Predicting Infectious Disease Outbreaks with Machine Learning and Epidemiological Data

Over the past several years, there has been a notable shift in the international public health arena, mostly driven by the use of machine learning methodologies and epidemiological data for the purpose of forecasting and controlling outbreaks of infectious diseases. This study explores the changing paradigm of disease outbreak prediction by examining current advancements and emerging patterns in the field of machine learning and epidemiology. In this paper, we explore the complex procedure of forecasting infectious disease outbreaks, a task of significant significance for global public health authorities. This paper examines the crucial role of machine learning algorithms in this undertaking, elucidating their capacity to analyze extensive and heterogeneous datasets in order to produce significant insights and predictions Our inquiry spans multiple facets of this complex topic. This study examines the transformative impact of machine learning models, namely deep learning and ensemble approaches, on the field. The individuals in question have exhibited remarkable proficiency in recognizing patterns, establishing correlations, and formulating predictions by utilizing past data. Consequently, this has greatly contributed to the prompt identification and readiness for potential outbreaks. Moreover, our study involves the incorporation of epidemiological data, including case reports, genetic sequencing, and population dynamics, into the machine learning architecture. This study investigates the enhanced predictive accuracy and improved comprehension of disease dynamics resulting from the integration of data-driven models and expert knowledge from the field of epidemiology. The integration of different approaches is of utmost importance when it comes to effectively tackling the distinct characteristics and problems presented by diverse infectious illnesses. Additionally, the research emphasizes the significance of incorporating a wide range of data sources, including not only data related to human health, but also environmental factors, socio-economic metrics, and patterns of human mobility. Non-conventional data sources provide essential contextual information for comprehending the dynamics of disease transmission, hence enhancing the robustness and comprehensiveness of forecasts

Optimizing Antibiotic Prescriptions and Infectious Disease Management in Hospitals using Neural Networks

This study introduces an innovative approach to antibiotic optimization and improved infectious disease management in healthcare facilities. Antibiotic stewardship and patient-specific outcomes are prioritized in the suggested strategy that uses neural networks to increase the precision and utility of antibiotic prescriptions. There are three primary algorithms at the heart of the technique. When it comes to identifying infectious illnesses from a picture, Algorithm 1 uses a Convolutional Neural Network (CNN). In order to provide educated antibiotic recommendations, Algorithm 2 uses a Recurrent Neural Network (RNN) containing Long Short-Term Memory (LSTM) cells. The third algorithm integrates reinforcement learning to automatically modify therapies based on patient results and antibiotic use. The outcomes prove that the suggested strategy is better than the status quo. The F1 score, recall, and precision all increase dramatically, and the overall diagnostic accuracy is much higher. Antibiotic stewardship also improves noticeably, leading to fewer antibiotic prescriptions, more effective measures against antibiotic resistance, better health outcomes for patients, and lower overall healthcare expenditures. Addressing the difficulties of fluctuating patient states and changing disease patterns, the suggested methodology provides a comprehensive strategy for managing infectious diseases. Using this method, antibiotic prescriptions may be optimized while still meeting all legal and ethical requirements. The ethical use of AI in healthcare is further ensured by constant monitoring and flexibility

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

Description of spreading dynamics by microscopic network models and macroscopic branching processes can differ due to coalescence

Spreading processes are conventionally monitored on a macroscopic level by counting the number of incidences over time. The spreading process can then be modeled either on the microscopic level, assuming an underlying interaction network, or directly on the macroscopic level, assuming that microscopic contributions are negligible. The macroscopic characteristics of both descriptions are commonly assumed to be identical. In this work, we show that these characteristics of microscopic and macroscopic descriptions can be different due to coalescence, i.e., a node being activated at the same time by multiple sources. In particular, we consider a (microscopic) branching network (probabilistic cellular automaton) with annealed connectivity disorder, record the macroscopic activity, and then approximate this activity by a (macroscopic) branching process. In this framework, we analytically calculate the effect of coalescence on the collective dynamics. We show that coalescence leads to a universal non-linear scaling function for the conditional expectation value of successive network activity. This allows us to quantify the difference between the microscopic model parameter and established macroscopic estimates. To overcome this difference, we propose a non-linear estimator that correctly infers the model branching parameter for all system sizes.Comment: 13 page

Modeling the outbreak and spread of infectious diseases using a bayesian machine learning approach

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe modeling of infectious diseases and their predictions on space and time is very important as it helps in devising the policies for preventive measures. These predictions should be generated from a probabilistic model to provide the uncertainties and thus the confidence. The phenomenon of spread of infectious diseases is so complex that there are lots of uncertainties in the data and in the process itself. Machine learning methods like neural networks are useful in modeling this complex problem, however, these approaches lack handling of uncertainties. Similarly, it is seen in literature that a combined approach of neural networks and Bayesian inferences have not been explored much. Thus to fill these gaps this thesis aims to develop a combined model containing neural network method and Bayesian inference for modeling and predicting the number of cases of infectious diseases in areal units such as municipalities or health-zones. To introduce the impact of human movement on the spread of infectious disease, the movement data has been used combined with the daily infection data to form a spatial factor and used as a covariate in this study. In addition to this, the spatial correlation due to spatial neighborhood as well as the mobility is taken into account in the model along with the temporal dependencies. The model was evaluated on the COVID-19 dataset for 245 healthzones of the autonomous community of Castilla-Leon, Spain. The results show that the model is generally able to predict the number of cases of infectious diseases with good accuracy. Similarly, the mobility factor was also found to have an influence on the model. However, the flexibility of the model still needs to be evaluated by applying the model to different scenarios

Inferring collective dynamical states from widely unobserved systems

When assessing spatially-extended complex systems, one can rarely sample the states of all components. We show that this spatial subsampling typically leads to severe underestimation of the risk of instability in systems with propagating events. We derive a subsampling-invariant estimator, and demonstrate that it correctly infers the infectiousness of various diseases under subsampling, making it particularly useful in countries with unreliable case reports. In neuroscience, recordings are strongly limited by subsampling. Here, the subsampling-invariant estimator allows to revisit two prominent hypotheses about the brain's collective spiking dynamics: asynchronous-irregular or critical. We identify consistently for rat, cat and monkey a state that combines features of both and allows input to reverberate in the network for hundreds of milliseconds. Overall, owing to its ready applicability, the novel estimator paves the way to novel insight for the study of spatially-extended dynamical systems.Comment: 7 pages + 12 pages supplementary information + 7 supplementary figures. Title changed to match journal referenc