Epidemiology
70 papers with code • 0 benchmarks • 1 datasets
Epidemiology is a scientific discipline that provides reliable knowledge for clinical medicine focusing on prevention, diagnosis and treatment of diseases. Research in Epidemiology aims at characterizing risk factors for the outbreak of diseases and at evaluating the efficiency of certain treatment strategies, e.g., to compare a new treatment with an established gold standard. This research is strongly hypothesis-driven and statistical analysis is the major tool for epidemiologists so far. Correlations between genetic factors, environmental factors, life style-related parameters, age and diseases are analyzed.
Source: Visual Analytics of Image-Centric Cohort Studies in Epidemiology
Benchmarks
These leaderboards are used to track progress in Epidemiology
PHM2017
Most implemented papers
Heterogeneity in susceptibility dictates the order of epidemiological models
The fundamental models of epidemiology describe the progression of an infectious disease through a population using compartmentalized differential equations, but do not incorporate population-level heterogeneity in disease susceptibility.
Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19 Pandemic: Leveraging Data Science, Epidemiology and Control Theory
We provide a SWOT analysis and a roadmap that goes from the access to data sources to the final decision-making step.
Data-driven Identification of Number of Unreported Cases for COVID-19: Bounds and Limitations
A critical factor that can hinder accurate long-term forecasts, is the number of unreported/asymptomatic cases.
COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes
This paper describes a large global dataset on people's discourse and responses to the COVID-19 pandemic over the Twitter platform.
Estimating Structural Target Functions using Machine Learning and Influence Functions
Within this framework, we propose two general learning algorithms that build on the idea of nonparametric plug-in bias removal via IFs: the 'IF-learner' which uses pseudo-outcomes motivated by uncentered IFs for regression in large samples and outputs entire target functions without confidence bands, and the 'Group-IF-learner', which outputs only approximations to a function but can give confidence estimates if sufficient information on coarsening mechanisms is available.
Learning Dynamical Systems with Side Information
yashlal/Microbial-Vector-Field-Analysis
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L4DC 2020
We then demonstrate the added value of side information for learning the dynamics of basic models in physics and cell biology, as well as for learning and controlling the dynamics of a model in epidemiology.
Referenced Thermodynamic Integration for Bayesian Model Selection: Application to COVID-19 Model Selection
The approach is shown to be useful in practice when applied to a real problem - to perform model selection for a semi-mechanistic hierarchical Bayesian model of COVID-19 transmission in South Korea involving the integration of a 200D density.
Disease control as an optimization problem
In the context of epidemiology, policies for disease control are often devised through a mixture of intuition and brute-force, whereby the set of logically conceivable policies is narrowed down to a small family described by a few parameters, following which linearization or grid search is used to identify the optimal policy within the set.
OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany
stefanradev93/AIAgainstCorona
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Mathematical models in epidemiology are an indispensable tool to determine the dynamics and important characteristics of infectious diseases.
Neural Spatio-Temporal Point Processes
facebookresearch/neural_stpp
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ICLR 2021
We propose a new class of parameterizations for spatio-temporal point processes which leverage Neural ODEs as a computational method and enable flexible, high-fidelity models of discrete events that are localized in continuous time and space.
