Application of Geographically Weighted Regression for Vulnerable Area Mapping of Leptospirosis in Bantul District

Widayani, Prima; Gunawan, Totok; Danoedoro, Projo; Mardihusodo, Sugeng Juwono

doi:10.22146/ijg.17601

“…The Akaike Information Criterion (AICc) value, which assesses the relative quality of models given trade-offs between model fit and model complexity [15], showed that the model with temperature seasonality (TS) was more efficient (AIC: 31743.42) in explaining the geographical distribution of leptospirosis transmission risk. This result corroborates findings from other studies showing that geographically weighted regression can offer improvements and additional insights over standard non-spatial regression models for eco-epidemiological studies of leptospirosis [13,15,16,17,18].…”

Section: Discussionsupporting

confidence: 90%

“…This, in turn, has fostered the utilization of GIS analytics and geospatial statistics for environmental analyses of infectious diseases [14]. Previous studies have adopted GIS analysis tools in the study of ecological models to explore and analyse spatial variations in relationships between local environmental factors and the occurrences of leptospirosis [13,15,16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Spatial Variation in the Relationship between Leptospirosis Incidence and Climate Determinants in Sarawak (Malaysia)

Kira

¹

,

Bilung²,

Ngui

³

et al. 2019

Preprint

0

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Background Leptospirosis is a zoonotic disease caused by spirochete bacteria in the genus Leptospira, and it has become a significant public health challenge in Malaysia. Environmental survival and persistence of this pathogen are highly dependent on environmental conditions such as moisture content, pH and temperature. These conditions are further adapted by the natural climate system including precipitation and humidity which is highly heterogeneous at a geographical scale. This paper described the spatial and temporal distribution of leptospirosis incidence with climate factors using Geographical Information System and stratified the climate factors based on their association with the disease incidence rate. Methods Leptospirosis surveillance data from 2012-2016 were integrated into this study along with seven geo-spatial climate variables for the state of Sarawak, Malaysia. High and low clustering of incidence rate was explored by Getis Ord Gi* statistics. Geographical Weighted Regression model was utilized to study the relationship between the incidence rate and selected climate variables. Results Spatial analysis revealed seven districts in the state of Sarawak as hot spot areas, and six cold spot areas with GiZ score varies between (-3.092 to 3.203). The cumulative incidence rate demonstrated an increasing trend towards the South-East region of Sarawak with an average of 162 cases per 100,000 population. The univariate analysis reported a significant relationship (p<0.05) between leptospirosis incidence rate and temperature seasonality with the lowest AIC value of 31741.4. The results showed that temperature seasonality explained 99% (R2: 0.99) of the spatial variances in incidence rate with 42.31% of the localities showing a significant positive relationship. Conclusion The present study highlighted the importance of temperature seasonality as a potential determinant for the spatial distribution of leptospirosis cases with different strength and direction of the association. In conclusion, this suggests that specific interventions at a locality with strong climate determinants should be implemented to combat the burden of leptospirosis.

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“…The Akaike Information Criterion (AICc) value, which assesses the relative quality of models given trade-offs between model fit and model complexity [15], showed that the model with temperature seasonality (TS) was more efficient (AIC: 31743.42) in explaining the geographical distribution of leptospirosis transmission risk. This result corroborates findings from other studies showing that geographically weighted regression can offer improvements and additional insights over standard non-spatial regression models for eco-epidemiological studies of leptospirosis 21 [13,15,16,17,18].…”

Section: Discussionsupporting

confidence: 90%

Spatial Variation in the Relationship between Leptospirosis Incidence and Climate Determinants in Sarawak (Malaysia)

Kira

¹

,

Ngui

²

,

Apun

³

et al. 2019

Preprint

0

View full text Add to dashboard Cite

Background Leptospirosis is a zoonotic disease caused by spirochete bacteria in the genus Leptospira, and it has become a significant public health challenge in Malaysia. Environmental survival and persistence of this pathogen are highly dependent on environmental conditions such as moisture content, pH and temperature. These conditions are further adapted by the natural climate system including precipitation and humidity which is highly heterogeneous at a geographical scale. This paper described the spatial and temporal distribution of leptospirosis incidence with climate factors using Geographical Information System and stratified the climate factors based on their association with the disease incidence rate. Methods Leptospirosis surveillance data from 2012-2016 were integrated into this study along with seven geo-spatial climate variables for the state of Sarawak, Malaysia. High and low clustering of incidence rate was explored by Getis Ord Gi* statistics. Geographical Weighted Regression model was utilized to study the relationship between the incidence rate and selected climate variables. Results Spatial analysis revealed seven districts in the state of Sarawak as hot spot areas, and six cold spot areas with GiZ score varies between (-3.092 to 3.203). The cumulative incidence rate demonstrated an increasing trend towards the South-East region of Sarawak with an average of 162 cases per 100,000 population. The univariate analysis reported a significant relationship (p<0.05) between leptospirosis incidence rate and temperature seasonality with the lowest AIC value of 31741.4. The results showed that temperature seasonality explained 99% (R2: 0.99) of the spatial variances in incidence rate with 42.31% of the localities showing a significant positive relationship. Conclusion The present study highlighted the importance of temperature seasonality as a potential determinant for the spatial distribution of leptospirosis cases with different strength and direction of the association. In conclusion, this suggests that specific interventions at a locality with strong climate determinants should be implemented to combat the burden of leptospirosis.

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“…While A is vegetation cover score, B annual precipitation score, C land degradation score and D harvest failure score. Then, food endurance level will be calculated by following formula [9]:…”

Section: Fig 4 Graphical Representation Of Land Degardationmentioning

confidence: 99%

“…2). Then mean annual precipetation value were assigned weightage according to Table 3 [8,9]. Soil map: We get soil map from GWSP (Digital Water Atlas http://atlas.gwsp.org/index.php) with 0.5 degree.…”

Section: Introductionmentioning

confidence: 99%

Food vulnerability analysis in the central dry zone of Myanmar

Boori¹,

Choudhary²,

Paringer³

et al. 2017

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The central dry zone of Myanmar is the most water stressed and also one of the most food insecure regions in the country. In the Dry Zone, the total population is 10.1 million people in 54 townships, in which approximately 43 % of people live below the poverty line and 40 -50 % of the rural population is landless. Agriculture is the most important economic sector in Myanmar as it is essential for the national food security and a major source of livelihood of the people. In this region the adverse effects of climate change such as a late or early onset of the monsoon season, longer dry spells, erratic rainfall, increasing temperatures, heavy rains, stronger typhoons, extreme spatialtemporal variability of rainfall, high intensities, limited rainfall events in the growing season, heat stress, drought, flooding, sea water intrusion, land degradation, desertification, deforestation, and other natural disasters are believed to be major constraints to food security. Theses extreme climatic events are likely to increase in frequency and magnitude, leading to serious drought periods and extreme floods. Food insecurity is an important thing that must be reviewed because it affects the lives of many people. For food vulnerability, we use the following indicators: slope, precipitation, vegetation, soil, erosion, land degradation and harvest failure in ArcGIS software. The erosion is influenced by rainfall and slope, while land degradation is directly related to vegetation, drainage and soil. In the meantime, the harvest failure can be generated by rainfall and flood potential zones. The results show that around 45 % of the area studied comes under a very high erosion danger level, 70 % are in the average harvest failure zone, 59 % are in the intermediate land degradation area, and overall around 45 % of the studied area comes under the insecure food vulnerability zone. Our analysis shows that an increase in the alluvial farming by 1745.33 km 2 since 1988 has helped reduce the insecure food vulnerability. The food vulnerability map is also relevant to increased population and low income areas. This paper is helpful for identifying the areas of food needs in central dry zone of Myanmar.

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Application of Geographically Weighted Regression for Vulnerable Area Mapping of Leptospirosis in Bantul District

Cited by 9 publications

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Spatial Variation in the Relationship between Leptospirosis Incidence and Climate Determinants in Sarawak (Malaysia)

Spatial Variation in the Relationship between Leptospirosis Incidence and Climate Determinants in Sarawak (Malaysia)

Spatial Variation in the Relationship between Leptospirosis Incidence and Climate Determinants in Sarawak (Malaysia)

Food vulnerability analysis in the central dry zone of Myanmar

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