Bayesian spatio-temporal distributed lag modeling for delayed climatic effects on sparse malaria incidence data

Abstract Background In many areas of the Greater Mekong Subregion (GMS), malaria endemic regions have shrunk to patches of predominantly low-transmission. With a regional goal of elimination by 2030, it is important to use appropriate methods to analyze and predict trends in incidence in these remaining transmission foci to inform planning efforts. Climatic variables have been associated with malaria incidence to varying degrees across the globe but the relationship is less clear in the GMS and standard methodologies may not be appropriate to account for the lag between climate and incidence and for locations with low numbers of cases. Methods In this study, a methodology was developed to estimate the spatio-temporal lag effect of climatic factors on malaria incidence in Thailand within a Bayesian framework. A simulation was conducted based on ground truth of lagged effect curves representing the delayed relation with sparse malaria cases as seen in our study population. A case study to estimate the delayed effect of environmental variables was used with malaria incidence at a fine geographic scale of sub-districts in a western province of Thailand. Results From the simulation study, the model assumptions which accommodated both delayed effects and excessive zeros appeared to have the best overall performance across evaluation metrics and scenarios. The case study demonstrated lagged climatic effect estimation of the proposed modeling with real data. The models appeared to be useful to estimate the shape of association with malaria incidence. Conclusions A new method to estimate the spatiotemporal effect of climate on malaria trends in low transmission settings is presented. The developed methodology has potential to improve understanding and estimation of past and future trends in malaria incidence. With further development, this could assist policy makers with decisions on how to more effectively distribute resources and plan strategies for malaria elimination.

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Spatio-temporal variation of malaria hotspots in central Senegal, 2008-2012

10.21203/rs.2.11659/v1 ◽

2019 ◽

Author(s):

Sokhna DIENG ◽

El Hadj Ba ◽

Badara Cissé ◽

Kankoe Sallah ◽

Abdoulaye Guindo ◽

...

Keyword(s):

Temporal Variation ◽

Incidence Rate ◽

Malaria Incidence ◽

Vegetation Type ◽

Control Strategies ◽

Entire Study ◽

Entire Area ◽

Low Transmission ◽

Malaria Incidence Rate ◽

Spatio Temporal

Abstract Background In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal, and to identify the meteorological, environmental, and preventive factors that influence this variation. Methods The weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population 523,908) during a trial of Seasonal Malaria Chemoprevention (SMC), were analysed. Data on weekly rainfall and annual vegetation types were obtained for each village from remote sensing data. The time series of weekly cumulative malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected for each period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. Results The cumulative malaria incidence rate for the entire area ranged from 0 to 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR=0.48, IC95%: 0.33-0.68). According to the spatial interpolation, 2 zones located in the south of the study area had the highest risk of being a hotspot (ORmin=1.90, 95%CI: 1.02-3.56; ORmax=60.65, 95%CI: 26.86-136.95). The association between rainfall and hotspot status was non-linear and depended on vegetation type and the amount of rainfall. Conclusion In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. Our analysis shows also the importance of adapting control strategies to the local context and dynamic patterns. Moreover, the issue of spatial hotspots and foci of malaria persistence during LTPs needs to be further addressed.

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ANALOG RESERVOIR MODELLING OF CHANNEL SANDS

The APPEA Journal ◽

10.1071/aj04034 ◽

2005 ◽

Vol 45 (1) ◽

pp. 439

Author(s):

D. Sherlock ◽

G. Weir ◽

K. Dodds

Keyword(s):

Ground Truth ◽

Real Data ◽

Time Lapse ◽

Seismic Inversion ◽

Reservoir Modelling ◽

Engineering Research ◽

Seismic Properties ◽

Before And After ◽

Deepwater Channel

This paper outlines the results of analog modelling of a sandy deepwater channel reservoir to gain insight into issues of uncertainty in reservoir simulations and their seismic expression. The project is unique in that it integrates seismic and reservoir engineering research in a controlled laboratory environment. Unlike numerical modelling investigations, this laboratory-based modelling study provides real data that does not rely on assumptions and is, therefore, a useful case study for comparing the actual production and seismic response against numerical predictions.The 1 m2 model comprised two intersecting synthetic sandstone channels within a transparent acrylic matrix. The model was initially oil-saturated with irreducible water and was produced through waterflooding of the upper channel. Careful attention was paid to scaling of both the fluid dynamics and the seismic properties to ensure that the response of the model was representative of the field-scale environment. Scaled time-lapse seismic data was recorded before and after production and data such as water cuts, recovery rates and pressure drop between injector and producers were also recorded.Analog reservoir modelling (ARM) provides a new tool that allows seismic attributes to be evaluated against ground truth results and the performance of seismic inversion schemes to be critically assessed.

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Regional Agroclimate Characteristic and Its Multiple Teleconnections: A Case Study in the Jianghan Plain (JHP) Region

Water ◽

10.3390/w13192789 ◽

2021 ◽

Vol 13 (19) ◽

pp. 2789

Author(s):

Wenhui Li ◽

Dongguo Shao ◽

Wenquan Gu ◽

Donghao Miao

Keyword(s):

Quantile Regression ◽

Climatic Factors ◽

Meteorological Data ◽

Central China ◽

Data Sets ◽

Jianghan Plain ◽

Teleconnection Patterns ◽

Spatio Temporal ◽

Quantile Regression Method

Agricultural production depends on local agroclimatic conditions to a great extent, affected by ENSO and other ocean-atmospheric climate modes. This paper analyzed the spatio-temporal distributions of climate elements in the Jianghan Plain (JHP), Central China, and explored the impacts from teleconnection patterns, aimed at providing references for dealing with climate change and guiding agricultural activities. Both linear and multifactorial regression models were constructed based on the frequentist quantile regression and Bayesian quantile regression method, with the daily meteorological data sets of 17 national stations in the plain and teleconnection climate characteristic indices. The results showed that precipitation in JHP had stronger spatial variability than evapotranspiration. El Niño probably induced less precipitation in summer while the weakening Arctic Oscillation might lead to more summertime precipitation. The Nash-Sutcliffe efficiency (NSE) of the multifactorial and linear regression model at the median level were 0.42–0.56 and 0.12–0.18, respectively. The mean relative error (MRE) ranged −2.95–−0.26% and −7.83–0.94%, respectively, indicating the much better fitting accuracy of the multiple climatic factors model. Meanwhile it confirmed that the agricultural climate in JHP was under the influence from multiple teleconnection patterns.

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Spatio-temporal variation of malaria hotspots in central Senegal, 2008-2012

10.21203/rs.2.11659/v2 ◽

2020 ◽

Author(s):

Sokhna DIENG ◽

El Hadj Ba ◽

Badara Cissé ◽

Kankoe Sallah ◽

Abdoulaye Guindo ◽

...

Keyword(s):

Temporal Variation ◽

Incidence Rate ◽

Malaria Incidence ◽

Vegetation Type ◽

Local Context ◽

Entire Study ◽

Entire Area ◽

Low Transmission ◽

Malaria Incidence Rate ◽

Spatio Temporal

Abstract Background In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal, and to identify the meteorological, environmental, and preventive factors that influence this variation. Methods The weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population 523,908) during a trial of Seasonal Malaria Chemoprevention (SMC), were analysed. Data on weekly rainfall and annual vegetation types were obtained for each village from remote sensing data. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. Results The malaria incidence rate for the entire area ranged from 0 to 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR=0.48, IC95%: 0.33-0.68). The association between rainfall and hotspot status was non-linear and depended on vegetation type and the amount of rainfall. The association between village location in the study area and the hotspot status was also showed. Conclusion In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. Knowing the similar environmental and meteorological particularities of hotspots, surveillance on these factors could lead targeted public health interventions in local context. Moreover, the issue of spatial hotspots and foci of malaria persistence during LTPs needs to be further addressed.

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Spatio-temporal variation of malaria hotspots in central Senegal, 2008-2012

10.21203/rs.2.11659/v3 ◽

2020 ◽

Author(s):

Sokhna DIENG ◽

El Hadj Ba ◽

Badara Cissé ◽

Kankoe Sallah ◽

Abdoulaye Guindo ◽

...

Keyword(s):

Temporal Variation ◽

Incidence Rate ◽

Malaria Incidence ◽

Vegetation Type ◽

Local Level ◽

Entire Study ◽

Entire Area ◽

Low Transmission ◽

Malaria Incidence Rate ◽

Spatio Temporal

Abstract Background: In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal and to identify the meteorological, environmental, and preventive factors that influence this variation.Methods: This study analysed the weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population approximately 500,000) as part of a trial of seasonal malaria chemoprevention (SMC). Data on weekly rainfall and annual vegetation types were obtained for each village through remote sensing. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model.Results : The malaria incidence for the entire area varied between 0 and 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR=0.48, IC95%: 0.33-0.68). The association between rainfall and hotspot status was non-linear and depended on both vegetation type and amount of rainfall. The association between village location in the study area and hotspot status was also shown.Conclusion : In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. By taking into consideration the environmental and meteorological characteristics common to all hotspots, monitoring of these factors could lead targeted public health interventions at the local level. Moreover, spatial hotspots and foci of malaria persisting during LTPs need to be further addressed.Trial registrationThe data used in this work were obtained from a clinical trial registered at www.clinicaltrials.gov under # NCT 00712374.

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SPATIO-TEMPORAL VARIATION OF VEGETATION COVERAGE AND ITS RESPONSE TO CLIMATIC FACTORS FROM 2001 TO 2015: A CASE STUDY IN ANHUI, CHINA

Environmental Engineering and Management Journal ◽

10.30638/eemj.2021.086 ◽

2021 ◽

Vol 20 (6) ◽

pp. 927-939

Author(s):

Hongfei Yang ◽

Hao Xu ◽

Xuanning Zhong ◽

Dandan Hu

Keyword(s):

Temporal Variation ◽

Climatic Factors ◽

Vegetation Coverage ◽

Spatio Temporal

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Spatio-temporal approach for identification of critical conservation areas: a case study with two pine species from a threatened temperate forest in Mexico

Biodiversity and Conservation ◽

10.1007/s10531-019-01767-y ◽

2019 ◽

Vol 28 (7) ◽

pp. 1863-1883 ◽

Cited By ~ 3

Author(s):

Agustín Molina Sánchez ◽

Patricia Delgado ◽

Antonio González-Rodríguez ◽

Clementina González ◽

A. Francisco Gómez-Tagle Rojas ◽

...

Keyword(s):

Temperate Forest ◽

Conservation Areas ◽

Spatio Temporal ◽

Pine Species

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G-Tric: generating three-way synthetic datasets with triclustering solutions

BMC Bioinformatics ◽

10.1186/s12859-020-03925-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

João Lobo ◽

Rui Henriques ◽

Sara C. Madeira

Keyword(s):

State Of The Art ◽

Synthetic Data ◽

Ground Truth ◽

Real Data ◽

Three Dimensions ◽

Additional Advantage ◽

Urban Dynamics ◽

Data Generator ◽

Real World Datasets ◽

Synthetic Datasets

Abstract Background Three-way data started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations $$\times$$ × features $$\times$$ × contexts). With increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount. These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real 3-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output. Results G-Tric can replicate real-world datasets and create new ones that match researchers needs across several properties, including data type (numeric or symbolic), dimensions, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled, by defining the amount of missing, noise or errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters. Conclusions Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric’s potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches.

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