Journal articles: 'Disease forecasting'

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Fackelmann, Kathleen. "Forecasting Alzheimer's Disease." Science News 149, no. 20 (May 18, 1996): 312. http://dx.doi.org/10.2307/3979714.

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Susinthra, M. Juno Isabel, and S. Vinitha. "Artificial Intelligence Assisted Weather Based Plant Disease Forecasting System." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 2362–67. http://dx.doi.org/10.31142/ijtsrd12734.

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Nath, R. K. "Plant Disease Forecasting Models." Indian Journal of Pure & Applied Biosciences 8, no. 4 (August 30, 2020): 454–61. http://dx.doi.org/10.18782/2582-2845.8280.

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Robbins, S., L. A. Kondili, S. Blach, I. Gamkrelidze, A. L. Zignego, M. R. Brunetto, G. Raimondo, et al. "Forecasting liver disease burden." Digestive and Liver Disease 50, no. 1 (February 2018): 10–11. http://dx.doi.org/10.1016/j.dld.2018.01.022.

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Yuen, Jonathan. "Bayesian Approaches to Plant Disease Forecasting." Plant Health Progress 4, no. 1 (January 2003): 20. http://dx.doi.org/10.1094/php-2003-1113-06-rv.

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Prediction of disease occurrence is a well known historical theme, and has begun to receive new interest due to internet-based prediction systems. The evaluation of these systems in a quantitative manner is an important step if they are to be used in modern agricultural production. Bayes's theorem is one way in which the performance of such predictors can be studied. In this way, the conditional probability of pest occurrence after a positive or negative prediction can be compared with the unconditional probability of pest occurrence. Both the specificity and the sensitivity of the predictive system are needed, along with the unconditional probability of pest occurrence, in order to make a Bayesian analysis. If there is little information on the prior probability of disease, most predictors will be useful, but for extremely common or extremely rare diseases, a Bayesian analysis indicates that a system predicting disease occurrence or non-occurrence will have limited usefulness. Accepted for publication 29 January 2002. Published 13 November 2003.

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Kshirsagar, D. P., C. V. Savalia, I. H. Kalyani, Rajeev Kumar, and D. N. Nayak. "Disease alerts and forecasting of zoonotic diseases: an overview." Veterinary World 6, no. 11 (October 14, 2013): 889–96. http://dx.doi.org/10.14202/vetworld.2013.889-896.

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Hasanov, A. G., D. G. Shaybakov, S. V. Zhernakov, A. M. Men’shikov, F. F. Badretdinova, I. F. Sufiyarov, and J. R. Sagadatova. "Neural Networks in Forecasting Disease Dynamics." Creative surgery and oncology 10, no. 3 (November 30, 2020): 198–204. http://dx.doi.org/10.24060/2076-3093-2020-10-3-198-204.

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Introduction.In recent years, computer technologies are more and more widely used in medicine. Thus, medical neuro‑ informatics solves diagnostic and forecasting tasks using neural networks.Materials and methods. Using the example of erysipelas, the possibility of forecasting the course and outcome of the dis‑ ease is demonstrated. A retrospective study of the medical histories of patients treated for erysipelas at the Ufa Clinical Hospital No.8 during 2006–2015 was carried out. Modern statistical packages and the MATLAB environment were used.Results and discussion.The conducted comparative analysis showed a 3-layer recurring network of direct distribution to be the most suitable neural network architecture. The optimal structure of the neural network was found to be: 27–6–1 (i.e. 27 neurons are used at the entrance; 6 — in a hidden layer; 1 — in the output layer). The best convergence of the network learning process is provided by the quasi-Newton and conjugated gradient algorithms. In order to assess the effectiveness of the proposed neural network in predicting the dynamics of inflammation, a comparative analysis was carried out using a number of conventional methods, such as exponential smoothing, moving average, least squares and group data handling.Conclusion.The proposed neural network based on approximation and extrapolation of variations in the patient’s medi‑ cal history over fixed time window segments (within the ‘sliding time window’) can be successfully used for forecasting the development and outcome of erysipelas.

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del Castillo, Manuel ??lvarez, and Juan Manuel Nava Caballero. "Forecasting survival after acute neurologic disease." Current Opinion in Critical Care 6, no. 2 (April 2000): 110–16. http://dx.doi.org/10.1097/00075198-200004000-00006.

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Jones, Rod. "Forecasting conundrum: a disease time cascade." British Journal of Healthcare Management 20, no. 2 (February 2014): 90–91. http://dx.doi.org/10.12968/bjhc.2014.20.2.90.

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Kondratyev, Mikhail Alexandrovich. "Forecasting methods and models of disease spread." Computer Research and Modeling 5, no. 5 (October 2013): 863–82. http://dx.doi.org/10.20537/2076-7633-2013-5-5-863-882.

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Dev, J., and N. Sharma. "ARIMA MODELLING FOR TIME SERIES DISEASE FORECASTING." Advances in Mathematics: Scientific Journal 9, no. 6 (July 18, 2020): 3947–53. http://dx.doi.org/10.37418/amsj.9.6.75.

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G, Malavika. "Alzheimer Disease Forecasting using Machine Learning Algorithm." Bioscience Biotechnology Research Communications 13, no. 11 (December 25, 2020): 15–19. http://dx.doi.org/10.21786/bbrc/13.11/4.

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De Cola, Lee. "Spatial Forecasting of Disease Risk and Uncertainty." Cartography and Geographic Information Science 29, no. 4 (January 2002): 363–80. http://dx.doi.org/10.1559/152304002782008413.

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Thomson, MC, T. Palmer, AP Morse, M. Cresswell, and SJ Connor. "Forecasting disease risk with seasonal climate predictions." Lancet 355, no. 9214 (April 2000): 1559–60. http://dx.doi.org/10.1016/s0140-6736(05)74616-x.

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Gibson, Graham Casey, Kelly R. Moran, Nicholas G. Reich, and Dave Osthus. "Improving probabilistic infectious disease forecasting through coherence." PLOS Computational Biology 17, no. 1 (January 6, 2021): e1007623. http://dx.doi.org/10.1371/journal.pcbi.1007623.

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With an estimated $10.4 billion in medical costs and 31.4 million outpatient visits each year, influenza poses a serious burden of disease in the United States. To provide insights and advance warning into the spread of influenza, the U.S. Centers for Disease Control and Prevention (CDC) runs a challenge for forecasting weighted influenza-like illness (wILI) at the national and regional level. Many models produce independent forecasts for each geographical unit, ignoring the constraint that the national wILI is a weighted sum of regional wILI, where the weights correspond to the population size of the region. We propose a novel algorithm that transforms a set of independent forecast distributions to obey this constraint, which we refer to as probabilistically coherent. Enforcing probabilistic coherence led to an increase in forecast skill for 79% of the models we tested over multiple flu seasons, highlighting the importance of respecting the forecasting system’s geographical hierarchy.

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Coombs, Amy. "Climate change concerns prompt improved disease forecasting." Nature Medicine 14, no. 1 (January 2008): 3. http://dx.doi.org/10.1038/nm0108-3.

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Generous, Nicholas, Geoffrey Fairchild, Alina Deshpande, Sara Y. Del Valle, and Reid Priedhorsky. "Global Disease Monitoring and Forecasting with Wikipedia." PLoS Computational Biology 10, no. 11 (November 13, 2014): e1003892. http://dx.doi.org/10.1371/journal.pcbi.1003892.

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DOWD, JOHN E., and KENNETH G. MANTON. "Forecasting Chronic Disease Risks in Developing Countries." International Journal of Epidemiology 19, no. 4 (1990): 1019–36. http://dx.doi.org/10.1093/ije/19.4.1019.

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Brookmeyer, Ron, Elizabeth Johnson, Kathryn Ziegler-Graham, and H. Michael Arrighi. "Forecasting the global burden of Alzheimer's disease." Alzheimer's & Dementia 3, no. 3 (July 2007): 186–91. http://dx.doi.org/10.1016/j.jalz.2007.04.381.

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Sell, T., L. Warmbrod, M. Trotochaud, S. Ravi, E. Martin, and C. Watson. "Using prediction polling for infectious disease forecasting." International Journal of Infectious Diseases 101 (December 2020): 374. http://dx.doi.org/10.1016/j.ijid.2020.09.984.

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Shi, Ming Wang, and Yan Zhou. "WebGIS Intelligent Diagnosis and Forecasting System of Plant Diseases." Key Engineering Materials 480-481 (June 2011): 1603–6. http://dx.doi.org/10.4028/www.scientific.net/kem.480-481.1603.

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With the computer and the rapid development of information technology, The plant disease intelligence diagnosis and the plant disease early warning network is possible. In this paper, relying on the Internet, the realization of intelligent online plant disease diagnosis, early warning, prevention and other information query and forecasting capabilities. The database base on Internet-based wide area network environment and the help of webGIS technology, including diagnostic module, check module and the prediction module. The intelligent prognosis is tested to the extent and happening of plant diseases, for example, Wheat, cotton, tomatoes and other 12 kinds of plant diseases, and epidemic curve is given. To disease diagnosis and prediction of the professional, technical problems become easy, for the effective control of plant diseases, provide a reference.

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Lee, Kookjin, Jaideep Ray, and Cosmin Safta. "The predictive skill of convolutional neural networks models for disease forecasting." PLOS ONE 16, no. 7 (July 9, 2021): e0254319. http://dx.doi.org/10.1371/journal.pone.0254319.

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In this paper we investigate the utility of one-dimensional convolutional neural network (CNN) models in epidemiological forecasting. Deep learning models, in particular variants of recurrent neural networks (RNNs) have been studied for ILI (Influenza-Like Illness) forecasting, and have achieved a higher forecasting skill compared to conventional models such as ARIMA. In this study, we adapt two neural networks that employ one-dimensional temporal convolutional layers as a primary building block—temporal convolutional networks and simple neural attentive meta-learners—for epidemiological forecasting. We then test them with influenza data from the US collected over 2010-2019. We find that epidemiological forecasting with CNNs is feasible, and their forecasting skill is comparable to, and at times, superior to, plain RNNs. Thus CNNs and RNNs bring the power of nonlinear transformations to purely data-driven epidemiological models, a capability that heretofore has been limited to more elaborate mechanistic/compartmental disease models.

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Sultana, Nashreen, Nonita Sharma, Krishna Pal Sharma, and Shobhit Verma. "A Sequential Ensemble Model for Communicable Disease Forecasting." Current Bioinformatics 15, no. 4 (June 11, 2020): 309–17. http://dx.doi.org/10.2174/1574893614666191202153824.

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Background: Ensemble building is a popular method for improving model accuracy for classification problems as well as regression. Objective: In this research work, we propose a sequential ensemble model to predict the number of incidences for communicable diseases like influenza, hand foot and mouth disease (HFMD), and diarrhea and compare it with applied models for prediction. Methods: The weekly dataset of the three diseases, namely, influenza, HFMD, and diarrhea, are collected from the official government site of Hong Kong from the year 2010 to 2018. The data was preprocessed by taking log transformation and z-score transformation. The proposed sequential ensemble model is applied to the processed dataset to predict future occurrences. Results: The result of the proposed ensemble model is compared against standard support vector regression (SVR) using different error metrics such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). In the case of all the threedisease datasets, the proposed ensemble model gives better results in comparison to the standard SVR model. Conclusion: The main objective of this research work is to minimize the prediction error; the proposed sequential ensemble model has shown a significant result in terms of prediction errors.

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Kim, Hyun-Gi, Cheol-Ju Yang, and Hyun Yoe. "Design and Implementation of Livestock Disease Forecasting System." Journal of Korea Information and Communications Society 37C, no. 12 (December 28, 2012): 1263–70. http://dx.doi.org/10.7840/kics.2012.37c.12.1263.

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Lafferty, Kevin D., and Eileen E. Hofmann. "Marine disease impacts, diagnosis, forecasting, management and policy." Philosophical Transactions of the Royal Society B: Biological Sciences 371, no. 1689 (March 5, 2016): 20150200. http://dx.doi.org/10.1098/rstb.2015.0200.

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Rekatsinas, Theodoros, Saurav Ghosh, Sumiko R. Mekaru, Elaine O. Nsoesie, John S. Brownstein, Lise Getoor, and Naren Ramakrishnan. "Forecasting rare disease outbreaks from open source indicators." Statistical Analysis and Data Mining: The ASA Data Science Journal 10, no. 2 (March 28, 2017): 136–50. http://dx.doi.org/10.1002/sam.11337.

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Soni, Umang, Nishu Gupta, and Sakshi. "An Artificial Intelligence Approach for Forecasting Ebola Disease." Journal of Physics: Conference Series 1950, no. 1 (August 1, 2021): 012038. http://dx.doi.org/10.1088/1742-6596/1950/1/012038.

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Corpas-Burgos, Francisca, and Miguel A. Martinez-Beneito. "An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting." Mathematics 9, no. 4 (February 14, 2021): 384. http://dx.doi.org/10.3390/math9040384.

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One of the more evident uses of spatio-temporal disease mapping is forecasting the spatial distribution of diseases for the next few years following the end of the period of study. Spatio-temporal models rely on very different modeling tools (polynomial fit, splines, time series, etc.), which could show very different forecasting properties. In this paper, we introduce an enhancement of a previous autoregressive spatio-temporal model with particularly interesting forecasting properties, given its reliance on time series modeling. We include a common spatial component in that model and show how that component improves the previous model in several ways, its predictive capabilities being one of them. In this paper, we introduce and explore the theoretical properties of this model and compare them with those of the original autoregressive model. Moreover, we illustrate the benefits of this new model with the aid of a comprehensive study on 46 different mortality data sets in the Valencian Region (Spain) where the benefits of the new proposed model become evident.

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Fenu, Gianni, and Francesca Maridina Malloci. "Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms." Big Data and Cognitive Computing 5, no. 1 (January 12, 2021): 2. http://dx.doi.org/10.3390/bdcc5010002.

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Every year, plant diseases cause a significant loss of valuable food crops around the world. The plant and crop disease management practice implemented in order to mitigate damages have changed considerably. Today, through the application of new information and communication technologies, it is possible to predict the onset or change in the severity of diseases using modern big data analysis techniques. In this paper, we present an analysis and classification of research studies conducted over the past decade that forecast the onset of disease at a pre-symptomatic stage (i.e., symptoms not visible to the naked eye) or at an early stage. We examine the specific approaches and methods adopted, pre-processing techniques and data used, performance metrics, and expected results, highlighting the issues encountered. The results of the study reveal that this practice is still in its infancy and that many barriers need to be overcome.

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Gónzalez-Bandala, Daniel Alejandro, Juan Carlos Cuevas-Tello, Daniel E. Noyola, Andreu Comas-García, and Christian A. García-Sepúlveda. "Computational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamics." International Journal of Environmental Research and Public Health 17, no. 12 (June 24, 2020): 4540. http://dx.doi.org/10.3390/ijerph17124540.

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The study of infectious disease behavior has been a scientific concern for many years as early identification of outbreaks provides great advantages including timely implementation of public health measures to limit the spread of an epidemic. We propose a methodology that merges the predictions of (i) a computational model with machine learning, (ii) a projection model, and (iii) a proposed smoothed endemic channel calculation. The predictions are made on weekly acute respiratory infection (ARI) data obtained from epidemiological reports in Mexico, along with the usage of key terms in the Google search engine. The results obtained with this methodology were compared with state-of-the-art techniques resulting in reduced root mean squared percentage error (RMPSE) and maximum absolute percent error (MAPE) metrics, achieving a MAPE of 21.7%. This methodology could be extended to detect and raise alerts on possible outbreaks on ARI as well as for other seasonal infectious diseases.

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Firanj Sremac, Ana, Branislava Lalić, Milena Marčić, and Ljiljana Dekić. "Toward a Weather-Based Forecasting System for Fire Blight and Downy Mildew." Atmosphere 9, no. 12 (December 7, 2018): 484. http://dx.doi.org/10.3390/atmos9120484.

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The aim of this research is to present a weather-based forecasting system for apple fire blight (Erwinia amylovora) and downy mildew of grapevine (Plasmopara viticola) under Serbian agroecological conditions and test its efficacy. The weather-based forecasting system contains Numerical Weather Prediction (NWP) model outputs and a disease occurrence model. The weather forecast used is a product of the high-resolution forecast (HRES) atmospheric model by the European Centre for Medium-Range Weather Forecasts (ECMWF). For disease modelling, we selected a biometeorological system for messages on the occurrence of diseases in fruits and vines (BAHUS) because it contains both diseases with well-known and tested algorithms. Several comparisons were made: (1) forecasted variables for the fifth day are compared against measurements from the agrometeorological network at seven locations for three months (March, April, and May) in the period 2012–2018 to determine forecast efficacy; (2) BAHUS runs driven with observed and forecast meteorology were compared to test the impact of forecasted meteorological data; and (3) BAHUS runs were compared with field disease observations to estimate system efficacy in plant disease forecasts. The BAHUS runs with forecasted and observed meteorology were in good agreement. The results obtained encourage further development, with the goal of fully utilizing this weather-based forecasting system.

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Henshall, W. R., and R. M. Beresford. "Performance of wetness sensors used in plant disease forecasting." Proceedings of the New Zealand Plant Protection Conference 50 (August 1, 1997): 107–11. http://dx.doi.org/10.30843/nzpp.1997.50.11370.

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Jeong, Seokkyun, Hoseok Jeong, Haengkon Kim, and Hyun Yoe. "Cloud Computing based Livestock Monitoring and Disease Forecasting System." International Journal of Smart Home 7, no. 6 (November 30, 2013): 313–20. http://dx.doi.org/10.14257/ijsh.2013.7.6.30.

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McLeish, Michael J., Aurora Fraile, and Fernando García‐Arenal. "Trends and gaps in forecasting plant virus disease risk." Annals of Applied Biology 176, no. 2 (November 19, 2019): 102–8. http://dx.doi.org/10.1111/aab.12553.

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Laporte, R. E. "How to improve monitoring and forecasting of disease patterns." BMJ 307, no. 6919 (December 18, 1993): 1573–74. http://dx.doi.org/10.1136/bmj.307.6919.1573.

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Viani, Ali, Parimal Sinha, Taru Sharma, and Lal Mohan Bhar. "A model for forecasting spot blotch disease in wheat." Australasian Plant Pathology 46, no. 6 (September 6, 2017): 601–9. http://dx.doi.org/10.1007/s13313-017-0514-z.

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Birley, M. H. "Forecasting the vector-borne disease implications of water development." Parasitology Today 1, no. 1 (July 1985): 34–36. http://dx.doi.org/10.1016/0169-4758(85)90105-x.

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Morales-Castilla, Ignacio, Paula Pappalardo, Maxwell J. Farrell, A. Alonso Aguirre, Shan Huang, Alyssa-Lois M. Gehman, Tad Dallas, Dominique Gravel, and T. Jonathan Davies. "Forecasting parasite sharing under climate change." Philosophical Transactions of the Royal Society B: Biological Sciences 376, no. 1837 (September 20, 2021): 20200360. http://dx.doi.org/10.1098/rstb.2020.0360.

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Species are shifting their distributions in response to climate change. This geographic reshuffling may result in novel co-occurrences among species, which could lead to unseen biotic interactions, including the exchange of parasites between previously isolated hosts. Identifying potential new host–parasite interactions would improve forecasting of disease emergence and inform proactive disease surveillance. However, accurate predictions of future cross-species disease transmission have been hampered by the lack of a generalized approach and data availability. Here, we propose a framework to predict novel host–parasite interactions based on a combination of niche modelling of future host distributions and parasite sharing models. Using the North American ungulates as a proof of concept, we show this approach has high cross-validation accuracy in over 85% of modelled parasites and find that more than 34% of the host–parasite associations forecasted by our models have already been recorded in the literature. We discuss potential sources of uncertainty and bias that may affect our results and similar forecasting approaches, and propose pathways to generate increasingly accurate predictions. Our results indicate that forecasting parasite sharing in response to shifts in host geographic distributions allow for the identification of regions and taxa most susceptible to emergent pathogens under climate change. This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.

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Sivrikaya, Orhan, and Enar Tunc. "Demand Forecasting for Domestic Air Transportation in Turkey." Open Transportation Journal 7, no. 1 (May 31, 2013): 20–26. http://dx.doi.org/10.2174/1874447820130508001.

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introduction Patients have the right to influence the care they receive, but their wish to participate in care decision-making is unclear. Aim This study investigates whether participation in nursing documentation influences patient participation in care decision-making, mastery, self-esteem, empowerment and depressive feelings among adult in-patients with chronic disease. Materials and Methodology Adult patients (n=39) with chronic diseases were randomized. The intervention group participated in nursing documentation. Upon departure, patients filled in questionnaires about participation in care decision-making, mastery, self-esteem, empowerment and depressive feelings. Results The majority of the patients preferred a collaborative or passive role regarding care decision-making. Lack of knowledge was one reason for non-participation. Having been diagnosed more than five years previously meant stronger empowerment. Conclusion It is a challenge for nurses to find strategies to assess patients’ wishes regarding participation in care decision-making. Nurses must support patients’ knowledge of their disease and empowerment.

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Doos, Lucy, Claire Packer, Derek Ward, Sue Simpson, and Andrew Stevens. "OP06 Past Speculations Of Future Health Technologies: What Did They Predict?" International Journal of Technology Assessment in Health Care 33, S1 (2017): 4. http://dx.doi.org/10.1017/s0266462317001167.

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INTRODUCTION:Rapid technological innovation is leading to new health technologies and interventions becoming available to healthcare markets at increasing speed; these often cost more than current alternatives and significantly affect the cost of healthcare services and delivery (1). Identifying future technologies supports service preparedness, long-term planning, and strategic decision making. The aim of this study was to describe and classify health technologies predicted in fifteen forecasting studies according to their type, purpose and clinical use, and relate these to the original purpose and timing of the forecasting studies.METHODS:This was a descriptive study of predicted healthcare technologies identified in fifteen forecasting studies included in a previously published systematic review (2). Outcomes related to (i) each forecast study including country, year, intent and forecasting methods used, and (ii) the predicted technology type, purpose, targeted clinical area and forecast timeframe.RESULTS:We identified 896 predicted health-related topics, of which 685 were health technologies. Of these, 19.1 percent were diagnostic or imaging tests and 14.3 percent devices or biomaterials; 38.1 percent were intended to treat or manage disease and 21.6 percent to diagnose or monitor disease. The most frequent targeted clinical areas were infectious diseases followed by cancer, circulatory and nervous system disorders. The mean timeframe for technology forecast was 11.6 years (Standard Deviation, SD = 6.6). The forecasting timeframe significantly differed by technology type (p = .002), the intent of the forecasting group (p < .0001), and the methods used (p < .0001).CONCLUSIONS:Our description and classification of predicted health-related technologies from prior forecasting studies provides an overview of the technological and clinical frontiers of innovation in health and healthcare provision.

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Yu, Gongchao, Huifen Feng, Shuang Feng, Jing Zhao, and Jing Xu. "Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA–NNAR hybrid model." PLOS ONE 16, no. 2 (February 5, 2021): e0246673. http://dx.doi.org/10.1371/journal.pone.0246673.

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Background Hand-foot-and-mouth disease_(HFMD) is one of the most typical diseases in children that is associated with high morbidity. Reliable forecasting is crucial for prevention and control. Recently, hybrid models have become popular, and wavelet analysis has been widely performed. Better prediction accuracy may be achieved using wavelet-based hybrid models. Thus, our aim is to forecast number of HFMD cases with wavelet-based hybrid models. Materials and methods We fitted a wavelet-based seasonal autoregressive integrated moving average (SARIMA)–neural network nonlinear autoregressive (NNAR) hybrid model with HFMD weekly cases from 2009 to 2016 in Zhengzhou, China. Additionally, a single SARIMA model, simplex NNAR model, and pure SARIMA–NNAR hybrid model were established for comparison and estimation. Results The wavelet-based SARIMA–NNAR hybrid model demonstrates excellent performance whether in fitting or forecasting compared with other models. Its fitted and forecasting time series are similar to the actual observed time series. Conclusions The wavelet-based SARIMA–NNAR hybrid model fitted in this study is suitable for forecasting the number of HFMD cases. Hence, it will facilitate the prevention and control of HFMD.

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Sokolova, Ekaterina, and Andrey Sokolov. "The problem of forecasting emergency situations of technical objects." MATEC Web of Conferences 298 (2019): 00052. http://dx.doi.org/10.1051/matecconf/201929800052.

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The problem of edge state forecasting has a wide field of application. To begin with, it arises when one develops a technical diagnostics system as the problem of forecasting emergency situations of technical objects. When the ecological system is concerned it appears as the problem of forecasting unfavorable development of the ecological situation. In case of investment analysis it evolves as the problem of forecasting the risks of no profit. In medical diagnostic automated systems it is the forecasting disease progression and transition.

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Abraham, V., A. C. Kushalappa, O. Carisse, G. Bourgeois, and P. Auclair. "Comparison of decision methods to initiate fungicide applications against cercospora blight of carrot." Phytoprotection 76, no. 3 (April 12, 2005): 91–99. http://dx.doi.org/10.7202/706088ar.

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In 1991 and 1992, two thresholds of a forecasting model were compared with two other decision methods for effectiveness in timing the first fungicide application against Cercospora blight of carrot induced by Cercospora carotae. The first fungicide application was made when : 1) the plants reached 15 cm in height (conventional method); 2) the intermediate (middle) leaves of 50% of the plants were diseased (50% disease incidence threshold method); 3) the cumulative infection equivalence (CE) was 14 (forecasting model CE 14); and 4) the CE was 18 (forecasting model CE 18). In all four treatments, subsequent applications of fungicide were made at 10-d intervals when there was no rain, and at 7-d intervals when there was rain. The CE was calculated based on duration of leaf wetness and temperature during the wet period, corrected for high humidity and interrupted wet periods, and was cumulative starting at crop emergence. For thresholds of CE 14 and CE 18, no yield losses were observed and the total number of fungicide applications needed was lower compared to conventional and 50% disease incidence threshold methods. In a separate study, the CE thresholds were related to the percentage of commercial fields that reached disease incidence thresholds of 50, 80 and 100% to establish low risk (CE 11-15) and high risk (CE 16-20) thresholds. The forecasting of low and high risk CE thresholds were too late for 3 and 19% of the commercial fields because those fields had more than 50 and 80% of the plants diseased, respectively.

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Ogden, NH, LR Lindsay, A. Ludwig, AP Morse, H. Zheng, and H. Zhu. "Prédictions des éclosions de maladies transmises par les moustiques selon les prévisions météorologiques au Canada." Relevé des maladies transmissibles au Canada 45, no. 5 (May 2, 2019): 141–47. http://dx.doi.org/10.14745/ccdr.v45i05a03f.

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Bartlow, Andrew W., Carrie Manore, Chonggang Xu, Kimberly A. Kaufeld, Sara Del Valle, Amanda Ziemann, Geoffrey Fairchild, and Jeanne M. Fair. "Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment." Veterinary Sciences 6, no. 2 (May 6, 2019): 40. http://dx.doi.org/10.3390/vetsci6020040.

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Infectious diseases are changing due to the environment and altered interactions among hosts, reservoirs, vectors, and pathogens. This is particularly true for zoonotic diseases that infect humans, agricultural animals, and wildlife. Within the subset of zoonoses, vector-borne pathogens are changing more rapidly with climate change, and have a complex epidemiology, which may allow them to take advantage of a changing environment. Most mosquito-borne infectious diseases are transmitted by mosquitoes in three genera: Aedes, Anopheles, and Culex, and the expansion of these genera is well documented. There is an urgent need to study vector-borne diseases in response to climate change and to produce a generalizable approach capable of generating risk maps and forecasting outbreaks. Here, we provide a strategy for coupling climate and epidemiological models for zoonotic infectious diseases. We discuss the complexity and challenges of data and model fusion, baseline requirements for data, and animal and human population movement. Disease forecasting needs significant investment to build the infrastructure necessary to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions. These investments can contribute to building a modeling community around the globe to support public health officials so as to reduce disease burden through forecasts with quantified uncertainty.

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Bom, M., and G. J. Boland. "Evaluation of disease forecasting variables for sclerotinia stem rot (Sclerotinia sclerotiorum) of canola." Canadian Journal of Plant Science 80, no. 4 (October 1, 2000): 889–98. http://dx.doi.org/10.4141/p99-071.

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Selected environmental, crop and pathogen variables were sampled weekly from winter and spring canola crops before and during flowering and evaluated for the ability to predict sclerotinia stem rot, caused by Sclertinia sclerotirum. Linear and nonlinear relationships were examined among variables but, because no strong correlations were observed between final disease incidence and any of the variables tested, a categorical approach (e.g., disease severity) was used instead. Disease severity in individual crops was categorized as low (< 20% diseased plants) or high (> 20% disease), and differences in weekly rainfall, soil moisture, crop height, percentage of petal infestation, and number of apothecia m−2 and clumps of apothecia m−2 were significantly associated with differences in disease severity within or between years. Two disease prediction models were compared for the ability to predict low or high disease severities using petal infestation alone, or petal infestation in combination with soil moisture. The model that included petal infestation and soil moisture predicted more fields correctly than the model using petal infestation alone, but the accuracy of both was affected by the timing of soil moisture measurements in relation to petal infestation, and threshold values used in discriminating categories of soil moisture and petal infestation. Key words: Brassica rapa, Brassica napus, Sclerotinia sclerotiorum, disease prediction

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Evans, Neal, Andreas Baierl, Mikhail A. Semenov, Peter Gladders, and Bruce D. L. Fitt. "Range and severity of a plant disease increased by global warming." Journal of The Royal Society Interface 5, no. 22 (August 21, 2007): 525–31. http://dx.doi.org/10.1098/rsif.2007.1136.

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Climate change affects plants in natural and agricultural ecosystems throughout the world but little work has been done on the effects of climate change on plant disease epidemics. To illustrate such effects, a weather-based disease forecasting model was combined with a climate change model predicting UK temperature and rainfall under high- and low-carbon emissions for the 2020s and 2050s. Multi-site data collected over a 15-year period were used to develop and validate a weather-based model forecasting severity of phoma stem canker epidemics on oilseed rape across the UK. This was combined with climate change scenarios to predict that epidemics will not only increase in severity but also spread northwards by the 2020s. These results provide a stimulus to develop models to predict the effects of climate change on other plant diseases, especially in delicately balanced agricultural or natural ecosystems. Such predictions can be used to guide policy and practice in adapting to effects of climate change on food security and wildlife.

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Weinstein, M. C., P. G. Coxson, L. W. Williams, T. M. Pass, W. B. Stason, and L. Goldman. "Forecasting coronary heart disease incidence, mortality, and cost: the Coronary Heart Disease Policy Model." American Journal of Public Health 77, no. 11 (November 1987): 1417–26. http://dx.doi.org/10.2105/ajph.77.11.1417.

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Shi, Ming-wang. "Based on time series and RBF network plant disease forecasting." Procedia Engineering 15 (2011): 2384–87. http://dx.doi.org/10.1016/j.proeng.2011.08.447.

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Kim, Choong Hoe. "Field Testing a Computerized Forecasting System for Rice Blast Disease." Phytopathology 78, no. 7 (1988): 931. http://dx.doi.org/10.1094/phyto-78-931.

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Journal articles on the topic 'Disease forecasting'

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