Sam Abbott seabbs

🏠

Working from home

Real-time infectious disease modelling. Developing tools and methods for outbreak response, routine surveillance, and pandemic preparedness.

246 followers · 377 following

@epinowcast @EpiAware @epiforecasts
Bristol
13:16 (UTC)
samabbott.co.uk
https://orcid.org/0000-0001-8057-8037
https://bsky.app/profile/seabbs.bsky.social
@seabbs

View GitHub Profile

Recently created

Least recently created

Recently updated

Least recently updated

seabbs / latent_Re_custom_prior.R

Created October 24, 2023 10:45

Latent reproduction number estimation with a joint nowcast and custom prior specification for the expectation model.

	# Load packages
	library(epinowcast)
	library(data.table)
	library(ggplot2)

	# Load and filter germany hospitalisations
	nat_germany_hosp <- germany_covid19_hosp[location == "DE"][age_group %in% "00+"]
	nat_germany_hosp <- enw_filter_report_dates(
	nat_germany_hosp,
	latest_date = "2021-10-01"

seabbs / interveral-censored-right-truncated-distribution-estimation-with-brms.R

Last active October 27, 2022 16:45

This gist shows how to estimate a doubly censored (i.e daily data) and right truncated (i.e due to epidemic phase) distribution using the brms package.

	# Load packages
	library(brms)
	library(cmdstanr)
	library(data.table) # here we use the development version of data.table install it with data.table::update_dev_pkg
	library(purrr)

	# Set up parallel cores
	options(mc.cores = 4)

	# Simulate some truncated and truncation data

seabbs / ct-piecewise.stan

Created February 23, 2022 13:25

	functions{
	#include functions/ct_trajectory.stan
	#include functions/truncated_normal_rng.stan
	#include functions/censor.stan
	}

	data {
	int P; // number of patients
	int N; // number of tests
	real c_lod; // Ct value at limit of detection

seabbs / deterministic-deconvolution.R

Last active February 7, 2022 16:02

A script containing the functions required to simulation infections from a Poisson process, calculate prevalence assuming a deterministic convolution with a vector of detection probabilities, deconvolve prevalence to infections by numerically estimating the inverse of the convolution matrix, and then plotting a simulation of this process. *Note …

	library(data.table)
	library(snakecase)
	library(ggplot2)

	# make a convolution matrix
	convolution_matrix <- function(vec1, vec2) {
	lvec1 <- length(vec1)
	lvec2 <- length(vec2)
	conv <- matrix(0, nrow = lvec1, ncol = lvec1)
	for (s in 1:lvec1) {

seabbs / covidregionaldata-0.9.1-overview.R

Created May 7, 2021 14:50

An overview of the features included in the 0.9.1 release of covidregionaldata. See the release notes for more details: https://github.com/epiforecasts/covidregionaldata/releases/tag/v0.9.1

	library(covidregionaldata)

	# set up a data cache
	start_using_memoise()
	#> Using a cache at: /tmp/RtmphiSeWY

	# check for supported countries
	get_available_datasets()
	#> # A tibble: 18 x 8
	#> origin class level_1_region level_2_region level_3_region type data_urls

seabbs / process-rt.R

Created March 9, 2021 11:18

Example script for processing samples.

	# Packages ----------------------------------------------------------------
	require(data.table, quietly = TRUE)
	require(EpiNow2, quietly = TRUE)
	require(purrr)
	require(ggplot2)

	# Target date -------------------------------------------------------------
	creation_date <- Sys.Date()
	extraction_date <- creation_date

seabbs / covid19-utla-rt-from-admissions.R

Last active June 9, 2021 12:17

R code using {covid19.nhs.data} and estimates from epiforecasts.io/covid to generate a gif of the effective reproduction for Covid-19 using hospital admissions by upper-tier local authority in England.

	# Packages ----------------------------------------------------------------
	library(covid19.nhs.data)
	library(vroom)
	library(dplyr)
	library(tidyr)
	library(lubridate)
	library(gganimate)
	#devtools::install_github("thomasp85/transformr")
	library(transformr)
	library(gifski)

seabbs / covid19-utla-admissions.R

Last active January 25, 2021 14:52

R code using {covid19.nhs.data} to generate a gif of Covid-19 hospital admissions byupper-tier local authority in England.

	# Packages ----------------------------------------------------------------
	library(covid19.nhs.data)
	library(dplyr)
	library(tidyr)
	library(lubridate)
	library(gganimate)
	#devtools::install_github("thomasp85/transformr")
	library(transformr)
	library(gifski)
	library(ggplot2)

seabbs / covid19-ltla-admissions.R

Last active January 25, 2021 14:53

R code using {covid19.nhs.data} to generate a gif of weekly Covid-19 hospital admissions by lower-tier local authority in England.

	# Packages ----------------------------------------------------------------
	library(covid19.nhs.data)
	library(dplyr)
	library(tidyr)
	library(lubridate)
	library(gganimate)
	#devtools::install_github("thomasp85/transformr")
	library(transformr)
	library(gifski)
	library(ggplot2)

seabbs / epinow2-covid-rt-region.R

Last active November 30, 2021 13:22

Example of using EpiNow2 to estimate the Rt of Covid-19 in last 3 months for a region in a country supported in covidregionaldata. See the documentation for more details and examples: https://epiforecasts.io/EpiNow2/

	# packages
	# install.packages(c("data.table", "remotes", "EpiNow2"))
	# remotes::install_github("epiforecasts/EpiNow2")
	# remotes::install_github("epiforecasts/covidregionaldata")
	library(data.table)
	library(EpiNow2)
	library(covidregionaldata)

	# target country (must be supported in covidregionaldata)
	country <- "uk" # harder to fit "india"

NewerOlder