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

Zimmer, Christoph; Yaesoubi, Reza; Cohen, Ted

doi:10.1371/journal.pcbi.1005257

Cited by 17 publications

(32 citation statements)

References 42 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

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%

“…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%

See 1 more Smart Citation

Real-time decision-making during emergency disease outbreaks

et al. 2018

View full text Add to dashboard Cite

In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect.Our results show that the control policy that would have been chosen using all the data is also identified from an early stage in an outbreak using only the available data, despite high variability in projections of epidemic size. Critically, we find that it is an improved understanding of the locations of infected farms, rather than improved estimates of transmission parameters, that drives improved prediction of the relative performance of control interventions. However, the ability to estimate undetected infectious premises is a function of uncertainty in the transmission parameters. Here, we demonstrate the need for both real-time model fitting and generating projections to evaluate alternative control interventions throughout an outbreak. Our results highlight the use of using models at outbreak onset to inform policy and the importance of state-dependent interventions that adapt in response to additional information throughout an outbreak.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-time decision-making during emergency disease outbreaks

et al. 2018

View full text Add to dashboard Cite

show abstract

“…[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^i−1 (see Zimmer et al 4 for details).…”

Section: Methodsmentioning

confidence: 99%

Accurate quantification of uncertainty in epidemic parameter estimates and predictions using stochastic compartmental models

Zimmer

Leuba

Cohen

et al. 2018

Stat Methods Med Res

Self Cite

View full text Add to dashboard Cite

Stochastic transmission dynamic models are needed to quantify the uncertainty in estimates and predictions during outbreaks of infectious diseases. We previously developed a calibration method for stochastic epidemic compartmental models, called Multiple Shooting for Stochastic Systems (MSS), and demonstrated its competitive performance against a number of existing state-of-the-art calibration methods. The existing MSS method, however, lacks a mechanism against filter degeneracy, a phenomenon that results in parameter posterior distributions that are weighted heavily around a single value. As such, when filter degeneracy occurs, the posterior distributions of parameter estimates will not yield reliable credible or prediction intervals for parameter estimates and predictions. In this work, we extend the MSS method by evaluating and incorporating two resampling techniques to detect and resolve filter degeneracy. Using simulation experiments, we demonstrate that an extended MSS method produces credible and prediction intervals with desired coverage in estimating key epidemic parameters (e.g. mean duration of infectiousness and R0) and short- and long-term predictions (e.g. one and three-week forecasts, timing and number of cases at the epidemic peak, and final epidemic size). Applying the extended MSS approach to a humidity-based stochastic compartmental influenza model, we were able to accurately predict influenza-like illness activity reported by U.S. Centers for Disease Control and Prevention from 10 regions as well as city-level influenza activity using real-time, city-specific Google search query data from 119 U.S. cities between 2003 and 2014.

show abstract

“…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%

Use of daily Internet search query data improves real-time projections of influenza epidemics

Zimmer

Leuba

Yaesoubi³

et al. 2018

J. R. Soc. Interface.

Self Cite

View full text Add to dashboard Cite

Seasonal influenza causes millions of illnesses and tens of thousands of deaths per year in the USA alone. While the morbidity and mortality associated with influenza is substantial each year, the timing and magnitude of epidemics are highly variable which complicates efforts to anticipate demands on the healthcare system. Better methods to forecast influenza activity would help policymakers anticipate such stressors. The US Centers for Disease Control and Prevention (CDC) has recognized the importance of improving influenza forecasting and hosts an annual challenge for predicting influenza-like illness (ILI) activity in the USA. The CDC data serve as the reference for ILI in the USA, but this information is aggregated by epidemiological week and reported after a one-week delay (and may be subject to correction even after this reporting lag). Therefore, there has been substantial interest in whether real-time Internet search data, such as Google, Twitter or Wikipedia could be used to improve influenza forecasting. In this study, we combine a previously developed calibration and prediction framework with an established humidity-based transmission dynamic model to forecast influenza. We then compare predictions based on only CDC ILI data with predictions that leverage the earlier availability and finer temporal resolution of Wikipedia search data. We find that both the earlier availability and the finer temporal resolution are important for increasing forecasting performance. Using daily Wikipedia search data leads to a marked improvement in prediction performance compared to weekly data especially for a three- to four-week forecasting horizon.

show abstract

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

Cited by 17 publications

References 42 publications

Real-time decision-making during emergency disease outbreaks

Real-time decision-making during emergency disease outbreaks

Accurate quantification of uncertainty in epidemic parameter estimates and predictions using stochastic compartmental models

Use of daily Internet search query data improves real-time projections of influenza epidemics

Contact Info

Product

Resources

About