Pre-submission inquiry: Episia - Open-source epidemiology and biostatistics for Python

[ArielShadrac]

Submitting Author: Fildouindé Ariel Shadrac Ouedraogo (@ArielShadrac)
Package Name: Episia
One-Line Description of Package: Open-source epidemiology and biostatistics library for Python, with validated measures, compartmental models, DHIS2 integration, and automated reporting.
Repository Link: https://github.com/Xcept-Health/episia
Version: 0.1.1

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Code of Conduct & Commitment to Maintain Package

• I agree to abide by [pyOpenSci's Code of Conduct][PyOpenSciCodeOfConduct] during the review process and in maintaining my package after should it be accepted.
• I have read and will commit to package maintenance after the review as per the [pyOpenSci Policies Guidelines][Commitment].

Description

Episia is an open-source Python library for epidemiologists, public health researchers, and biostatisticians. It provides a modern, unified, and fully offline toolkit for the entire epidemiological workflow: from raw surveillance data processing (including DHIS2 integration) to classical biostatistical measures (risk/odds ratios, confidence intervals, diagnostic tests, sample size calculations), compartmental epidemic modeling (SIR/SEIR/SEIRD), Monte Carlo sensitivity analysis, alert detection, and automated self-contained HTML report generation. All core statistical functions are systematically validated against the reference OpenEpi tool. The package is designed especially for practical use in resource-limited settings such as public health systems in Africa.

Community Partnerships

(Aucune case cochée – Episia n’adhère pas encore aux standards Astropy ou Pangeo)

Scope

• Data processing/munging
• Data visualization
• Scientific software wrappers

Domain Specific
(Aucune case cochée)

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Explanation

Episia falls under Data processing/munging, Data visualization, and Scientific software wrappers because it transforms raw surveillance data (e.g. from CSV or DHIS2) into structured epidemiological datasets, implements and extends validated epidemiological calculations, and provides rich visualizations (epidemic curves, ROC curves, endemic channels) with dual backends (Plotly + Matplotlib).

• Who is the target audience and what are the scientific applications of this package?
  Target audience: Field epidemiologists, public health officers, biostatisticians, students and researchers working in low-resource settings (particularly in Africa).
  Applications: Outbreak investigation, routine disease surveillance and early warning systems, epidemic modeling & forecasting, diagnostic test evaluation, sample size planning for studies, and production of epidemiological bulletins for ministries of health.

• Are there other Python packages that accomplish similar things? If so, how does yours differ?
  The closest equivalent is OpenEpi (a popular web-based JavaScript tool). Episia reimplements and validates its core calculations in Python while adding major new capabilities: compartmental models (SIR/SEIR/SEIRD), Monte Carlo sensitivity analysis, DHIS2 integration, unified result objects, alert engines, and automated reporting. General packages like statsmodels or scipy lack these domain-specific epidemiological tools and the integrated public-health workflow. No other comprehensive, offline-first Python library currently offers this combination.

• Any other questions or issues we should be aware of:
  This is the first submission of Episia (version 0.1.1, Alpha status). The package has 1390 tests (80% coverage), ReadTheDocs documentation, and a detailed README with examples and validation notebook against OpenEpi. I am open to any feedback on scope or improvements needed before a full submission.

P.S. Have feedback/comments about our review process? Leave a comment [on our discourse forum][Comments]

[kysolvik]

Hi @ArielShadrac! My name is Kylen, I'm an editor over here at pyOpenSci.

Thanks for your pre-submission inquiry regarding Episia! After looking at your inquiry and the package, it looks like it is in scope. There's a few things to think about/address before we could move ahead with a full submission:

1. Some links are broken, including links to full documentation and the examples folder.
2. We're in the process of rewriting some of our guidelines due to an influx of submissions that is stretching our community thin. As part of that, we're asking that packages have a public development history spanning 3-6 months before submitting to let the package mature some and undergo iterative updates. We are also implementing some guidelines for the use of generative AI. You can see the (draft) updated guidelines our software-peer-review repo. I'm sorry for the moving goalposts, since I know this updated guideline hasn't yet published on the website. Before going ahead with a submission, we'd ask you to wait another month or two to let Episia mature further before review, and for you to review the new guidelines to determine if Episia's development meets them.
3. Episia is an impressively large package with a lot of features, and so just note that if review does move forward the process could take longer than usual.

[ArielShadrac]

Hi @kysolvik,

Thank you again for the detailed feedback. Here is a concrete update on each point you raised.

Broken links
Fixed. Both are now accessible:

• Documentation: https://episia.readthedocs.io/en/latest/
• Examples: https://github.com/Xcept-Health/episia/tree/main/exemples

Development maturity
Episia is now at v0.1.2 (released today), with 5 tagged releases over ~2 months. The changelog since v0.1.1a includes prevalence(), cumulative_incidence(), and incidence_rate() exposed on the epi singleton, a new IncidenceRateResult dataclass, and API consistency improvements. Active development is ongoing.

Generative AI guidelines
I have not yet read the draft policy in full, but I will do so carefully before the formal submission.
I plan to submit formally in 4–6 weeks. Happy to address any additional concerns in the meantime.

[kysolvik]

Hi @ArielShadrac! Just wanted to check in. I'm tagging this issue as submission requested and closing it for now.

When you open a full submission, make sure to reference this presubmission inquiry. Also, the full generative AI guidelines are available now. Be sure to review them before submitting and include the checklist at the bottom of that page in your full submission.

We'll keep an eye out for your full submission! Let me know if you have any questions in the meantime. Thanks!