Spatiotemporal Infectious Disease Modeling: A BME-SIR Approach

Angulo, J. M.; Yu, Hwa‐Lung; Langousis, Andreas; Kolovos, Alexander; Wang, Jinfeng; Madrid, Ana E.; Christakos, George

doi:10.1371/journal.pone.0072168

Cited by 44 publications

(36 citation statements)

References 33 publications

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“…A notable exception is the agent-based model proposed by Chao et al (2011), which, however, was not calibrated against data but relied on parameter values taken from literature. Other possible approaches to model stochasticity in disease transmission are the use of Langevin-type differential equations (Azaele et al 2010;Mukandavire et al 2013) or of spatiotemporal random fields which account for multiple sources of uncertainty (Angulo et al 2012(Angulo et al , 2013.…”

Section: Introductionmentioning

confidence: 99%

On the probability of extinction of the Haiti cholera epidemic

Bertuzzo

Finger

Mari

et al. 2014

Stoch Environ Res Risk Assess

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More than three years after its appearance in Haiti, cholera has already caused more than 8,500 deaths and 695,000 infections and it is feared to become endemic. However, no clear evidence of a stable environmental reservoir of pathogenic Vibrio cholerae, the infective agent of the disease, has emerged so far, suggesting the possibility that the transmission cycle of the disease is being maintained by bacteria freshly shed by infected individuals. Should this be the case, cholera could in principle be eradicated from Haiti. Here, we develop a framework for the estimation of the probability of extinction of the epidemic based on current information on epidemiological dynamics and health-care practice. Cholera spreading is modeled by an individual-based spatially-explicit stochastic model that accounts for the dynamics of susceptible, infected and recovered individuals hosted in different local communities connected through hydrologic and human mobility networks. Our results indicate that the probability that the epidemic goes extinct before the end of 2016 is of the order of 1 %. This low probability of extinction highlights the need for more targeted and effective interventions to possibly stop cholera in Haiti.

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Section: Introductionmentioning

confidence: 99%

On the probability of extinction of the Haiti cholera epidemic

Bertuzzo

Finger

Mari

et al. 2014

Stoch Environ Res Risk Assess

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“…Average annual spatial distributions of meteorological factors are important estimators, and the acquisition of accurate estimated values within the studied area is a prerequisite for building regression model of the relationship between the distribution of multivariate meteorological factors and historical disaster-related losses. Some studies have focused on finding the potential distribution regularity of meteorological factors in different regions or the laws governing the spread of infectious diseases by integrating hard data and soft data into a BME framework [22]. These studies show that meteorological factors not only are suitable regionalized variables but also are correlated with other variables and if these other variables are not taken into consideration, more accurate estimates cannot be made.…”

Section: Discussionmentioning

confidence: 99%

“…The BME approach has been applied to a variety of studies with considerable success [18,22,25,[37][38][39][40][41]. We implement the BME estimation for the distribution of seven meteorological factors by using the BMElib suite of functions in Matlab [39].…”

Section: Bme Methodologymentioning

confidence: 99%

“…If these variables are not taken into consideration, accurate estimates cannot be made. Bayesian maximum entropy (BME) approach provides a numerical estimation framework for spatiotemporal analysis and mapping [21][22][23]. Some BME applications [24,25] have been shown to be more accurate than previous spatial interpolation techniques, and all these advantages facilitate the process of spatially mapping multiple meteorological factors accurately.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Estimation of Losses Attributable to Meteorological Disasters in a Specific Area (105.0°E–115.0°E, 25°N–35°N) Using Bayesian Maximum Entropy and Partial Least Squares Regression

Zhang

Zhong

Yang

et al. 2016

Advances in Meteorology

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The spatial mapping of losses attributable to such disasters is now well established as a means of describing the spatial patterns of disaster risk, and it has been shown to be suitable for many types of major meteorological disasters. However, few studies have been carried out by developing a regression model to estimate the effects of the spatial distribution of meteorological factors on losses associated with meteorological disasters. In this study, the proposed approach is capable of the following: (a) estimating the spatial distributions of seven meteorological factors using Bayesian maximum entropy, (b) identifying the four mapping methods used in this research with the best performance based on the cross validation, and (c) establishing a fitted model between the PLS components and disaster losses information using partial least squares regression within a specific research area. The results showed the following: (a) best mapping results were produced by multivariate Bayesian maximum entropy with probabilistic soft data; (b) the regression model using three PLS components, extracted from seven meteorological factors by PLS method, was the most predictive by means ofPRESS/SStest; (c) northern Hunan Province sustains the most damage, and southeastern Gansu Province and western Guizhou Province sustained the least.

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“…This is the SIR model framework used as the conceptual backbone for much of the mechanistic modeling of infectious disease in humans [4]. Generating forecasts or inference in real-world systems may require integration of demography, as well as environmental variability and stochasticity into this conceptual model [5][6][7][8][9][10]. The oft-cited threshold parameter (R 0 ), describing the likely number of newly infected individuals generated by the introduction of a single infected host into a --54 Shannon L. LADEAU and Barbara A. HAN pool of susceptibles, is derived from this conceptual model [11,12].…”

Section: Fundamental Principlesmentioning

confidence: 99%

Disases Ecology（疾病生態学）の出現

Shannon

Barbara

2016

Jpn. J. Zoo Wildl. Med.

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The number of newly recognized diseases affecting humans, domestic animals and wildlife has increased in recent decades and many of these diseases 'emerge' when environmental conditions change to alter contact rates between species. While traditional disease biology or epidemiological studies strive to understand the patterns of outbreak in a single species of interest, it is increasingly evident that management strategies demand more comprehensive understanding of the ecological interactions across wildlife, human, domestic animal and potential vector populations. This paper will introduce the ecological principles underlying pathogen dynamics in ecological systems and highlight current research frontiers in Disease Ecology. Pathogens and parasites are often considered as a disturbance rather than an inherent part of ecological systems, yet the fundamental principles of disease ecology derive from classical theory in population and community ecology.

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Spatiotemporal Infectious Disease Modeling: A BME-SIR Approach

Cited by 44 publications

References 33 publications

On the probability of extinction of the Haiti cholera epidemic

On the probability of extinction of the Haiti cholera epidemic

Spatial Estimation of Losses Attributable to Meteorological Disasters in a Specific Area (105.0°E–115.0°E, 25°N–35°N) Using Bayesian Maximum Entropy and Partial Least Squares Regression

Disases Ecology（疾病生態学）の出現

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