This vignette outlines a basic workflow of:
We can create a synthetic matrix from all POLYMOD data by using the
extrapolate_polymod function. First, let’s extract an age
distribution from the ABS data.
fairfield <- abs_age_lga("Fairfield (C)")
fairfield
#> # A tibble: 18 × 4 (conmat_population)
#> - age: lower.age.limit
#> - population: population
#> lga lower.age.limit year population
#> <chr> <dbl> <dbl> <dbl>
#> 1 Fairfield (C) 0 2020 12261
#> 2 Fairfield (C) 5 2020 13093
#> 3 Fairfield (C) 10 2020 13602
#> 4 Fairfield (C) 15 2020 14323
#> 5 Fairfield (C) 20 2020 15932
#> 6 Fairfield (C) 25 2020 16190
#> 7 Fairfield (C) 30 2020 14134
#> 8 Fairfield (C) 35 2020 13034
#> 9 Fairfield (C) 40 2020 12217
#> 10 Fairfield (C) 45 2020 13449
#> 11 Fairfield (C) 50 2020 13419
#> 12 Fairfield (C) 55 2020 13652
#> 13 Fairfield (C) 60 2020 12907
#> 14 Fairfield (C) 65 2020 10541
#> 15 Fairfield (C) 70 2020 8227
#> 16 Fairfield (C) 75 2020 5598
#> 17 Fairfield (C) 80 2020 4006
#> 18 Fairfield (C) 85 2020 4240Note that this is a conmat_population object, which is
just a data frame that knows which columns represent the
age and population information.
We then extrapolate this to home, work, school, other and all settings, using the full POLYMOD data. This gives us a setting prediction matrix.
age_breaks_0_80_plus <- c(seq(0, 80, by = 5), Inf)
synthetic_fairfield_5y <- extrapolate_polymod(
population = fairfield,
age_breaks = age_breaks_0_80_plus
)
synthetic_fairfield_5y
#>
#> ── Setting Prediction Matrices ─────────────────────────────────────────────────
#> A list of matrices containing the model predicted contact rate between ages in
#> each setting.
#> There are 17 age breaks, ranging 0-80+ years, with a regular 5 year interval
#> • home: a 17x17 <matrix>
#> • work: a 17x17 <matrix>
#> • school: a 17x17 <matrix>
#> • other: a 17x17 <matrix>
#> • all: a 17x17 <matrix>
#> ℹ Access each <matrix> with `x$name`
#> ℹ e.g., `x$home`
synthetic_fairfield_5y$home
#> [0,5) [5,10) [10,15) [15,20) [20,25) [25,30)
#> [0,5) 0.52331991 0.43331026 0.21604527 0.14520119 0.20706225 0.39297905
#> [5,10) 0.45819719 0.74130671 0.48785205 0.17693732 0.11536809 0.19943984
#> [10,15) 0.24053181 0.51364426 0.84142890 0.43448783 0.14787240 0.11032767
#> [15,20) 0.17249165 0.19877597 0.46360462 0.74624759 0.37589888 0.14292140
#> [20,25) 0.26436767 0.13929633 0.16957694 0.40399925 0.63610888 0.34651307
#> [25,30) 0.50625036 0.24297098 0.12765936 0.15498702 0.34962956 0.54785508
#> [30,35) 0.63832925 0.49444540 0.23140771 0.12060556 0.13960249 0.30266672
#> [35,40) 0.44367676 0.60110421 0.44986554 0.20873270 0.10533083 0.11842552
#> [40,45) 0.22980603 0.38985778 0.51253279 0.38269461 0.17221002 0.08519447
#> [45,50) 0.15723783 0.20068263 0.33214619 0.43868096 0.31697065 0.14167357
#> [50,55) 0.16762689 0.13835773 0.17273223 0.28568338 0.36182341 0.26267731
#> [55,60) 0.19626851 0.14336034 0.11546058 0.14166508 0.22350893 0.28806870
#> [60,65) 0.17259891 0.15185593 0.10648350 0.08193061 0.09666623 0.15947239
#> [65,70) 0.10640195 0.11872082 0.09779557 0.06316338 0.04709905 0.06025166
#> [70,75) 0.05651045 0.06939264 0.07043323 0.05234543 0.03355246 0.02817056
#> [75,80) 0.03050367 0.03639643 0.03940738 0.03582935 0.02740058 0.02045005
#> [80,Inf) 0.02395838 0.03015550 0.03077152 0.02945082 0.02978919 0.02983457
#> [30,35) [35,40) [40,45) [45,50) [50,55) [55,60)
#> [0,5) 0.53781315 0.41150516 0.22216716 0.14725517 0.15134817 0.17599794
#> [5,10) 0.44051273 0.58953802 0.39854569 0.19873609 0.13209621 0.13593754
#> [10,15) 0.21706622 0.46453568 0.55165538 0.34631439 0.17363394 0.11527056
#> [15,20) 0.12071240 0.22998365 0.43950993 0.48804535 0.30641947 0.15090987
#> [20,25) 0.15017141 0.12473017 0.21256135 0.37900067 0.41709748 0.25589349
#> [25,30) 0.32850901 0.14149786 0.10610254 0.17092213 0.30552869 0.33277368
#> [30,35) 0.47860229 0.28321487 0.11127348 0.08021956 0.13306181 0.24107694
#> [35,40) 0.25727295 0.40856551 0.22887741 0.08817326 0.06688246 0.11370183
#> [40,45) 0.09697502 0.21958015 0.36144787 0.20731196 0.08507515 0.06541272
#> [45,50) 0.07216946 0.08732367 0.21400765 0.37912952 0.23092412 0.09433839
#> [50,55) 0.12416712 0.06870484 0.09109356 0.23952415 0.44032574 0.26149895
#> [55,60) 0.22650897 0.11760319 0.07052185 0.09852468 0.26329736 0.47773278
#> [60,65) 0.22823247 0.19661252 0.10653881 0.06504924 0.09249990 0.25326537
#> [65,70) 0.11336576 0.17658002 0.15281395 0.08122303 0.05070187 0.07696039
#> [70,75) 0.04150215 0.08312102 0.12669812 0.10702841 0.05933634 0.04034025
#> [75,80) 0.01958401 0.02952000 0.05688943 0.08648014 0.07835045 0.04720490
#> [80,Inf) 0.02658190 0.02319721 0.02836220 0.05237460 0.09304130 0.10996504
#> [60,65) [65,70) [70,75) [75,80) [80,Inf)
#> [0,5) 0.16458757 0.11988506 0.08281025 0.06268887 0.04010743
#> [5,10) 0.15312433 0.14144768 0.10752816 0.07909526 0.05338108
#> [10,15) 0.11304961 0.12267679 0.11491077 0.09016617 0.05735142
#> [15,20) 0.09281177 0.08454320 0.09112384 0.08747322 0.05856830
#> [20,25) 0.11769044 0.06775402 0.06277506 0.07189612 0.06366980
#> [25,30) 0.19590271 0.08745415 0.05317982 0.05414128 0.06434031
#> [30,35) 0.25831507 0.15160402 0.07218372 0.04776979 0.05281614
#> [35,40) 0.20214433 0.21451044 0.13132809 0.06541029 0.04186919
#> [40,45) 0.10508685 0.17809841 0.19204680 0.12093482 0.04911215
#> [45,50) 0.06623503 0.09771948 0.16747150 0.18977593 0.09362128
#> [50,55) 0.09769375 0.06327118 0.09630363 0.17833898 0.17250817
#> [55,60) 0.26932574 0.09669986 0.06592301 0.10818534 0.20528872
#> [60,65) 0.46952709 0.26582005 0.09957592 0.07061294 0.14341967
#> [65,70) 0.22497344 0.43027814 0.24501515 0.09086061 0.07270457
#> [70,75) 0.06479746 0.18838793 0.34734890 0.18950830 0.05470861
#> [75,80) 0.03276461 0.04981417 0.13512800 0.23649619 0.08450442
#> [80,Inf) 0.08169591 0.04893390 0.04788987 0.10374097 0.16430280By full POLYMOD data, we mean these data:
polymod_setting <- get_polymod_setting_data()
polymod_population <- get_polymod_population()
polymod_setting
#>
#> ── Setting Data ────────────────────────────────────────────────────────────────
#> A list of <data.frame>s containing the number of contacts between ages in each
#> setting.
#> There are 86 age breaks, ranging 0-90 years, with an irregular year interval,
#> (on average, 1.05 years)
#> • home: a 8,787x5 <data.frame>
#> • work: a 8,787x5 <data.frame>
#> • school: a 8,787x5 <data.frame>
#> • other: a 8,787x5 <data.frame>
#> ℹ Access each <data.frame> with `x$name`
#> ℹ e.g., `x$home`
polymod_setting$home
#> # A tibble: 8,787 × 5
#> setting age_from age_to contacts participants
#> <chr> <int> <dbl> <int> <int>
#> 1 home 0 0 10 92
#> 2 home 0 1 7 92
#> 3 home 0 2 11 92
#> 4 home 0 3 15 92
#> 5 home 0 4 12 92
#> 6 home 0 5 6 92
#> 7 home 0 6 8 92
#> 8 home 0 7 9 92
#> 9 home 0 8 6 92
#> 10 home 0 9 6 92
#> # ℹ 8,777 more rows
polymod_population
#> # A tibble: 21 × 2 (conmat_population)
#> - age: lower.age.limit
#> - population: population
#> lower.age.limit population
#> <int> <dbl>
#> 1 0 1898966.
#> 2 5 2017632.
#> 3 10 2192410.
#> 4 15 2369985.
#> 5 20 2467873.
#> 6 25 2484327.
#> 7 30 2649826.
#> 8 35 3043704.
#> 9 40 3117812.
#> 10 45 2879510.
#> # ℹ 11 more rowsThe extrapolate_polymod() function does the
following:
polymod_setting_models) of
the contact rate to the full POLYMOD data aboveIt also has options to predict to specified age brackets, defaulting to 5 year age groups up to 75, then 75 and older.
This object, synthetic_fairfield_5y, contains a matrix
of predictions for each of the settings, home, work, school, other, and
all settings, which is summarised when you print the object to the
console:
synthetic_fairfield_5y
#>
#> ── Setting Prediction Matrices ─────────────────────────────────────────────────
#> A list of matrices containing the model predicted contact rate between ages in
#> each setting.
#> There are 17 age breaks, ranging 0-80+ years, with a regular 5 year interval
#> • home: a 17x17 <matrix>
#> • work: a 17x17 <matrix>
#> • school: a 17x17 <matrix>
#> • other: a 17x17 <matrix>
#> • all: a 17x17 <matrix>
#> ℹ Access each <matrix> with `x$name`
#> ℹ e.g., `x$home`You can see more detail by using str if you like:
str(synthetic_fairfield_5y)
#> List of 5
#> $ home : 'conmat_age_matrix' num [1:17, 1:17] 0.523 0.458 0.241 0.172 0.264 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> ..- attr(*, "age_breaks")= num [1:18] 0 5 10 15 20 25 30 35 40 45 ...
#> $ work : 'conmat_age_matrix' num [1:17, 1:17] 0.00287 0.00482 0.00379 0.00422 0.00911 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> ..- attr(*, "age_breaks")= num [1:18] 0 5 10 15 20 25 30 35 40 45 ...
#> $ school: 'conmat_age_matrix' num [1:17, 1:17] 0.9176 0.3803 0.0677 0.0361 0.0506 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> ..- attr(*, "age_breaks")= num [1:18] 0 5 10 15 20 25 30 35 40 45 ...
#> $ other : 'conmat_age_matrix' num [1:17, 1:17] 0.769 0.424 0.162 0.103 0.133 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> ..- attr(*, "age_breaks")= num [1:18] 0 5 10 15 20 25 30 35 40 45 ...
#> $ all : 'conmat_age_matrix' num [1:17, 1:17] 2.213 1.268 0.474 0.316 0.457 ...
#> ..- attr(*, "age_breaks")= num [1:18] 0 5 10 15 20 25 30 35 40 45 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> - attr(*, "age_breaks")= num [1:18] 0 5 10 15 20 25 30 35 40 45 ...
#> - attr(*, "class")= chr [1:2] "conmat_setting_prediction_matrix" "list"Once infected, a person can transmit an infectious disease to another, creating generations of infected individuals. We can define a matrix describing the number of newly infected individuals in given categories, such as age, for consecutive generations. This matrix is called a “next generation matrix” (NGM).
We can generate an NGM using the population data
fairfield_ngm_age_data <- generate_ngm(
fairfield,
age_breaks = age_breaks_0_80_plus,
R_target = 1.5
)Or if you’ve already got the fitted settings contact matrices, then
you can pass that to generate_ngm instead:
fairfield_ngm <- generate_ngm(
synthetic_fairfield_5y,
age_breaks = age_breaks_0_80_plus,
R_target = 1.5
)However, note in these cases the age breaks specified in
generate_ngm must be the same as the age breaks specified
in the synthetic contact matrix, otherwise it will error as it is trying
to multiple incompatible matrices.
You can also specify your own transmission matrix, like so:
# using our own transmission matrix
new_transmission_matrix <- get_setting_transmission_matrices(
age_breaks = age_breaks_0_80_plus,
# is normally 0.5
asymptomatic_relative_infectiousness = 0.75
)
new_transmission_matrix
#>
#> ── Transmission Probability Matrices ───────────────────────────────────────────
#> A list of matrices, each <matrix> containing the relative probability of
#> individuals in a given age group infecting an individual in another age group,
#> for that setting.
#> Warning in min(x, na.rm = na.rm): no non-missing arguments to min; returning
#> Inf
#> Warning in max(x, na.rm = na.rm): no non-missing arguments to max; returning
#> -Inf
#> There are -1 age breaks, ranging Inf--Inf years,
#> • home: a 17x17 <matrix>
#> • school: a 17x17 <matrix>
#> • work: a 17x17 <matrix>
#> • other: a 17x17 <matrix>
#> ℹ Access each <matrix> with `x$name`
#> ℹ e.g., `x$home`
fairfield_ngm_0_80_new_tmat <- generate_ngm(
synthetic_fairfield_5y,
age_breaks = age_breaks_0_80_plus,
R_target = 1.5,
setting_transmission_matrix = new_transmission_matrix
)We can also generate an NGM for Australian specific data like so, which refits and extrapolates the data based on the Australian state or LGA provided.
ngm_fairfield <- generate_ngm_oz(
lga_name = "Fairfield (C)",
age_breaks = age_breaks_0_80_plus,
R_target = 1.5
)The output of this is a matrix for each of the settings, where each value is the number of newly infected individuals
ngm_fairfield$home
#> [0,5) [5,10) [10,15) [15,20) [20,25)
#> [0,5) 0.049751622 0.040326077 0.019485789 0.013032638 0.019030739
#> [5,10) 0.053182053 0.084215832 0.053700622 0.019381429 0.012942333
#> [10,15) 0.032909393 0.068774302 0.109139465 0.056079336 0.019549859
#> [15,20) 0.028790666 0.032460863 0.073315802 0.117427939 0.060603892
#> [20,25) 0.063842451 0.032889089 0.038735483 0.091811642 0.148219412
#> [25,30) 0.147016557 0.068948382 0.035019970 0.042294436 0.097881870
#> [30,35) 0.196488679 0.148690329 0.067251439 0.034865505 0.041412101
#> [35,40) 0.135481077 0.179327721 0.129705737 0.059865238 0.030997797
#> [40,45) 0.067287445 0.111541015 0.141750795 0.105287866 0.048607571
#> [45,50) 0.045226217 0.056406176 0.090252702 0.118578944 0.087895934
#> [50,55) 0.048930483 0.039463890 0.047626875 0.078358626 0.101815289
#> [55,60) 0.059234212 0.042272398 0.032905479 0.040161433 0.065014732
#> [60,65) 0.053998421 0.046410582 0.031447534 0.024068519 0.029141592
#> [65,70) 0.032420299 0.035341406 0.028135855 0.018076520 0.013830850
#> [70,75) 0.015696372 0.018837332 0.018487126 0.013668130 0.008986538
#> [75,80) 0.007929359 0.009248453 0.009684949 0.008760233 0.006870408
#> [80,Inf) 0.006067669 0.007465989 0.007369258 0.007016746 0.007277967
#> [25,30) [30,35) [35,40) [40,45) [45,50) [50,55)
#> [0,5) 0.037037984 0.052017962 0.040748064 0.022613399 0.01540295 0.01638965
#> [5,10) 0.022947552 0.052024273 0.071292417 0.049551148 0.02539756 0.01748183
#> [10,15) 0.014962915 0.030222466 0.066239718 0.080892026 0.05220899 0.02711564
#> [15,20) 0.023644094 0.020507478 0.040025864 0.078686270 0.08986290 0.05847245
#> [20,25) 0.082916049 0.036933255 0.031451595 0.055192578 0.10131744 0.11572157
#> [25,30) 0.157610276 0.097203461 0.042955348 0.033195670 0.05510437 0.10235283
#> [30,35) 0.092285882 0.150135559 0.091175135 0.036930820 0.02744534 0.04732850
#> [35,40) 0.035821264 0.080058989 0.130470394 0.075347429 0.02992059 0.02359359
#> [40,45) 0.024711357 0.028931907 0.067213890 0.114030145 0.06739870 0.02874215
#> [45,50) 0.040368829 0.021149864 0.026254330 0.066307691 0.12104035 0.07660174
#> [50,55) 0.075957903 0.036930301 0.020965555 0.028648859 0.07762725 0.14829186
#> [55,60) 0.086121192 0.069661729 0.037113983 0.022941742 0.03303561 0.09176693
#> [60,65) 0.049419029 0.072771512 0.064340245 0.035946910 0.02262744 0.03345673
#> [65,70) 0.018185349 0.035200607 0.056265148 0.050196141 0.02750097 0.01784567
#> [70,75) 0.007751960 0.011744058 0.024127398 0.037892832 0.03297696 0.01899083
#> [75,80) 0.005266989 0.005185488 0.008015839 0.015911748 0.02491071 0.02343304
#> [80,Inf) 0.007486513 0.006856862 0.006135944 0.007726659 0.01469283 0.02709625
#> [55,60) [60,65) [65,70) [70,75) [75,80) [80,Inf)
#> [0,5) 0.01981747 0.01943735 0.01461158 0.01029844 0.007870232 0.005050639
#> [5,10) 0.01871236 0.02211669 0.02109088 0.01636300 0.012151851 0.008226539
#> [10,15) 0.01873051 0.01928354 0.02160942 0.02066219 0.016370247 0.010444993
#> [15,20) 0.02998075 0.01937018 0.01823048 0.02006491 0.019451316 0.013065095
#> [20,25) 0.07404131 0.03585596 0.02136306 0.02023430 0.023415939 0.020806210
#> [25,30) 0.11643632 0.07232439 0.03346576 0.02082582 0.021434648 0.025562252
#> [30,35) 0.08961793 0.10141111 0.06173339 0.03009494 0.020139311 0.022347072
#> [35,40) 0.04191584 0.07868841 0.08660178 0.05428119 0.027337520 0.017561639
#> [40,45) 0.02308379 0.03913356 0.06875047 0.07587258 0.048303122 0.019685451
#> [45,50) 0.03268193 0.02420769 0.03701508 0.06491465 0.074363576 0.036814324
#> [50,55) 0.09198726 0.03626280 0.02434448 0.03792181 0.070995970 0.068917257
#> [55,60) 0.17397838 0.10354858 0.03855302 0.02690534 0.044644614 0.085018974
#> [60,65) 0.09575775 0.18753298 0.11014691 0.04225231 0.030300572 0.061765927
#> [65,70) 0.02830701 0.08737373 0.17330705 0.10103337 0.037884774 0.030423337
#> [70,75) 0.01347933 0.02283150 0.06877202 0.12972522 0.071540580 0.020724591
#> [75,80) 0.01473126 0.01077391 0.01696122 0.04705206 0.083222204 0.029838223
#> [80,Inf) 0.03340941 0.02614652 0.01621339 0.01622475 0.035517099 0.056441839
str(ngm_fairfield)
#> List of 5
#> $ home : 'conmat_age_matrix' num [1:17, 1:17] 0.0498 0.0532 0.0329 0.0288 0.0638 ...
#> ..- attr(*, "age_breaks")= num [1:18] 0 5 10 15 20 25 30 35 40 45 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> $ school: 'conmat_age_matrix' num [1:17, 1:17] 0.02341 0.01177 0.00246 0.00158 0.00313 ...
#> ..- attr(*, "age_breaks")= num [1:18] 0 5 10 15 20 25 30 35 40 45 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> $ work : 'conmat_age_matrix' num [1:17, 1:17] 7.31e-05 1.49e-04 1.37e-04 1.85e-04 5.64e-04 ...
#> ..- attr(*, "age_breaks")= num [1:18] 0 5 10 15 20 25 30 35 40 45 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> $ other : 'conmat_age_matrix' num [1:17, 1:17] 0.01962 0.01314 0.00588 0.00451 0.00824 ...
#> ..- attr(*, "age_breaks")= num [1:18] 0 5 10 15 20 25 30 35 40 45 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> $ all : 'conmat_age_matrix' num [1:17, 1:17] 0.0929 0.0782 0.0414 0.0351 0.0758 ...
#> ..- attr(*, "age_breaks")= num [1:18] 0 5 10 15 20 25 30 35 40 45 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> .. ..$ : chr [1:17] "[0,5)" "[5,10)" "[10,15)" "[15,20)" ...
#> - attr(*, "raw_eigenvalue")= num 3.08
#> - attr(*, "scaling")= num 0.487
#> - attr(*, "age_breaks")= num [1:18] 0 5 10 15 20 25 30 35 40 45 ...
#> - attr(*, "class")= chr [1:2] "ngm_setting_matrix" "list"It is important to understand the effect of vaccination on the next
generation of infections. We can use apply_vaccination() to
return the percentage reduction in acquisition and transmission in each
age group.
It takes two key arguments:
The vaccination effect could look like the following:
vaccination_effect_example_data
#> # A tibble: 17 × 4
#> age_band coverage acquisition transmission
#> <chr> <dbl> <dbl> <dbl>
#> 1 0-4 0 0 0
#> 2 5-11 0.782 0.583 0.254
#> 3 12-15 0.997 0.631 0.295
#> 4 16-19 0.965 0.786 0.469
#> 5 20-24 0.861 0.774 0.453
#> 6 25-29 0.997 0.778 0.458
#> 7 30-34 0.998 0.803 0.493
#> 8 35-39 0.998 0.829 0.533
#> 9 40-44 0.999 0.841 0.551
#> 10 45-49 0.993 0.847 0.562
#> 11 50-54 0.999 0.857 0.579
#> 12 55-59 0.996 0.864 0.591
#> 13 60-64 0.998 0.858 0.581
#> 14 65-69 0.999 0.864 0.591
#> 15 70-74 0.999 0.867 0.597
#> 16 75-79 0.999 0.866 0.595
#> 17 80+ 0.999 0.844 0.556Each row contains information, for each age band:
Then you need to specify the columns in the vaccination effect data frame related to coverage, acquisition, and transmission.
# Apply vaccination effect to next generation matrices
ngm_nsw_vacc <- apply_vaccination(
ngm = ngm_fairfield,
data = vaccination_effect_example_data,
coverage_col = coverage,
acquisition_col = acquisition,
transmission_col = transmission
)
ngm_nsw_vacc
#>
#> ── Vaccination Setting Matrices ────────────────────────────────────────────────
#> A list of matrices, each <matrix> containing the adjusted number of newly
#> infected individuals for age groups. These numbers have been adjusted based on
#> proposed vaccination rates in age groups
#> There are 17 age breaks, ranging 0-80+ years, with a regular 5 year interval
#> • home: a 17x17 <matrix>
#> • school: a 17x17 <matrix>
#> • work: a 17x17 <matrix>
#> • other: a 17x17 <matrix>
#> • all: a 17x17 <matrix>
#> ℹ Access each <matrix> with `x$name`
#> ℹ e.g., `x$home`In the examples so far we have focussed on using
extrapolate_polymod to fit the contact model - this is very
useful because it doesn’t involve many lines of code to fit:
fairfield <- abs_age_lga("Fairfield (C)")
age_breaks_0_80_plus <- c(seq(0, 80, by = 5), Inf)
synthetic_fairfield_5y <- extrapolate_polymod(
population = fairfield,
age_breaks = age_breaks_0_80_plus
)It also fits quite quickly, since it uses a pre-computed model,
polymod_setting_models, (See
?polymod_setting_models for more details).
Under the hood of extrapolate_polymod, this uses this
already fit model for each setting (home, work, school, other), and then
predicts using that model, and the provided data, to predict the new
contact rates.
So the process is:
Let’s show each step and unpack them.
First let’s create a model that predicts contact rate for each setting:
polymod_setting_data <- get_polymod_setting_data()
polymod_population <- get_polymod_population()
contact_setting_model_not_sym <- fit_setting_contacts(
contact_data_list = polymod_setting_data,
population = polymod_population,
symmetrical = FALSE
)Here, we first get the polymod setting data
(polymod_setting_data), and the polymod population
(polymod_population), to create a model for each setting.
These data look like this, if you are interested.
polymod_setting_data
#>
#> ── Setting Data ────────────────────────────────────────────────────────────────
#> A list of <data.frame>s containing the number of contacts between ages in each
#> setting.
#> There are 86 age breaks, ranging 0-90 years, with an irregular year interval,
#> (on average, 1.05 years)
#> • home: a 8,787x5 <data.frame>
#> • work: a 8,787x5 <data.frame>
#> • school: a 8,787x5 <data.frame>
#> • other: a 8,787x5 <data.frame>
#> ℹ Access each <data.frame> with `x$name`
#> ℹ e.g., `x$home`
polymod_population
#> # A tibble: 21 × 2 (conmat_population)
#> - age: lower.age.limit
#> - population: population
#> lower.age.limit population
#> <int> <dbl>
#> 1 0 1898966.
#> 2 5 2017632.
#> 3 10 2192410.
#> 4 15 2369985.
#> 5 20 2467873.
#> 6 25 2484327.
#> 7 30 2649826.
#> 8 35 3043704.
#> 9 40 3117812.
#> 10 45 2879510.
#> # ℹ 11 more rowsWe also specify the symmetrical = FALSE option - by
default this is TRUE. Briefly, this changes some of the terms we use in
creating the model, to use terms that aren’t strictly symmetric.
Now that we’ve got our model, we can predict to our fairfield data, like so:
fairfield_hh <- get_abs_per_capita_household_size(lga = "Fairfield (C)")
fairfield_hh
#> [1] 4.199372
contact_model_pred <- predict_setting_contacts(
population = fairfield,
contact_model = contact_setting_model_not_sym,
age_breaks = age_breaks_0_80_plus,
per_capita_household_size = fairfield_hh
)population is our population to predict tocontact_model is our contact rate model for each
settingage_breaks are our age breaks to predict toper_capita_household_size is the household size for
that population, in our case we have a helper function,
get_abs_per_capita_household_size which works for each LGA
in Australia.alternatively, you can use the estimate_setting_contacts
function to do a similar task:
contact_model_pred_est <- estimate_setting_contacts(
contact_data_list = polymod_setting_data,
survey_population = polymod_population,
prediction_population = fairfield,
age_breaks = age_breaks_0_80_plus,
per_capita_household_size = fairfield_hh,
symmetrical = FALSE
)This is a bit briefer than the two step process, and might be preferable to creating a separate model.