From 6358d930620b6554481d464d63047fb3ef78451d Mon Sep 17 00:00:00 2001 From: KeithJF82 <32104570+KeithJF82@users.noreply.github.com> Date: Wed, 3 Jul 2024 16:43:36 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20mrc-ide/?= =?UTF-8?q?YEP@325057f3186b77f2d248890bc2cba946da11a3f6=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- pkgdown.yml | 2 +- reference/mcmc_prelim_fit.html | 2 -- search.json | 2 +- 3 files changed, 2 insertions(+), 4 deletions(-) diff --git a/pkgdown.yml b/pkgdown.yml index 9edcde2..83062f9 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -13,7 +13,7 @@ articles: CGuideCModelResultsProcess: CGuideCModelResultsProcess.html CGuideDEpiDataGenerate: CGuideDEpiDataGenerate.html CGuideELikelihood: CGuideELikelihood.html -last_built: 2024-06-27T17:39Z +last_built: 2024-07-03T16:43Z urls: reference: https://mrc-ide.github.io/YEP/reference article: https://mrc-ide.github.io/YEP/articles diff --git a/reference/mcmc_prelim_fit.html b/reference/mcmc_prelim_fit.html index a06c918..ba37b8f 100644 --- a/reference/mcmc_prelim_fit.html +++ b/reference/mcmc_prelim_fit.html @@ -133,8 +133,6 @@

Arguments -If type = "flat", prior probability is zero if log parameter values in designated ranges log_params_min and log_params_max, - -Inf otherwise; log_params_min and log_params_max included in prior_settings as vectors of same length as log_params_ini
If type = "norm", prior probability is given by dnorm calculation on parameter values with settings based on vectors of values in prior_settings:
norm_params_mean and norm_params_sd (vectors of mean and standard deviation values applied to log FOI/R0 diff --git a/search.json b/search.json index 169ab15..3173627 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://mrc-ide.github.io/YEP/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Keith Fraser. Author, maintainer.","code":""},{"path":"https://mrc-ide.github.io/YEP/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Fraser K (2024). YEP: Yellow Fever Epidemic Prediction. R package version 0.2.0, https://mrc-ide.github.io/YEP/.","code":"@Manual{, title = {YEP: Yellow Fever Epidemic Prediction}, author = {Keith Fraser}, year = {2024}, note = {R package version 0.2.0}, url = {https://mrc-ide.github.io/YEP/}, }"},{"path":"https://mrc-ide.github.io/YEP/index.html","id":"yep---yellow-fever-epidemic-prevention","dir":"","previous_headings":"","what":"Yellow Fever Epidemic Prediction","title":"Yellow Fever Epidemic Prediction","text":"package running dynamic SEIRV model yellow fever, creating input data processing output data. package can also used : -Generate datasets based existing yellow fever epidemiological datasets (annual reported case data seroprevalence survey results). -Estimate values epidemiological parameters model parameters including vaccine efficacy case reporting rates, based epidemiological data yellow fever burden one regions. -Generate yellow fever burden data multiple countries based approach used Vaccine Impact Modelling Consortium. Additional packages use package: YEPaux: Auxiliary functions processing model output parameter estimation results YellowFeverDynamics: Alternate versions basic model additional functions outbreak risk estimation response modelling","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Generate_Dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate_Dataset — Generate_Dataset","title":"Generate_Dataset — Generate_Dataset","text":"Generate annual serological /case/death data","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Generate_Dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate_Dataset — Generate_Dataset","text":"","code":"Generate_Dataset( input_data = list(), FOI_values = c(), R0_values = c(), sero_template = NULL, case_template = NULL, vaccine_efficacy = 1, p_severe_inf = 0.12, p_death_severe_inf = 0.39, p_rep_severe = 1, p_rep_death = 1, mode_start = 1, start_SEIRV = NULL, dt = 1, n_reps = 1, deterministic = FALSE, mode_parallel = FALSE, cluster = NULL, output_frame = FALSE )"},{"path":"https://mrc-ide.github.io/YEP/reference/Generate_Dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate_Dataset — Generate_Dataset","text":"input_data List population vaccination data multiple regions standard format [TBA] FOI_values Vector values force infection due spillover sylvatic reservoir R0_values Vector values basic reproduction number human-human transmission sero_template Seroprevalence data template - data frame region, year, minimum/maximum age, vc_factor [TBA] number samples case_template Annual reported case/death data template - data frame region year vaccine_efficacy Fractional vaccine efficacy p_severe_inf Probability infection severe p_death_severe_inf Probability severe infection resulting death p_rep_severe Probability reporting severe non-fatal infection p_rep_death Probability reporting fatal infection mode_start Flag indicating set initial population immunity level addition vaccination mode_start=0, vaccinated individuals mode_start=1, shift non-vaccinated individuals recovered give herd immunity (uniform age, R0 based ) mode_start=2, use SEIRV input list previous run(s) mode_start=3, shift non-vaccinated individuals recovered give herd immunity (stratified age) start_SEIRV SEIRV data end previous run use input (list datasets, one per region) dt Time increment days use model (either 1.0, 2.5 5.0 days) n_reps number stochastic repetitions deterministic TRUE/FALSE - set model run deterministic mode TRUE mode_parallel TRUE/FALSE - set model run parallel using cluster TRUE cluster Cluster threads use mode_parallel=TRUE output_frame TRUE/FALSE - indicate whether output complete data frame results template format (TRUE) calculated values (FALSE) '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Generate_Dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate_Dataset — Generate_Dataset","text":"function used generate annual serological /case/death data based templates; normally used single_posterior_calc() function. [TBA - Explanation breakdown regions model set lengths FOI_values R0_values]","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/MCMC.html","id":null,"dir":"Reference","previous_headings":"","what":"MCMC — MCMC","title":"MCMC — MCMC","text":"Combined MCMC Multi-Region - series MCMC iterations one regions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/MCMC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MCMC — MCMC","text":"","code":"MCMC( log_params_ini = c(), input_data = list(), obs_sero_data = NULL, obs_case_data = NULL, filename_prefix = \"Chain\", Niter = 1, mode_start = 0, prior_settings = list(type = \"zero\"), dt = 1, n_reps = 1, enviro_data = list(), p_severe_inf = 0.12, p_death_severe_inf = 0.39, add_values = list(vaccine_efficacy = 1, p_rep_severe = 1, p_rep_death = 1, m_FOI_Brazil = 1), deterministic = FALSE, mode_parallel = FALSE, cluster = NULL )"},{"path":"https://mrc-ide.github.io/YEP/reference/MCMC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MCMC — MCMC","text":"log_params_ini Initial values parameters estimated. always log() values actual parameters, ordered follows: 1) number environmental coefficients used calculate spillover force infection values environmental covariates equal number environmental covariates listed enviro_data frame. Values order columns environmental data frame. 2) number environmental coefficients used calculate basic reproduction number values environmental covariates equal number environmental covariates listed enviro_data frame. Values order columns environmental data frame. 3) Values additional parameters (reported vaccination effectiveness vaccine_efficacy, severe case reporting probability p_rep_severe, fatal case reporting probability p_rep_death, Brazil spillover FOI multiplier m_FOI_Brazil); estimated, order vaccine_efficacy->p_rep_severe->p_rep_death->m_FOI_Brazil. parameters estimated, values separately supplied function (see add_values ) set NA. input_data List population vaccination data multiple regions (created using data input creation code usually loaded RDS file) obs_sero_data Seroprevalence data comparison, region, year & age group, format . samples/. positives obs_case_data Annual reported case/death data comparison, region year, format . cases/. deaths filename_prefix Prefix names output files; function outputs CSV file every 10,000 iterations name format: \"(filename_prefix)XX.csv\", e.g. Chain00.csv Niter Total number iterations run mode_start Flag indicating set initial population immunity level addition vaccination mode_start = 0, vaccinated individuals mode_start = 1, shift non-vaccinated individuals recovered give herd immunity (uniform age, R0 based ) mode_start = 3, shift non-vaccinated individuals recovered give herd immunity (stratified age) prior_settings List containing settings priors: must contain text named \"type\": type = \"zero\", prior probability always zero type = \"flat\", prior probability zero log parameter values designated ranges log_params_min log_params_max, -Inf otherwise; log_params_min log_params_max included prior_settings vectors length log_params_ini type = \"norm\", prior probability given dnorm calculation parameter values settings based vectors values prior_settings: norm_params_mean norm_params_sd (vectors mean standard deviation values applied log FOI/R0 parameters actual values additional parameters) + FOI_mean + FOI_sd (mean + standard deviation computed FOI, single values) + R0_mean + R0_sd (mean + standard deviation computed R0, single values) dt time increment days (must 1 5) n_reps Number times repeat calculations get average likelihood iteration enviro_data Data frame values environmental covariates (columns) region (rows) p_severe_inf Probability infection severe p_death_severe_inf Probability severe infection resulting death add_values List parameters addition governing FOI/R0, either giving fixed value giving NA indicate part fitted parameter set vaccine_efficacy Vaccine efficacy (proportion reported vaccinations causing immunity) (must present) p_rep_severe Probability observation severe infection p_rep_death Probability observation death m_FOI_Brazil Multiplier spillover FOI Brazil regions (relevant regions Brazil considered) deterministic TRUE/FALSE - set model run deterministic mode TRUE mode_parallel TRUE/FALSE - indicate whether use parallel processing supplied cluster speed cluster Cluster threads use mode_parallel = TRUE '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/MCMC.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MCMC — MCMC","text":"master function running Markov chain optimize parameters yellow fever model based calculated likelihood observing supplied data given particular set parameters.","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Model_Run.html","id":null,"dir":"Reference","previous_headings":"","what":"Model_Run — Model_Run","title":"Model_Run — Model_Run","text":"Run SEIRV model single region (Model_Run_Multi_Input can used run multiple regions parallel)","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Model_Run.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model_Run — Model_Run","text":"","code":"Model_Run( FOI_spillover = 0, R0 = 1, vacc_data = list(), pop_data = list(), years_data = c(1940:1941), start_SEIRV = list(), output_type = \"full\", year0 = 1940, mode_start = 0, vaccine_efficacy = 1, dt = 1, n_particles = 1, n_threads = 1, deterministic = FALSE )"},{"path":"https://mrc-ide.github.io/YEP/reference/Model_Run.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model_Run — Model_Run","text":"FOI_spillover Force infection due spillover sylvatic reservoir R0 Basic reproduction number urban spread infection vacc_data Projected vaccination-based immunity (assuming vaccine_efficacy = 1) age group year pop_data Population age group year years_data Incremental vector years denoting years save data start_SEIRV SEIRV data end previous run use input output_type Type data output: \"full\" = SEIRVC + FOI steps ages \"case\" = annual total new infections (C) summed across ages \"sero\" = annual SEIRV \"case+sero\" = annual SEIRVC, cases summed across ages \"case_alt\" = annual total new infections combined age \"case_alt2\" = total new infections combined age steps year0 First year population/vaccination data mode_start Flag indicating set initial population immunity level addition vaccination mode_start = 0, vaccinated individuals mode_start = 1, shift non-vaccinated individuals recovered give herd immunity (uniform age, R0 based ) mode_start = 2, use SEIRV input list previous run(s) mode_start = 3, shift non-vaccinated individuals recovered give herd immunity (stratified age) vaccine_efficacy Proportional vaccine efficacy dt Time increment days use model (1.0, 2.5 5.0 days) n_particles number particles use n_threads number threads use deterministic TRUE/FALSE - set model run deterministic mode TRUE '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Model_Run.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Model_Run — Model_Run","text":"Accepts epidemiological + population parameters model settings; runs SEIRV model one region specified time period number particles/threads outputs time-dependent SEIRV values, infection numbers /total force infection values.","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Model_Run_Many_Reps.html","id":null,"dir":"Reference","previous_headings":"","what":"Model_Run_Many_Reps — Model_Run_Many_Reps","title":"Model_Run_Many_Reps — Model_Run_Many_Reps","text":"Run SEIRV model single region large number repetitions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Model_Run_Many_Reps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model_Run_Many_Reps — Model_Run_Many_Reps","text":"","code":"Model_Run_Many_Reps( FOI_spillover = 0, R0 = 1, vacc_data = list(), pop_data = list(), years_data = c(1940:1941), start_SEIRV = list(), output_type = \"full\", year0 = 1940, mode_start = 0, vaccine_efficacy = 1, dt = 1, n_reps = 1, division = 10 )"},{"path":"https://mrc-ide.github.io/YEP/reference/Model_Run_Many_Reps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model_Run_Many_Reps — Model_Run_Many_Reps","text":"FOI_spillover Force infection due spillover sylvatic reservoir R0 Basic reproduction number urban spread infection vacc_data Projected vaccination-based immunity (assuming vaccine_efficacy = 1) age group year pop_data Population age group year years_data Incremental vector years denoting years save data start_SEIRV SEIRV data end previous run use input output_type Type data output: \"full\" = SEIRVC + FOI steps ages \"case\" = annual total new infections (C) summed across ages \"sero\" = annual SEIRV \"case+sero\" = annual SEIRVC, C summed across ages \"case_alt\" = annual total new infections combined age \"case_alt2\" = total new infections combined age steps year0 First year population/vaccination data mode_start Flag indicating set initial population immunity level addition vaccination mode_start = 0, vaccinated individuals mode_start = 1, shift non-vaccinated individuals recovered give herd immunity (uniform age, R0 based ) mode_start = 2, use SEIRV input list previous run(s) mode_start = 3, shift non-vaccinated individuals recovered give herd immunity (stratified age) vaccine_efficacy Proportional vaccine efficacy dt Time increment days use model (1.0, 2.5 5.0 days) n_reps Number repetitions (used set number particles threads) division Number particles/threads run one go (20) '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Model_Run_Many_Reps.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Model_Run_Many_Reps — Model_Run_Many_Reps","text":"Accepts epidemiological + population parameters model settings; runs SEIRV model one region specified time period number repetitions outputs time-dependent SEIRV values, infection numbers /total force infection values. Variation Model_Run() used running large number repetitions (>20).","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_calculate.html","id":null,"dir":"Reference","previous_headings":"","what":"case_data_calculate — case_data_calculate","title":"case_data_calculate — case_data_calculate","text":"Calculate reported case data SEIRV model output","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_calculate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"case_data_calculate — case_data_calculate","text":"","code":"case_data_calculate( model_data = list(), n_p = 1, p_severe_inf = 0.12, p_death_severe_inf = 0.39, p_rep_severe = 1, p_rep_death = 1, output_type = \"annual\", deterministic = FALSE )"},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_calculate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"case_data_calculate — case_data_calculate","text":"model_data SEIRV output Model_Run similar functions n_p Particle select model_data p_severe_inf Probability infection severe p_death_severe_inf Probability severe infection resulting death p_rep_severe Probability reporting severe non-fatal infection p_rep_death Probability reporting fatal infection output_type Type output produce: \"annual\" - Total reported cases reported deaths year \"pts\" - Total reported cases reported deaths every time point, summed age groups \"full\" - Reported cases reported deaths every time point age group deterministic Indicates whether calculate results deterministically (TRUE) stochastically (FALSE) '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_calculate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"case_data_calculate — case_data_calculate","text":"[TBA]","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_calculate_multi.html","id":null,"dir":"Reference","previous_headings":"","what":"case_data_calculate_multi — case_data_calculate_multi","title":"case_data_calculate_multi — case_data_calculate_multi","text":"Calculate reported case data SEIRV model output across multiple repetitions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_calculate_multi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"case_data_calculate_multi — case_data_calculate_multi","text":"","code":"case_data_calculate_multi( model_data = list(), p_severe_inf = 0.12, p_death_severe_inf = 0.39, p_rep_severe = 1, p_rep_death = 1, output_type = \"annual\", deterministic = FALSE )"},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_calculate_multi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"case_data_calculate_multi — case_data_calculate_multi","text":"model_data SEIRV output Model_Run similar functions p_severe_inf Probability infection severe p_death_severe_inf Probability severe infection resulting death p_rep_severe Probability reporting severe non-fatal infection p_rep_death Probability reporting fatal infection output_type Type output produce: \"annual\" - Total reported cases reported deaths year \"pts\" - Total reported cases reported deaths every time point, summed age groups \"full\" - Reported cases reported deaths every time point age group deterministic Indicates whether calculate results deterministically (TRUE) stochastically (FALSE) '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_calculate_multi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"case_data_calculate_multi — case_data_calculate_multi","text":"[TBA]","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_compare.html","id":null,"dir":"Reference","previous_headings":"","what":"case_data_compare — case_data_compare","title":"case_data_compare — case_data_compare","text":"Compare modelled observed case death data using negative binomial function","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_compare.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"case_data_compare — case_data_compare","text":"","code":"case_data_compare(model_values, obs_values)"},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_compare.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"case_data_compare — case_data_compare","text":"model_values Modelled reported case death values obs_values Observed template case death values '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_compare.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"case_data_compare — case_data_compare","text":"Compares modelled data (dataset generation functions) reported cases deaths per year observed data, calculating logarithmiclikelihood observing latter given former, using negative binomial formula.","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/create_input_data.html","id":null,"dir":"Reference","previous_headings":"","what":"create_input_data — create_input_data","title":"create_input_data — create_input_data","text":"Creates input data set correct format use functions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/create_input_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"create_input_data — create_input_data","text":"","code":"create_input_data( vacc_data = list(), pop_data = list(), regions = c(), years = c() )"},{"path":"https://mrc-ide.github.io/YEP/reference/create_input_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"create_input_data — create_input_data","text":"vacc_data Data frame containing vaccination coverage data region column 1, year column 2 coverage values age remaining columns pop_data Data frame containing population data region column 1, year column 2 population values age remaining columns regions Vector regions extract data vacc_data pop_data (alphabetical order) years Vector years extract data vacc_data pop_data '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/create_input_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"create_input_data — create_input_data","text":"Takes vaccination population data data frames (columns age columns showing region year row), extracts number age groups (verifying data frame), extracts data specified regions years, creates list format used functions (vectors region names, years age groups, 3-dimensional arrays vaccination population data).","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/create_param_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"create_param_labels — create_param_labels","title":"create_param_labels — create_param_labels","text":"Apply names parameters set used data matching parameter fitting","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/create_param_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"create_param_labels — create_param_labels","text":"","code":"create_param_labels(enviro_data = NULL, extra_estimated_params = c(\"vacc_eff\"))"},{"path":"https://mrc-ide.github.io/YEP/reference/create_param_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"create_param_labels — create_param_labels","text":"enviro_data Environmental data frame, containing relevant environmental covariate values regions interest extra_estimated_params Vector strings listing variable parameters besides ones determining FOI/R0 (may include vaccine efficacy /infection/death reporting probabilities /Brazil FOI adjustment factor)","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/create_param_labels.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"create_param_labels — create_param_labels","text":"Takes environmental covariate data along names additional parameters (vaccine efficacy reporting probabilities) generates list names parameter set use input fitting functions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/imm_fraction_function.html","id":null,"dir":"Reference","previous_headings":"","what":"imm_fraction_function — imm_fraction_function","title":"imm_fraction_function — imm_fraction_function","text":"Function estimate notional FOI herd immunity based R0 population age distribution","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/imm_fraction_function.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"imm_fraction_function — imm_fraction_function","text":"","code":"imm_fraction_function( log_lambda = -4, R0 = 1, ages = c(0:100), pop_fraction = rep(1/101, 101) )"},{"path":"https://mrc-ide.github.io/YEP/reference/imm_fraction_function.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"imm_fraction_function — imm_fraction_function","text":"log_lambda Natural logarithm force infection R0 Basic reproduction number ages List age values pop_fraction Population age group proportion total '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/imm_fraction_function.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"imm_fraction_function — imm_fraction_function","text":"[TBA]","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_check.html","id":null,"dir":"Reference","previous_headings":"","what":"input_data_check — input_data_check","title":"input_data_check — input_data_check","text":"Check input data correctly formatted","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"input_data_check — input_data_check","text":"","code":"input_data_check(input_data = list())"},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"input_data_check — input_data_check","text":"input_data List population vaccination data multiple regions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_check.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"input_data_check — input_data_check","text":"function takes list input data use functions checks correctly formatted, including containing necessary elements years ages sequence","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_process.html","id":null,"dir":"Reference","previous_headings":"","what":"input_data_process — input_data_process","title":"input_data_process — input_data_process","text":"Cross-reference input data serological /annual case/death data comparison","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_process.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"input_data_process — input_data_process","text":"","code":"input_data_process( input_data = list(), obs_sero_data = NULL, obs_case_data = NULL )"},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_process.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"input_data_process — input_data_process","text":"input_data List population vaccination data multiple regions (created using create_input_data() function usually loaded RDS file) obs_sero_data Seroprevalence data comparison, region, year & age group, format . samples/. positives obs_case_data Annual reported case/death data comparison, region year, format . cases/. deaths","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_process.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"input_data_process — input_data_process","text":"function, used prepare input data functions used calculate likelihood observed data, amends list population vaccination data used input functions, cross-referencing seroprevalence /case data adding connection information.","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_truncate.html","id":null,"dir":"Reference","previous_headings":"","what":"input_data_truncate — input_data_truncate","title":"input_data_truncate — input_data_truncate","text":"Truncate input data list shorter set regions /years","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_truncate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"input_data_truncate — input_data_truncate","text":"","code":"input_data_truncate(input_data = list(), regions_new = NULL, years_new = NULL)"},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_truncate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"input_data_truncate — input_data_truncate","text":"input_data List population vaccination data multiple regions (created using create_input_data() function usually loaded RDS file) regions_new Vector regions (subset input_data$region_labels) years_new Vector years (subset input_data$years_labels)","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_truncate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"input_data_truncate — input_data_truncate","text":"TBA","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/mcmc_checks.html","id":null,"dir":"Reference","previous_headings":"","what":"mcmc_checks — mcmc_checks","title":"mcmc_checks — mcmc_checks","text":"Perform checks MCMC inputs","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/mcmc_checks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"mcmc_checks — mcmc_checks","text":"","code":"mcmc_checks( log_params_ini = c(), n_regions = 1, prior_settings = list(type = \"zero\"), enviro_data = list(), add_values = list(vaccine_efficacy = 1, p_rep_severe = 1, p_rep_death = 1, m_FOI_Brazil = 1), extra_estimated_params = list() )"},{"path":"https://mrc-ide.github.io/YEP/reference/mcmc_checks.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"mcmc_checks — mcmc_checks","text":"log_params_ini Initial values parameters estimated (natural logarithm actual parameters; see documentation MCMC() function details) n_regions Number regions prior_settings List containing settings priors; see documentation MCMC() function details) enviro_data Data frame values environmental covariates (columns) region (rows) add_values List parameters addition governing FOI/R0, either giving fixed value giving NA indicate part parameter set estimated vaccine_efficacy Vaccine efficacy (proportion reported vaccinations causing immunity) (must present) p_rep_severe Probability observation severe infection p_rep_death Probability observation death m_FOI_Brazil Multiplier spillover FOI Brazil regions (relevant regions Brazil considered) extra_estimated_params Vector names parameters estimated addition governing FOI R0; see add_values","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/mcmc_checks.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"mcmc_checks — mcmc_checks","text":"function, called MCMC(), performs number checks data used fitting ensure proper functionality. verifies number parameters estimated consistent settings certain values outwith sensible boundaries (e.g. probabilities must 0 1).","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/mcmc_prelim_fit.html","id":null,"dir":"Reference","previous_headings":"","what":"mcmc_prelim_fit — mcmc_prelim_fit","title":"mcmc_prelim_fit — mcmc_prelim_fit","text":"Test multiple sets parameters randomly drawn range maximum minimum values order find approximate values giving maximum posterior likelihood","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/mcmc_prelim_fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"mcmc_prelim_fit — mcmc_prelim_fit","text":"","code":"mcmc_prelim_fit( n_iterations = 1, n_param_sets = 1, n_bounds = 1, log_params_min = NULL, log_params_max = NULL, input_data = list(), obs_sero_data = list(), obs_case_data = list(), mode_start = 0, prior_settings = list(type = \"zero\"), dt = 1, n_reps = 1, enviro_data = list(), p_severe_inf = 0.12, p_death_severe_inf = 0.39, add_values = list(vaccine_efficacy = 1, p_rep_severe = 1, p_rep_death = 1, m_FOI_Brazil = 1), deterministic = TRUE, mode_parallel = FALSE, cluster = NULL )"},{"path":"https://mrc-ide.github.io/YEP/reference/mcmc_prelim_fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"mcmc_prelim_fit — mcmc_prelim_fit","text":"n_iterations = Number times run adjust maximum/minimum n_param_sets = Number parameter sets run iteration n_bounds = Number parameter sets (highest likelihood values) take iteration create new maximum/minimum values log_params_min Initial lower limits estimated parameter values (natural logarithm actual limits) log_params_max Initial upper limits estimated parameter values (natural logarithm actual limits) input_data List population vaccination data multiple regions (created using data input creation code usually loaded RDS file) obs_sero_data Seroprevalence data comparison, region, year & age group, format . samples/. positives obs_case_data Annual reported case/death data comparison, region year, format . cases/. deaths mode_start Flag indicating set initial population immunity level addition vaccination mode_start = 0, vaccinated individuals mode_start = 1, shift non-vaccinated individuals recovered give herd immunity (uniform age, R0 based ) mode_start = 3, shift non-vaccinated individuals recovered give herd immunity (stratified age) prior_settings List containing settings priors: must contain text named \"type\": type = \"zero\", prior probability always zero type = \"flat\", prior probability zero log parameter values designated ranges log_params_min log_params_max, -Inf otherwise; log_params_min log_params_max included prior_settings vectors length log_params_ini type = \"norm\", prior probability given dnorm calculation parameter values settings based vectors values prior_settings: norm_params_mean norm_params_sd (vectors mean standard deviation values applied log FOI/R0 parameters actual values additional parameters) + FOI_mean + FOI_sd (mean + standard deviation computed FOI, single values) + R0_mean + R0_sd (mean + standard deviation computed R0, single values) dt time increment days (must 1 5) n_reps Number repetitions enviro_data Data frame values environmental covariates (columns) region (rows) p_severe_inf Probability infection severe p_death_severe_inf Probability severe infection resulting death add_values List parameters addition governing FOI/R0, either giving fixed value giving NA indicate part fitted parameter set vaccine_efficacy Vaccine efficacy (proportion reported vaccinations causing immunity) (must present) p_rep_severe Probability observation severe infection p_rep_death Probability observation death m_FOI_Brazil Multiplier spillover FOI Brazil regions (relevant regions Brazil considered) deterministic TRUE/FALSE - set model run deterministic mode TRUE mode_parallel TRUE/FALSE - indicate whether use parallel processing supplied cluster speed cluster Cluster threads use mode_parallel = TRUE '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/mcmc_prelim_fit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"mcmc_prelim_fit — mcmc_prelim_fit","text":"function used estimate model parameter values giving maximum posterior likelihood; primarily intended used generate initial parameter values Markov Chain Monte Carlo fitting (using mcmc() function).","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/param_calc_enviro.html","id":null,"dir":"Reference","previous_headings":"","what":"param_calc_enviro — param_calc_enviro","title":"param_calc_enviro — param_calc_enviro","text":"Parameter calculation environmental covariates","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/param_calc_enviro.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"param_calc_enviro — param_calc_enviro","text":"","code":"param_calc_enviro(enviro_coeffs = c(), enviro_covar_values = c())"},{"path":"https://mrc-ide.github.io/YEP/reference/param_calc_enviro.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"param_calc_enviro — param_calc_enviro","text":"enviro_coeffs Values environmental coefficients enviro_covar_values Values environmental covariates '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/param_calc_enviro.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"param_calc_enviro — param_calc_enviro","text":"Takes set coefficients environmental covariates covariate values calculates values spillover force infection reproduction number.","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/param_prop_setup.html","id":null,"dir":"Reference","previous_headings":"","what":"param_prop_setup — param_prop_setup","title":"param_prop_setup — param_prop_setup","text":"Set proposed new log parameter values next iteration chain","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/param_prop_setup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"param_prop_setup — param_prop_setup","text":"","code":"param_prop_setup(log_params = c(), chain_cov = 1, adapt = 0)"},{"path":"https://mrc-ide.github.io/YEP/reference/param_prop_setup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"param_prop_setup — param_prop_setup","text":"log_params Previous log parameter values used input chain_cov Covariance calculated previous iterations chain adapt 0/1 flag indicating type covariance use proposition value (TBA) '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/param_prop_setup.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"param_prop_setup — param_prop_setup","text":"Takes current values parameter set used Markov Chain Monte Carlo fitting proposes new values multivariate normal distribution existing values form mean standard deviation based chain covariance (flag \"adapt\" set 1) flat value based number parameters.","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/parameter_setup.html","id":null,"dir":"Reference","previous_headings":"","what":"Parameter setup — parameter_setup","title":"Parameter setup — parameter_setup","text":"Set parameters input model","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/parameter_setup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parameter setup — parameter_setup","text":"","code":"parameter_setup( FOI_spillover = 0, R0 = 1, vacc_data = list(), pop_data = list(), year0 = 1940, years_data = c(1941:1942), mode_start = 0, vaccine_efficacy = 1, start_SEIRV = list(), dt = 1 )"},{"path":"https://mrc-ide.github.io/YEP/reference/parameter_setup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parameter setup — parameter_setup","text":"FOI_spillover Force infection due spillover sylvatic reservoir R0 Reproduction number urban spread infection vacc_data Projected vaccination-based immunity (assuming vaccine_efficacy = 1) age group year pop_data Population age group year year0 First year population/vaccination data years_data Incremental vector years denoting years save data mode_start Flag indicating set initial population immunity level addition vaccination mode_start = 0, vaccinated individuals mode_start = 1, shift non-vaccinated individuals recovered give herd immunity (uniform age, R0 based ) mode_start = 2, use SEIRV input list previous run(s) mode_start = 3, shift non-vaccinated individuals recovered give herd immunity (stratified age) vaccine_efficacy Proportional vaccine efficacy start_SEIRV SEIRV data end previous run use input dt Time increment days use model (either 1.0 5.0 days) '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/parameter_setup.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Parameter setup — parameter_setup","text":"Takes multiple inputs, outputs list use odin SEIRV model.","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/regions_breakdown.html","id":null,"dir":"Reference","previous_headings":"","what":"regions_breakdown — regions_breakdown","title":"regions_breakdown — regions_breakdown","text":"Break regions datasets get list unique regions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/regions_breakdown.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"regions_breakdown — regions_breakdown","text":"","code":"regions_breakdown(region_labels = c())"},{"path":"https://mrc-ide.github.io/YEP/reference/regions_breakdown.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"regions_breakdown — regions_breakdown","text":"region_labels Vector region labels","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/regions_breakdown.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"regions_breakdown — regions_breakdown","text":"Takes vector region labels, potentially including labels contain one region (e.g. labels countrywide data adm1 level regions one label separated comma) produces alphabetically ordered list region labels, breaking comma-separated groups. example, supplying vector labels \"BRA.1_1,BRA.2_1,BRA.3_1\" return vector length 3 - c(\"BRA.1_1\",\"BRA.2_1\",\"BRA.3_1\").","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_calculate.html","id":null,"dir":"Reference","previous_headings":"","what":"sero_calculate — sero_calculate","title":"sero_calculate — sero_calculate","text":"Calculate seroprevalence unvaccinated people modelled data one years one age range","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_calculate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"sero_calculate — sero_calculate","text":"","code":"sero_calculate( age_min = 0, age_max = 101, years = NULL, vc_factor = 0, data = list(), n_p = 1 )"},{"path":"https://mrc-ide.github.io/YEP/reference/sero_calculate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"sero_calculate — sero_calculate","text":"age_min = Minimum age age group age_max = Maximum age age group years = Years calculate average annual seroprevalence vc_factor = Proportion patients tested vaccine status unknown data = Output Basic_Model_Run Full_Model_Run n_p = Particle select data '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_calculate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"sero_calculate — sero_calculate","text":"Takes information minimum maximum ages desired range, year(s) calculate seroprevalence, factor representing proportion patients unknown vaccine status, SEIRV model output data, calculates seroprevalence unvaccinated people specified age range specified year(s).","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_calculate2.html","id":null,"dir":"Reference","previous_headings":"","what":"sero_calculate2 — sero_calculate2","title":"sero_calculate2 — sero_calculate2","text":"Calculate number \"samples\" number \"positives\" modelled data specified age range(s) year(s)","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_calculate2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"sero_calculate2 — sero_calculate2","text":"","code":"sero_calculate2(sero_data = list(), model_data = list(), n_p = 1)"},{"path":"https://mrc-ide.github.io/YEP/reference/sero_calculate2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"sero_calculate2 — sero_calculate2","text":"sero_data Data frame containing years, minimum maximum ages, values vc_factor (proportion people vaccination status unknown) model_data SEIRV output Model_Run similar functions n_p Particle select model_data '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_calculate2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"sero_calculate2 — sero_calculate2","text":"Takes information minimum maximum ages desired range(s), year(s) calculate number \"samples\" (people eligible testing) \"positives\" (people test positive), plus vc_factor (proportion people vaccination status unknown)","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_data_compare.html","id":null,"dir":"Reference","previous_headings":"","what":"sero_data_compare — sero_data_compare","title":"sero_data_compare — sero_data_compare","text":"Take seroprevalence results dataset generation functions, compare comparison observed/template seroprevalence data calculate likelihood","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_data_compare.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"sero_data_compare — sero_data_compare","text":"","code":"sero_data_compare(model_sero_values = c(), obs_sero_data = list())"},{"path":"https://mrc-ide.github.io/YEP/reference/sero_data_compare.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"sero_data_compare — sero_data_compare","text":"model_sero_values Seroprevalence values dataset generation function (. positives/. samples) obs_sero_data Seroprevalence data comparison, year age group, format . samples/. positives '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_data_compare.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"sero_data_compare — sero_data_compare","text":"[TBA]","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/single_posterior_calc.html","id":null,"dir":"Reference","previous_headings":"","what":"single_posterior_calc — single_posterior_calc","title":"single_posterior_calc — single_posterior_calc","text":"Function calculates outputs posterior likelihood observing simulated data","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/single_posterior_calc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"single_posterior_calc — single_posterior_calc","text":"","code":"single_posterior_calc( log_params_prop = c(), input_data = list(), obs_sero_data = NULL, obs_case_data = NULL, ... )"},{"path":"https://mrc-ide.github.io/YEP/reference/single_posterior_calc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"single_posterior_calc — single_posterior_calc","text":"log_params_prop Proposed values parameters estimated (natural logarithm actual parameters) input_data List population vaccination data multiple regions (created using data input creation code usually loaded RDS file), cross-reference tables added using input_data_process MCMC obs_sero_data Seroprevalence data comparison, region, year & age group, format . samples/. positives obs_case_data Annual reported case/death data comparison, region year, format . cases/. deaths ... = Constant parameters/flags/etc. loaded determined mcmc() mcmc_prelim_fit, including mode_start, prior_settings, dt, n_reps, enviro_data, p_severe_inf, p_death_severe_inf, add_values list, extra_estimated_params, deterministic, mode_parallel, cluster","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/single_posterior_calc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"single_posterior_calc — single_posterior_calc","text":"function calculates posterior likelihood observing set observations (across multiple regions data types) given proposed parameter set. [TBA]","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/template_region_xref.html","id":null,"dir":"Reference","previous_headings":"","what":"template_region_xref — template_region_xref","title":"template_region_xref — template_region_xref","text":"Cross-reference template data individual regions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/template_region_xref.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"template_region_xref — template_region_xref","text":"","code":"template_region_xref(template = list(), regions = c())"},{"path":"https://mrc-ide.github.io/YEP/reference/template_region_xref.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"template_region_xref — template_region_xref","text":"template List containing one sets template data (serological data, case data burden data) regions Vector individual regions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/template_region_xref.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"template_region_xref — template_region_xref","text":"Examines template data (serological, case burden) compares vector region names order check lines set template data require model data region(s). example, line set serological data region given \"AGO.1_1,AGO.2_1,AGO.3_1\" compared vector regions c(\"AGO.1_1\",\"AGO.2_1\",\"AGO.3_1\",...), line requires data regions 1, 2 3. function used generating dataset one templates; normally used functions Generate_Dataset, Generate_Sero_Dataset, Generate_Case_Dataset, Generate_VIMC_Burden_Dataset Generate_Multiple_Datasets. returns list containing [TBA].","code":""}] +[{"path":"https://mrc-ide.github.io/YEP/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Keith Fraser. Author, maintainer.","code":""},{"path":"https://mrc-ide.github.io/YEP/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Fraser K (2024). YEP: Yellow Fever Epidemic Prediction. R package version 0.2.0, https://mrc-ide.github.io/YEP/.","code":"@Manual{, title = {YEP: Yellow Fever Epidemic Prediction}, author = {Keith Fraser}, year = {2024}, note = {R package version 0.2.0}, url = {https://mrc-ide.github.io/YEP/}, }"},{"path":"https://mrc-ide.github.io/YEP/index.html","id":"yep---yellow-fever-epidemic-prevention","dir":"","previous_headings":"","what":"Yellow Fever Epidemic Prediction","title":"Yellow Fever Epidemic Prediction","text":"package running dynamic SEIRV model yellow fever, creating input data processing output data. package can also used : -Generate datasets based existing yellow fever epidemiological datasets (annual reported case data seroprevalence survey results). -Estimate values epidemiological parameters model parameters including vaccine efficacy case reporting rates, based epidemiological data yellow fever burden one regions. -Generate yellow fever burden data multiple countries based approach used Vaccine Impact Modelling Consortium. Additional packages use package: YEPaux: Auxiliary functions processing model output parameter estimation results YellowFeverDynamics: Alternate versions basic model additional functions outbreak risk estimation response modelling","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Generate_Dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate_Dataset — Generate_Dataset","title":"Generate_Dataset — Generate_Dataset","text":"Generate annual serological /case/death data","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Generate_Dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate_Dataset — Generate_Dataset","text":"","code":"Generate_Dataset( input_data = list(), FOI_values = c(), R0_values = c(), sero_template = NULL, case_template = NULL, vaccine_efficacy = 1, p_severe_inf = 0.12, p_death_severe_inf = 0.39, p_rep_severe = 1, p_rep_death = 1, mode_start = 1, start_SEIRV = NULL, dt = 1, n_reps = 1, deterministic = FALSE, mode_parallel = FALSE, cluster = NULL, output_frame = FALSE )"},{"path":"https://mrc-ide.github.io/YEP/reference/Generate_Dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate_Dataset — Generate_Dataset","text":"input_data List population vaccination data multiple regions standard format [TBA] FOI_values Vector values force infection due spillover sylvatic reservoir R0_values Vector values basic reproduction number human-human transmission sero_template Seroprevalence data template - data frame region, year, minimum/maximum age, vc_factor [TBA] number samples case_template Annual reported case/death data template - data frame region year vaccine_efficacy Fractional vaccine efficacy p_severe_inf Probability infection severe p_death_severe_inf Probability severe infection resulting death p_rep_severe Probability reporting severe non-fatal infection p_rep_death Probability reporting fatal infection mode_start Flag indicating set initial population immunity level addition vaccination mode_start=0, vaccinated individuals mode_start=1, shift non-vaccinated individuals recovered give herd immunity (uniform age, R0 based ) mode_start=2, use SEIRV input list previous run(s) mode_start=3, shift non-vaccinated individuals recovered give herd immunity (stratified age) start_SEIRV SEIRV data end previous run use input (list datasets, one per region) dt Time increment days use model (either 1.0, 2.5 5.0 days) n_reps number stochastic repetitions deterministic TRUE/FALSE - set model run deterministic mode TRUE mode_parallel TRUE/FALSE - set model run parallel using cluster TRUE cluster Cluster threads use mode_parallel=TRUE output_frame TRUE/FALSE - indicate whether output complete data frame results template format (TRUE) calculated values (FALSE) '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Generate_Dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate_Dataset — Generate_Dataset","text":"function used generate annual serological /case/death data based templates; normally used single_posterior_calc() function. [TBA - Explanation breakdown regions model set lengths FOI_values R0_values]","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/MCMC.html","id":null,"dir":"Reference","previous_headings":"","what":"MCMC — MCMC","title":"MCMC — MCMC","text":"Combined MCMC Multi-Region - series MCMC iterations one regions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/MCMC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MCMC — MCMC","text":"","code":"MCMC( log_params_ini = c(), input_data = list(), obs_sero_data = NULL, obs_case_data = NULL, filename_prefix = \"Chain\", Niter = 1, mode_start = 0, prior_settings = list(type = \"zero\"), dt = 1, n_reps = 1, enviro_data = list(), p_severe_inf = 0.12, p_death_severe_inf = 0.39, add_values = list(vaccine_efficacy = 1, p_rep_severe = 1, p_rep_death = 1, m_FOI_Brazil = 1), deterministic = FALSE, mode_parallel = FALSE, cluster = NULL )"},{"path":"https://mrc-ide.github.io/YEP/reference/MCMC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MCMC — MCMC","text":"log_params_ini Initial values parameters estimated. always log() values actual parameters, ordered follows: 1) number environmental coefficients used calculate spillover force infection values environmental covariates equal number environmental covariates listed enviro_data frame. Values order columns environmental data frame. 2) number environmental coefficients used calculate basic reproduction number values environmental covariates equal number environmental covariates listed enviro_data frame. Values order columns environmental data frame. 3) Values additional parameters (reported vaccination effectiveness vaccine_efficacy, severe case reporting probability p_rep_severe, fatal case reporting probability p_rep_death, Brazil spillover FOI multiplier m_FOI_Brazil); estimated, order vaccine_efficacy->p_rep_severe->p_rep_death->m_FOI_Brazil. parameters estimated, values separately supplied function (see add_values ) set NA. input_data List population vaccination data multiple regions (created using data input creation code usually loaded RDS file) obs_sero_data Seroprevalence data comparison, region, year & age group, format . samples/. positives obs_case_data Annual reported case/death data comparison, region year, format . cases/. deaths filename_prefix Prefix names output files; function outputs CSV file every 10,000 iterations name format: \"(filename_prefix)XX.csv\", e.g. Chain00.csv Niter Total number iterations run mode_start Flag indicating set initial population immunity level addition vaccination mode_start = 0, vaccinated individuals mode_start = 1, shift non-vaccinated individuals recovered give herd immunity (uniform age, R0 based ) mode_start = 3, shift non-vaccinated individuals recovered give herd immunity (stratified age) prior_settings List containing settings priors: must contain text named \"type\": type = \"zero\", prior probability always zero type = \"flat\", prior probability zero log parameter values designated ranges log_params_min log_params_max, -Inf otherwise; log_params_min log_params_max included prior_settings vectors length log_params_ini type = \"norm\", prior probability given dnorm calculation parameter values settings based vectors values prior_settings: norm_params_mean norm_params_sd (vectors mean standard deviation values applied log FOI/R0 parameters actual values additional parameters) + FOI_mean + FOI_sd (mean + standard deviation computed FOI, single values) + R0_mean + R0_sd (mean + standard deviation computed R0, single values) dt time increment days (must 1 5) n_reps Number times repeat calculations get average likelihood iteration enviro_data Data frame values environmental covariates (columns) region (rows) p_severe_inf Probability infection severe p_death_severe_inf Probability severe infection resulting death add_values List parameters addition governing FOI/R0, either giving fixed value giving NA indicate part fitted parameter set vaccine_efficacy Vaccine efficacy (proportion reported vaccinations causing immunity) (must present) p_rep_severe Probability observation severe infection p_rep_death Probability observation death m_FOI_Brazil Multiplier spillover FOI Brazil regions (relevant regions Brazil considered) deterministic TRUE/FALSE - set model run deterministic mode TRUE mode_parallel TRUE/FALSE - indicate whether use parallel processing supplied cluster speed cluster Cluster threads use mode_parallel = TRUE '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/MCMC.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MCMC — MCMC","text":"master function running Markov chain optimize parameters yellow fever model based calculated likelihood observing supplied data given particular set parameters.","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Model_Run.html","id":null,"dir":"Reference","previous_headings":"","what":"Model_Run — Model_Run","title":"Model_Run — Model_Run","text":"Run SEIRV model single region (Model_Run_Multi_Input can used run multiple regions parallel)","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Model_Run.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model_Run — Model_Run","text":"","code":"Model_Run( FOI_spillover = 0, R0 = 1, vacc_data = list(), pop_data = list(), years_data = c(1940:1941), start_SEIRV = list(), output_type = \"full\", year0 = 1940, mode_start = 0, vaccine_efficacy = 1, dt = 1, n_particles = 1, n_threads = 1, deterministic = FALSE )"},{"path":"https://mrc-ide.github.io/YEP/reference/Model_Run.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model_Run — Model_Run","text":"FOI_spillover Force infection due spillover sylvatic reservoir R0 Basic reproduction number urban spread infection vacc_data Projected vaccination-based immunity (assuming vaccine_efficacy = 1) age group year pop_data Population age group year years_data Incremental vector years denoting years save data start_SEIRV SEIRV data end previous run use input output_type Type data output: \"full\" = SEIRVC + FOI steps ages \"case\" = annual total new infections (C) summed across ages \"sero\" = annual SEIRV \"case+sero\" = annual SEIRVC, cases summed across ages \"case_alt\" = annual total new infections combined age \"case_alt2\" = total new infections combined age steps year0 First year population/vaccination data mode_start Flag indicating set initial population immunity level addition vaccination mode_start = 0, vaccinated individuals mode_start = 1, shift non-vaccinated individuals recovered give herd immunity (uniform age, R0 based ) mode_start = 2, use SEIRV input list previous run(s) mode_start = 3, shift non-vaccinated individuals recovered give herd immunity (stratified age) vaccine_efficacy Proportional vaccine efficacy dt Time increment days use model (1.0, 2.5 5.0 days) n_particles number particles use n_threads number threads use deterministic TRUE/FALSE - set model run deterministic mode TRUE '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Model_Run.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Model_Run — Model_Run","text":"Accepts epidemiological + population parameters model settings; runs SEIRV model one region specified time period number particles/threads outputs time-dependent SEIRV values, infection numbers /total force infection values.","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Model_Run_Many_Reps.html","id":null,"dir":"Reference","previous_headings":"","what":"Model_Run_Many_Reps — Model_Run_Many_Reps","title":"Model_Run_Many_Reps — Model_Run_Many_Reps","text":"Run SEIRV model single region large number repetitions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Model_Run_Many_Reps.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model_Run_Many_Reps — Model_Run_Many_Reps","text":"","code":"Model_Run_Many_Reps( FOI_spillover = 0, R0 = 1, vacc_data = list(), pop_data = list(), years_data = c(1940:1941), start_SEIRV = list(), output_type = \"full\", year0 = 1940, mode_start = 0, vaccine_efficacy = 1, dt = 1, n_reps = 1, division = 10 )"},{"path":"https://mrc-ide.github.io/YEP/reference/Model_Run_Many_Reps.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model_Run_Many_Reps — Model_Run_Many_Reps","text":"FOI_spillover Force infection due spillover sylvatic reservoir R0 Basic reproduction number urban spread infection vacc_data Projected vaccination-based immunity (assuming vaccine_efficacy = 1) age group year pop_data Population age group year years_data Incremental vector years denoting years save data start_SEIRV SEIRV data end previous run use input output_type Type data output: \"full\" = SEIRVC + FOI steps ages \"case\" = annual total new infections (C) summed across ages \"sero\" = annual SEIRV \"case+sero\" = annual SEIRVC, C summed across ages \"case_alt\" = annual total new infections combined age \"case_alt2\" = total new infections combined age steps year0 First year population/vaccination data mode_start Flag indicating set initial population immunity level addition vaccination mode_start = 0, vaccinated individuals mode_start = 1, shift non-vaccinated individuals recovered give herd immunity (uniform age, R0 based ) mode_start = 2, use SEIRV input list previous run(s) mode_start = 3, shift non-vaccinated individuals recovered give herd immunity (stratified age) vaccine_efficacy Proportional vaccine efficacy dt Time increment days use model (1.0, 2.5 5.0 days) n_reps Number repetitions (used set number particles threads) division Number particles/threads run one go (20) '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/Model_Run_Many_Reps.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Model_Run_Many_Reps — Model_Run_Many_Reps","text":"Accepts epidemiological + population parameters model settings; runs SEIRV model one region specified time period number repetitions outputs time-dependent SEIRV values, infection numbers /total force infection values. Variation Model_Run() used running large number repetitions (>20).","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_calculate.html","id":null,"dir":"Reference","previous_headings":"","what":"case_data_calculate — case_data_calculate","title":"case_data_calculate — case_data_calculate","text":"Calculate reported case data SEIRV model output","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_calculate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"case_data_calculate — case_data_calculate","text":"","code":"case_data_calculate( model_data = list(), n_p = 1, p_severe_inf = 0.12, p_death_severe_inf = 0.39, p_rep_severe = 1, p_rep_death = 1, output_type = \"annual\", deterministic = FALSE )"},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_calculate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"case_data_calculate — case_data_calculate","text":"model_data SEIRV output Model_Run similar functions n_p Particle select model_data p_severe_inf Probability infection severe p_death_severe_inf Probability severe infection resulting death p_rep_severe Probability reporting severe non-fatal infection p_rep_death Probability reporting fatal infection output_type Type output produce: \"annual\" - Total reported cases reported deaths year \"pts\" - Total reported cases reported deaths every time point, summed age groups \"full\" - Reported cases reported deaths every time point age group deterministic Indicates whether calculate results deterministically (TRUE) stochastically (FALSE) '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_calculate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"case_data_calculate — case_data_calculate","text":"[TBA]","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_calculate_multi.html","id":null,"dir":"Reference","previous_headings":"","what":"case_data_calculate_multi — case_data_calculate_multi","title":"case_data_calculate_multi — case_data_calculate_multi","text":"Calculate reported case data SEIRV model output across multiple repetitions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_calculate_multi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"case_data_calculate_multi — case_data_calculate_multi","text":"","code":"case_data_calculate_multi( model_data = list(), p_severe_inf = 0.12, p_death_severe_inf = 0.39, p_rep_severe = 1, p_rep_death = 1, output_type = \"annual\", deterministic = FALSE )"},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_calculate_multi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"case_data_calculate_multi — case_data_calculate_multi","text":"model_data SEIRV output Model_Run similar functions p_severe_inf Probability infection severe p_death_severe_inf Probability severe infection resulting death p_rep_severe Probability reporting severe non-fatal infection p_rep_death Probability reporting fatal infection output_type Type output produce: \"annual\" - Total reported cases reported deaths year \"pts\" - Total reported cases reported deaths every time point, summed age groups \"full\" - Reported cases reported deaths every time point age group deterministic Indicates whether calculate results deterministically (TRUE) stochastically (FALSE) '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_calculate_multi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"case_data_calculate_multi — case_data_calculate_multi","text":"[TBA]","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_compare.html","id":null,"dir":"Reference","previous_headings":"","what":"case_data_compare — case_data_compare","title":"case_data_compare — case_data_compare","text":"Compare modelled observed case death data using negative binomial function","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_compare.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"case_data_compare — case_data_compare","text":"","code":"case_data_compare(model_values, obs_values)"},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_compare.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"case_data_compare — case_data_compare","text":"model_values Modelled reported case death values obs_values Observed template case death values '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/case_data_compare.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"case_data_compare — case_data_compare","text":"Compares modelled data (dataset generation functions) reported cases deaths per year observed data, calculating logarithmiclikelihood observing latter given former, using negative binomial formula.","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/create_input_data.html","id":null,"dir":"Reference","previous_headings":"","what":"create_input_data — create_input_data","title":"create_input_data — create_input_data","text":"Creates input data set correct format use functions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/create_input_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"create_input_data — create_input_data","text":"","code":"create_input_data( vacc_data = list(), pop_data = list(), regions = c(), years = c() )"},{"path":"https://mrc-ide.github.io/YEP/reference/create_input_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"create_input_data — create_input_data","text":"vacc_data Data frame containing vaccination coverage data region column 1, year column 2 coverage values age remaining columns pop_data Data frame containing population data region column 1, year column 2 population values age remaining columns regions Vector regions extract data vacc_data pop_data (alphabetical order) years Vector years extract data vacc_data pop_data '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/create_input_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"create_input_data — create_input_data","text":"Takes vaccination population data data frames (columns age columns showing region year row), extracts number age groups (verifying data frame), extracts data specified regions years, creates list format used functions (vectors region names, years age groups, 3-dimensional arrays vaccination population data).","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/create_param_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"create_param_labels — create_param_labels","title":"create_param_labels — create_param_labels","text":"Apply names parameters set used data matching parameter fitting","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/create_param_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"create_param_labels — create_param_labels","text":"","code":"create_param_labels(enviro_data = NULL, extra_estimated_params = c(\"vacc_eff\"))"},{"path":"https://mrc-ide.github.io/YEP/reference/create_param_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"create_param_labels — create_param_labels","text":"enviro_data Environmental data frame, containing relevant environmental covariate values regions interest extra_estimated_params Vector strings listing variable parameters besides ones determining FOI/R0 (may include vaccine efficacy /infection/death reporting probabilities /Brazil FOI adjustment factor)","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/create_param_labels.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"create_param_labels — create_param_labels","text":"Takes environmental covariate data along names additional parameters (vaccine efficacy reporting probabilities) generates list names parameter set use input fitting functions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/imm_fraction_function.html","id":null,"dir":"Reference","previous_headings":"","what":"imm_fraction_function — imm_fraction_function","title":"imm_fraction_function — imm_fraction_function","text":"Function estimate notional FOI herd immunity based R0 population age distribution","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/imm_fraction_function.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"imm_fraction_function — imm_fraction_function","text":"","code":"imm_fraction_function( log_lambda = -4, R0 = 1, ages = c(0:100), pop_fraction = rep(1/101, 101) )"},{"path":"https://mrc-ide.github.io/YEP/reference/imm_fraction_function.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"imm_fraction_function — imm_fraction_function","text":"log_lambda Natural logarithm force infection R0 Basic reproduction number ages List age values pop_fraction Population age group proportion total '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/imm_fraction_function.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"imm_fraction_function — imm_fraction_function","text":"[TBA]","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_check.html","id":null,"dir":"Reference","previous_headings":"","what":"input_data_check — input_data_check","title":"input_data_check — input_data_check","text":"Check input data correctly formatted","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_check.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"input_data_check — input_data_check","text":"","code":"input_data_check(input_data = list())"},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_check.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"input_data_check — input_data_check","text":"input_data List population vaccination data multiple regions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_check.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"input_data_check — input_data_check","text":"function takes list input data use functions checks correctly formatted, including containing necessary elements years ages sequence","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_process.html","id":null,"dir":"Reference","previous_headings":"","what":"input_data_process — input_data_process","title":"input_data_process — input_data_process","text":"Cross-reference input data serological /annual case/death data comparison","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_process.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"input_data_process — input_data_process","text":"","code":"input_data_process( input_data = list(), obs_sero_data = NULL, obs_case_data = NULL )"},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_process.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"input_data_process — input_data_process","text":"input_data List population vaccination data multiple regions (created using create_input_data() function usually loaded RDS file) obs_sero_data Seroprevalence data comparison, region, year & age group, format . samples/. positives obs_case_data Annual reported case/death data comparison, region year, format . cases/. deaths","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_process.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"input_data_process — input_data_process","text":"function, used prepare input data functions used calculate likelihood observed data, amends list population vaccination data used input functions, cross-referencing seroprevalence /case data adding connection information.","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_truncate.html","id":null,"dir":"Reference","previous_headings":"","what":"input_data_truncate — input_data_truncate","title":"input_data_truncate — input_data_truncate","text":"Truncate input data list shorter set regions /years","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_truncate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"input_data_truncate — input_data_truncate","text":"","code":"input_data_truncate(input_data = list(), regions_new = NULL, years_new = NULL)"},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_truncate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"input_data_truncate — input_data_truncate","text":"input_data List population vaccination data multiple regions (created using create_input_data() function usually loaded RDS file) regions_new Vector regions (subset input_data$region_labels) years_new Vector years (subset input_data$years_labels)","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/input_data_truncate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"input_data_truncate — input_data_truncate","text":"TBA","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/mcmc_checks.html","id":null,"dir":"Reference","previous_headings":"","what":"mcmc_checks — mcmc_checks","title":"mcmc_checks — mcmc_checks","text":"Perform checks MCMC inputs","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/mcmc_checks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"mcmc_checks — mcmc_checks","text":"","code":"mcmc_checks( log_params_ini = c(), n_regions = 1, prior_settings = list(type = \"zero\"), enviro_data = list(), add_values = list(vaccine_efficacy = 1, p_rep_severe = 1, p_rep_death = 1, m_FOI_Brazil = 1), extra_estimated_params = list() )"},{"path":"https://mrc-ide.github.io/YEP/reference/mcmc_checks.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"mcmc_checks — mcmc_checks","text":"log_params_ini Initial values parameters estimated (natural logarithm actual parameters; see documentation MCMC() function details) n_regions Number regions prior_settings List containing settings priors; see documentation MCMC() function details) enviro_data Data frame values environmental covariates (columns) region (rows) add_values List parameters addition governing FOI/R0, either giving fixed value giving NA indicate part parameter set estimated vaccine_efficacy Vaccine efficacy (proportion reported vaccinations causing immunity) (must present) p_rep_severe Probability observation severe infection p_rep_death Probability observation death m_FOI_Brazil Multiplier spillover FOI Brazil regions (relevant regions Brazil considered) extra_estimated_params Vector names parameters estimated addition governing FOI R0; see add_values","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/mcmc_checks.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"mcmc_checks — mcmc_checks","text":"function, called MCMC(), performs number checks data used fitting ensure proper functionality. verifies number parameters estimated consistent settings certain values outwith sensible boundaries (e.g. probabilities must 0 1).","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/mcmc_prelim_fit.html","id":null,"dir":"Reference","previous_headings":"","what":"mcmc_prelim_fit — mcmc_prelim_fit","title":"mcmc_prelim_fit — mcmc_prelim_fit","text":"Test multiple sets parameters randomly drawn range maximum minimum values order find approximate values giving maximum posterior likelihood","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/mcmc_prelim_fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"mcmc_prelim_fit — mcmc_prelim_fit","text":"","code":"mcmc_prelim_fit( n_iterations = 1, n_param_sets = 1, n_bounds = 1, log_params_min = NULL, log_params_max = NULL, input_data = list(), obs_sero_data = list(), obs_case_data = list(), mode_start = 0, prior_settings = list(type = \"zero\"), dt = 1, n_reps = 1, enviro_data = list(), p_severe_inf = 0.12, p_death_severe_inf = 0.39, add_values = list(vaccine_efficacy = 1, p_rep_severe = 1, p_rep_death = 1, m_FOI_Brazil = 1), deterministic = TRUE, mode_parallel = FALSE, cluster = NULL )"},{"path":"https://mrc-ide.github.io/YEP/reference/mcmc_prelim_fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"mcmc_prelim_fit — mcmc_prelim_fit","text":"n_iterations = Number times run adjust maximum/minimum n_param_sets = Number parameter sets run iteration n_bounds = Number parameter sets (highest likelihood values) take iteration create new maximum/minimum values log_params_min Initial lower limits estimated parameter values (natural logarithm actual limits) log_params_max Initial upper limits estimated parameter values (natural logarithm actual limits) input_data List population vaccination data multiple regions (created using data input creation code usually loaded RDS file) obs_sero_data Seroprevalence data comparison, region, year & age group, format . samples/. positives obs_case_data Annual reported case/death data comparison, region year, format . cases/. deaths mode_start Flag indicating set initial population immunity level addition vaccination mode_start = 0, vaccinated individuals mode_start = 1, shift non-vaccinated individuals recovered give herd immunity (uniform age, R0 based ) mode_start = 3, shift non-vaccinated individuals recovered give herd immunity (stratified age) prior_settings List containing settings priors: must contain text named \"type\": type = \"zero\", prior probability always zero type = \"norm\", prior probability given dnorm calculation parameter values settings based vectors values prior_settings: norm_params_mean norm_params_sd (vectors mean standard deviation values applied log FOI/R0 parameters actual values additional parameters) + FOI_mean + FOI_sd (mean + standard deviation computed FOI, single values) + R0_mean + R0_sd (mean + standard deviation computed R0, single values) dt time increment days (must 1 5) n_reps Number repetitions enviro_data Data frame values environmental covariates (columns) region (rows) p_severe_inf Probability infection severe p_death_severe_inf Probability severe infection resulting death add_values List parameters addition governing FOI/R0, either giving fixed value giving NA indicate part fitted parameter set vaccine_efficacy Vaccine efficacy (proportion reported vaccinations causing immunity) (must present) p_rep_severe Probability observation severe infection p_rep_death Probability observation death m_FOI_Brazil Multiplier spillover FOI Brazil regions (relevant regions Brazil considered) deterministic TRUE/FALSE - set model run deterministic mode TRUE mode_parallel TRUE/FALSE - indicate whether use parallel processing supplied cluster speed cluster Cluster threads use mode_parallel = TRUE '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/mcmc_prelim_fit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"mcmc_prelim_fit — mcmc_prelim_fit","text":"function used estimate model parameter values giving maximum posterior likelihood; primarily intended used generate initial parameter values Markov Chain Monte Carlo fitting (using mcmc() function).","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/param_calc_enviro.html","id":null,"dir":"Reference","previous_headings":"","what":"param_calc_enviro — param_calc_enviro","title":"param_calc_enviro — param_calc_enviro","text":"Parameter calculation environmental covariates","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/param_calc_enviro.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"param_calc_enviro — param_calc_enviro","text":"","code":"param_calc_enviro(enviro_coeffs = c(), enviro_covar_values = c())"},{"path":"https://mrc-ide.github.io/YEP/reference/param_calc_enviro.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"param_calc_enviro — param_calc_enviro","text":"enviro_coeffs Values environmental coefficients enviro_covar_values Values environmental covariates '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/param_calc_enviro.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"param_calc_enviro — param_calc_enviro","text":"Takes set coefficients environmental covariates covariate values calculates values spillover force infection reproduction number.","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/param_prop_setup.html","id":null,"dir":"Reference","previous_headings":"","what":"param_prop_setup — param_prop_setup","title":"param_prop_setup — param_prop_setup","text":"Set proposed new log parameter values next iteration chain","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/param_prop_setup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"param_prop_setup — param_prop_setup","text":"","code":"param_prop_setup(log_params = c(), chain_cov = 1, adapt = 0)"},{"path":"https://mrc-ide.github.io/YEP/reference/param_prop_setup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"param_prop_setup — param_prop_setup","text":"log_params Previous log parameter values used input chain_cov Covariance calculated previous iterations chain adapt 0/1 flag indicating type covariance use proposition value (TBA) '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/param_prop_setup.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"param_prop_setup — param_prop_setup","text":"Takes current values parameter set used Markov Chain Monte Carlo fitting proposes new values multivariate normal distribution existing values form mean standard deviation based chain covariance (flag \"adapt\" set 1) flat value based number parameters.","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/parameter_setup.html","id":null,"dir":"Reference","previous_headings":"","what":"Parameter setup — parameter_setup","title":"Parameter setup — parameter_setup","text":"Set parameters input model","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/parameter_setup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parameter setup — parameter_setup","text":"","code":"parameter_setup( FOI_spillover = 0, R0 = 1, vacc_data = list(), pop_data = list(), year0 = 1940, years_data = c(1941:1942), mode_start = 0, vaccine_efficacy = 1, start_SEIRV = list(), dt = 1 )"},{"path":"https://mrc-ide.github.io/YEP/reference/parameter_setup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parameter setup — parameter_setup","text":"FOI_spillover Force infection due spillover sylvatic reservoir R0 Reproduction number urban spread infection vacc_data Projected vaccination-based immunity (assuming vaccine_efficacy = 1) age group year pop_data Population age group year year0 First year population/vaccination data years_data Incremental vector years denoting years save data mode_start Flag indicating set initial population immunity level addition vaccination mode_start = 0, vaccinated individuals mode_start = 1, shift non-vaccinated individuals recovered give herd immunity (uniform age, R0 based ) mode_start = 2, use SEIRV input list previous run(s) mode_start = 3, shift non-vaccinated individuals recovered give herd immunity (stratified age) vaccine_efficacy Proportional vaccine efficacy start_SEIRV SEIRV data end previous run use input dt Time increment days use model (either 1.0 5.0 days) '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/parameter_setup.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Parameter setup — parameter_setup","text":"Takes multiple inputs, outputs list use odin SEIRV model.","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/regions_breakdown.html","id":null,"dir":"Reference","previous_headings":"","what":"regions_breakdown — regions_breakdown","title":"regions_breakdown — regions_breakdown","text":"Break regions datasets get list unique regions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/regions_breakdown.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"regions_breakdown — regions_breakdown","text":"","code":"regions_breakdown(region_labels = c())"},{"path":"https://mrc-ide.github.io/YEP/reference/regions_breakdown.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"regions_breakdown — regions_breakdown","text":"region_labels Vector region labels","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/regions_breakdown.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"regions_breakdown — regions_breakdown","text":"Takes vector region labels, potentially including labels contain one region (e.g. labels countrywide data adm1 level regions one label separated comma) produces alphabetically ordered list region labels, breaking comma-separated groups. example, supplying vector labels \"BRA.1_1,BRA.2_1,BRA.3_1\" return vector length 3 - c(\"BRA.1_1\",\"BRA.2_1\",\"BRA.3_1\").","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_calculate.html","id":null,"dir":"Reference","previous_headings":"","what":"sero_calculate — sero_calculate","title":"sero_calculate — sero_calculate","text":"Calculate seroprevalence unvaccinated people modelled data one years one age range","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_calculate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"sero_calculate — sero_calculate","text":"","code":"sero_calculate( age_min = 0, age_max = 101, years = NULL, vc_factor = 0, data = list(), n_p = 1 )"},{"path":"https://mrc-ide.github.io/YEP/reference/sero_calculate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"sero_calculate — sero_calculate","text":"age_min = Minimum age age group age_max = Maximum age age group years = Years calculate average annual seroprevalence vc_factor = Proportion patients tested vaccine status unknown data = Output Basic_Model_Run Full_Model_Run n_p = Particle select data '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_calculate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"sero_calculate — sero_calculate","text":"Takes information minimum maximum ages desired range, year(s) calculate seroprevalence, factor representing proportion patients unknown vaccine status, SEIRV model output data, calculates seroprevalence unvaccinated people specified age range specified year(s).","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_calculate2.html","id":null,"dir":"Reference","previous_headings":"","what":"sero_calculate2 — sero_calculate2","title":"sero_calculate2 — sero_calculate2","text":"Calculate number \"samples\" number \"positives\" modelled data specified age range(s) year(s)","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_calculate2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"sero_calculate2 — sero_calculate2","text":"","code":"sero_calculate2(sero_data = list(), model_data = list(), n_p = 1)"},{"path":"https://mrc-ide.github.io/YEP/reference/sero_calculate2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"sero_calculate2 — sero_calculate2","text":"sero_data Data frame containing years, minimum maximum ages, values vc_factor (proportion people vaccination status unknown) model_data SEIRV output Model_Run similar functions n_p Particle select model_data '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_calculate2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"sero_calculate2 — sero_calculate2","text":"Takes information minimum maximum ages desired range(s), year(s) calculate number \"samples\" (people eligible testing) \"positives\" (people test positive), plus vc_factor (proportion people vaccination status unknown)","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_data_compare.html","id":null,"dir":"Reference","previous_headings":"","what":"sero_data_compare — sero_data_compare","title":"sero_data_compare — sero_data_compare","text":"Take seroprevalence results dataset generation functions, compare comparison observed/template seroprevalence data calculate likelihood","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_data_compare.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"sero_data_compare — sero_data_compare","text":"","code":"sero_data_compare(model_sero_values = c(), obs_sero_data = list())"},{"path":"https://mrc-ide.github.io/YEP/reference/sero_data_compare.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"sero_data_compare — sero_data_compare","text":"model_sero_values Seroprevalence values dataset generation function (. positives/. samples) obs_sero_data Seroprevalence data comparison, year age group, format . samples/. positives '","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/sero_data_compare.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"sero_data_compare — sero_data_compare","text":"[TBA]","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/single_posterior_calc.html","id":null,"dir":"Reference","previous_headings":"","what":"single_posterior_calc — single_posterior_calc","title":"single_posterior_calc — single_posterior_calc","text":"Function calculates outputs posterior likelihood observing simulated data","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/single_posterior_calc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"single_posterior_calc — single_posterior_calc","text":"","code":"single_posterior_calc( log_params_prop = c(), input_data = list(), obs_sero_data = NULL, obs_case_data = NULL, ... )"},{"path":"https://mrc-ide.github.io/YEP/reference/single_posterior_calc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"single_posterior_calc — single_posterior_calc","text":"log_params_prop Proposed values parameters estimated (natural logarithm actual parameters) input_data List population vaccination data multiple regions (created using data input creation code usually loaded RDS file), cross-reference tables added using input_data_process MCMC obs_sero_data Seroprevalence data comparison, region, year & age group, format . samples/. positives obs_case_data Annual reported case/death data comparison, region year, format . cases/. deaths ... = Constant parameters/flags/etc. loaded determined mcmc() mcmc_prelim_fit, including mode_start, prior_settings, dt, n_reps, enviro_data, p_severe_inf, p_death_severe_inf, add_values list, extra_estimated_params, deterministic, mode_parallel, cluster","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/single_posterior_calc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"single_posterior_calc — single_posterior_calc","text":"function calculates posterior likelihood observing set observations (across multiple regions data types) given proposed parameter set. [TBA]","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/template_region_xref.html","id":null,"dir":"Reference","previous_headings":"","what":"template_region_xref — template_region_xref","title":"template_region_xref — template_region_xref","text":"Cross-reference template data individual regions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/template_region_xref.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"template_region_xref — template_region_xref","text":"","code":"template_region_xref(template = list(), regions = c())"},{"path":"https://mrc-ide.github.io/YEP/reference/template_region_xref.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"template_region_xref — template_region_xref","text":"template List containing one sets template data (serological data, case data burden data) regions Vector individual regions","code":""},{"path":"https://mrc-ide.github.io/YEP/reference/template_region_xref.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"template_region_xref — template_region_xref","text":"Examines template data (serological, case burden) compares vector region names order check lines set template data require model data region(s). example, line set serological data region given \"AGO.1_1,AGO.2_1,AGO.3_1\" compared vector regions c(\"AGO.1_1\",\"AGO.2_1\",\"AGO.3_1\",...), line requires data regions 1, 2 3. function used generating dataset one templates; normally used functions Generate_Dataset, Generate_Sero_Dataset, Generate_Case_Dataset, Generate_VIMC_Burden_Dataset Generate_Multiple_Datasets. returns list containing [TBA].","code":""}]