Results 81 to 90 of about 3,927,001 (165)

Bayesian spatio-temporal analysis of the COVID-19 pandemic in Catalonia

Scientific Reports
In this study, we modelled the incidence of COVID-19 cases and hospitalisations by basic health areas (ABS) in Catalonia. Spatial, temporal and spatio-temporal incidence trends were described using estimation methods that allow to borrow strength from ...
Pau Satorra, Cristian Tebé
doaj   +1 more source

Network meta-analysis with integrated nested Laplace approximations

, 2015
Analyzing the collected evidence of a systematic review in form of a network meta-analysis (NMA) enjoys increasing popularity and provides a valuable instrument for decision making.
Leonhard Held, Held, Leonhard, Rafael Sauter, Sauter, Rafael, Sauter R, Held L   +5 more
core   +1 more source

The association of climate-induced stressors on risk of negative sentiment: An analysis from 462 million geotagged tweets in Europe

iScience
Summary: This study examines how climate-induced health risks influence negative sentiments on European social tweets from 2015 to 2022. Analyzing over 400 million tweets using NLP tools (NLTK, LIWC22) and spatial-temporal aggregation at the NUTS2 weekly
Tareq Al-Ahdal, Sandra Barman, Barrak Alahmad, Stella Dafka, Elisa Gallo, Joan Ballester, Mikhail Sofiev, Marina Romanello, Till Bärnighausen, Michael Gertz, Joacim Rocklöv   +10 more
doaj   +1 more source

Spatial Models Using Laplace Approximation Methods

, 2019
Bayesian inference has been at the center of the development of spatial statistics in recent years. In particular, Bayesian hierarchical models including several fixed and random effects have become very popular in many different fields.
Håvard Rue, Virgilio Gómez-Rubio, Gómez-Rubio, Virgilio, Roger S. Bivand, Rue, Haavard, Bivand, Roger S.   +5 more
core   +1 more source

Breakpoint detection on latent autoregressive time series of counts using integrated nested Laplace approximation. [PDF]

, 2020
Abrupt changes in a data source can weaken models that fail at addressing these. Structural change detection has traditionally been done with a frequentist approach, but recently approaches based on Bayesian models and Markov Chain Monte Carlo (MCMC ...
Ahmed, Amir
core   +1 more source

JSTMapp: A web-based joint spatiotemporal modelling and mapping application for epidemiologists.

PLoS ONE
Disease mapping models help create disease risk maps, which public health policymakers can use to design disease control and monitoring programmes.
Alfred Ngwira, Samuel O M Manda, Esron D Karimuribo, Sharadhuli I Kimera   +3 more
doaj   +1 more source

Designing Proposal Distributions for Particle Filters using Integrated Nested Laplace Approximation [PDF]

State-space models are used to describe and analyse dynamical systems. They are ubiquitously used in many scientific fields such as signal processing, finance and ecology to name a few.
Amri, Alaa
core  

Bayesian analysis of spatio-temporal patterns in colorectal cancer mortality in Chile: a study linking national quality of life and mortality data

Scientific Reports
Colorectal cancer (CRC) is the second leading cause of cancer death globally, surpassed only by lung cancer, representing a significant public health challenge in Chile due to its biopsychosocial impact. This study analyzed standardized mortality ratios (
Claudia Barría-Sandoval, Matthias Fierro-Rodríguez, Guillermo Ferreira, Jorge Urrutia-Hernández   +3 more
doaj   +1 more source

Mapping and understanding the regional farmland SOC distribution in southern China using a Bayesian spatial model

Geoderma
Information on the spatial distribution of soil organic carbon (SOC) in regional farmland is crucial for improving management and production. Mapping SOC in farmlands is challenging due to the strong variation of SOC caused by the influence of natural ...
Bifeng Hu, Yibo Geng, Hanjie Ni, Zhou Shi, Zheng Wang, Nan Wang, Jipeng Luo, Modian Xie, Qian Zou, Thomas Optiz, Hongyi Li   +10 more
doaj   +1 more source

Bayesian spatio-temporal modeling for policy evaluation: Sensitivity of policy effect estimates in the context of COVID-19 stay-at-home orders.

PLoS ONE
This study applies a Bayesian spatio-temporal model to demonstrate the sensitivity of policy effect estimates to spatial and temporal structure, using COVID-19 stay-at-home orders as a case study.
Pyung Kim, Sunghye Choi, Dohyeong Kim, Chang-Kil Lee   +3 more
doaj   +1 more source