Prospective Infectious Disease Outbreak Detection Using Markov Switching Models

Lu, Hsin-Min; Zeng, Daniel; Chen, HsinChun

doi:10.1109/tkde.2009.115

Cited by 50 publications

(29 citation statements)

References 51 publications

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“…Recall from Eqs. (5,8) that the input gate i control how much new information is updated into memory c. The gate can be modified to reflect the risk level of admission type as follows:…”

Section: Admission As Inputmentioning

confidence: 99%

See 1 more Smart Citation

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Pham

Tran

Phung

et al. 2016

Lecture Notes in Computer Science

221

142

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Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-toend deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -diabetes and mental health -the results show improved modeling and risk prediction accuracy.

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“…Recall from Eqs. (5,8) that the input gate i control how much new information is updated into memory c. The gate can be modified to reflect the risk level of admission type as follows:…”

Section: Admission As Inputmentioning

confidence: 99%

“…Existing methods are poor in handling such complexity. They inadequately capture variable length [4] and ignore the long-term dependencies [7,8]. Temporal models based on Markovian assumption are unable to model temporal irregularity and have no memory, and thus can completely forget previous major illness given an irrelevant episode [9].…”

Section: Introductionmentioning

confidence: 99%

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Pham

Tran

Phung

et al. 2016

Lecture Notes in Computer Science

221

142

View full text Add to dashboard Cite

show abstract

“…(17) by Eq. (16). The formula for C is derived by expanding L(θ) around the maximal likelihood estimatorθ as a single point Gaussian approximation and by applying the Laplace's method [29] for calculating a finite integral.…”

Section: Maximal Likelihood Selectormentioning

confidence: 99%

Detecting a trend change in cross-border epidemic transmission

Maeno¹

2016

Physica A: Statistical Mechanics and its Applications

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h i g h l i g h t s• A method is developed to detect a trend change in cross-border epidemic transmission.• International travels decrease one week after the WHO alert for the SARS outbreak.• The WHO alert of phase 5 for the flu pandemic did not affect international travels. a b s t r a c t A method for a system of Langevin equations is developed for detecting a trend change in cross-border epidemic transmission. The equations represent a standard epidemiological SIR compartment model and a meta-population network model. The method analyzes a time series of the number of new cases reported in multiple geographical regions. The method is applicable to investigating the efficacy of the implemented public health intervention in managing infectious travelers across borders. It is found that the change point of the probability of travel movements was one week after the WHO worldwide alert on the SARS outbreak in 2003. The alert was effective in managing infectious travelers. On the other hand, it is found that the probability of travel movements did not change at all for the flu pandemic in 2009. The pandemic did not affect potential travelers despite the WHO alert.

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“…HMMs have been applied in very diverse fields such as financial economics (Bhar and Hamori 2004), computer vision (Bunke and Caelli 2001), hydrology (Khadr 2016;Vasas et al 2007) or biological sequence analysis (Yoon 2009). In the field of temporal surveillance of influenza and ILI, HMM have been applied both under the frequentist paradigm (Le Strat andCarrat 1999, Rafei et al 2015) and the Bayesian paradigm (Lu et al 2010;Madigan 2005;Rath et al 2003;Sun and Cai 2009), while MSMs have been used mostly under the Bayesian paradigm Lu et al 2010;Lytras et al 2018;. Spatio-temporal HMM models have been more scarce.…”

Section: Introductionmentioning

confidence: 99%

A spatio-temporal hierarchical Markov switching model for the early detection of influenza outbreaks

Amorós

Conesa

López‐Quílez

et al. 2020

Stoch Environ Res Risk Assess

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Rapidly detecting the beginning of influenza outbreaks helps health authorities to reduce their impact. Accounting for the spatial distribution of the data can greatly improve the performance of an outbreak detection method by promptly detecting the first foci of infection. The use of Hidden Markov chains in temporal models has shown to be great tools for classifying the epidemic or endemic state of influenza data, though their use in spatio-temporal models for outbreak detection is scarce.In this work, we present a spatio-temporal Bayesian Markov switching model over the differentiated incidence rates for the rapid detection of influenza outbreaks. This model focuses its attention on the incidence variations to better detect the higher increases of early epidemic rates even when the rates themselves are relatively low. The differentiated rates are modelled by a Gaussian distribution with different mean and variance according to the epidemic or endemic state. A temporal autoregressive term and a spatial conditional autoregressive model are added to capture the spatio-temporal structure of the epidemic mean. The proposed model has been tested over the USA Google Flu Trends database to assess the relevance of the whole structure.

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Prospective Infectious Disease Outbreak Detection Using Markov Switching Models

Cited by 50 publications

References 51 publications

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Detecting a trend change in cross-border epidemic transmission

A spatio-temporal hierarchical Markov switching model for the early detection of influenza outbreaks

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