1,354 research outputs found
Epidemiological Prediction using Deep Learning
Department of Mathematical SciencesAccurate and real-time epidemic disease prediction plays a significant role in the health system and is of great importance for policy making, vaccine distribution and disease control. From the SIR model by Mckendrick and Kermack in the early 1900s, researchers have developed a various mathematical model to forecast the spread of disease. With all attempt, however, the epidemic prediction has always been an ongoing scientific issue due to the limitation that the current model lacks flexibility or shows poor performance. Owing to the temporal and spatial aspect of epidemiological data, the problem fits into the category of time-series forecasting. To capture both aspects of the data, this paper proposes a combination of recent Deep Leaning
models and applies the model to ILI (influenza like illness) data in the United States. Specifically, the graph convolutional network (GCN) model is used to capture the geographical feature of the U.S. regions and the gated recurrent unit (GRU) model is used to capture the temporal dynamics of ILI. The result was compared with the Deep Learning model proposed by other researchers, demonstrating the proposed model outperforms the previous methods.clos
Neural network based country wise risk prediction of COVID-19
The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened
up new challenges to the research community. Artificial intelligence (AI)
driven methods can be useful to predict the parameters, risks, and effects of
such an epidemic. Such predictions can be helpful to control and prevent the
spread of such diseases. The main challenges of applying AI is the small volume
of data and the uncertain nature. Here, we propose a shallow long short-term
memory (LSTM) based neural network to predict the risk category of a country.
We have used a Bayesian optimization framework to optimize and automatically
design country-specific networks. The results show that the proposed pipeline
outperforms state-of-the-art methods for data of 180 countries and can be a
useful tool for such risk categorization. We have also experimented with the
trend data and weather data combined for the prediction. The outcome shows that
the weather does not have a significant role. The tool can be used to predict
long-duration outbreak of such an epidemic such that we can take preventive
steps earlie
Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19
Forecasting influenza in a timely manner aids health organizations and
policymakers in adequate preparation and decision making. However, effective
influenza forecasting still remains a challenge despite increasing research
interest. It is even more challenging amidst the COVID pandemic, when the
influenza-like illness (ILI) counts are affected by various factors such as
symptomatic similarities with COVID-19 and shift in healthcare seeking patterns
of the general population. Under the current pandemic, historical influenza
models carry valuable expertise about the disease dynamics but face
difficulties adapting. Therefore, we propose CALI-Net, a neural transfer
learning architecture which allows us to 'steer' a historical disease
forecasting model to new scenarios where flu and COVID co-exist. Our framework
enables this adaptation by automatically learning when it should emphasize
learning from COVID-related signals and when it should learn from the
historical model. Thus, we exploit representations learned from historical ILI
data as well as the limited COVID-related signals. Our experiments demonstrate
that our approach is successful in adapting a historical forecasting model to
the current pandemic. In addition, we show that success in our primary goal,
adaptation, does not sacrifice overall performance as compared with
state-of-the-art influenza forecasting approaches.Comment: Appears in AAAI-2
Data-Centric Epidemic Forecasting: A Survey
The COVID-19 pandemic has brought forth the importance of epidemic
forecasting for decision makers in multiple domains, ranging from public health
to the economy as a whole. While forecasting epidemic progression is frequently
conceptualized as being analogous to weather forecasting, however it has some
key differences and remains a non-trivial task. The spread of diseases is
subject to multiple confounding factors spanning human behavior, pathogen
dynamics, weather and environmental conditions. Research interest has been
fueled by the increased availability of rich data sources capturing previously
unobservable facets and also due to initiatives from government public health
and funding agencies. This has resulted, in particular, in a spate of work on
'data-centered' solutions which have shown potential in enhancing our
forecasting capabilities by leveraging non-traditional data sources as well as
recent innovations in AI and machine learning. This survey delves into various
data-driven methodological and practical advancements and introduces a
conceptual framework to navigate through them. First, we enumerate the large
number of epidemiological datasets and novel data streams that are relevant to
epidemic forecasting, capturing various factors like symptomatic online
surveys, retail and commerce, mobility, genomics data and more. Next, we
discuss methods and modeling paradigms focusing on the recent data-driven
statistical and deep-learning based methods as well as on the novel class of
hybrid models that combine domain knowledge of mechanistic models with the
effectiveness and flexibility of statistical approaches. We also discuss
experiences and challenges that arise in real-world deployment of these
forecasting systems including decision-making informed by forecasts. Finally,
we highlight some challenges and open problems found across the forecasting
pipeline.Comment: 67 pages, 12 figure
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