Michael DeWitt medewitt

age_group	pct_sympto_hospital	pct_hosp_icu	infection_fatality_ratio
0-9	0.00	0.05	0.00
10-19	0.00	0.05	0.00
20-29	0.01	0.05	0.00
30-39	0.03	0.05	0.00
40-49	0.05	0.06	0.00
50-59	0.10	0.12	0.01
60-69	0.17	0.27	0.02
70-79	0.24	0.43	0.05
80+	0.27	0.71	0.09

The Shenzhen CDC identified 391 SARS-CoV-2 cases from January 14 to February 12, 2020 and 1286 close contacts. We compare cases identified through symptomatic surveillance and contact tracing, and estimate the time from symptom onset to confirmation, isolation, and hospitalization. We estimate metrics of disease transmission and analyze factors influencing transmission risk.

Cases were older than the general population (mean age 45) and balanced between males (187) and females (204). Ninety-one percent had mild or moderate clinical severity at initial assessment. Three have died, 225 have recovered (median time to recovery is 21 days). Cases were isolated on average 4.6 days after developing symptoms; contact tracing reduced this by

	---
	title: "Paper summary"
	date: "`r Sys.Date()`"
	bibliography: my_bib.bib
	---

	# Paper:
	## Author:
	## Journal:
	## Keywords:

	library(tidyverse)
	library(tidyqwi)

	# see census.gov/population/estimates/metro-city/0312msa.txt

	yearz <- 2009:2018

	state <- 37

	arguments <- data.frame(yearz, state)

	#' Stan Model Base General
	#'
	#' @param dat the dat sent to the model
	#' @param iter 2400
	#' @param warmup 1000
	#' @param chains 4
	#' @param control default controls
	#' @param ... Arguments passed to `rstan::sampling` (e.g. iter, chains).
	#' @return An object of class `stanfit` returned by `rstan::sampling`
	#' @export

	library(data.table)
	library(tidyverse)

	path_seirds_model <- system.file("examples/discrete_stochastic_seirds.R", package = "odin")

	seirds_generator <- odin::odin(path_seirds_model)

	x <- seirds_generator()

	x_res <- as.data.frame(replicate(100, x$run(0:365)[, -1]))

	library(tidyverse)
	n.sim <- 1000
	lambda <- 8/45 # 8 nursing homes in 45 days


	set.seed(336)
	n.sim <- 1000
	mu <- 20 # 20 hospitalizations mean
	sigma <- 5 ## =/- 10 patients
	sigma.sq <- sigma^2