Generating contact matrix for Vietnam
2024-03-06
Introduction
In the realm of epidemiology and public health, understanding social mixing patterns is critical for developing effective strategies to control the spread of infectious diseases. Social mixing matrices provide a structured framework for quantifying interactions between different demographic groups within a population, enabling the simulation of disease transmission dynamics. This document focuses on constructing a social mixing matrix for Vietnam, with a specific emphasis on interactions between different age groups. Given the limited availability of detailed data, our efforts concentrate on elucidating the patterns of social mixing between various age cohorts within the Vietnamese population. By delineating these interactions, we aim to provide valuable insights into the dynamics of disease transmission and inform the design of targeted public health interventions in Vietnam.
The study conducted a diary-based survey in North Vietnam (2007) to explore social contact patterns, crucial for infectious disease spread modeling. It utilized generalized estimating equations, accounting for household sampling and demographic weighting towards the Vietnamese population. Key findings include assortative mixing by age and differences in contact intensity within household and community settings compared to European data. The study emphasizes the importance of context-specific data for accurate infectious disease modeling, particularly in developing countries. For a detailed understanding of the methods and results, please refer to the full article here.
Using R and available packages
This work will use socialmixr package and conmat.
Firstly, load the package socialmixr and required packges
Then load the data from the social contact matrix survey of Horby et. al (2011), available on Zenodo.
The survey object returns a dictionary with contacts and participants datatable
Participants dataframe:
summary(vietnam$participants)## part_id hh_id part_age part_gender
## Length:865 Length:865 Min. : 0.00 Length:865
## Class :character Class :character 1st Qu.:14.00 Class :character
## Mode :character Mode :character Median :29.00 Mode :character
## Mean :30.52
## 3rd Qu.:45.00
## Max. :90.00
##
## part_occupation_detail country hh_size sday_id
## Length:865 Vietnam:865 Min. :1.000 Mode:logical
## Class :character 1st Qu.:3.000 NA's:865
## Mode :character Median :4.000
## Mean :3.918
## 3rd Qu.:5.000
## Max. :9.000
##
## day month year dayofweek
## Mode:logical Mode:logical Min. :2007 Min. :0.000
## NA's:865 NA's:865 1st Qu.:2007 1st Qu.:3.000
## Median :2007 Median :3.000
## Mean :2007 Mean :3.451
## 3rd Qu.:2007 3rd Qu.:4.000
## Max. :2007 Max. :6.000
## NA's :6Contact dataframe:
summary(vietnam$contacts)## part_id cont_id cnt_age_exact cnt_age_est_min
## Length:6675 Length:6675 Mode:logical Min. : 0.00
## Class :character Class :character NA's:6675 1st Qu.: 6.00
## Mode :character Mode :character Median :26.00
## Mean :25.95
## 3rd Qu.:35.00
## Max. :65.00
##
## cnt_age_est_max cnt_gender cnt_home cnt_work
## Min. : 5.0 Length:6675 Mode :logical Mode :logical
## 1st Qu.: 15.0 Class :character FALSE:2317 FALSE:6009
## Median : 34.0 Mode :character TRUE :4358 TRUE :666
## Mean : 37.7
## 3rd Qu.: 49.0
## Max. :100.0
##
## cnt_school cnt_transport cnt_leisure cnt_otherplace
## Mode :logical Mode :logical Mode :logical Mode :logical
## FALSE:5827 FALSE:6435 FALSE:6564 FALSE:6366
## TRUE :848 TRUE :240 TRUE :111 TRUE :309
##
##
##
##
## frequency_multi phys_contact duration_multi
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:3.000
## Median :1.000 Median :2.000 Median :5.000
## Mean :1.217 Mean :1.678 Mean :3.766
## 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:5.000
## Max. :5.000 Max. :2.000 Max. :5.000
## NA's :139 NA's :652 NA's :103Creating a contact matrix object:
The contact_matrix function in R is
used to construct contact matrices from survey data. Specifically, it
allows customization of age groups, weighting by age, and applying
various filters to refine the analysis. Input is survey data from
Vietnam with age limits of (0,5,15,35,50,70) and weighting by age
enabled (weigh.age = TRUE), taking into account the age distribution of
the population and computes the mean of contacts per day for each age
group. For detailed usage and argument explanations, please refer to the
documentation.
mr <- vncm$matrix
mr## contact.age.group
## [0,5) [5,15) [15,35) [35,50) [50,70) 70+
## [1,] 1.0081382 1.0345059 2.679943 1.015341 0.4792535 0.06372883
## [2,] 0.4333715 3.6475693 1.559493 1.266675 0.3174873 0.17016764
## [3,] 0.5748874 0.7985706 3.030877 1.438238 0.7411519 0.19228457
## [4,] 0.4100289 1.2210695 2.707548 2.775660 1.2706702 0.46192042
## [5,] 0.3516335 0.5560628 2.534982 2.308633 2.5107631 0.79998885
## [6,] 0.1091823 0.6959308 1.535690 1.959661 1.8679943 1.20843314Plotting the matrix:
matrix_plot(mr, main = "Contact matrix")
The we use bootstrap times:
mb <- Reduce("+", lapply(m["matrix", ], function(x) x / ncol(m)))
matrix_plot(mb, main = "Contact matrix using 50-time bootstrap")
Comparing two matrices:
dm <- mr - mb
matrix_plot(dm, main = "Difference between two contact matrices")
Use conmat:
vnp <-wpp_age("Vietnam", 2020)## Warning in wpp_age("Vietnam", 2020): Don't have historial population data
## available after 2015. Will make use of the median projection of population
## counts from the WPP2017 package.vn_2020_pop <- as_conmat_population(
data = vnp,
age = lower.age.limit,
population = population
)Genereting matrix using polymod:
age_breaks <- c(0,5,15,35,50,70,Inf)
vietnam_contact <- extrapolate_polymod(
population = vn_2020_pop,
age_breaks = age_breaks
)
matrix_plot(vietnam_contact$all, main = "Contact matrix by polymod")
Generate NGM by conmat:
vn_2020_ngm <- generate_ngm(
vn_2020_pop,
age_breaks = age_breaks,
R_target = 1.5
)
matrix_plot(vn_2020_ngm$all, main = "Contact matrix by Conmat package")