OutbreakFlow: Model-based Bayesian inference of disease outbreak
dynamics with invertible neural networks and its application to the COVID-19
pandemics in Germany
- URL: http://arxiv.org/abs/2010.00300v4
- Date: Tue, 2 Nov 2021 11:09:05 GMT
- Title: OutbreakFlow: Model-based Bayesian inference of disease outbreak
dynamics with invertible neural networks and its application to the COVID-19
pandemics in Germany
- Authors: Stefan T. Radev, Frederik Graw, Simiao Chen, Nico T. Mutters, Vanessa
M. Eichel, Till B\"arnighausen and Ullrich K\"othe
- Abstract summary: We present a novel combination of epidemiological modeling with specialized neural networks.
We are able to obtain reliable probabilistic estimates for important disease characteristics, such as generation time, fraction of undetected infections, likelihood of transmission before symptom onset, and reporting delays using a very moderate amount of real-world observations.
- Score: 0.19791587637442667
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Mathematical models in epidemiology are an indispensable tool to determine
the dynamics and important characteristics of infectious diseases. Apart from
their scientific merit, these models are often used to inform political
decisions and intervention measures during an ongoing outbreak. However,
reliably inferring the dynamics of ongoing outbreaks by connecting complex
models to real data is still hard and requires either laborious manual
parameter fitting or expensive optimization methods which have to be repeated
from scratch for every application of a given model. In this work, we address
this problem with a novel combination of epidemiological modeling with
specialized neural networks. Our approach entails two computational phases: In
an initial training phase, a mathematical model describing the epidemic is used
as a coach for a neural network, which acquires global knowledge about the full
range of possible disease dynamics. In the subsequent inference phase, the
trained neural network processes the observed data of an actual outbreak and
infers the parameters of the model in order to realistically reproduce the
observed dynamics and reliably predict future progression. With its flexible
framework, our simulation-based approach is applicable to a variety of
epidemiological models. Moreover, since our method is fully Bayesian, it is
designed to incorporate all available prior knowledge about plausible parameter
values and returns complete joint posterior distributions over these
parameters. Application of our method to the early Covid-19 outbreak phase in
Germany demonstrates that we are able to obtain reliable probabilistic
estimates for important disease characteristics, such as generation time,
fraction of undetected infections, likelihood of transmission before symptom
onset, and reporting delays using a very moderate amount of real-world
observations.
Related papers
- Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events.
Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
arXiv Detail & Related papers (2024-07-28T02:42:36Z) - Metapopulation Graph Neural Networks: Deep Metapopulation Epidemic
Modeling with Human Mobility [14.587916407752719]
We propose a novel hybrid model called MepoGNN for multi-step multi-region epidemic forecasting.
Our model can not only predict the number of confirmed cases but also explicitly learn the epidemiological parameters.
arXiv Detail & Related papers (2023-06-26T17:09:43Z) - Approaching epidemiological dynamics of COVID-19 with physics-informed
neural networks [23.95944607153291]
A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases.
The method is applied to COVID-19 data reported for Germany and shows that it can accurately identify and predict virus spread trends.
arXiv Detail & Related papers (2023-02-17T10:36:58Z) - SPADE4: Sparsity and Delay Embedding based Forecasting of Epidemics [2.578242050187029]
We propose Sparsity and Delay Embedding based Forecasting (SPADE4) for predicting epidemics.
We show that our approach outperforms compartmental models when applied to both simulated and real data.
arXiv Detail & Related papers (2022-11-11T23:39:48Z) - Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
This survey delves into various data-driven methodological and practical advancements.
We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting.
We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
arXiv Detail & Related papers (2022-07-19T16:15:11Z) - EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting.
We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
arXiv Detail & Related papers (2022-02-21T18:59:03Z) - Mixed Effects Neural ODE: A Variational Approximation for Analyzing the
Dynamics of Panel Data [50.23363975709122]
We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data.
We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem.
We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
arXiv Detail & Related papers (2022-02-18T22:41:51Z) - Discrepancies in Epidemiological Modeling of Aggregated Heterogeneous
Data [1.433758865948252]
We show that state-of-the-art models for estimating epidemiological parameters, e.g.transmission rates, can be inappropriate when faced with complex systems.
We generate three complex outbreak scenarios by combining incidence curves from multiple epidemics.
We evaluate two data-generating models within this Bayesian inference framework.
arXiv Detail & Related papers (2021-06-20T03:41:19Z) - Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease
Progression [71.7560927415706]
latent hybridisation model (LHM) integrates a system of expert-designed ODEs with machine-learned Neural ODEs to fully describe the dynamics of the system.
We evaluate LHM on synthetic data as well as real-world intensive care data of COVID-19 patients.
arXiv Detail & Related papers (2021-06-05T11:42:45Z) - An Optimal Control Approach to Learning in SIDARTHE Epidemic model [67.22168759751541]
We propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data.
We forecast the epidemic evolution in Italy and France.
arXiv Detail & Related papers (2020-10-28T10:58:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.