epiweeks
Epidemiological weeks calculation based on CDC and ISO week numbering systems
Decision gist · record as of 2026-08-14
Yes. Active maintenance, zero dependencies, permissive license, production-stable status, and validated accuracy against CDC data make this a reliable choice for any epidemiological or public health application that needs precise week calculations. The low install friction and broad Python version support (3.10–3.14) add to its appeal.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low friction: pure Python wheel with zero runtime dependencies and active maintenance.
- Last commit 2026-08-06, production-stable status, supports Python 3.10 through 3.14.
License · maintenance · safety
MIT (permissive) — MIT license (permissive): you may use, modify, and distribute this package freely in commercial and private projects, with minimal restrictions.
last release 2026-01-07 (219 days) · last repo commit 2026-08-06 · 20 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 187,718 downloads/mo, #9,959 on PyPI
Alternatives
Verify before relying
from epiweeks import Week, Year
week = Week(2019, 1)
print(week.enddate())
# 2019-01-05
for week in Year(2019).iterweeks():
print(week.enddate())- Whether CDC reference validation covers all edge cases or only specific year ranges
- Performance characteristics when iterating large numbers of weeks or years
What it is and what it does
EpiWeeks is a Python package for calculating epidemiological weeks according to two standards: the US CDC's MMWR (Morbidity and Mortality Weekly Report) system and the ISO week numbering system. It provides week-to-date conversions, date-to-week lookups, and iteration over weeks within a year, all validated against CDC reference data. The package is designed for disease surveillance, public health reporting, and epidemiological research where precise week boundaries matter.
The package offers a clean API with Week and Year objects that support comparison, arithmetic (addition and subtraction), and containment checks. It includes full type annotations, comprehensive input validation, and zero runtime dependencies, making it straightforward to integrate into public health applications or data pipelines.
Use it for
- Generate CDC MMWR week boundaries for disease surveillance reports and epidemiological data aggregation.
- Convert between calendar dates and epidemiological week numbers for public health data analysis.
- Iterate over all weeks in a year to populate surveillance dashboards or export weekly summaries.
- Validate and normalize week-based data from multiple public health sources using a single standard.
- Build time-series analyses aligned to epidemiological weeks rather than calendar months or ISO weeks.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Active maintenance, zero dependencies, permissive license, production-stable status, and validated accuracy against CDC data make this a reliable choice for any epidemiological or public health application that needs precise week calculations. The low install friction and broad Python version support (3.10–3.14) add to its appeal.
Install
epiweeks on PyPI
Before you install
Low friction: pure Python wheel with zero runtime dependencies and active maintenance. Last commit 2026-08-06, production-stable status, supports Python 3.10 through 3.14.
Requires Python 3.10 or later.
License in practice
MIT license (permissive): you may use, modify, and distribute this package freely in commercial and private projects, with minimal restrictions.
Quickstart
from epiweeks import Week, Year
week = Week(2019, 1)
print(week.enddate())
# 2019-01-05
for week in Year(2019).iterweeks():
print(week.enddate())Verify before relying
- Whether CDC reference validation covers all edge cases or only specific year ranges
- Performance characteristics when iterating large numbers of weeks or years
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 219 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 187,718 / month, #9,959 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Healthcare IndustryIntended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: UtilitiesTyping :: Typed |
Evidence: epiweeks-2.4.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “epidemiological week calculation”
- epiweeksCalculates epidemiological weeks using US CDC (MMWR) and ISO week…
- iso-week-dateParses and manipulates ISO Week date strings in YYYY-WNN and…
- isoweekProvides a Week class representing ISO 8601 weeks (Monday–Sunday)…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also isoweek · iso-week-date · convertdate · datedelta · simple-icd-10-cm · workadays · jdcal · ummalqura · suntimes · hijridate