A latent shared-component generative model for real-time disease surveillance using Twitter data
Exploiting the large amount of available data for addressing relevant social problems has been one of the key challenges in data mining. Such efforts have been recently named "data science for social good" and attracted the attention of several researchers and institutions. We give a contribution in this objective in this paper considering a difficult public health problem, the timely monitoring of dengue epidemics in small geographical areas. We develop a generative simple yet effective model to connect the fluctuations of disease cases and disease-related Twitter posts. We considered a hidden Markov process driving both, the fluctuations in dengue reported cases and the tweets issued in each region. We add a stable but random source of tweets to represent the posts when no disease cases are recorded. The model is learned through a Markov chain Monte Carlo algorithm that produces the posterior distribution of the relevant parameters. Using data from a significant number of large Brazilian towns, we demonstrate empirically that our model is able to predict well the next weeks of the disease counts using the tweets and disease cases jointly.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
SerpentFlow: Generative Unpaired Domain Alignment via Shared-Structure Decomposition
Domain alignment refers broadly to learning correspondences between data distributions from distinct domains. In this work, we focus on a setting where domains share underlying structural patterns despite differences in …
Domain AdaptationUnderstanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective
Multiple views of data, both naturally acquired (e.g., image and audio) and artificially produced (e.g., via adding different noise to data samples), have proven useful in enhancing representation learning. Natural views…
ClusteringDisentanglementMultiview LearningRepresentation Learning+1Nonlinear Multiview Analysis: Identifiability and Neural Network-assisted Implementation
Multiview analysis aims at extracting shared latent components from data samples that are acquired in different domains, e.g., image, text, and audio. Classic multiview analysis, e.g., canonical correlation analysis (CCA…
Variational Inference for Deep Probabilistic Canonical Correlation Analysis
In this paper, we propose a deep probabilistic multi-view model that is composed of a linear multi-view layer based on probabilistic canonical correlation analysis (CCA) description in the latent space together with deep…
MULTI-VIEW LEARNINGVariational InferenceNoiseGate: Learning Per-Latent Timestep Schedules as Information Gating in World Action Models
World Action Models (WAMs) are an emerging family of policies that tie robot action generation to future-observation modeling. In this work, we focus on the joint video--action modeling paradigm, where actions and imagin…