196,189 research outputs found
Modelled gridded population estimates for Kongo-Central Province in the Democratic Republic of Congo, version 4.3
No full textThis data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100 m grid cells) for Kongo-Central Province in the Democratic Republic of Congo (DRC), along with estimates of the number of people belonging to various age-sex groups. The project team used the Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP) collected as part of anti-malarial campaigns in the Democratic Republic of the Congo for 2023, settlement footprints and geospatial covariates to model and estimate population numbers at grid cell level using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2023. This time period corresponds to the PDRS survey date for Kongo-Central. Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, are most likely to remain.
These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 – Phase 2 Scaling project, with funding from the Bill & Melinda Gates Foundation (INV-044979). Project partners included GRID3, the Center for Integrated Earth System Information (CIESIN) within the Columbia Climate School at Columbia University, and WorldPop at the University of Southampton. The final statistical modelling was designed, developed, and implemented by Ortis Yankey. Data processing was done by Ortis Yankey with additional support from Heather Chamberlain. Project oversight was done by Chris Nnanatu, Attila Lazar, and Andy Tatem. The PDRS data from the malaria insecticide treated net (ITN) distribution campaigns were collected, processed, anonymised, and shared by the PNLP and its implementing partners. The settlement extent data was prepared and shared by CIESIN (2024). The data has been clipped to GRID3-CIESIN health area extent (version 6.0) (CIESIN, 2025)
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Modelled gridded population estimates for Tshuapa Province in the Democratic Republic of Congo, version 4.4.
No full textThis data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100 m grid cells) for Tshuapa Province in the Democratic Republic of Congo (DRC), along with estimates of the number of people belonging to various age-sex groups. The project team used the Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP) collected as part of anti-malarial campaigns in the Democratic Republic of the Congo for 2023, settlement footprints and geospatial covariates to model and estimate population numbers at grid cell level using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2023. This time period corresponds to the PDRS survey date for Tshuapa. Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, are most likely to remain.
These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 – Phase 2 Scaling project, with funding from the Bill & Melinda Gates Foundation (INV-044979). Project partners included GRID3, the Center for Integrated Earth System Information (CIESIN) within the Columbia Climate School at Columbia University, and WorldPop at the University of Southampton. The final statistical modelling was designed, developed, and implemented by Ortis Yankey. Data processing was done by Ortis Yankey with additional support from Heather Chamberlain. Project oversight was done by Chris Nnanatu, Attila Lazar, and Andy Tatem. The PDRS data from the malaria insecticide treated net (ITN) distribution campaigns were collected, processed, anonymised, and shared by the PNLP and its implementing partners. The settlement footprint data was prepared and shared by CIESIN (2024). The data has been clipped to GRID3-CIESIN health area extent (version 8.0) (CIESIN, 2025).
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Modelled gridded population estimates for Kongo-Central Province in the Democratic Republic of Congo, version 4.4.
No full textThis data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100 m grid cells) for Kongo-Central Province in the Democratic Republic of Congo (DRC), along with estimates of the number of people belonging to various age-sex groups. The project team used the Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP) collected as part of anti-malarial campaigns in the Democratic Republic of the Congo for 2023, settlement footprints and geospatial covariates to model and estimate population numbers at grid cell level using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2023. This time period corresponds to the PDRS survey date for Kongo-Central. Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, are most likely to remain.
These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 – Phase 2 Scaling project, with funding from the Gates Foundation (INV-044979). Project partners included GRID3, the Center for Integrated Earth System Information (CIESIN) within the Columbia Climate School at Columbia University, and WorldPop at the University of Southampton. The final statistical modelling was designed, developed, and implemented by Ortis Yankey. Data processing was done by Ortis Yankey with additional support from Heather Chamberlain. Project oversight was done by Chris Nnanatu, Attila Lazar, and Andy Tatem. The PDRS data from the malaria insecticide treated net (ITN) distribution campaigns were collected, processed, anonymised, and shared by the PNLP and its implementing partners. The settlement extent data was prepared and shared by CIESIN (2024). The data has been clipped to GRID3-CIESIN health area extent (version 8.0) (CIESIN, 2025).
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“Io sono un mondo in me stesso”: Jacopo Ortis e il paesaggio delle sue lettere
No full textIl saggio indica la resa del paesaggio nel romanzo «Ultime lettere di Jacopo Ortis»
Colli, laghi, città: paesaggi a confronto nelle "Ultime lettere di Jacopo Ortis"
No full textIl saggio indica la presenza e l'uso del paesaggio nel romanzo epistolare «Ultime lettere di Jacopo Ortis»
Una confessione di Jacopo Ortis
No full textUn caso di "omicidio colposo", narrato nelle "Ultime lettere di Jacopo Ortis" è studiato in termini di critica del biografismo (per dimostrare che esso non pertiene in alcun modo alla vita dell'autore), e ricondotto a un topos settecentesco: quello dell'incidente in carrozza con il quale il signore uccide il popolano. In questo modo, un episodio quasi in parentesi, nel romanzo, rivela tutti i suoi addentellati con la recente tradizione della letteratura tardo-settecentesca, e nello stesso tempo la sua funzione di critica della differenza di classe
Dernières lettres de Jacopo Ortis. Fragments d'une traduction inédite par M. Phelippe-Beaulieux
No full text[Ultime lettere di Jacopo Ortis (français). Extrait]Avec mode text
The effect of sociodemographic factors on the risk of poor mental health in Akron (Ohio): a Bayesian hierarchical spatial analysis
No full textWe examined the association of sociodemographic factors on mental health risk within the city of Akron (Ohio). A Spatial Bayesian Hierarchical model was used in this study. We found that the risk of poor mental health was positively associated with the proportion of people lacking sufficient sleep (RR = 0.42, 95% CI:0.22-0.62), the percentage of people below poverty (RR = 0.12, 95% CI: 0.09, 0.16), and the percentage of married couples (RR = 0.02, 95% CI: -0.05, 0.08). On the contrary, the percentage of female population (RR = -0.06, 95% CI: -0.13, 0.01), the percentage of the black population (RR = -0.05, 95% CI: -0.08, -0.02), and the college-educated population (RR = -0.03, 95% CI: -0.09, 0.04) was negatively associated with the risk of poor mental health. We also found that the sociodemographic variables have spatially varying effects across different neighborhoods. Future studies will examine the joint spatial effect of poor mental health risk and suicide ideation in the study area
Practical method of control of TBC in a population of students belonging to the Sapienza University of Rome, Italy.
No full textTo describe the surveillance and control protocol for tuberculosis in healthcare students in use at the CMO of University "Sapienza" and founded on a dedicated database. Analysis of health certificates through a dedicated data storage software based on Microsoft Access 2007. Students resulting positive to Mantoux test who did not undergo second-level examination (chest X-ray, TB Gold, prophylaxis) are invited to refer themselves within ten days to the infectious disease specialist. the methodological analysis, refined over the years by the CMO staff of the Sapienza University of Rome, allowed to match clinical certification data, to rapidly focus upon TBC spread control among healthcare settings, to register new students time zero before their admission to courses and potential professional exposure, to perform epidemiological studies aimed to increase TBC control through healthcare system released clinical certification confirmation
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