Package 'first90'

Title: The first90 model
Description: Implements the Shiny90 model for estimating progress towards the UNAIDS "first 90" target for HIV awareness of status in sub-Saharan Africa.
Authors: Jeffrey Eaton [aut, cre] , Mathieu Maheu-Giroux [aut], Katia Giguère [aut], Roma Bhatkoti [aut]
Maintainer: Jeffrey Eaton <[email protected]>
License: MIT + file LICENSE
Version: 1.6.11
Built: 2024-10-09 05:30:41 UTC
Source: https://github.com/mrc-ide/first90release

Help Index


Add incidences corresponding to state space

Description

Add incidences corresponding to state space

Usage

add_ss_indices(dat, ss)

Arguments

dat

a data.frame with particular columns coded in particular ways

ss

state space definition (fp$ss)

Examples

## Not run: 
data(survey_hts)
dat <- subset(survey_hts, survey_hts$country == "Malawi" & outcome == "evertest")
df <- add_ss_indices(dat, fp$ss)

## End(Not run)

ART coverage among age 15-49

Description

ART coverage among age 15-49

Usage

artcov15to49(mod, sex = "both")

Calculate ASFR from TFR and fertility distribution

Description

Calculate ASFR from TFR and fertility distribution

Usage

calc_asfr(tfr, asfd)

Arguments

tfr

vector of annual TFR values

asfd

array of proportion of births by 5 year age group 15-49

Value

array of age-specific fertility rate by single-year of age 15-49.


Combine PJNZ inputs

Description

Combine PJNZ inputs

Usage

combine_inputs(lst)

Arguments

lst

a list of inputs, each returned from extract_pjnz()


Beers coefficients to distribute from 5-year to single-year of age

Description

Beers coefficients to distribute from 5-year to single-year of age

Usage

create_beers(n5yr)

Create HIV testing rates from parameter inputs

Description

Create HIV testing rates from parameter inputs

Usage

create_hts_param(theta, fp)

Details

This function creates two arrays hts_rate and diagn_rate. The hts_rate array summarizes HIV testing rates for the HIV negative population stratified by:

  • HIV aggregated age groups (hAG: 1-9)

  • sex (1 = male; 2 = female)

  • testing history (1 = never tested; 2 = previously tested negative)

  • year

Array diagn_rate summarizes HIV testing rates for the HIV positive population stratified by:

  • CD4 stage (hDS: 1-7)

  • HIV age group (hAG: 1-9)

  • sex (1 = male; 2 = female)

  • testing and treatment history (1 = never tested; 2 = previously tested negative; 3 = diagnosed, not treated; 4 = on ART)

  • year


Proportion diagnosed among HIV+ by age, sex, or HIV status

Description

This function calculates proportion diagnosed among the HIV positive population stratified by age group, sex, HIV status, and year.

Usage

diagnosed(mod, fp, df, VERSION = "C")

Arguments

mod

simulation model output

fp

simulation model parameter inputs

df

a data.frame with indices for prediction, see Details.

Details

Age groups are specified in terms of aggregate HIV age groups: 15-16, 17-19, 20-24, ..., 45-49, 50+. Another function could be added to handle other age groups if needed, with additional computational complexity.

The argument df must contain the following columns:

  • haidx: HIV age group (1 = 15-16, 2 = 17-19, 3 = 20-24, ..., 8 = 45-49, 9 = 50+)

  • sidx: sex (1 = male, 2 = female, 0 = both)

  • yidx: year index

  • hagspan: number of HIV age groups to span

Value

a vector

Examples

## Not run: 
data(survey_hts)
dat <- subset(survey_hts, country == "Malawi" & outcome == "aware")
df <- add_ss_indices(dat, fp$ss)

df$pred <- diagnosed(mod, fp, df)

## End(Not run)

Proportion ever tested by age, sex, or HIV status

Description

This function calculates proportion of ever tested among a population stratified by age group, sex, HIV status, and year.

Usage

evertest(mod, fp, df, VERSION = "C")

Arguments

mod

simulation model output

fp

simulation model parameter inputs

df

a data.frame with indices for prediction, see Details.

Details

Age groups are specified in terms of aggregate HIV age groups: 15-16, 17-19, 20-24, ..., 45-49, 50+. Another function could be added to handle other age groups if needed, with additional computational complexity.

The argument df must contain the following columns:

  • haidx: HIV age group (1 = 15-16, 2 = 17-19, 3 = 20-24, ..., 8 = 45-49, 9 = 50+)

  • sidx: sex (1 = male, 2 = female, 0 = both)

  • hvidx: HIV status (1 = negative, 2 = positive, 0 = all)

  • yidx: year index

  • hagspan: number of HIV age groups to span

Value

a vector

Examples

## Not run: 
data(survey_hts)
dat <- subset(survey_hts, country == "Malawi" & outcome == "evertest")
df <- add_ss_indices(dat, fp$ss)

df$pred <- evertest(mod, fp, df)

## End(Not run)

Internal helper functions

Description

Internal helper functions

Usage

exists_dptag(dp, tag, tagcol = 1)

Extract outputs from PJNZ needed for first90 model

Description

Extract outputs from PJNZ needed for first90 model

Usage

extract_pjnz(pjnz = NULL, dp_file = NULL, pjn_file = NULL)

Arguments

pjnz

filepath to PJNZ file

dp_file

filepath to a .DP file

pjn_file

filepath to a .PJN file

Value

a list


Extract AIM module parameters

Description

Extract AIM module parameters

Usage

get_dp_frr(dp, proj_years)

Get sex ratio at birth

Description

Get sex ratio at birth

Usage

get_dp_srb(dp, proj_years)

Get mortality probability (Sx) from Spectrum DP file

Description

Get mortality probability (Sx) from Spectrum DP file

Usage

get_dp_Sx(dp, proj_years)

Details

This function extracts Sx for ages 0:79 and 80+. Spectrum calculates a separate Sx for age 80. The population projection model in EPP-ASM needs to be updated to handle this.


Get age-specific fertility rate by single-year

Description

Get age-specific fertility rate by single-year

Usage

get_dp_tfr(dp, proj_years)

Extract arrays from Spectrum DP file

Description

Extract arrays from Spectrum DP file

Usage

get_dp_totpop(dp, proj_years)

Get country name from parsed PJN

Description

Get country name from parsed PJN

Usage

get_pjn_country(pjn)

Arguments

pjn

parsed PJN file

Details

pjn should be via first90_read_csv_character(pjn_file)


Get subnational region from parsed PJN

Description

Get subnational region from parsed PJN

Usage

get_pjn_region(pjn)

Arguments

pjn

parsed PJN file

Details

pjn should be via first90_read_csv_character(pjn_file)


Number of new diagnoses by age and sex

Description

Number of new diagnoses by age and sex

Usage

number_diagnoses(mod, fp, df, VERSION = "R")

Value

A data frame reporting new diagnoses in two ways:

  1. Approximated from the testing rates and mid-year populations as in the function number_tests().

  2. Based on the number of new diagnoses recorded in each model time step.

The column late_diagnoses reports the number who are initiated to ART directly from the undiagnosed population.

The two approaches are largely for debugging purposes to understand the implications of the model choices.


Calculate number adn proportion of retests conducted or number tested by year funtion based on number test in the outputs.R script

Description

Calculate number adn proportion of retests conducted or number tested by year funtion based on number test in the outputs.R script

Usage

number_retests(mod, fp, df)

Arguments

mod

model output of class 'eppasm'

fp

parameter inputs (class 'specfp')

df

a data.frame with indices for prediction. See evertest() for more information.

Details

Number of tests (or number tested in past 12 months) are approximated by mid-year counts and annual testing rates within each stratum.

Value

a data.frame consisting of the number of tests, and population size corresponding to rows of df


Calculate number of tests conducted or number tested by year

Description

Calculate number of tests conducted or number tested by year

Usage

number_tests(mod, fp, df, tested12m = FALSE, VERSION = "C")

Arguments

mod

model output of class 'eppasm'

fp

parameter inputs (class 'specfp')

df

a data.frame with indices for prediction. See evertest() for more information.

Details

Number of tests (or number tested in past 12 months) are approximated by mid-year counts and annual testing rates within each stratum.

Value

a data.frame consisting of the number of tests, number tested in the past 12 months, and population size corresponding to rows of df


Create model inputs from Spectrum PJNZ

Description

Create model inputs from Spectrum PJNZ

Usage

prepare_inputs(pjnzlist)

Arguments

pjnzlist

a vector of PJNZ file names to aggregate

Details

The aggregation makes a number of assumptions:

  • Progression parameters are the same in all files, and values frome the first file are used.

  • Special populations ART eligibility is the same in all files.

Examples

pjnzlist <- list.files("~/Documents/Data/Spectrum files/2018 final/SSA/",
  "CotedIvoire.*PJNZ$", full.names=TRUE, ignore.case=TRUE)
pjnzlist <- "~/Documents/Data/Spectrum files/2018 final/SSA/Malawi_2018_version_8.PJNZ"

Create model inputs from aggregated Spectrum PJNZ

Description

Create model inputs from aggregated Spectrum PJNZ

Usage

prepare_inputs_from_extracts(pjnz_in)

Arguments

pjnz_in

a list of outputs from extract_pjnz


Process survey data on hiv testing behaviors

Description

Process survey data on hiv testing behaviors

Usage

select_hts(survey_hts, cnt, age_group)

Process programmatic data on number of tests

Description

Process programmatic data on number of tests

Usage

select_prgmdata(prgm_dat, cnt, age_group)

Simulate EPP-ASM model

Description

Simulate EPP-ASM model

Usage

simmod(fp, VERSION = "C")

Table to write CSV outputs for Spectrum

Description

Table to write CSV outputs for Spectrum

Usage

spectrum_output_table(mod, fp)

Details

Presently this returns point estimates for age 15+ population by sex:

  • Number PLHIV,

  • Ever tested among PLHIV

  • Aware of HIV+ status

  • On ART

PLHIV is mid-year estimate. All other outcomes are end of year estimate.

Value

a data.frame to write to CSV file for ingestion into Spectrum

Examples

## Not run: 
write.csv(spectrum_output_table(mod, fp), 
          "~/Downloads/Malawi-shiny90-example-output.csv", 
           row.names = FALSE)

## End(Not run)

Function to present the survey data on shinny interface

Description

Function to present the survey data on shinny interface

Usage

svy_hts_interface(survey_hts, cnt, age_group = c("15-24", "25-34", "35-49"))