Spatio-Temporal Diffusion Pattern and Hotspot Detection of Dengue in Chachoengsao Province, Thailand

Jeefoo, Phaisarn; Tripathi, Nimisha; Souris, Marc

doi:10.3390/ijerph8010051

Cited by 103 publications

(90 citation statements)

References 40 publications

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“…All the years studied, except 2009, showed clustered patterns. The application of LISA is known for its ability to effectively locate the spatially active local clusters ( Figure 5), which are missed (by default) by the global Moran I (Jeefoo et al, 2011). Using the LISA approach, we observed two highhigh clusters marking a hotspot in 2010.…”

Section: Articlementioning

confidence: 98%

Space-time epidemiology and effect of meteorological parameters on influenza-like illness in Phitsanulok, a northern province in Thailand

Nimbalkar

Tripathi

2016

Geospat Health

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Influenza-like illness (ILI) is an acute respiratory disease that remains a public health concern for its ability to circulate globally affecting any age group and gender causing serious illness with mortality risk. Comprehensive assessment of the spatio-temporal dynamics of ILI is a prerequisite for effective risk assessment and application of control measures. Though meteorological parameters, such as rainfall, average relative humidity and temperature, influence ILI and represent crucial information for control of this disease, the relation between the disease and these variables is not clearly understood in tropical climates. The aim of this study was to analyse the epidemiology of ILI cases using integrated methods (space-time analysis, spatial autocorrelation and other correlation statistics). After 2009s H1N1 influenza pandemic, Phitsanulok Province in northern Thailand was strongly affected by ILI for many years. This study is based on ILI cases in villages in this province from 2005 to 2012. We used highly precise weekly incidence records covering eight years, which allowed accurate estimation of the ILI outbreak. Comprehensive methodology was developed to analyse the global and local patterns of the spread of the disease. Significant space-time clusters were detected over the study region during eight different periods. ILI cases showed seasonal clustered patterns with a peak in 2010 (P>0.05-9.999 iterations). Local indicators of spatial association identified hotspots for each year. Statistically, the weather pattern showed a clear influence on ILI cases and it strongly correlated with humidity at a lag of 1 month, while temperature had a weaker correlation.

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Section: Articlementioning

confidence: 98%

Space-time epidemiology and effect of meteorological parameters on influenza-like illness in Phitsanulok, a northern province in Thailand

Nimbalkar

Tripathi

2016

Geospat Health

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“…Typically, the most significant time lags between temperature/precipitation and dengue are found to be around 1-2 months (Arcari et al, 2007;Cheong et al, 2013;Descloux et al, 2012;García et al, 2011;Gharbi et al, 2011;Gomes et al, 2012;Jeefoo et al, 2010;Lowe et al, 2011;Wu et al, 2007) , although some studies report lags of around 3-4 months (Bi et al, 2001;Chen et al, 2012;Depradine and Lovell, 2004;Yu et al, 2011) .…”

Section: Introductionmentioning

confidence: 99%

Quantifying the added value of climate information in a spatio-temporal dengue model

Lowe

Cazelles

Rácz

et al. 2015

Stoch Environ Res Risk Assess

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Dengue is the world's most important vector-borne viral disease. The dengue mosquito and virus are sensitive to climate variability and change. Temperature, humidity and precipitation influence mosquito biology, abundance and habitat, and the virus replication speed. In this study, we develop a modelling procedure to quantify the added value of including climate information in a dengue model for the 76 provinces of Thailand, from 1982-2013. We first developed a seasonal-spatial model, to account for dependency structures from one month to the next and between provinces. We then tested precipitation and temperature variables at varying time lags, using linear and nonlinear functional forms, to determine an optimum combination of time lags to describe dengue relative risk. Model parameters were estimated using Integrated Nested Laplace Approximation (INLA).This approach provides a novel opportunity to perform model selection in a Bayesian framework, while accounting for underlying spatial and temporal dependency structures and linear or nonlinear functional forms. We quantified the additional variation explained by interannual climate variations, above that provided by the seasonal-spatial model. Overall, an additional 8% of the variance in dengue relative risk can be explained by accounting for interannual variations in precipitation and temperature in the previous month. The inclusion of nonlinear functions of climate in the model framework improved the model for 79% of the provinces. Therefore, climate forecast information could significantly contribute to a national dengue early warning system in Thailand.

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“…Hotspot detection is one of the methods for testing global aggregation, which not only can measure the degree of local correlation and analyze the characteristics of spatial distribution, but also can identify the clustering characteristics of high-or low-value species richness [42,43]. CrimeStat is spatial statistics software for the analysis of incident locations that can interface with most desktop geographic information system programs [44]. First, the data setup section required that we define the "Primary" file, which is a shape file of threatened plant species distribution incidence with X and Y coordinates and attribute fields.…”

Section: Spatial Autocorrelation Analysis and Hotspot Detectionmentioning

confidence: 99%

Threatened Plants in China’s Sanjiang Plain: Hotspot Distributions and Gap Analysis

Zheng²,

Liu

et al. 2018

Sustainability

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Abstract:Global biodiversity is markedly decreasing in response to climate change and human disturbance. Sanjiang Plain is recognized as a biodiversity hotspot in China due to its high forest and wetland coverage, but species are being lost at an unprecedented rate, induced by anthropogenic activities. Identifying hotspot distributions and conservation gaps of threatened species is of particular significance for enhancing the conservation of biodiversity. Specifically, we integrated the principles and methods of spatial hotspot inspection, geographic information system (GIS) technology and spatial autocorrelation analysis along with fieldwork to determine the spatial distribution patterns and unprotected hotspots of vulnerable and endangered plants in Sanjiang Plain. A gap analysis of the conservation status of vulnerable and endangered plants was conducted. Our results indicate that six nationally-protected plants were not observed in nature reserves or were without any protection, while the protection rates were <10% for 10 other nationally-protected plants. Protected areas (PAs) cover <5% of the distribution areas for 31 threatened plant species, while only five species are covered by national nature reserves (NNRs) within >50% of the distribution areas. We found 30 hotspots with vulnerable and endangered plants in the study area, but the area covered by NNRs is very limited. Most of the hotspots were located in areas with a high-high aggregation of plant species. Therefore, it is necessary to expand the area of existing nature reserves, establish miniature protection plots and create new PAs and ecological corridors to link the existing PAs. Our findings can contribute to the design of a PA network for botanical conservation.

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Spatio-Temporal Diffusion Pattern and Hotspot Detection of Dengue in Chachoengsao Province, Thailand

Cited by 103 publications

References 40 publications

Space-time epidemiology and effect of meteorological parameters on influenza-like illness in Phitsanulok, a northern province in Thailand

Space-time epidemiology and effect of meteorological parameters on influenza-like illness in Phitsanulok, a northern province in Thailand

Quantifying the added value of climate information in a spatio-temporal dengue model

Threatened Plants in China’s Sanjiang Plain: Hotspot Distributions and Gap Analysis

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