scholarly journals Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016-2017

2021 ◽  
Author(s):  
C&eacutedric St&eacutephane Bationo ◽  
Jean Gaudart ◽  
Dieng Sokhna ◽  
Mady Cissoko ◽  
Paul Taconet ◽  
...  
Keyword(s):  
Burkina Faso ◽  
Incidence Rate ◽  
Temporal Dynamics ◽  
Meteorological Data ◽  
Time Lag ◽  
Temporal Analysis ◽  
Health District ◽  
Control And Prevention ◽  
Spatio Temporal ◽  
Spatial Hotspots

Background: Malaria control and prevention programs are more efficient and cost-effective when they target hotspots or select the best periods of year to implement interventions. This study aimed to identify the spatial distribution of malaria hotspots at the village level in Diébougou health district, Burkina Faso, and to model the temporal dynamics of malaria cases as a function of meteorological conditions and of the distance between villages and health centers (HCs). Methods: Case data for 27 villages were collected in 13 HCs using continuous passive case detection. Meteorological data were obtained through remote sensing. Two synthetic meteorological indicators (SMIs) were created to summarize meteorological variables. Spatial hotspots were detected using the Kulldorf scanning method. A General Additive Model was used to determine the time lag between cases and SMIs and to evaluate the effect of SMIs and distance to HC on the temporal evolution of malaria cases. The multivariate model was fitted with data from the epidemic year to predict the number of cases in the following outbreak. Results: Overall, the incidence rate in the area was 429.13 cases per 1,000 person-year with important spatial and temporal heterogeneities. Four spatial hotspots, involving 7 of the 27 villages, were detected, for an incidence rate of 854.02 cases per 1,000 person-year. The hotspot with the highest risk (relative risk = 4.06) consisted of a single village, with an incidence rate of 1,750.75 cases per 1,000 person-years. The multivariate analysis found greater variability in incidence between HCs than between villages linked to the same HC. The epidemic year was characterized by a major peak during the second part of the rainy season and a secondary peak during the dry-hot season. The time lag that generated the better predictions of cases was 9 weeks for SMI1 (positively correlated with precipitation variables and associated with the first peak of cases) and 16 weeks for SMI2 (positively correlated with temperature variables and associated with the secondary peak of cases). Euclidian distance to HC was not found to be a predictor of malaria cases recorded in HC. The prediction followed the overall pattern of the time series of reported cases and predicted the onset of the following outbreak with a precision of less than 3 weeks. Conclusions: Our spatio-temporal analysis of malaria cases in Diebougou health district, Burkina Faso, provides a powerful prospective method for identifying and predicting high-risk areas and high-transmission periods that could be targeted in future malaria control and prevention campaigns. Keywords Geo-epidemiology, Spatial Clusters, temporal dynamics, nonlinear relationship, prediction.

scholarly journals Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016–2017

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Cédric S. Bationo ◽  
Jean Gaudart ◽  
Sokhna Dieng ◽  
Mady Cissoko ◽  
Paul Taconet ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Burkina Faso ◽  
Incidence Rate ◽  
Malaria Control ◽  
Meteorological Data ◽  
Time Lag ◽  
Health District ◽  
Scanning Method ◽  
Control And Prevention ◽  
Spatial Hotspots

AbstractMalaria control and prevention programs are more efficient and cost-effective when they target hotspots or select the best periods of year to implement interventions. This study aimed to identify the spatial distribution of malaria hotspots at the village level in Diébougou health district, Burkina Faso, and to model the temporal dynamics of malaria cases as a function of meteorological conditions and of the distance between villages and health centres (HCs). Case data for 27 villages were collected in 13 HCs. Meteorological data were obtained through remote sensing. Two synthetic meteorological indicators (SMIs) were created to summarize meteorological variables. Spatial hotspots were detected using the Kulldorf scanning method. A General Additive Model was used to determine the time lag between cases and SMIs and to evaluate the effect of SMIs and distance to HC on the temporal evolution of malaria cases. The multivariate model was fitted with data from the epidemic year to predict the number of cases in the following outbreak. Overall, the incidence rate in the area was 429.13 cases per 1000 person-year with important spatial and temporal heterogeneities. Four spatial hotspots, involving 7 of the 27 villages, were detected, for an incidence rate of 854.02 cases per 1000 person-year. The hotspot with the highest risk (relative risk = 4.06) consisted of a single village, with an incidence rate of 1750.75 cases per 1000 person-years. The multivariate analysis found greater variability in incidence between HCs than between villages linked to the same HC. The time lag that generated the better predictions of cases was 9 weeks for SMI1 (positively correlated with precipitation variables) and 16 weeks for SMI2 (positively correlated with temperature variables. The prediction followed the overall pattern of the time series of reported cases and predicted the onset of the following outbreak with a precision of less than 3 weeks. This analysis of malaria cases in Diébougou health district, Burkina Faso, provides a powerful prospective method for identifying and predicting high-risk areas and high-transmission periods that could be targeted in future malaria control and prevention campaigns.

scholarly journals Spatio-temporal analysis of COVID-19 incidence rate using GIS: a case study—Tehran metropolitan, Iran

GeoJournal ◽  
2021 ◽  
Author(s):  
R. Nasiri ◽  
S. Akbarpour ◽  
AR. Zali ◽  
N. Khodakarami ◽  
MH. Boochani ◽  
...  
Keyword(s):  
Incidence Rate ◽  
Temporal Analysis ◽  
Spatio Temporal

scholarly journals Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Behzad Kiani ◽  
Amene Raouf Rahmati ◽  
Robert Bergquist ◽  
Soheil Hashtarkhani ◽  
Neda Firouraghi ◽  
...  
Keyword(s):  
High Risk ◽  
Incidence Rate ◽  
Policy Development ◽  
Temporal Pattern ◽  
Temporal Analysis ◽  
High Rate ◽  
Spatial Scan Statistic ◽  
Cross Sectional ◽  
Risk Areas ◽  
Spatio Temporal

Abstract Background Effective reduction of tuberculosis (TB) requires information on the distribution of TB incidence rate across time and location. This study aims to identify the spatio-temporal pattern of TB incidence rate in Iran between 2008 and 2018. Methods This cross-sectional study was conducted on aggregated TB data (50,500 patients) at the provincial level provided by the Ministry of Health in Iran between 2008 and 2018. The Anselin Local Moran’s I and Getis-Ord Gi* were performed to identify the spatial variations of the disease. Furthermore, spatial scan statistic was employed for purely temporal and spatio-temporal analyses. In all instances, the null hypothesis of no clusters was rejected at p ≤ 0.05. Results The overall incidence rate of TB decreased from 13.46 per 100,000 (95% CI: 13.19–13.73) in 2008 to 10.88 per 100,000 (95% CI: 10.65–11.11) in 2018. The highest incidence rate of TB was observed in southeast and northeast of Iran for the whole study period. Additionally, spatial cluster analysis discovered Khuzestan Province, in the West of the country, having significantly higher rates than neighbouring provinces in terms of both total TB and smear-positive pulmonary TB (SPPTB). Purely temporal analysis showed that high-rate and low-rate clusters were predominantly distributed in the time periods 2010–2014 and 2017–2018. Spatio-temporal results showed that the statistically significant clusters were mainly distributed from centre to the east during the study period. Some high-trend TB and SPPTB statistically significant clusters were found. Conclusion The results provided an overview of the latest TB spatio-temporal status In Iran and identified decreasing trends of TB in the 2008–2018 period. Despite the decreasing incidence rate, there is still need for screening, and targeting of preventive interventions, especially in high-risk areas. Knowledge of the spatio-temporal pattern of TB can be useful for policy development as the information regarding the high-risk areas would contribute to the selection of areas needed to be targeted for the expansion of health facilities.

scholarly journals Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China

2020 ◽  
Vol 17 (7) ◽  
pp. 2563 ◽  
Author(s):  
Wentao Yang ◽  
Min Deng ◽  
Chaokui Li ◽  
Jincai Huang
Keyword(s):  
Temporal Dynamics ◽  
Spatial Interaction ◽  
Temporal Trends ◽  
Temporal Patterns ◽  
Temporal Analysis ◽  
Hubei Province ◽  
Incidence Rates ◽  
County Level ◽  
Spatial Clusters ◽  
Spatio Temporal

Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann–Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran’s I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic.

Fog climatology analyses in coastal fog ecosystem at the Atacama Desert/Chile – spatio-temporal analysis of fog water characteristics and variability

2020 ◽  
Author(s):  
Juan Carlos Pastene ◽  
Alexander Siegmund ◽  
Camilo del Río ◽  
Pablo Osses
Keyword(s):  
Temporal Dynamics ◽  
Local Level ◽  
Global Radiation ◽  
Atacama Desert ◽  
Temporal Analysis ◽  
Local Network ◽  
Fog Water ◽  
Water Characteristics ◽  
Vertical Range ◽  
Spatio Temporal

<p>The coastal Chilean Atacama Desert comprise some of the driest areas of the world with anual mean precipitation partly less than 1 mm/year, like in the Tarapacá region. It is in these environments, where fog plays a relevant role for local ecosystems, like the so called <em>Tillandsia</em> Lomas. These fog ecosystems contain <em>Tillandsia landbeckii</em> as an endemic species, which covers a vertical range of about 800 to 1,250 m, related to fog availability. The study area “Oyarbide” (20°29’ S, 70°03’ W) is situated inland desert, over a range of 300 m elevation where the advective and orographic fog penetrate far enough to reach the east border of the site at around 1,200 m.</p> <p>On local level, the understanding of the fog climate characteristics and variability is still poor as well as knowledge about the driving parameters, the temporal dynamics and spatial gradients. For this reason, various parameters of fog climate are analysed and characterised on the basis of a local station network in order to determine the local fog climatology.</p> <p>From 2016, several high quality climatological stations (Thies Clima) were installed in “Oyarbide”, located in a transect from ca. 1,160 m to ca. 1,350 m in a distance between 10.3 km to 10.7 km from the coast. The local network of climate stations is generating a high temporal and spatial acquisition of climatological data of standard fog water (2 m), air temperature & humidity (2 m), surface temperature (5 cm), wind speed & direction (10 m & 2 m), air pressure, global radiation, leaf wetness and dew every 10 minutes until nowadays. Additionally, ten mini fog collectors (Mini FCs) were installed at the beginning 2019, covering a surface of ca. 3 km<sup>2</sup>, generating a monthly data of ground fog water collected (50 cm).</p> <p>First spatio-temporal analyses of different parameters of the local fog climate will be presented. The results of the study show a seasonal, monthly and daily variability, with altitudinal and vertical differences and oscillation. The results will serve as input for the understanding of the fog variability into hyperarid zones.</p>

scholarly journals Spatio-temporal dynamics of dengue in Brazil: seasonal travelling waves and determinants of regional synchrony

2018 ◽  
Author(s):  
Mikhail Churakov ◽  
Christian J. Villabona-Arenas ◽  
Moritz U.G. Kraemer ◽  
Henrik Salje ◽  
Simon Cauchemez
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Mosquito Control ◽  
Human Mobility ◽  
Travelling Waves ◽  
Seasonal Pattern ◽  
Temporal Analysis ◽  
Vector Ecology ◽  
Spatio Temporal ◽  
Dengue Epidemics

AbstractDengue continues to be the most important vector-borne viral disease globally and in Brazil, where more than 1.4 million cases and over 500 deaths were reported in 2016. Mosquito control programmes and other interventions have not stopped the alarming trend of increasingly large epidemics in the past few years.Here, we analyzed monthly dengue cases reported in Brazil between 2001 and 2016 to better characterize the key drivers of dengue epidemics. Spatio-temporal analysis revealed recurring travelling waves of disease occurrence. Using wavelet methods, we characterised the average seasonal pattern of dengue in Brazil, which starts in the western states of Acre and Rondônia, then travels eastward to the coast before reaching the northeast of the country. Only two states in the north of Brazil (Roraima and Amapá) did not follow the countrywide pattern and had inconsistent timing of dengue epidemics throughout the study period.We also explored epidemic synchrony and timing of annual dengue cycles in Brazilian regions. Using gravity style models combined with climate factors, we showed that both human mobility and vector ecology contribute to spatial patterns of dengue occurrence.This study offers a characterization of the spatial dynamics of dengue in Brazil and its drivers, which could inform intervention strategies against dengue and other arboviruses.Author summaryIn this paper we studied the synchronization of dengue epidemics in Brazilian regions. We found that a typical dengue season in Brazil can be described as a wave travelling from the western part of the country towards the east, with the exception of the two most northern equatorial states that experienced inconsistent seasonality of dengue epidemics.We found that the spatial structure of dengue cases is driven by both climate and human mobility patterns. In particular, precipitation was the most important factor for the seasonality of dengue at finer spatial resolutions.Our findings increase our understanding of large scale dengue patterns and could be used to enhance national control programs against dengue and other arboviruses.

scholarly journals The integrated water balance and soil data set of the Rollesbroich hydrological observatory

2016 ◽  
Author(s):  
Wei Qu ◽  
Heye R. Bogena ◽  
Johan A. Huisman ◽  
Marius Schmidt ◽  
Ralf Kunkel ◽  
...  
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Temporal Dynamics ◽  
Meteorological Data ◽  
Temporal Variations ◽  
Hydrological Models ◽  
Data Set ◽  
Western Germany ◽  
Spatio Temporal

Abstract. The Rollesbroich headwater catchment located in Western Germany is a densely instrumented hydrological observatory and part of the TERENO (Terrestrial Environmental Observatories) initiative. The measurements acquired in this observatory present a comprehensive dataset that contains key hydrological fluxes in addition to important hydrological states and properties. Meteorological data (i.e. precipitation, air temperature, air humidity, radiation components, and wind speed) are continuously recorded and actual evapotranspiration is measured using the eddy covariance technique. Runoff is measured at the catchment outlet with a gauging station. In addition, spatio-temporal variations in soil water content and temperature are measured at high resolution with a wireless sensor network (SoilNet). Soil physical properties were determined using standard laboratory procedures from samples taken at a large number of locations in the catchment. This comprehensive data set can be used to validate remote sensing retrievals and hydrological models; to improve the understanding of spatial temporal dynamics of soil water content; to optimize data assimilation and inverse techniques for hydrological models; and to develop upscaling and downscaling procedures of soil water content information. The complete data set is freely available online (http://www.tereno.net).

scholarly journals Multiple Sclerosis incidence rate in southern Iran: A Bayesian epidemiological study

2020 ◽  
Author(s):  
Naeimehossadat Asmarian ◽  
Zahra Sharafi ◽  
Amin Mousavi ◽  
Reis Jacques ◽  
Ibon Tamayo ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Vitamin D ◽  
Relative Risk ◽  
Incidence Rate ◽  
Temporal Analysis ◽  
Incidence Rates ◽  
Vitamin D Supplementation ◽  
Lower Percentage ◽  
Fars Province ◽  
Spatio Temporal

Abstract Background: Multiple Sclerosis (MS) remains to be a public health challenge, due to its unknown biological mechanisms and clinical impacts on young people. The prevalence of this disease in Iran is reported to be 5.30 to 74.28 per 100,000-person. Because of high prevalence of this disease in Fars province, the purpose of this study was to assess the spatial pattern of MS incidence rate by modeling both the effects of spatial dependence between neighboring regions and risk factors in a Bayesian Poisson model, which can lead to the improvement of health resource allocation decisions. Method: Data from 5,468 patients diagnosed with MS were collected, according to the McDonald’s criteria. New cases of MS were reported by the MS Society of Fars province from 1991 until 2016. The association between the percentage of people with low vitamin D intake, smoking, abnormal BMI and alcohol consumption in addition to spatial structure in a Bayesian spatio-temporal hierarchical model were used to determine the relative risk and trend of MS incidence rate in 29 counties of Fars province. Results: County-level crude incidence rates ranged from 0.22 to 11.31 cases per 100,000-person population. The highest relative risk was estimated at 1.80 in the county of Shiraz, the capital of Fars province, while the lowest relative risk was estimated at 0.11 in Zarindasht county in southern of Fars. The percentages of vitamin D supplementation intake and smoking were significantly associated with the incidence rate of MS. The results showed that 1% increase in vitamin D supplementation intake is associated with 2% decrease in the risk of MS and 1% increase in smoking is associated with 16% increase in the risk of MS. Conclusion: Bayesian spatio-temporal analysis of MS incidence rate revealed that trend is less steep than the mean trend of this disease in the south and south east of Fars province, which is due to the association between the higher percentage of vitamin D supplementation intake and the lower percentage of smoking. Previous studies have also shown that smoking and low vitamin D, among all covariates might be associated with high incidence of MS

scholarly journals Spatio-temporal analysis of influenza-like illness and prediction of incidence in high-risk regions, in the United States from 2011 to 2020

2021 ◽  
Author(s):  
Zhijuan Song ◽  
Xiaocan Jia ◽  
Junzhe Bao ◽  
Yongli Yang ◽  
Huili Zhu ◽  
...  
Keyword(s):  
United States ◽  
Statistical Significance ◽  
Temporal Analysis ◽  
The United States ◽  
Incidence Rates ◽  
Influenza Like Illness ◽  
Temporal Cluster ◽  
Control And Prevention ◽  
Spatio Temporal ◽  
And Control

Abstract Introduction: About 8% of Americans get influenza during an average season from the Centers for Disease Control and Prevention in the United States. It is necessary to strengthen the early warning of influenza and the prediction of public health. Methods In this study, we analyzed the characteristics of Influenza-like Illness (ILI) by Geographic Information System and SARIMA model, respectively. Spatio-temporal cluster analysis detected 23 clusters of ILI during the study period. Results The highest incidence of ILI was mainly concentrated in the states of Louisiana, District of Columbia and Virginia. The Local spatial autocorrelation analysis revealed the High-High cluster was mainly located in Louisiana and Mississippi. This means that if the influenza incidence is high in Louisiana and Mississippi, the neighboring states will also have higher influenza incidence rates. The regression model SARIMA(1, 0, 0)(1, 1, 0)52 with statistical significance was obtained to forecast the ILI incidence of Mississippi. Conclusions The study showed, the ILI incidence will begin to increase in the 45th week 2020 and peak in the 6th week 2021. To conclude, notable epidemiological differences were observed across states, indicating that some states should pay more attention to prevent and control respiratory infectious diseases.

Spatio-Temporal Analysis of Freight Flows in Southern California

2021 ◽  
pp. 036119812110041
Author(s):  
Daniel Rivera-Royero ◽  
Miguel Jaller ◽  
Chang-Mo Kim
Keyword(s):  
Southern California ◽  
Temporal Dynamics ◽  
Capacity Utilization ◽  
Temporal Analysis ◽  
Heavy Duty ◽  
Additional Information ◽  
Weigh In Motion ◽  
Spatio Temporal ◽  
Truck Load ◽  
Vehicle Capacity

This paper analyses the spatio-temporal patterns of freight flows in Southern California using weigh-in-motion (WIM) data between 2003 and 2015. The study explores the spatial relationships between truck volumes, load ratios, and gross vehicle weights for different vehicle classes, through econometric and centrographic analyses during the study period. Overall, the results confirmed the existence of the logistics sprawl phenomenon, highlighted the effect of the 2008 to 2009 major recession in the concentration of freight facilities and flows, indicated that the changes in flow patterns vary for different vehicle classes, and found low vehicle capacity utilization for light- (WIM classes 5–7) and medium- (WIM classes 8–10) heavy-duty trucks, though recently improving. These results are consistent with the growth in residential deliveries owing to e-commerce, showing increased light-heavy-duty trucks flows concentrated closer to the consumption areas, and experiencing larger flow reductions compared to heavy vehicle flows as the distance from the area increases; and showing that medium-heavy-duty vehicles used in both full-truck-load, and less-than-truck-load vocations are prevalent throughout the study area, whereas there is a trade-off between light- and heavy-heavy duty trucks (WIM classes 11–13) at the proximity, and the outskirts of the consumption markets, respectively. Moreover, the study shows the usefulness of the WIM data in identifying spatial and temporal dynamics in freight demand, providing additional information for planning, maintenance, and rehabilitation of the infrastructure. More importantly, the results, coupled with other evidence from the literature, show how major disruptions such as the recession significantly affect truck traffic.

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