Relevant bibliographies by topics / Disease forecasting

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Disease forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "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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Dissertations / Theses on the topic "Disease forecasting"

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Törmänen, Patrik. "Forecasting important disease spreaders from temporal contact data." Thesis, Umeå universitet, Institutionen för fysik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-56747.

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Langston, David Barnes Jr. "The Role of Host, Environment, and Fungicide Use Patterns in Algorithms for Improving Control of Sclerotinia Blight of Peanut." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/30434.

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An algorithm was developed for assessing disease risk and improving fungicide timing for control of Sclerotinia blight of peanut, caused by Sclerotinia minor. A 5-day index (FDI) of disease risk was calculated daily by multiplying indices of moisture, soil temperature, vine growth and canopy density and summing the values for the previous 5 days. Spray thresholds of FDI 16, 24, 32, 40, 48 were compared to a 60, 90, 120 DAP (days after planting) schedule and the standard demand program. Field trials in 1994 indicated that fluazinam (0.58 kg a.i./ha) applied at an FDI of 32 performed similarly to the demand program and was more efficient than the DAP schedule. However, the original FDI 32 algorithm triggered sprays 13 days subsequent to disease onset in 1995, indicating the need for improved vine growth and temperature parameters as well as DAP-dependent FDI thresholds. Results from 1996 and 1997 demonstrated that algorithms with new vine growth and temperature parameters coupled with DAP-dependent thresholds performed as well or better than the original FDI 32 algorithm, demand program, or DAP schedule. Protection intervals of 7 and 14 days improved the performance of iprodione (1.12 kg a.i./ha) while fluazinam provided protection for up to 21 days when applied according to the original FDI 32 algorithm. Planting date was evaluated for its effect on disease and fungicide use patterns. Late planting (20-28 May) delayed disease onset and reduced early season disease incidence three of the four years tested. When averaged across planting dates, the original FDI 32 algorithm performed as well or better than the demand program in 1994 and 1995, as did algorithms utilizing new vine growth and temperature parameters with DAP-dependent thresholds in 1996 and 1997. Chemicals for altering plant architecture were compared to defoliation by corn earworm and leaf spot for suppression of Sclerotinia blight. Chlorimuron (8.8 g a.i./ha) and withholding fungicide for leaf spot control demonstrated the most significant disease suppression and yield improvement. Results show the importance of fungicide timing and plant growth and canopy architecture modification for control of Sclerotinia blight of peanut.
Ph. D.
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Gessman, Daniel J. "Pollen Forecasting in Sarasota, Florida." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6845.

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Current predictions of pollen levels rely strictly on historical Averages, regardless of environmental factors that might affect the timing of pollen release by different plants. For this thesis, the goal was to develop a statistical model that will accurately forecast pollen levels by correlating those daily counts to atmospheric and meteorological conditions. This project used ARIMA modeling on IBM’s SPSS Statistics 24 of daily pollen count information for multiple allergenic pollens in the Sarasota County, Florida area over a 11-year period. The pollen species in question for this project are oak and cypress trees, grass, and ragweed pollens; and Alternaria and Cladosporium mold spores. The total pollen counts for weeds, grass, trees, and overall total are also included in the 11 years of data. The atmospheric variables used to predict pollen levels are high temperature, low temperature, average temperature, precipitation, humidity, wind direction, and wind speed for daily observations over the 11-year period. Results for these models showed that maximum temperature, precipitation, humidity, and wind direction were the driving predictors behind the pollen counts in Sarasota, Florida. The analysis of the pollination periods also showed that there were phenological changes according to the specific species. The models and phenological changes are specific to the Sarasota, Florida area, and would serve as a framework for studying other pollination regions.
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Peterson, Kelly(Kelly Nicole). "Personalized Gaussian process-based machine learning models for forecasting Alzheimer's Disease progression." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121678.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 81-90).
In this thesis, I address the problem of predicting behavioral and cognitive metrics from highly heterogeneous datasets (e.g. genetic, clinical/patient history, neuropsychological, biohumoral, molecular) with missing or incomplete data, using Personalized Machine Learning (PML) [71, 72]. In specific, my thesis work focuses on exploring the application of personalized machine learning techniques to the problem of predicting behavioral and cognitive metrics given a pre-organized dataset containing multimodal subject data collected from the longitudinal Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Thus, this thesis explores the impact of PML in the context of predicting the progression of Alzheimer's disease (AD) by predicting various cognitive, clinical, and behavioral metrics known to be indicative of AD diagnosis. To do this, we employ Gaussian Process (GP) Regression as a modeling framework. Using this framework, we design and implement two novel methods for personalized prediction of key cognitive metrics associated with the AD progression (e.g., ADAS-Cog13). Our experimental evaluations show that the proposed personalized model yields significant gains in performance over non-personalized ("one size fits all") approaches applied to the target estimation tasks using the ADNI database. The techniques proposed have the potential to advance and revolutionize disease treatment and clinical research in AD and other health-related domains. We also provide an extensive overview of methods that deal with missing data in ADNI dataset, being one of the main challenges when working with real-world data of AD.
by Kelly Peterson.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Lega, Joceline, and Heidi E. Brown. "Data-driven outbreak forecasting with a simple nonlinear growth model." ELSEVIER SCIENCE BV, 2016. http://hdl.handle.net/10150/622814.

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Recent events have thrown the spotlight on infectious disease outbreak response. We developed a data-driven method, EpiGro, which can be applied to cumulative case reports to estimate the order of magnitude of the duration, peak and ultimate size of an ongoing outbreak. It is based on a surprisingly simple mathematical property of many epidemiological data sets, does not require knowledge or estimation of disease transmission parameters, is robust to noise and to small data sets, and runs quickly due to its mathematical simplicity. Using data from historic and ongoing epidemics, we present the model. We also provide modeling considerations that justify this approach and discuss its limitations. In the absence of other information or in conjunction with other models, EpiGro may be useful to public health responders. (C) 2016 The Authors. Published by Elsevier B.V.
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Walsh, Brenda. "Epidemiology and disease forecasting system for dollar spot caused by Sclerotinia homoeocarpa F.T. Bennett." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ56297.pdf.

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Twengström, Eva. "Epidemiology and forecasting of Sclerotinia stem rot on spring sown oilseed rape in Sweden /." Uppsala : Swedish Univ. of Agricultural Sciences (Sveriges lantbruksuniv.), 1999. http://epsilon.slu.se/avh/1999/91-576-5722-X.pdf.

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Morales, Nicolàs Gerard. "Integrated management of bacterial spot disease of stone fruits caused by Xanthomonas arboricola pv. pruni: development of a disease forecasting system." Doctoral thesis, Universitat de Girona, 2018. http://hdl.handle.net/10803/523516.

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Bacterial spot disease of stone fruits, caused by Xanthomonas arboricola pv. pruni, is of high economic importance in the major stone-fruit-producing areas worldwide. Disease control is mainly based on preventive measures, such as quarantine regulation, breeding for resistance or preventive copper spray applications, since no effective chemical control is available. Therefore, a better understanding of disease epidemiology can be valuable in developing disease management strategies. This thesis was aimed at developing a mechanistic forecasting system for bacterial spot disease of stone fruits, which is based on three components: i) epiphytic inoculum potential, ii) weather conditions conducive to infections, and iii) disease symptom appearance. The effects of environmental parameters and inoculum populations on different steps of the disease cycle were quantified and modeled. The results contributed with new knowledge on the epidemiology of bacterial spot disease of stone fruits and offer new possibilities in its management
La taca bacteriana dels fruiters de pinyol, causada per Xanthomonas arboricola pv. pruni, té un gran impacte econòmic a les principals zones productores de tot el món. El control de la malaltia es basa principalment en mesures preventives, com ara una regulació de quarantena, la selecció de varietats d’hostes resistents o aplicacions preventives de coure, ja que no es disposa de cap mètode de control químic curatiu i efectiu. Per tant, l’estudi de l'epidemiologia de la malaltia pot ser un factor valuós en el desenvolupament d'estratègies per al seu maneig. L’objectiu d’aquesta tesi va ser el desenvolupament d'un sistema de predicció del desenvolupament de la taca bacteriana dels fruiters de pinyol, el qual es basa en tres components: i) el potencial d'inòcul epífit, ii) les condicions meteorològiques favorables en el procés d’infecció, i iii) l’aparició dels símptomes de la malaltia. Els efectes dels paràmetres ambientals i del potencial d'inòcul es van quantificar i modelar en diferents processos clau del cicle de la malaltia. Els resultats obtinguts aporten nous coneixements sobre l'epidemiologia de la taca bacteriana dels fruiters de pinyol que ofereixen noves possibilitats en el seu maneig
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Cresswell, Mark Philip. "Developing an integrated approach to epidemic forecasting, through the monitoring and prediction of meteorological variables associated with disease." Thesis, University of Liverpool, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250341.

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MacManus, Gerard P. V. "Development and extension of a disease forecasting and chemical control system for onion downy mildew /." St. Lucia, Qld, 2000. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16115.pdf.

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Books on the topic "Disease forecasting"

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Ridley, Matt. Disease. London: Phoenix, 1997.

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Wells, N. E. J. The AIDSvirus: Forecasting its impact. London: Office of Health Economics, 1986.

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Wells, N. E. J. The AIDS virus: Forecasting its impact. London: Office of Health Economics, 1986.

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Wells, Nicholas E. J. The AIDS virus: Forecasting its impact. London: Office of Health Economics, 1986.

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Goreham, Gary. Projected prevalence of Alzheimer's disease among North Dakota's elderly. Fargo, N.D: North Dakota Census Data Center, 1986.

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Namibia. Ministry of Health and Social Services. Directorate of Special Programmes. Response Monitoring & Evaluation Subdivision. 2011/12 estimates and projections of the impact of HIV and AIDS in Namibia. Windhoek, Namibia: Ministry of Health and Social Services, Directorate of Special Programmes, Response Monitoring and Evaluation Sub-division, 2012.

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Future plagues: Biohazard, disease and pestilence : mankind's battle for survival. London: Blandford, 1997.

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Joint WHO/FAO/UNEP/UNCHS Panel of Experts on Environmental Management for Vector Control. Guidelines for forecasting the vector-borne disease implications of water resources development. 2nd ed. Geneva: World Health Organization, 1991.

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Manton, Kenneth G. Chronic disease modelling: Measurement and evaluation of the risks of chronic disease processes. London: Charles Griffin & Co. Ltd., 1988.

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Eric, Stallard, ed. Chronic disease modelling: Measurement and evaluation of the risks of chronic disease processes. London: Charles Griffin & Co., 1988.

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Book chapters on the topic "Disease forecasting"

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Hardwick, N. V. "Disease forecasting." In The Epidemiology of Plant Diseases, 207–30. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-017-3302-1_10.

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Singh Saharan, Govind, Naresh Mehta, and Prabhu Dayal Meena. "Epidemiology and Forecasting." In Alternaria Diseases of Crucifers: Biology, Ecology and Disease Management, 99–124. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-10-0021-8_5.

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Saharan, Govind Singh, Naresh Mehta, and Prabhu Dayal Meena. "Epidemiology and Forecasting." In Downy Mildew Disease of Crucifers: Biology, Ecology and Disease Management, 183–98. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7500-1_9.

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Saharan, Govind Singh, Naresh K. Mehta, and Prabhu Dayal Meena. "Epidemiology and Disease Forecasting." In Powdery Mildew Disease of Crucifers: Biology, Ecology and Disease Management, 145–75. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9853-7_6.

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Manton, Kenneth G., Burton H. Singer, and Eric Stallard. "Cancer Forecasting: Cohort Models of Disease Progression and Mortality." In Forecasting the Health of Elderly Populations, 109–36. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4613-9332-0_5.

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Attanayake, A. M. C. H., and S. S. N. Perera. "Time Series Analysis for Modeling the Transmission of Dengue Disease." In Recent Advances in Time Series Forecasting, 11–35. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003102281-2.

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Tolley, H. Dennis, Kenneth G. Manton, and J. Richard Bumgarner. "Risk Factors Affecting Multiple-Disease Efficacy and Effectiveness of Intervention Programs." In Forecasting the Health of Elderly Populations, 183–203. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4613-9332-0_8.

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Krakauer, Henry. "A Forecasting Model for the Assessment of Medical Technologies: End-Stage Renal Disease." In Forecasting the Health of Elderly Populations, 239–61. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4613-9332-0_10.

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Holmes, G. J., C. E. Main, and Z. T. Keever. "Cucurbit Downy Mildew: A Unique Pathosystem for Disease Forecasting." In Advances in Downy Mildew Research — Volume 2, 69–80. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-2658-4_3.

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Manton, Kenneth G. "Biomedical Research and Changing Concepts of Disease and Aging: Implications for Long-Term Health Forecasts for Elderly Populations." In Forecasting the Health of Elderly Populations, 319–65. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4613-9332-0_15.

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Conference papers on the topic "Disease forecasting"

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Banu, M. A. Nishara, and B. Gomathy. "Disease Forecasting System Using Data Mining Methods." In 2014 International Conference on Intelligent Computing Applications (ICICA). IEEE, 2014. http://dx.doi.org/10.1109/icica.2014.36.

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Shi, Ming-wang, Qing-lian Wang, Xi-ling Chen, Ju-huai Zhai, Tian-fu Deng, Fan-bin Kong, Xue-yong Li, Qi-Li Liu, and Jian-fen Lang. "Plant Disease Forecasting System Based on Datacollection." In 2009 International Conference on Management and Service Science (MASS). IEEE, 2009. http://dx.doi.org/10.1109/icmss.2009.5301610.

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Forster, Alina, Jens Behley, Jan Behmann, and Ribana Roscher. "Hyperspectral Plant Disease Forecasting Using Generative Adversarial Networks." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8898749.

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Rekatsinas, Theodoros, Saurav Ghosh, Sumiko R. Mekaru, Elaine O. Nsoesie, John S. Brownstein, Lise Getoor, and Naren Ramakrishnan. "SourceSeer: Forecasting Rare Disease Outbreaks Using Multiple Data Sources." In Proceedings of the 2015 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974010.43.

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Park, Sangshin, and Hyun Yoe. "System Framework of Livestock Disease Forecasting based on Cloud." In CIA 2015. Science & Engineering Research Support soCiety, 2015. http://dx.doi.org/10.14257/astl.2015.95.34.

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Hua, Ting, Chandan K. Reddy, Lei Zhang, Lijing Wang, Liang Zhao, Chang-Tien Lu, and Naren Ramakrishnan. "Social Media based Simulation Models for Understanding Disease Dynamics." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/528.

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In this modern era, infectious diseases, such as H1N1, SARS, and Ebola, are spreading much faster than any time in history. Efficient approaches are therefore desired to monitor and track the diffusion of these deadly epidemics. Traditional computational epidemiology models are able to capture the disease spreading trends through contact network, however, one unable to provide timely updates via real-world data. In contrast, techniques focusing on emerging social media platforms can collect and monitor real-time disease data, but do not provide an understanding of the underlying dynamics of ailment propagation. To achieve efficient and accurate real-time disease prediction, the framework proposed in this paper combines the strength of social media mining and computational epidemiology. Specifically, individual health status is first learned from user's online posts through Bayesian inference, disease parameters are then extracted for the computational models at population-level, and the outputs of computational epidemiology model are inversely fed into social media data based models for further performance improvement. In various experiments, our proposed model outperforms current disease forecasting approaches with better accuracy and more stability.
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Makkar, Garima. "Real-Time Disease Forecasting using Climatic Factors: Supervised Analytical Methodology." In 2018 IEEE Punecon. IEEE, 2018. http://dx.doi.org/10.1109/punecon.2018.8745369.

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Gomez, Carlos, Roberto Hornero, Angela Mediavilla, Alberto Fernandez, and Daniel Abasolo. "Nonlinear forecasting measurement of magnetoencephalogram recordings from Alzheimer's disease patients." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4649620.

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Танченко, Ольга, Ol'ga Tanchenko, Светлана Нарышкина, and Svetlana Naryshkina. "FORECASTING METABOLIC SYNDROME IN PATIENTS WITH CHRONIC OBSTRUCTIVE PULMONARY DISEASE." In XII International Scientific Conference (correspondence, electronic) "System analysis in medicine" (SAM 2018). Far Eastern Scientific Center of Physiology and Pathology of Respiration, 2018. http://dx.doi.org/10.12737/conferencearticle_5bdaacdd573097.07301550.

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Zhang, Yanrong. "Study on the forest disease forecasting based on gray model." In Advanced Information Technology and Sensor Application 2014. Science & Engineering Research Support soCiety, 2014. http://dx.doi.org/10.14257/astl.2014.53.91.

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Reports on the topic "Disease forecasting"

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Mccabe, Kirsten, and Rebecca McDonald. Global Disease Modeling & Forecasting Center. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1671063.

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Ray, Jaideep, Katherine Regina Cauthen, Sophia Lefantzi, and Lynne Burks. Conditioning multi-model ensembles for disease forecasting. Office of Scientific and Technical Information (OSTI), January 2019. http://dx.doi.org/10.2172/1492995.

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Atkeson, Andrew, Karen Kopecky, and Tao Zha. Estimating and Forecasting Disease Scenarios for COVID-19 with an SIR Model. Cambridge, MA: National Bureau of Economic Research, June 2020. http://dx.doi.org/10.3386/w27335.

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