Epicas stands for Epidemiological Forecasting. Epicas is an AutoML framework based on TensorFlow and statsmodels.

Pipeline: What Can It Do?

Epicas automates data loading, merging, feature engineering, model building, training, validating, testing, hyperparameter finding, predicting, and explaining.

tl;dr It does all kinds of stuffs but data cleaning.

Inspiration: Why Epicas?

"Large delays in the timing of epidemic peaks and the prevention of epidemics in a large number of locations could be achieved with the most extreme interventions, but only when they were made sufficiently early."[1]

The main goal for epidemiological forecasting is to predict when and where the disease outbreaks will happen. However, these predictions only matter if they are made early and trustworthy enough for extreme interventions to be made.

The status quo:

1. Public health officials, epidemiologists are those who have early access to gold-standard data yet may feel uncomfortable building machine learning pipelines.

2. ML engineers, data scientists, scholars are the ones who usually join forecasting on a voluntary basis, thus having late access to good data and needing to get a model pipeline built quickly for fine-tuning.

Epicas hopes to bring machine learning closer to everyone and specifically for these two groups of interest.

Installation

Epicas is only compatible to Python 3.7.1 or later.

It can be installed via the Python Package Index (PyPI).

pip install epicas

Basic Usage

Data Loading

jhu = epicas.StructuredData(
      'demo/datasets/covid.xz',
      location = 'FIPS',
      date = 'date',
      incidence = 'confirmed_cases',
      )

population = epicas.StructuredData(
            'demo/datasets/Reichlab_Population.csv',
            location = 'location',
            usecols = ['location', 'population']
            )

mobility = epicas.StructuredData(
          'demo/datasets/mobility.csv.gz',
          location = 'FIPS',
          date = 'date'
          )

merged = jhu + population + mobility

Feature Engineering

merged = epicas.EpiData(merged, y='incidence', disease='covid19').imputation().target_to_ma(window=3)

merged = merged.lag_reduction(subset=['fb_movement_change', 'fb_stationary'], sliding_window=21)

Model Selecting and Forecasting

forecasts = epicas.Ensemble(merged, ['attention', 'ARIMA'], '2021-09-01').get_predict()

This framework is being documented. At the moment, please read examples from Jupyter notebooks.

Development

Epicas is still under development, yet most basic usage is already functional (0.1.0).

• StructuredData (data loading and merging)
• EpiData (feature engineering)
• ARIMA
• Seasonal ARIMA
• LSTM
• Bidirectional LSTM
• GRU
• Self-attention based + BiLSTM
• Unweighted Average Ensemble
• Weighted Average Ensemble
• LIME/SHAP (blackbox model explainers)
• RNN Hyperparameter Selecting

All model questions or improvements are very welcomed! Please forward them to caominhduy@gmail.com or create a pull request.

References

[1] Cooper BS, Pitman RJ, Edmunds WJ, Gay NJ (2006) Delaying the international spread of pandemic influenza. PLoS Med 3(6): e212. DOI: 10.1371/journal.pmed.0030212