2017
DOI: 10.1371/journal.pcbi.1005257
|View full text |Cite
|
Sign up to set email alerts
|

A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models

Abstract: Stochastic transmission dynamic models are especially useful for studying the early emergence of novel pathogens given the importance of chance events when the number of infectious individuals is small. However, methods for parameter estimation and prediction for these types of stochastic models remain limited. In this manuscript, we describe a calibration and prediction framework for stochastic compartmental transmission models of epidemics. The proposed method, Multiple Shooting for Stochastic systems (MSS),… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
30
0
1

Year Published

2018
2018
2024
2024

Publication Types

Select...
8
1

Relationship

2
7

Authors

Journals

citations
Cited by 17 publications
(32 citation statements)
references
References 42 publications
(64 reference statements)
0
30
0
1
Order By: Relevance
“…Recent research has outlined frameworks that combine Bayesian parameter estimation and a mathematical model for generating real-time forecasts of outbreak severity throughout the course of an epidemic, aiming to assimilate available surveillance data into model estimates as rapidly as possible [ 7 10 ]. The efficacy of these frameworks has typically been evaluated using the accuracy of forecasts.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Recent research has outlined frameworks that combine Bayesian parameter estimation and a mathematical model for generating real-time forecasts of outbreak severity throughout the course of an epidemic, aiming to assimilate available surveillance data into model estimates as rapidly as possible [ 7 10 ]. The efficacy of these frameworks has typically been evaluated using the accuracy of forecasts.…”
Section: Introductionmentioning
confidence: 99%
“…In contrast to previous work in real-time forecasting [ 7 10 ], we used an individual-based model and included uncertainty regarding the location of infected and undetected farms [ 11 , 12 ]. As surveillance data become available, we improve our understanding, in a cumulative manner, of both how the outbreak is unfolding, via estimates of transmission parameters, and of where likely new infections may be located, via estimates of the spatial distribution of infected but undetected farms.…”
Section: Introductionmentioning
confidence: 99%
“…[State estimate] = [Maximum over possible states] of [Probability for observation conditioned on specific current state] × [Transition probability to move from previous state estimate to specific current state] . At the beginning of each period, the ODE system to propagate the epidemic state to the next period is initialized with the state estimate trueν^ Since the MSSa approach assumes a point distribution for the belief state , the LNA method to characterize the distribution of the epidemic state in the next period is initialized with zero variance for trueΣ^i1 (see Zimmer et al 4 for details).…”
Section: Methodsmentioning
confidence: 99%
“…As in previous work [ 30 32 ], we employ a linear noise approximation (LNA) method to approximate the transition probability p (of equation ( 2.6 )). The LNA assumes that the probability distribution of ν i | ν i −1 can be properly approximated by a normal distribution where x i is the solution of the ordinary differential equation (ODE) representation of the system on the interval [ t i −1 , t i ] where Γ is a matrix describing the instantaneous change of each transition on each compartment and the vector Λ the rate of the instantaneous change of each transition.…”
Section: Methodsmentioning
confidence: 99%