Interactive spatiotemporal modelling of health systems: the SEKS–GUI framework

Self Cite

Dengue Fever (DF) has been identified by the World Health organization (WHO) as one of the most serious vector-borne infectious diseases in tropical and subtropical areas. During 2007, in particular, there were over 2,000 DF cases in Taiwan, which was the highest number of cases in the recorded history of Taiwan epidemics. Most DF studies have focused mainly on temporal DF patterns and its close association with climatic covariates, whereas they have understated spatial DF patterns (spatial dependence and clustering) and composite space-time effects. The present study proposes a spatio-temporal DF prediction approach based on stochastic Bayesian Maximum Entropy (BME) analysis. Core and site-specific knowledge bases are considered, including climate and health datasets under conditions of uncertainty, space-time dependence functions, and a Poisson regression model of climatic variables contributing to DF occurrences in southern Taiwan during 2007. The results show that the DF outbreaks in the study area are highly influenced by climatic conditions. Furthermore, the analysis can provide the required ''oneweek-ahead'' outbreak warnings based on spatio-temporal predictions of DF distributions. Therefore, the proposed approach can provide the Taiwan Disease Control Agency with a valuable tool to timely identify, control, and even efficiently prevent DF spreading across space-time.

Section: Methodsmentioning

confidence: 99%

A spatio-temporal climate-based model of early dengue fever warning in southern Taiwan

Yang

Yen

et al. 2010

Self Cite

“…Further statistical analysis of the samples spatial distribution and the application of BME methodology were carried out using the SEKS-GUI software library (Yu et al 2007). This methodology has already been applied successfully to ground water modeling problems (Christakos 1998;Serre et al 2003a).…”

Section: The Prior Stagementioning

confidence: 99%

Uncertainty management of a hydrogeological data set in a greek lignite basin, using BME

Modis

Vatalis²,

Papantonopoulos

et al. 2009

The occurrence of chemical pollutants in ground water is an issue of considerable interest. In the case of Ptolemais lignite opencast mining area in Greece, ammonium, nitrites, nitrates, iron total and total manganese concentrations, as well as various other elements have been monitored since the early 2000s through a borehole network. The continuous, though, alteration of the surface topography due to the intensive mining works, limits the life span of the water boreholes and results to irregular spatiotemporal distribution of the samples. Regarding the problem of mapping the water contamination, the coarse and irregular sampling pattern, combined with absence of seasonal variations and temporal trends, does not facilitate spatiotemporal processing of the data. On the other hand, a mere spatial analysis requires the attribution of the whole set of monitored values for each borehole, to a single point in space. The objective of this work is to develop a methodology to cope with the problem of uncertainty caused due to the above assumptions. The proposed solution is based on the consideration of interval data. The Bayesian maximum entropy (BME) theory (Christakos, Modern spatiotemporal geostatistics. Oxford University Press, New York, 2000) is an essential component of this methodology. The main reason for this is that it is the only theory in the framework of Geostatistics that offers powerful tools to merge the uncertainty sources with the rest of conventional measurements. This ability is a result of its generalized view on the problem of estimation that focuses on the knowledge of a natural variable and not the variable itself. The application of the proposed methodology led to sharper posterior distributions, an indication of the increased certainty in estimated values which is induced by the broader utilization of data.

“…The occurrence of As in drinking water and food is an issue of considerable public health interest (Yu et al 2007). Risk assessment is a frequently employed method which discusses potential threats to human health through an exposure-bioaccumulation-ingestion pathway of toxic substances in aquatic organisms (Pohl et al 2003;Hung et al 2004).…”

Section: Introductionmentioning

confidence: 99%

“…However, only a small proportion of in-situ data can be analyzed in a survey owing to time and cost constraints. Risk predictions made with sparse measurement data are typically subject to considerable uncertainty (Yu et al 2007). Moreover, overestimated risks lead to social panic and fisher's economic losses.…”

Section: Introductionmentioning

confidence: 99%

Risk-based assessment of arsenic-affected aquacultural water in blackfoot disease hyperendemic areas

Jang

Lin

Liu

et al. 2008

This work explored a risk-based arsenic (As) regulation in farmed pond water by ingesting tilapia (Oreochromis mossambicus) in blackfoot disease hyperendemic areas and discussed a rational As regulation in pond water. Monte Carlo analysis was used to propagate the parameter uncertainty and to assess probabilistically regulation risks. A dynamic scheme of groundwater management was proposed that curves of utilization ratios against As concentrations in groundwater were established based on the risk-based regulation. The 5th to 95th percentiles of risks range from 3.5 9 10 -7 to 6.0 9 10 -5 via ingesting the farmed tilapia under the current As regulation in farmed pond water in Taiwan, 50 lg/L. To compare to inorganic As regulation in drinking water, the current As regulation in farmed pond water does not pose a great threat to human health, but it is unsafe. Therefore, this study suggests that the regulation of As in farmed pond water is revised to be 25 lg/L.