This repository is meant to hold resources to help researchers manipulate wastewater-based epidemiology data that conforms to the Ottawa Data Model for the surveillance of the SARS-CoV-2 virus in wastewater.

The code included in this repository will let you:

1. Get data into the data model format using mappers;
2. Validate your data;
3. Visualize data;
4. Perform common manipulations.

• wbe_odm: The package where most of the code is located.

• tests: Where the test code is located.

• Data: Location of example data files.

• notebooks: Location of Jupyter Notebooks that use the wbe_odm package for different purposes (e.g., reading in data from different sources, making plots, etc.)

The wbe_odm package has the following structure:

The larger rectange represents the wbe_odm package or directory. This is where the code base lives.

The odm.py module contains the Odm object. The Odm object is a python data class meant to hold the same data as the Ottawa Data Model, using the same tables. In the Odm object, tables are stored as pandas DataFrames.

Odmobjects also have helper methods to manipulate and tranform their own data:

• load_from lets an empty Odm object take in data from an object of the mapper class.

• append_odm appends tables from an Odm objects and add it to the calling object.

• append_from appends tables from objects of the mapper class.and add it to the calling object.

• to_csv saves the tables inside the Odm object in .csvfiles.

• to_sqlite3 adds the data inside the Odm object to the right tables inside a sqlite3 database.

• combine_per_sample creates a wide table (one row = one sample) with all characteristics recored in the other tables of the data model.

• get_geoJSON returns a geoJSON representation of the data held in the polygontable of the data model.

The module also includes a OdmEncoder object which turns Odm objects into a JSON string.

The data needed to do Wastewater-based surveillance come in many shapes and formats. To help researchers gather all that data into the ODM format, mapper obejcts can be used to parse, type-cast and organize data from any source. and then load that data into a single Odm object. This repo contains the following mappers:

• BaseMapper: this is an abstract mapper that all other mappers should inherit from. It has one required method:

  • read: this method is called by the user to get data from an input source. Any number of arguments can be used. BaseMapper also has two methods that can be inhereited or overridden by all mappers:
  • validates: this method is called by the Odm object before trying to ingest data from a mapper.
  • type_cast_table is a utility used to ensure that the columns of each tables conform to the data type defined by the ODM.

• ExcelTemplateMapper reads data from the long version of the official Excel Data Template from the Ottawa Data Model

• Sqlite3Mapper loads the contents of a sqlite3 database.

• SerialzedMapper reads JSON strings generated by the OdmEncoder object. In addition to these general-purpose mappers, the subpackage contains mappers that are designed for specific, local data sources. These can be used as inspiration for the development of more mappers for other locales.

• modelEauMapper reads in data from the ModelEAU lab for wastewater sample characterization.

• VdQPlantMapper reads data from the internal site measurement data sheet for the Ville de Québec wastewater treatment plants.

• Finally, McGillMapper reads the laboratory data sheet developped at McGill University to track the processing and analysis of wastewater samples for viral detection. This mapper is further explained in mcgill_mapper.md

This modules contains helper functions to help other function run.

This subpackage contains the following

1. Get to the root of the repo using your terminal.
2. Activate your python environment of choice.
3. Enter the command pip install -e . (the dot is important)
4. The package should now be installed. You can therefore import it just like any other package using the statement import wbe_odm