can see that `reanalysis_specific_humidity_g_per_kg` and reanalysis_dew_point_temp_k are firmly correlated to total cases. These variables are related to the humidity of the climate.
  • Average and Min temperature is also strongly correlated to the target variable.

BUILDING RANDOM FOREST MODEL

Predictions vs Actual total cases

Building RNN LSTM Model

MAKING PREDICTIONS

Predictions vs Actual cases with RNN model

CONCLUSION

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