1,422 research outputs found

    Epidemiological Prediction using Deep Learning

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    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

    On the dynamics of a class of multi-group models for vector-borne diseases

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    The resurgence of vector-borne diseases is an increasing public health concern, and there is a need for a better understanding of their dynamics. For a number of diseases, e.g. dengue and chikungunya, this resurgence occurs mostly in urban environments, which are naturally very heterogeneous, particularly due to population circulation. In this scenario, there is an increasing interest in both multi-patch and multi-group models for such diseases. In this work, we study the dynamics of a vector borne disease within a class of multi-group models that extends the classical Bailey-Dietz model. This class includes many of the proposed models in the literature, and it can accommodate various functional forms of the infection force. For such models, the vector-host/host-vector contact network topology gives rise to a bipartite graph which has different properties from the ones usually found in directly transmitted diseases. Under the assumption that the contact network is strongly connected, we can define the basic reproductive number R0\mathcal{R}_0 and show that this system has only two equilibria: the so called disease free equilibrium (DFE); and a unique interior equilibrium---usually termed the endemic equilibrium (EE)---that exists if, and only if, R0>1\mathcal{R}_0>1. We also show that, if R01\mathcal{R}_0\leq1, then the DFE equilibrium is globally asymptotically stable, while when R0>1\mathcal{R}_0>1, we have that the EE is globally asymptotically stable

    On the Dynamics of Dengue Virus type 2 with Residence Times and Vertical Transmission

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    A two-patch mathematical model of Dengue virus type 2 (DENV-2) that accounts for vectors' vertical transmission and between patches human dispersal is introduced. Dispersal is modeled via a Lagrangian approach. A host-patch residence-times basic reproduction number is derived and conditions under which the disease dies out or persists are established. Analytical and numerical results highlight the role of hosts' dispersal in mitigating or exacerbating disease dynamics. The framework is used to explore dengue dynamics using, as a starting point, the 2002 outbreak in the state of Colima, Mexico

    The Euler characteristic as a topological marker for outbreaks in vector-borne disease

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    Abstract. Epidemic outbreaks represent a significant concern for the current state of global health, particularly in Brazil, the epicentre of several vector-borne disease outbreaks and where epidemic control is still a challenge for the scientific community. Data science techniques applied to epidemics are usually made via standard statistical and modelling approaches, which do not always lead to reli- able predictions, especially when the data lacks a piece of reliable surveillance information needed for precise parameter estimation. In particular, dengue out- breaks reported over the past years raise concerns for global health care, and thus novel data-driven methods are necessary to predict the emergence of out- breaks. In this work, we propose a parameter-free approach based on geometric and topological techniques, which extracts geometrical and topological invariants as opposed to statistical summaries used in established methods. Specifically, our procedure generates a time-varying network from a time-series of new epidemic cases based on synthetic time-series and real dengue data across several dis- tricts of Recife, the fourth-largest urban area in Brazil. Subsequently, we use the Euler characteristic (EC) to extract key topological invariant of the epidemic time-varying network and we finally compared the results with the effective reproduction number (Rt) for each data set. Our results unveil a strong cor- relation between epidemic outbreaks and the EC. In fact, sudden changes in the EC curve preceding and/or during an epidemic period emerge as a warn- ing sign for an outbreak in the synthetic data, the EC transitions occur close to the periods of epidemic transitions, which is also corroborated. In the real dengue data, where data is intrinsically noise, the EC seems to show a better sign-to-noise ratio once compared to Rt. In analogy with later studies on noisy data by using EC in positron emission tomography scans, the EC estimates the number of regions with high connectivity in the epidemic network and thus has potential to be a signature of the emergence of an epidemic state. Our results open the door to the development of alternative/complementary topological and geometrical data-driven methods to characterise vector-borne disease outbreaks, specially when the conventional epidemic surveillance methods are not effective in a scenario of extreme noise and lack of robustness in the data

    Mathematical modeling and computation of dengue fever caused by climate change in Jeju Island

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    Department of Mathematical SciencesDengue fever which is a vector-borne infectious disease that spreads rapidly in subtropical or tropical countries is rarely recognized as a public health concern in South Korea, especially Jeju Island which is target district for study. However, there is a high possibility that the outbreak of dengue fever occurs in Korea within a few years since global warming is accelerating and the medium mosquitoes for dengue are also inhabit in Korea. The purpose of this study is predicting how many patients would occur when there is an outbreak of dengue fever by using climate change scenario. Based on RCPs provided by Korea Meterological Administration, the parameters related to mosquitoes represented as fitting functions and specific function by using climatic factors such as temperature, precipitation, and relative humidity are formulated. The simulation for deterministic models is performed by using two methods, one of applying climate data of all the four seasons, and the other applying climate data of seasons excluding winter. This study show the relation between climate change and outbreaks of dengue, which could be an important indicator to establish polices to reduce spread of the disease

    Machine learning in drug supply chain management during disease outbreaks: a systematic review

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    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

    Some Remarks about the Complexity of Epidemics Management

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    Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that the assumptions underlying the established theory of epidemics management are too idealistic. For an improvement of procedures and organizations involved in fighting epidemics, extended models of epidemics management are required. The necessary extensions consist in a representation of the management loop and the potential frictions influencing the loop. The effects of the non-deterministic frictions can be taken into account by including the measures of robustness and risk in the assessment of management options. Thus, besides of the increased structural complexity resulting from the model extensions, the computational complexity of the task of epidemics management - interpreted as an optimization problem - is increased as well. This is a serious obstacle for analyzing the model and may require an additional pre-processing enabling a simplification of the analysis process. The paper closes with an outlook discussing some forthcoming problems
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