Vivarium Population Spenser

Welcome to the Vivarium Population Spenser repository.

This library is used by Daedalous spatial microsimulation pipeline that allows users to produce (custom) population projections for the whole of the United Kingdom at the local authority (LA) level.

This library is being developed in collaboration between Leeds Institute for Institute Data Analytics and the Alan Turing Institute as part of the SPENSER (Synthetic Population Estimation and Scenario Projection Model) project.

This library is largely based in the vivarium_public_health library, but it has been modified to cater for the needs of the SPENSER project.

Installation

To install the Vivarium Population Spenser library follow the next steps:

> git clone https://github.com/alan-turing-institute/vivarium_population_spenser.git

> cd vivarium_population_spenser

> python install .

If you plan to run the microsimulation using this library we recommend it to run if from the Daedalous pipeline that contains many useful scripts to interact with this library.

Documentation

The Vivarium Population Spenser library contains 6 main components used to simulate the evolution of an input population. You can find all the computational workflow protocols of all of these components in here.

There components are the following:

Population

The Spenser Population component is used define the starting point of the simulation from an input population (input_population) to be used in the microsimulation. It creates a dataframe with following fields:

population = pd.DataFrame( {'age': input_population['age'], 'entrance_time': creation_time, 'sex': input_population['sex'], 'alive': pd.Series('alive', index=index), 'location': input_population['location'], 'ethnicity': input_population['ethnicity'], 'exit_time': pd.NaT, 'MSOA': input_population['MSOA']}, index=index)

where creation_time is the starting time of the simulation.

Mortality:

The mortality module contains tools modeling all cause mortality based on individuals characteristics as gender, age, location (local authority level) and ethnicity.

This module uses as input table to establish the morality rates the table Mortality2011LEEDS12.csv.

Details about the computational workflow protocol followed by this component can be found here: dx.doi.org/10.17504/protocols.io.bn79mhr6

Fertility:

A model of fertility based on individual characteristics as, age, location (local authority level) and ethnicity.

This module uses as input table to establish the morality rates the table Fertility2011LEEDS12.csv.

Details about the computational workflow protocol followed by this component can be found here: dx.doi.org/10.17504/protocols.io.bn8bmhsn

Emigration:

This module models emigration based on individuals characteristics as gender, age, location (local authority level) and ethnicity.

This module uses as input table to establish the morality rates the table Emig20112012_LEEDS2.csv.

Details about the computational workflow protocol followed by this component can be found here: dx.doi.org/10.17504/protocols.io.bn8emhte.

Immigration

A deterministic immigration model in which the total number of immigrants is read from a file level and the characteristics of the immigrants are sampled from the migration rate file.

The immigrants are assigned to a local authority and then based on their age and gender to an MSOA.

The input table to establish the total number of immigrants and their characteristics Immig20112012_LEEDS2.csv

The input table to assigned them an MSOA ImmigrationMSOAM_F.csv.

Details about the computational workflow protocol followed by this component can be found here: dx.doi.org/10.17504/protocols.io.bn9dmh26

Internal migration

This module models the internal_migration between MSOAs (and their respective LADs) of individuals based on their gender, age, initial location (local authority level) and ethnicity.

The input table to establish the pool of migrants that internally migrate based on their characteristics is InternalOutmig2011_LEEDS2.csv.

Once the pool of migrants is chosen they are assigned to a new MSOA based on their age and gender using the MSOA migration matrices in od_matrices.

Details about the computational workflow protocol followed by this component can be found here: dx.doi.org/10.17504/protocols.io.bn9imh4e

Note:

For details of how all the tables were produced, please contact Nik Lomax and Luke Archer.