2,260 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
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
EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes
Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones.
This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances.
The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises
Machine learning in drug supply chain management during disease outbreaks: a systematic review
The drug supply chain is inherently complex. The challenge is not only the number of stakeholders and the supply chain from producers to users but also production and demand gaps. Downstream, drug demand is related to the type of disease outbreak. This study identifies the correlation between drug supply chain management and the use of predictive parameters in research on the spread of disease, especially with machine learning methods in the last five years. Using the Publish or Perish 8 application, there are 71 articles that meet the inclusion criteria and keyword search requirements according to Kitchenham's systematic review methodology. The findings can be grouped into three broad groupings of disease outbreaks, each of which uses machine learning algorithms to predict the spread of disease outbreaks. The use of parameters for prediction with machine learning has a correlation with drug supply management in the coronavirus disease case. The area of drug supply risk management has not been heavily involved in the prediction of disease outbreaks
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
A DECISION SUPPORT SYSTEM FRAMEWORK FOR SEASONAL ZOONOSIS PREDICTION
The arising number of zoonosis epidemics and the potential threat to human
highlight the need to apply stringent system to contend zoonosis outbreak. Zoonosis is
any infectious disease that is able to be transmitted from other animals, both wild and
domestic, to humans. The increasing number of zoonotic diseases coupled with the
frequency of occurrences, especially lately, has made the need to study and develop a
framework to predict future number of zoonosis incidence. Unfortunately, study of
literatures showed most prediction models are case-specific and often based on a
single forecasting technique.
This research analyses and presents the application of a decision support system
(DSS) that applied multi forecasting methods to support and provide prediction on the
number of zoonosis human incidence. The focus of this research is to identify and to
design a DSS framework on zoonosis that is able to handle two seasonal time series
type, namely additive seasonal model and multiplicative seasonal model. The first
dataset describes the seasonal data pattern that exhibited the constant variation, while
the second dataset showed the upward/downward trend. Two case studies were
selected to evaluate the proposed framework: Salmonellosis and Tuberculosis for
additive time series and Tuberculosis for multiplicative time series. Data was
collected from the number of human Salmonellosis and Tuberculosis incidence in the
United States published by Centers for Disease Control and Prevention (CDC). These
data were selected based on availability and completeness.
The proposed framework consists of three components: database management
subsystem, model management subsystem, and dialog generation and management
subsystem. A set of 168 monthly data (1993–2006) of Salmonellosis and Tuberculosis
was used for developing the database management subsystem. Six forecasting
methods, including five statistical methods and one soft computing method, were
applied in the model management subsystem. They were regression analysis, moving average, decomposition, Holt-Winter’s, ARIMA, and neural network. The results of
each method were compared using ANOVA, while Duncan Multiple Range Test was
employed to identify the compatibility of each method to the time series. Coefficient
of Variation (CV) was used to determine the most appropriate method among them. In
the user interface subsystem, “What If” (sensitivity) analysis was chosen to construct
this component. This analysis provided the fluctuation of forecasting results which
was influenced by the changes in data. The sensitivity analysis was able to determine
method with the highest fluctuation based on data update. Observation of the result
showed that regression analysis was the fittest method for Salmonellosis and neural
network was the fittest method of Tuberculosis. Thus, it could be concluded that
results difference of both cases was affected by the available data series. Finally, the
design of Graphical User Interface (GUI) was presented to show the connectivity flow
between all DSS components.
The research resulted in the development of a DSS theoretical framework for a
zoonosis prediction system. The results are also expected to serve as a guide for
further research and development of DSS for other zoonosis, not only for seasonal
zoonosis but also for nonseasonal zoonosis
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