Synthetic contact matrices for 200 countries

This analysis pipeline produces csv files of synthetic contact matrices generated for all countries listed in the United Nations’ World Population Prospects (2017), using the conmat package.¹ The conmat package is motivated by the contact matrices generated in Prem, Cook, and Jit (2017).

These instructions will assist you in downloading the csv files for synthetic contact matrices, and to modify the analysis pipeline to suit your own needs.

Download synthetic contact matrices

You can download the synthetic contact matrices by navigating to our Zenodo repository and downloading the zip file. The contact matrices can be found in the folder output-contact-matrices.

Each csv file is named in the convention {Country}_{Environment}_2015.csv; for example, AUS_work_2015.csv. Country names are in ISO-3 format. The five environments for each country are: home, school, work, other, and all.

Alternatively, if you would like to load specific contact matrices, here is how you would do so for Australia in all settings:

library(readr)
url <- "https://raw.githubusercontent.com/idem-lab/syncomat/main/output-contact-matrices/AUS_all_2015.csv"
aus_all_cm <- read_csv(url)

The following is how the synthetic contact matrix looks for Australia in all settings, with the columns being “age group from” and the rows being “age group to.”

age_groups	[0,5)	[5,10)	[10,15)	[15,20)	[20,25)	[25,30)	[30,35)	[35,40)	[40,45)	[45,50)	[50,55)	[55,60)	[60,65)	[65,70)	[70,75)	[75,80)	[80,Inf)
[0,5)	2.36549	1.32146	0.47311	0.29575	0.40005	0.77449	1.15301	0.99993	0.64134	0.47867	0.44268	0.41760	0.35185	0.26851	0.19701	0.13537	0.07686
[5,10)	1.27674	7.34953	1.66447	0.41945	0.30169	0.48904	0.91914	1.13686	0.84775	0.57294	0.47211	0.40883	0.36431	0.32358	0.26286	0.18962	0.11482
[10,15)	0.44699	1.63060	10.87195	1.76465	0.48154	0.39025	0.57920	0.90744	0.98157	0.72667	0.51018	0.35212	0.25293	0.22886	0.20394	0.15588	0.09758
[15,20)	0.28800	0.42315	1.81674	9.51635	1.74482	0.68813	0.57022	0.68927	0.92875	0.96830	0.69633	0.39610	0.22885	0.18520	0.17954	0.15649	0.11044
[20,25)	0.41929	0.32756	0.53419	1.87586	4.95907	1.74664	0.97801	0.79992	0.85797	1.06005	1.03984	0.66555	0.34331	0.22024	0.19820	0.18753	0.15496
[25,30)	0.85879	0.56176	0.45801	0.78355	1.84767	3.01242	1.71204	1.09817	0.91736	0.96623	1.09704	0.96923	0.58925	0.32092	0.22601	0.19945	0.18568
[30,35)	1.27998	1.05702	0.68055	0.65003	1.03589	1.71400	2.39046	1.54667	1.05167	0.91530	0.91991	0.94739	0.80838	0.50495	0.28419	0.19238	0.18528
[35,40)	1.07154	1.26205	1.02924	0.75849	0.81787	1.06129	1.49302	2.01722	1.37952	0.99688	0.84227	0.76306	0.75673	0.67159	0.42916	0.22866	0.18398
[40,45)	0.67188	0.92003	1.08839	0.99913	0.85758	0.86669	0.99245	1.34863	1.75816	1.26520	0.89163	0.66054	0.56228	0.60097	0.55999	0.33882	0.21542
[45,50)	0.49238	0.61054	0.79116	1.02282	1.04038	0.89635	0.84813	0.95692	1.24230	1.60706	1.14430	0.69300	0.47216	0.43510	0.49951	0.45012	0.30138
[50,55)	0.44340	0.48987	0.54087	0.71622	0.99373	0.99096	0.83001	0.78726	0.85249	1.11424	1.51131	0.97152	0.54348	0.39952	0.39560	0.45260	0.42315
[55,60)	0.39430	0.39989	0.35189	0.38405	0.59958	0.82532	0.80580	0.67234	0.59534	0.63611	0.91582	1.30405	0.80135	0.48014	0.39168	0.39196	0.46427
[60,65)	0.29942	0.32117	0.22782	0.19999	0.27875	0.45223	0.61969	0.60094	0.45676	0.39061	0.46175	0.72225	1.07536	0.70064	0.46088	0.35168	0.35169
[65,70)	0.19510	0.24355	0.17600	0.13818	0.15268	0.21028	0.33049	0.45536	0.41681	0.30733	0.28981	0.36947	0.59820	0.98187	0.65803	0.34134	0.22572
[70,75)	0.11373	0.15719	0.12460	0.10643	0.10916	0.11766	0.14778	0.23118	0.30857	0.28031	0.22799	0.23946	0.31263	0.52279	0.84829	0.43245	0.19277
[75,80)	0.05961	0.08650	0.07265	0.07076	0.07879	0.07920	0.07631	0.09396	0.14241	0.19268	0.19897	0.18279	0.18197	0.20686	0.32988	0.46024	0.18492
[80,Inf)	0.04845	0.07497	0.06510	0.07148	0.09319	0.10554	0.10520	0.10821	0.12961	0.18467	0.26628	0.30992	0.26048	0.19581	0.21048	0.26470	0.18688

Each cell is the expected number of people that an individual will have contact with per day. In the contact matrix above, the number 2.36549 in the first column and first row indicates that we expect a 0-5 year old to be in contact with two other 0-5 year olds per day. The number 1.27998 in the first column, seventh row (30-35 age group) indicates that a 0-5 year old is, on average, in contact with one 30-35 year old per day.

Running the analysis pipeline

To run this analysis pipeline, you would need to be familiar with the targets workflow.² This pipeline also utilises renv.³

First, download the zip file of this analysis pipeline from our Zenodo repository. Open the project in a new RStudio session.

Once you’ve opened the project in a new RStudio session, you will be prompted to run renv::restore(). Run renv::restore() to install the packages used in this analysis to your workspace. If you then come across issues, run renv::status(). Also run ?renv::status() for advice on resolving these issues.

Open the _targets.R file, and run all lines of code under the Set-up section to load the R packages required for this pipeline. You can then run tar_make() to run the entire pipeline.

If you need more information about the pipeline, the Methodology page will explain each target object and how you can modify each object to suit your own analysis needs.

This pipeline takes approximately 13 minutes⁴ to run, which will generate contact matrices for 200 countries.

Data sources

The age-specific population data that forms the basis for this analysis were derived from the wpp_age() function in the socialmixr package, which uses data from the wpp2017 package.

Notes

The contact matrices created are transposed in comparison to those discussed by Prem, Cook, and Jit (2017) and Mossong et al. (2008). In other words, the rows are “age group to” and the columns are “age group from”.

Footnotes

1. For more information on the conmat package, refer to its documentation. ↩

2. For information on the targets workflow, refer to the targets user manual. ↩

3. For information on how renv works, refer to the Introduction to renv vignette. For a quick overview, refer to its website. ↩

4. This pipeline took 13 minutes to run on a computer equipped with an Intel Core i7-8565U and 16 GB of RAM running a 64-bit version of Windows. ↩