Infections Estimation

Functions for estimating infection trajectories. More...

Collaboration diagram for Infections Estimation:

Subgroups
	Estimates Smoothing
	Functions for smoothing estimates using Gaussian processes.
	Observation Model
	Functions for modeling the observation process.

Functions
real	update_infectiousness (vector infections, vector gt_rev_pmf, int seeding_time, int index)
vector	renewal_infections (vector seed, vector R, vector gt_rev_pmf, real pop, int use_pop, data real pop_floor, int nht)
vector	generate_infections (vector R, int uot, vector gt_rev_pmf, array[] real initial_infections, real pop, int use_pop, data real pop_floor, int ht, int obs_scale, real fraction_observed, int initial_as_scale)
	Generate infections using a renewal equation approach.
vector	deconvolve_infections (vector shifted_cases, vector noise, int fixed, int prior)

Description

Functions for estimating infection trajectories.

This group contains functions for estimating and modeling infection dynamics, including implementation of the renewal equation and backcalculation approaches. Core infection estimation functions from infections.stan and related generated quantities functions.

Function Documentation

deconvolve_infections()

vector deconvolve_infections	(	vector	shifted_cases,
		vector	noise,
		int	fixed,
		int	prior )

Backcalculate infections from cases

This function estimates infections by working backwards from observed cases, applying noise to account for uncertainty in the process.

Parameters

shifted_cases	Vector of shifted case counts
noise	Vector of noise values
fixed	Whether to use fixed (1) or variable (0) noise
prior	Prior type to use (0: noise only, 1: cases * noise, 2: random walk)

Returns

A vector of infection counts

Example

With shifted_cases = {10, 20, 30}, no noise (noise = {0, 0, 0}, so exp(noise) = 1) and prior = 1, each case count is scaled by 1 and offset by the 1e-5 numerical floor: deconvolve_infections(shifted_cases, noise, 0, 1) returns approximately {10, 20, 30}.

Definition at line 141 of file infections.stan.

generate_infections()

vector generate_infections	(	vector	R,
		int	uot,
		vector	gt_rev_pmf,
		array[]real	initial_infections,
		real	pop,
		int	use_pop,
		data real	pop_floor,
		int	ht,
		int	obs_scale,
		real	fraction_observed,
		int	initial_as_scale )

Generate infections using a renewal equation approach.

This function implements the renewal equation to generate a time series of infections based on reproduction numbers and the generation time distribution. It can also account for population depletion if a population size is specified. It sets the seeding infections and then calls renewal_infections().

Parameters

R	Vector of reproduction numbers
uot	Unobserved time (seeding time)
gt_rev_pmf	Vector of reversed generation time PMF
initial_infections	Array of initial infection values
pop	Initial susceptible population (0 for unlimited)
use_pop	Population adjustment mode (0=none, 1=forecast only, 2=all)
pop_floor	Minimum susceptible population (floor to prevent instability)
ht	Horizon time
obs_scale	Whether to scale by fraction observed (1) or not (0)
fraction_observed	Fraction of infections that are observed
initial_as_scale	Whether initial infections are a scaling factor (1) or not (0)

Returns

A vector of infection counts

Definition at line 93 of file infections.stan.

References R_to_r(), and renewal_infections().

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renewal_infections()

vector renewal_infections	(	vector	seed,
		vector	R,
		vector	gt_rev_pmf,
		real	pop,
		int	use_pop,
		data real	pop_floor,
		int	nht )

Run the renewal equation from the seeding infections.

Implemented in C++, with a hand-written gradient, in inst/include/epinow2/renewal_infections.hpp, which gives the maths.

Parameters

seed	Vector of seeding infections
R	Vector of reproduction numbers
gt_rev_pmf	Vector of reversed generation time PMF
pop	Initial susceptible population
use_pop	Population adjustment mode (0=none, 1=after nht, 2=all)
pop_floor	Minimum susceptible population
nht	Number of time steps before the adjustment when use_pop = 1

Returns

A vector of infections, starting with the seeding infections

Referenced by generate_infections().

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update_infectiousness()

real update_infectiousness	(	vector	infections,
		vector	gt_rev_pmf,
		int	seeding_time,
		int	index )

Infection Modeling Functions

This group of functions handles the generation, calculation, and backcalculation of infection time series in the model. These functions implement the core epidemiological dynamics, including the renewal equation approach. Calculate infectiousness for a single time point

This function computes the weighted sum of past infections with the generation time distribution to determine the current infectiousness.

Parameters

infections	Vector of infection counts
gt_rev_pmf	Vector of reversed generation time PMF
seeding_time	Number of time steps used for seeding
index	Current time index (relative to seeding_time)

Returns

The infectiousness at the specified time point

Example

With infections = {10, 20, 30, 40, 50}, gt_rev_pmf = {0.2, 0.5, 0.3}, seeding_time = 0 and index = 3, the infectiousness is the weighted sum of the three most recent infections up to time 3: update_infectiousness(infections, gt_rev_pmf, 0, 3) returns 10 * 0.2 + 20 * 0.5 + 30 * 0.3 = 21.

Definition at line 32 of file infections.stan.

Referenced by calculate_growth_infness(), and calculate_Rt().

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