epydemics 0.11.2
Advanced epidemiological modeling and forecasting using discrete SIRD/SIRDV models with time series analysis
Requires Python: >=3.9
Keywords: epidemiology, modeling, forecasting, sird, time-series, var, monte-carlo, covid, pandemic

# Epydemics: Forecasting COVID-19 using time series and machine learning

**Version 0.11.1** - Publication-Ready Reporting Tools with ModelReport

`epydemics` is a Python library for epidemiological modeling and forecasting. It provides tools for creating, fitting, and evaluating discrete SIRD/SIRDV models with time-dependent parameters. The library is designed to be flexible and extensible, allowing users to easily incorporate their own data and models.

**📖 New Users?** Start with the **[User Guide](docs/USER_GUIDE.md)** to understand when and how to use epydemics.

✅ **v0.11.1 New**: `ModelReport` class for one-line exports to Markdown, LaTeX tables, and publication-ready 300-600 DPI figures. Automated summary statistics, forecast accuracy evaluation, and model comparison utilities.

## Features

-   **Discrete SIRD Model**: A discrete Susceptible-Infected-Recovered-Deceased (SIRD) model with time-dependent parameters.
-   **SIRDV Vaccination Model** (v0.7.0): Extended model including Vaccinated compartment (V) and vaccination rate (δ).
-   **Time Series Forecasting**: Use of VAR (Vector Autoregression) models to forecast epidemic rates with logit transformation.
-   **Multi-Frequency Support** (v0.9.0): Native processing of daily, business day, weekly, monthly, and annual data with automatic detection and handler-based validation (no artificial reindexing).
-   **Temporal Aggregation** (v0.9.0): Frequency-aware aggregation that skips resampling when source and target frequencies match.
-   **Business Day Support** (v0.9.0): Dedicated handler for trading-day calendars (252 days/year) with validated lags and recovery windows.
-   **Data Container**: A convenient class for loading, preprocessing, and storing epidemiological data.
-   **Parallel Simulations**: Multi-core support for faster Monte Carlo simulations (27 scenarios for SIRD, 81 for SIRDV).
-   **Result Caching**: File-based caching to avoid recomputing identical analyses.
-   **Model Evaluation**: Tools for evaluating model performance with MAE, MSE, RMSE, MAPE, SMAPE metrics.
-   **Visualization**: Professional plotting functions for results and forecasts.
-   **Publication Tools** (v0.11.1): `ModelReport` class for Markdown, LaTeX, and high-DPI figure generation in one call. Model comparison utilities and automated summary statistics.

> **✅ Native Multi-Frequency**: v0.9.0+ processes annual/monthly/weekly/business-day data without artificial daily reindexing. See [User Guide](docs/USER_GUIDE.md#annual-surveillance-data-workaround) for frequency guidance.

> **✅ Publication-Ready Reports**: v0.11.1 includes `ModelReport` for generating Markdown reports, LaTeX tables, and multi-panel figures. See [examples/notebooks/07_reporting_and_publication.ipynb](examples/notebooks/07_reporting_and_publication.ipynb).

## SIRDV Model (New in v0.7.0)

The SIRDV model extends the classical SIRD model by incorporating vaccination:

**Compartments:**
- S: Susceptible
- I: Infected (active cases)
- R: Recovered
- D: Deaths
- V: Vaccinated (new)

**Rates:**
- α: Infection rate
- β: Recovery rate
- γ: Mortality rate
- δ: Vaccination rate (new)

**Key Features:**
- Automatic detection from vaccination data
- 81 simulation scenarios (3⁴ confidence levels)
- Conservation law: N = S + I + R + D + V
- Parallel execution recommended for performance

## Getting Started

To get started with `epydemics`, we recommend following the tutorial in [TUTORIAL.md](TUTORIAL.md).

## Installation

You can install `epydemics` from PyPI:

```bash
pip install epydemics
```

To install the latest development version, you can clone this repository and install it in editable mode:

```bash
git clone https://github.com/julihocc/epydemics.git
cd epydemics
pip install -e .
```

## Documentation

**User Documentation**:
-   **[User Guide](docs/USER_GUIDE.md)**: Complete guide on when to use epydemics, data preparation, and frequency handling
-   **[Tutorial](TUTORIAL.md)**: Step-by-step guide for COVID-19 forecasting
-   **[CHANGELOG.md](CHANGELOG.md)**: Version history and detailed changes

**Developer Documentation**:
-   **[Architecture](ARCHITECTURE.md)**: High-level architecture overview
-   **[Contributing](CONTRIBUTING.md)**: Contribution guidelines and workflow
-   **[CLAUDE.md](CLAUDE.md)**: Developer guide for AI assistants
-   **[Documentation Index](docs/INDEX.md)**: Complete documentation organization and navigation

**Release Documentation**:
-   **[v0.10.0 Release Notes](RELEASE_NOTES_v0.10.0.md)**: Latest release details (fractional recovery lag fix)
-   **[v0.9.0 Release Notes](docs/releases/RELEASE_NOTES_v0.9.0.md)**: Native multi-frequency support
-   **[Migration Guide](docs/releases/MIGRATION_GUIDE.md)**: Upgrade from v0.8.0 to v0.9.0
-   **[PyPI Publication Guide](docs/releases/PYPI_PUBLICATION_GUIDE.md)**: Publishing to PyPI

## Further work

Recent additions in v0.10.0:
-   ✅ **Fractional Recovery Lag**: Annual frequency now uses 14/365 years (0.0384) instead of 0, fixing LinAlgError with incidence mode
-   ✅ **Annual + Incidence Mode**: Production-ready native support for annual surveillance data with incident cases
-   ✅ **Enhanced Testing**: 10 new tests for fractional lag validation (421/423 tests passing)

Recent additions in v0.9.0:
-   ✅ **Native Multi-Frequency**: Daily, business day, weekly, monthly, annual handlers; no artificial reindexing
-   ✅ **Frequency-Aware Aggregation**: Skip resampling when source and target frequencies match
-   ✅ **Frequency-Aware Seasonality**: Adaptive seasonal detection per frequency
-   ✅ **Business Day Support**: Trading-day defaults (252 days/year, 10-lag VAR, 10-day recovery)

Previous releases (v0.6.1-v0.7.0):
-   ✅ **SIRDV Model Support**: Automatic detection and modeling of vaccination data
-   ✅ **Parallel Simulations**: Multi-core execution for improved performance
-   ✅ **Result Caching**: Optional caching to avoid recomputation
-   ✅ **Enhanced Testing**: Comprehensive test coverage with slow test markers

Future directions (v1.0.0+):

-   **Incidence-First Mode**: Direct modeling of incident cases (not just cumulative)
-   **Importation Modeling**: Handle external case introductions for eliminated diseases
-   **Probabilistic & Scenario Forecasting**: Monte Carlo and intervention comparison workflows
-   **More advanced time series models**: SARIMAX, Prophet, or deep learning approaches
-   **Support for other epidemiological models**: Extend to SEIR, SEIRD, or metapopulation models
-   **Improved visualization**: Interactive dashboards and real-time updating plots

## References

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**Andrade u.a. 2021** Andrade, Marinho G. ; Achcar, Jorge A. ; Conce
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**Hawas 2020** Hawas, Mohamed: Generated time-series prediction data of
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**Maleki u.a. 2020** Maleki, Mohsen ; Mahmoudi, Mohammad R. ; Wraith,
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**Singh u.a. 2020** Singh, Vijander ; Poonia, Ramesh C. ; Kumar, Sandeep ;
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**Wacker und Schluter 2020** Wacker, Benjamin; Schluter, Jan: Time
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