Publications by Adam Elder

Mean Degree Report

28.12.2020

## 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...

9563 sym R (29576 sym/23 pcs) 11 img

Cumulative Number of Partners Calculation

28.12.2020

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 ...

5449 sym R (8958 sym/5 pcs)

EpiModel Diagnostics

17.12.2020

## 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

07.12.2020

## 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...

8903 sym R (26250 sym/26 pcs) 9 img

Relationship duration information

18.12.2020

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...

1752 sym R (8500 sym/8 pcs) 2 img 1 tbl

Arrival Module

09.01.2021

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...

4049 sym R (4509 sym/17 pcs) 1 img

Cure Model

09.01.2021

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...

5571 sym R (1062 sym/2 pcs) 1 tbl

Testing Hazards

11.01.2021

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...

128 sym R (3110 sym/11 pcs) 4 img

EpiModel Mean Degree Report

14.01.2021

## 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...

194 sym R (13136 sym/9 pcs) 4 img

Instantaneous Degree distribution

02.02.2021

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 =...

203 sym R (1661 sym/6 pcs) 6 img