Publications by Adam Elder
Mean Degree Report
## For other simulations, just use a different file here. ## However, do keep the name sim. suppressPackageStartupMessages(library(tidyverse)) options(dplyr.summarise.inform = FALSE) ## Set path to model run you want to get diagnostics for: # sim <- readRDS("../../../Data/EpiModelSims/ergm3_ca_nohiv_sim_with15yo_boost.rds") if (params$sim == "Non...
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Cumulative Number of Partners Calculation
1 Notes on calculation of cumulative number of partner numbers This document details the current procedure used to calculate the number of partners each node had during the previous year across all of the years of the simulation. The code used to make these calculations is found in two places. Information used to calculate the cumulative number ...
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EpiModel Diagnostics
## Registered S3 method overwritten by 'tergm': ## method from ## simulate_formula.network ergm 1 Demographics 1.1 Number of individuals of each race 1.2 Mean age accross races 1.3 Number of ties in each network 2 HIV+ Diagnosis 2.1 Number diagnosed accross races 2.2 Incident counts accross races ## `geom_smooth()` us...
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Summary of No-HIV run
## For other simulations, just use a different file here. ## However, do keep the name sim. suppressPackageStartupMessages(library(tidyverse)) ## Set path to model run you want to get diagnostics for: # sim <- readRDS("../../../Data/EpiModelSims/ergm3_ca_nohiv_sim_with15yo_boost.rds") sim <- readRDS("../sim_epimodel3/episim_dec3_nohiv.rds") ## Re...
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Relationship duration information
0.1 Tracking of relationship duration and age During the simulation, as ties form and dissolve, this information is stored inside of the dat object. However, because of the large number of relationships that occur during an entire simulation, the list of relationships is trimmed each year to prevent the dat object from becoming too large. 0.2 Wh...
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Arrival Module
1 Notes on Arrival module There is a part of the EpiModel simulation in which we attempting to achieve a network that has the correct Joint age / race population distribution Marginal age and race-specific prevalences. Joint age / race / network specific mean degree distributions Importantly, we seek to hit all of these targets in a specific ye...
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Cure Model
1 Notes on the cure module This document details the current procedure used for curing HIV positive individuals. The current goal of this procedure is to hit both the Age-group and Race specific prevalence targets. 1.1 Prevalence targets 1.1.1 Need for joint age / race prevalence targets To hit the prevalence target for any subgroup it is neces...
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Testing Hazards
Comparison of Hazards ### Hazards based on Model pred_dat <- readRDS("../../Data/Intermediate/coxdata.RDS") pred_dat <- pred_dat %>% filter(!(never_tested == "T" & age > 45)) covardf <- with(pred_dat, expand.grid( age.young = unique(age.young), race2 = unique(race2), region.ewa = unique(region.ewa), snap.grp3 = unique(snap.grp3))) # defin...
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EpiModel Mean Degree Report
## For other simulations, just use a different file here. ## However, do keep the name sim. suppressPackageStartupMessages(library(tidyverse)) options(dplyr.summarise.inform = FALSE) ## Set path to model run you want to get diagnostics for: # sim <- readRDS("../../../Data/EpiModelSims/ergm3_ca_nohiv_sim_with15yo_boost.rds") if (params$sim == "Non...
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Instantaneous Degree distribution
0.1 Broken out Across Race 0.1.1 Main make_deg_dist_dat <- function(attr, sim_dat, pcut, deg_type, deg.name){ attr_val <- enexpr(attr) degtype_val <- enexpr(deg_type) sim_brk <- sim_dat %>% mutate(deg.name = pmin(n, pcut)) %>% group_by(!!attr_val, deg.name) %>% count() %>% ungroup() %>% group_by(!!attr_val) %>% mutate(Percent =...
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