Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-23T15:33:10.093Z Has data issue: false hasContentIssue false
  • English
  • Français

A review of knowledge discovery process in control and mitigation of avian influenza

Part of: Big Data

Published online by Cambridge University Press:  16 September 2019

Samira Yousefi Naghani ,
Rozita Dara ,
Zvonimir Poljak  and
Shayan Sharif
Samira Yousefi Naghani
Affiliation:
School of Computer Science, University of Guelph, Guelph, Ontario, Canada
Rozita Dara*
Affiliation:
School of Computer Science, University of Guelph, Guelph, Ontario, Canada
Zvonimir Poljak
Affiliation:
Department of Population Medicine, University of Guelph, Guelph, Ontario, Canada
Shayan Sharif
Affiliation:
Department of Pathobiology, University of Guelph, Guelph, Ontario, Canada
*
Author for correspondence: Rozita Dara, School of Computer Science, University of Guelph, Guelph, Ontario, Canada. E-mail: drozita@uoguelph.ca
Get access Rights & Permissions [Opens in a new window]

Abstract

In the last several decades, avian influenza virus has caused numerous outbreaks around the world. These outbreaks pose a significant threat to the poultry industry and also to public health. When an avian influenza (AI) outbreak occurs, it is critical to make informed decisions about the potential risks, impact, and control measures. To this end, many modeling approaches have been proposed to acquire knowledge from different sources of data and perspectives to enhance decision making. Although some of these approaches have shown to be effective, they do not follow the process of knowledge discovery in databases (KDD). KDD is an iterative process, consisting of five steps, that aims at extracting unknown and useful information from the data. The present review attempts to survey AI modeling methods in the context of KDD process. We first divide the modeling techniques used in AI into two main categories: data-intensive modeling and small-data modeling. We then investigate the existing gaps in the literature and suggest several potential directions and techniques for future studies. Overall, this review provides insights into the control of AI in terms of the risk of introduction and spread of the virus.

Keywords

Avian influenzadata miningknowledge discovery processmachine learningmodeling
Type
Review Article
Information
Animal Health Research Reviews , Volume 20 , Issue 1 , June 2019 , pp. 61 - 71
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Achrekar, H, Gandhe, A, Lazarus, R, Yu, SH and Liu, B (2011) Predicting flu trends using Twitter data. In: Proceedings of 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Shanghai, P.R. China, pp. 702–707.CrossRefGoogle Scholar
Alpaydin, E (2014) Introduction to machine learning. Cambridge, MA, USA: MIT press.Google Scholar
Arab, A (2015) Spatial and spatio-temporal models for modelling epidemiological data with excess zeros. International Journal of Environmental Research and Public Health 12, 10536–10548.CrossRefGoogle Scholar
Araque, O, Corcuera-Platas, I, Sanchez-Rada, JF and Iglesias, CA (2017) Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Systems with Applications 77, 236–246.CrossRefGoogle Scholar
Backer, JA, van Roermund, HJW, Fischer, EAJ, van Asseldonk, MAPM and Bergevoet, RHM (2015) Controlling highly pathogenic avian influenza outbreaks: an epidemiological and economic model analysis. Preventive Veterinary Medicine 121, 142–150.CrossRefGoogle ScholarPubMed
Bavinck, V, Bouma, A, Van Boven, M, Bos, MEH, Stassen, E and Stegeman, JA (2009) The role of backyard poultry flocks in the epidemic of highly pathogenic avian influenza virus (H7N7) in the Netherlands in 2003. Preventive Veterinary Medicine 88, 247–254.CrossRefGoogle ScholarPubMed
Belkhiria, J, Hijmans, RJ, Boyce, W, Crossley, BM and Martínez-López, B (2018) Identification of high risk areas for avian influenza outbreaks in California using disease distribution models. PLoS ONE 13, e0190824.CrossRefGoogle ScholarPubMed
Bellman, R (2013) Dynamic Programming. Princeton, NJ: Courier Corporation, Princeton University Press.Google Scholar
Blumenberg, C and Barros, AJD (2016) Electronic data collection in epidemiological research. Applied Clinical Informatics 7, 672–681.Google ScholarPubMed
Boender, GJ, Elbers, ARW and de Jong, MCM (2007) Spread of avian influenza in the Netherlands: identifying areas of high-risk. Veterinaria Italiana 43, 605–609.Google ScholarPubMed
Bos, MEH, Nielen, M, Koch, G, Bouma, A, De Jong, MCM and Stegeman, A (2009) Back-calculation method shows that within-flock transmission of highly pathogenic avian influenza (H7N7) virus in the Netherlands is not influenced by housing risk factors. Preventive Veterinary Medicine 88, 278–285.CrossRefGoogle Scholar
Bos, MEH, Nielen, M, Toson, M, Comin, A, Marangon, S and Busani, L (2010) Within-flock transmission of H7N1 highly pathogenic avian influenza virus in turkeys during the Italian epidemic in 1999–2000. Preventive Veterinary Medicine 95, 297–300.CrossRefGoogle ScholarPubMed
Bouma, AM, Claassen, I, Natih, K, Klinkenberg, D, Donnelly, CA, Koch, G and Van Boven, M (2009) Estimation of transmission parameters of H5N1 avian influenza virus in chickens. PLoS Pathogens 5, e1000281.CrossRefGoogle ScholarPubMed
Box, GEP, Jenkins, GM, Reinsel, GC and Ljung, GM (2015) Time Series Analysis: Forecasting and Control, 5th Edn.Hoboken, New Jersey, United States: John Wiley & Sons.Google Scholar
Busani, L, Valsecchi, MG, Rossi, E, Toson, M, Ferre, N, Dalla Pozza, M and Marangon, S (2009) Risk factors for highly pathogenic H7N1 avian influenza virus infection in poultry during the 1999–2000 epidemic in Italy. The Veterinary Journal 181, 171–177.CrossRefGoogle ScholarPubMed
Byrd, K, Mansurov, A and Baysal, O (2016) Mining Twitter data for influenza detection and surveillance. In: Proceedings of IEEE/ACM International Workshop on Software Engineering in Healthcare Systems (SEHS), Austin, Texas, pp. 43–49.Google Scholar
CDC (2010) Centers for Disease Control and Prevention. Available at https://www.cdc.gov/flu/avianflu.Google Scholar
Chadsuthi, S, Iamsirithaworn, S, Triampo, W and Modchang, C (2015) Modelling seasonal influenza transmission and its association with climate factors in Thailand using time-series and ARIMAX analyses. Computational and Mathematical Methods in Medicine 2015, 436495.CrossRefGoogle Scholar
Chapelle, O, Scholkopf, B and Zien, A (2009) Semi-supervised learning. IEEE Transactions on Neural Networks 20, 542–542.CrossRefGoogle Scholar
Chen, L, Hossain, KSMT, Butler, P, Ramakrishnan, N and Prakash, BA (2016) Syndromic surveillance of flu on twitter using weakly supervised temporal topic models. Data Mining and Knowledge Discovery 30, 681–710.CrossRefGoogle Scholar
Chi, CL (2009) Medical Decision Support Systems Based on Machine Learning Methods (Ph.D. thesis). The University of Iowa.Google Scholar
Coburn, BJ, Wagner, BG and Blower, S (2009) Modeling influenza epidemics and pandemics: insights into the future of swine flu (H1N1). BMC Medicine 7, 30.CrossRefGoogle Scholar
Comin, A, Klinkenberg, D, Marangon, S, Toffan, A and Stegeman, A (2011) Transmission dynamics of low pathogenicity avian influenza infections in Turkey flocks. PLoS ONE 6, e26935.CrossRefGoogle ScholarPubMed
Dorigatti, I, Mulatti, P, Rosà, R, Pugliese, A and Busani, L (2010) Modelling the spatial spread of H7N1 avian influenza virus among poultry farms in Italy. Epidemics 2, 29–35.CrossRefGoogle ScholarPubMed
Dorjee, S, Poljak, Z, Revie, CW, Bridgland, J, McNab, B, Leger, E and Sanchez, J (2013) A review of simulation modelling approaches used for the spread of zoonotic influenza viruses in animal and human populations. Zoonoses and Public Health 60, 383–411.CrossRefGoogle ScholarPubMed
Duhamel, A, Nuttens, MC, Devos, P, Picavet, M and Beuscart, R (2003) A preprocessing method for improving data mining techniques. Application to a large medical diabetes database. Studies in Health Technology and Informatics 95, 269–274.Google ScholarPubMed
Dusetzina, SB, Tyree, S, Meyer, AM, Meyer, A, Green, L and Carpenter, WR (2014) Linking data for health services research: a framework and instructional guide [Internet]. Rockville, USA: Agency for Healthcare Research and Quality. Available at http://www.ncbi.nlm.nih.gov/books/NBK253312/.Google Scholar
Erraguntla, M, Ramachandran, S, Wu, CN and Mayer, RJ (2010) Avian influenza data mining using environment, epidemiology, and etiology surveillance and analysis toolkit (E3SAT). In: Proceedings of 43rd Hawaii International Conference on System Sciences (HICSS), Honolulu, Hawaii, pp. 1–7.Google Scholar
Fayyad, U, Piatetsky-Shapiro, G and Smyth, P (1996) From data mining to knowledge discovery in databases. AI Magazine 17, 37.Google Scholar
Galvin, CJ, Rumbos, A, Vincent, JI and Salvato, M (2014) Modeling the effects of avian flu (H5N1) vaccination strategies on poultry. CODEE Journal 10, 1.CrossRefGoogle Scholar
García, S, Ramírez-Gallego, S, Luengo, J, Benítez, JM and Herrera, F (2016) Big data preprocessing: methods and prospects. Big Data Analytics 1, 9.CrossRefGoogle Scholar
Ghosh, S, Chakraborty, P, Nsoesie, EO, Cohn, E, Mekaru, SR, Brownstein, JS and Ramakrishnan, N (2017) Temporal topic modelling to assess associations between news trends and infectious disease outbreaks. Scientific Reports 7, 40841.CrossRefGoogle Scholar
Gilbert, M and Pfeiffer, DU (2012) Risk factor modelling of the spatio-temporal patterns of highly pathogenic avian influenza (HPAIV) H5N1: a review. Spatial and Spatio-Temporal Epidemiology 3, 173–183.CrossRefGoogle ScholarPubMed
Gilbert, M, Xiao, X, Chaitaweesub, P, Kalpravidh, W, Premashthira, S, Boles, S and Slingenbergh, J (2007) Avian influenza, domestic ducks and rice agriculture in Thailand. Agriculture, Ecosystems & Environment 119, 409–415.CrossRefGoogle ScholarPubMed
Gilbert, M, Golding, N, Zhou, H, Wint, GRW, Robinson, TP, Tatem, AJ, Lai, S, Zhou, S, Jiang, H and Guo, D (2014) Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia. Nature Communications 5, 4116.CrossRefGoogle ScholarPubMed
Gonzales, JL, Boender, GJ, Elbers, ARW, Stegeman, JA and de Koeijer, AA (2014) Risk-based surveillance for early detection of low pathogenic avian influenza outbreaks in layer chickens. Preventive Veterinary Medicine 117, 251–259.CrossRefGoogle ScholarPubMed
Gonzales, JL, Van Der Goot, JA, Stegeman, JA, Elbers, ARW and Koch, G (2011) Transmission between chickens of an H7N1 low pathogenic avian influenza virus isolated during the epidemic of 1999 in Italy. Veterinary Microbiology 152, 187–190.CrossRefGoogle ScholarPubMed
Gonzales Rojas, JL (2012) Surveillance of Low Pathogenic Avian Influenza in Layer Chickens: Risk Factors, Transmission and Early Detection (Ph.D. thesis). Utrecht University.Google Scholar
Herrick, KA (2013) Predictive Modelling of Avian Influenza in Wild Birds (Ph.D. thesis). University of Alaska Fairbanks (UAF).Google Scholar
Herrick, KA, Huettmann, F and Lindgren, MA (2013) A global model of avian influenza prediction in wild birds: the importance of northern regions. Veterinary Research 44, 42.CrossRefGoogle ScholarPubMed
Hira, ZM and Gillies, DF (2015) A review of feature selection and feature extraction methods applied on microarray data. Advances in Bioinformatics 2015, 198363.CrossRefGoogle ScholarPubMed
Höhle, M and Jørgensen, E (2002) Estimating Parameters for Stochastic Epidemics. Dina Research Report 102, Danish Institute of Agricultural Sciences, Tjele, Denmark.Google Scholar
Hosseini, PR, Fuller, T, Harrigan, R, Zhao, D, Arriola, CS, Gonzalez, A, Miller, MJ, Xiao, X, Smith, TB and Jones, JH (2013) Metapopulation dynamics enable persistence of influenza A, including A/H5N1, in poultry. PLoS ONE 8, e80091.CrossRefGoogle ScholarPubMed
Imai, C, Armstrong, B, Chalabi, Z, Mangtani, P and Hashizume, M (2015) Time series regression model for infectious disease and weather. Environmental Research 142, 319–327.CrossRefGoogle ScholarPubMed
Jayawardhana, UK (2016) An Ontology-Based Framework for Formulating Spatio-Temporal Influenza (flu) Outbreaks from Twitter (Ph.D. thesis). Bowling Green State University.Google Scholar
Kane, MJ, Price, N, Scotch, M and Rabinowitz, P (2014) Comparison of ARIMA and random forest time series models for prediction of avian influenza H5N1 outbreaks. BMC Bioinformatics 15, 276.CrossRefGoogle ScholarPubMed
Kilpatrick, AM, Chmura, AA, Gibbons, DW, Fleischer, RC, Marra, PP and Daszak, P (2006) Predicting the global spread of H5N1 avian influenza. Proceedings of the National Academy of Sciences 103, 19368–19373.CrossRefGoogle ScholarPubMed
Kostkova, P, Szomszor, M and St Louis, C (2014) The use of Twitter as an early warning and risk communication tool in the 2009 swine flu pandemic. ACM Transactions on Management Information Systems 5, 8.CrossRefGoogle Scholar
Lebl, K, Lentz, HHK, Pinior, B and Selhorst, T (2016) Impact of network activity on the spread of infectious diseases through the German pig trade network. Frontiers in Veterinary Science 3, 48.CrossRefGoogle ScholarPubMed
Lee, K, Agrawal, A and Choudhary, A (2013) Real-time digital flu surveillance using twitter data. In: Proceedings of the Second Workshop on Data Mining for Medicine and Healthcare, Austin, Texas, pp. 19–27.Google Scholar
Lee, HJ, Suh, K, Jung, NS, Lee, IB, Seo, IH, Moon, OK and Lee, JJ (2014) Prediction of the spread of highly pathogenic avian influenza using a multifactor network: part 2 – comprehensive network analysis with direct/indirect infection route. Biosystems Engineering 118, 115–127.CrossRefGoogle Scholar
Lewis, N, Dorjee, S, Dubé, C, VanLeeuwen, J and Sanchez, J (2017) Assessment of effectiveness of control strategies against simulated outbreaks of highly pathogenic avian influenza in Ontario, Canada. Transboundary and Emerging Diseases 64, 938–950.CrossRefGoogle ScholarPubMed
Lim, S, Tucker, CS and Kumara, S (2017) An unsupervised machine learning model for discovering latent infectious diseases using social media data. Journal of Biomedical Informatics 66, 82–94.CrossRefGoogle ScholarPubMed
Ma, X, Tao, Z, Wang, Y, Yu, H and Wang, Y (2015) Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research, Part C: Emerging Technologies 54, 187–197.CrossRefGoogle Scholar
Maidstone, R (2012) Discrete event simulation, system dynamics and agent based simulation: discussion and comparison. System 2012, 1–6.Google Scholar
Mannelli, A, Busani, L, Toson, M, Bertolini, S and Marangon, S (2007) Transmission parameters of highly pathogenic avian influenza (H7N1) among industrial poultry farms in northern Italy in 1999–2000. Preventive Veterinary Medicine 81, 318–322.CrossRefGoogle ScholarPubMed
Marathe, MV and Ramakrishnan, N (2013) Recent advances in computational epidemiology. IEEE Intelligent Systems 28, 96–101.CrossRefGoogle ScholarPubMed
Martin, V, Zhou, X, Marshall, E, Jia, B, Fusheng, G, France Dixon, MA, DeHaan, N, Pfeiffer, DU, Magalhães, RJS and Gilbert, M (2011 a) Risk-based surveillance for avian influenza control along poultry market chains in South China: the value of social network analysis. Preventive Veterinary Medicine 102, 196–205.CrossRefGoogle ScholarPubMed
Martin, V, Pfeiffer, DU, Zhou, X, Xiao, X, Prosser, DJ, Guo, F and Gilbert, M (2011 b) Spatial distribution and risk factors of highly pathogenic avian influenza (HPAI) H5N1 in China. PLoS Pathogens 7, e1001308.CrossRefGoogle Scholar
Martínez-López, B, Perez, AM and Sánchez-Vizcaíno, JM (2009) Social network analysis. Review of general concepts and use in preventive veterinary medicine. Transboundary and Emerging Diseases 56, 109–120.CrossRefGoogle ScholarPubMed
Maseleno, A, Hasan, MM, Tuah, N and Tabbu, CR (2015) Fuzzy logic and mathematical theory of evidence to detect the risk of disease spreading of highly pathogenic avian influenza H5N1. Procedia Computer Science 57, 348–357.CrossRefGoogle Scholar
Moyen, N, Ahmed, G, Gupta, S, Tenzin, T, Khan, R, Khan, T, Debnath, N, Yamage, M, Pfeiffer, DU and Fournie, G (2018) A large-scale study of a poultry trading network in Bangladesh: implications for control and surveillance of avian influenza viruses. BMC Veterinary Research 14, 12.CrossRefGoogle ScholarPubMed
Mu, JE, McCarl, BA, Wu, X and Ward, MP (2014) Climate change and the risk of highly pathogenic avian influenza outbreaks in birds. British Journal of Environment and Climate Change 4, 166–185.CrossRefGoogle Scholar
Mulatti, P, Bos, MEH, Busani, L, Nielen, M and Marangon, S (2010) Evaluation of interventions and vaccination strategies for low pathogenicity avian influenza: spatial and space-time analyses and quantification of the spread of infection. Epidemiology & Infection 138, 813–824.CrossRefGoogle ScholarPubMed
Nakamori, Y (2011) Knowledge science: modelling the knowledge creation process. In: Proceedings of the 55th Annual Meeting of the ISSS 2011, Hull, UK, pp. 17–22.CrossRefGoogle Scholar
Neumann, U, Riemenschneider, M, Sowa, JP, Baars, T, Kälsch, J, Canbay, A and Heider, D (2016) Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach. BioData Mining 9, 36.CrossRefGoogle ScholarPubMed
Ngattia, AK, Coulibaly, D, Nzussouo, NT, Kadjo, HA, Chérif, D, Traoré, Y, Kouakou, BK, Kouassi, PD, Ekra, KD and Dagnan, NS (2016) Effects of climatological parameters in modeling and forecasting seasonal influenza transmission in Abidjan, Cote d'Ivoire. BMC Public Health 16, 972.CrossRefGoogle Scholar
Nguyen, VL (2013) The Epidemiology of Avian Influenza in the Mekong River Delta of Viet Nam: A Dissertation Presented (Ph.D. thesis). New Zealand: Massey University.Google Scholar
Nickbakhsh, S, Hall, MD, Dorigatti, I, Lycett, SJ, Mulatti, P, Monne, I, Fusaro, A, Woolhouse, MEJ, Rambaut, A and Kao, RR (2016) Modelling the impact of co-circulating low pathogenic avian influenza viruses on epidemics of highly pathogenic avian influenza in poultry. Epidemics 17, 27–34.CrossRefGoogle ScholarPubMed
Nishiguchi, A, Kobayashi, S, Yamamoto, T, Ouchi, Y, Sugizaki, T and Tsutsui, T (2007) Risk factors for the introduction of avian influenza virus into commercial layer chicken farms during the outbreaks caused by a low-pathogenic H5N2 virus in Japan in 2005. Zoonoses and Public Health 54, 337–343.CrossRefGoogle ScholarPubMed
Nöremark, M, Håkansson, N, Lewerin, SS, Lindberg, A and Jonsson, A (2011) Network analysis of cattle and pig movements in Sweden: measures relevant for disease control and risk-based surveillance. Preventive Veterinary Medicine 99, 78–90.CrossRefGoogle ScholarPubMed
Noy, NF and McGuinness, DL (2001) Ontology development 101: A guide to creating your first ontology. Technical Report KSL-01-05, Stanford University, Palo Alto, Stanford, CA.Google Scholar
Padmanabhan, A, Wang, S, Cao, G, Hwang, M, Zhao, Y, Zhang, Z and Gao, Y (2013) FluMapper: an interactive CyberGIS environment for massive location-based social media data analysis. In: Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery, pp. 33.CrossRefGoogle Scholar
Patyk, KA, Helm, J, Martin, MK, Forde-Folle, KN, Olea-Popelka, FJ, Hokanson, JE, Fingerlin, T and Reeves, A (2013) An epidemiologic simulation model of the spread and control of highly pathogenic avian influenza (H5N1) among commercial and backyard poultry flocks in South Carolina, United States. Preventive Veterinary Medicine 110, 510–524.CrossRefGoogle ScholarPubMed
Pepin, KM, Spackman, E, Brown, JD, Pabilonia, KL, Garber, LP, Weaver, JT, Kennedy, DA, Patyk, KA, Huyvaert, KP and Miller, RS (2014) Using quantitative disease dynamics as a tool for guiding response to avian influenza in poultry in the United States of America. Preventive Veterinary Medicine 113, 376–397.CrossRefGoogle ScholarPubMed
Pérez, J, Iturbide, E, Olivares, V, Hidalgo, M, Martínez, A and Almanza, N (2015) A data preparation methodology in data mining applied to mortality population databases. Journal of Medical Systems 39, 152.CrossRefGoogle ScholarPubMed
Permanasari, AE, Utami, IK, Hidayah, I and Kusumawardani, SS (2015) Forecasting avian influenza incidence in Java and Madura area. In: Proceedings of 2015 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC), Indonesia, pp. 212–216.CrossRefGoogle Scholar
Pesquita, C, Ferreira, JD, Couto, FM and Silva, MJ (2014) The epidemiology ontology: an ontology for the semantic annotation of epidemiological resources. Journal of Biomedical Semantics 5, 4.CrossRefGoogle ScholarPubMed
Poetri, ON (2014) Towards an Improved Vaccination Programme Against Highly Pathogenic Avian Influenza in Indonesia (Ph.D. thesis). Utrecht University.Google Scholar
Poetri, ON, Bouma, A, Murtini, S, Claassen, I, Koch, G, Soejoedono, RD, Stegeman, JA and Van Boven, M (2009) An inactivated H5N2 vaccine reduces transmission of highly pathogenic H5N1 avian influenza virus among native chickens. Vaccine 27, 2864–2869.CrossRefGoogle ScholarPubMed
Qi, L (2008) Advancing knowledge discovery and data mining. In: Proceedings of the First International Workshop on Knowledge Discovery and Data Mining, Adelaide, SA, Australia, pp. 3–5.Google Scholar
RamrezGallego, S, Garca, S, MourioTaln, H, MartnezRego, D, BolnCanedo, V, AlonsoBetanzos, A, Bentez, JM and Herrera, F (2016) Data discretization: taxonomy and big data challenge. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6, 5–21.Google Scholar
Ray, EL and Reich, NG (2018) Prediction of infectious disease epidemics via weighted density ensembles. PLoS Computational Biology 14, e1005910.CrossRefGoogle ScholarPubMed
Reeves, A (2012) Construction and Evaluation of Epidemiologic Simulation Models for the Within-and Among-Unit Spread and Control of Infectious Diseases of Livestock and Poultry (Ph.D. thesis). Colorado State University.Google Scholar
Robertson, C and Yee, L (2016) Avian influenza risk surveillance in North America with online media. PLoS ONE 11, e0165688.CrossRefGoogle ScholarPubMed
Rohani, P, Breban, R, Stallknecht, DE and Drake, JM (2009) Environmental transmission of low pathogenicity avian influenza viruses and its implications for pathogen invasion. Proceedings of the National Academy of Sciences 106, 10365–10369.CrossRefGoogle ScholarPubMed
Saenz, RA, Essen, SC, Brookes, SM, Iqbal, M, Wood, JLN, Grenfell, BT, McCauley, JW, Brown, IH and Gog, JR (2012) Quantifying transmission of highly pathogenic and low pathogenicity H7N1 avian influenza in turkeys. PLoS ONE 7, e45059.CrossRefGoogle ScholarPubMed
Salathe, M, Bengtsson, L, Bodnar, TJ, Brewer, DD, Brownstein, JS, Buckee, C, Campbell, EM, Cattuto, C, Khandelwal, S and Mabry, PL (2012) Digital epidemiology. PLoS Computational Biology 8, e1002616.CrossRefGoogle ScholarPubMed
Santillana, M, Nguyen, AT, Dredze, M, Paul, MJ, Nsoesie, EO and Brownstein, JS (2015) Combining search, social media, and traditional data sources to improve influenza surveillance. PLoS Computational Biology 11, e1004513.CrossRefGoogle ScholarPubMed
Savill, NJ, St Rose, SG, Keeling, MJ and Woolhouse, MEJ (2006) Silent spread of H5N1 in vaccinated poultry. Nature 442, 757.CrossRefGoogle ScholarPubMed
Sharpe, D, Hopkins, R, Cook, RL and Striley, CW (2017) Using a Bayesian method to assess Google, Twitter, and Wikipedia for ILI surveillance. Online Journal of Public Health Informatics 9, e26.CrossRefGoogle Scholar
Si, Y, de Boer, WF and Gong, P (2013) Different environmental drivers of highly pathogenic avian influenza H5N1 outbreaks in poultry and wild birds. PLoS ONE 8, e53362.CrossRefGoogle ScholarPubMed
Siettos, CI and Russo, L (2013) Mathematical modelling of infectious disease dynamics. Virulence 4, 295–306.CrossRefGoogle ScholarPubMed
Signorini, A (2014) Use of Social Media to Monitor and Predict Outbreaks and Public Opinion on Health Topics (Ph.D. thesis). University of Iowa.Google Scholar
Soebiyanto, RP, Adimi, F and Kiang, RK (2010) Modeling and predicting seasonal influenza transmission in warm regions using climatological parameters. PLoS ONE 5, e9450.CrossRefGoogle ScholarPubMed
Song, X, Xiao, J, Deng, J, Kang, Q, Zhang, Y and Xu, J (2016) Time series analysis of influenza incidence in Chinese provinces from 2004 to 2011. Medicine 95, e3929.CrossRefGoogle ScholarPubMed
Ssematimba, A, Hagenaars, TJ and De Jong, MCM (2012) Modelling the wind-borne spread of highly pathogenic avian influenza virus between farms. PLoS ONE 7, e31114.CrossRefGoogle ScholarPubMed
Stegeman, A, Bouma, A, Elbers, ARW, de Jong, MCM, Nodelijk, G, de Klerk, F, Koch, G and van Boven, M (2004) Avian influenza A virus (H7N7) epidemic in the Netherlands in 2003: course of the epidemic and effectiveness of control measures. The Journal of Infectious Diseases 190, 2088–2095.CrossRefGoogle ScholarPubMed
Szomszor, M, Kostkova, P and Louis, CS (2011) Twitter informatics: tracking and understanding public reaction during the 2009 swine flu pandemic. In: 2011 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Lyon, France, pp. 320–323.Google Scholar
Taylor, N (2003) Review of the use of models in informing disease control policy development and adjustment. A Report for the Department for Environmental, Food, and Rural Affairs (DEFRA), UK.Google Scholar
Thakur, KK (2015) Simulation models for between farm transmission of PRRS virus in Canadian swine herds. Ph.D. thesis, University of Prince Edward Island.Google Scholar
Tiensin, T, Nielen, M, Vernooij, H, Songserm, T, Kalpravidh, W, Chotiprasatintara, S, Chaisingh, A, Wongkasemjit, S, Chanachai, K and Thanapongtham, W (2007) Transmission of the highly pathogenic avian influenza virus H5N1 within flocks during the 2004 epidemic in Thailand. The Journal of Infectious Diseases 196, 1679–1684.CrossRefGoogle ScholarPubMed
Tsumoto, S (2000) Clinical knowledge discovery in hospital information systems: two case studies. In: Proceedings of European Conference on Principles of Data Mining and Knowledge Discovery, Lyon, France, pp. 652–656.CrossRefGoogle Scholar
Valdes-Donoso, P, VanderWaal, K, Jarvis, LS, Wayne, SR and Perez, AM (2017) Using machine learning to predict swine movements within a regional program to improve control of infectious diseases in the US. Frontiers in Veterinary Science 4, 2.CrossRefGoogle ScholarPubMed
Van der Goot, JA, De Jong, MCM, Koch, G and Van Boven, M (2003) Comparison of the transmission characteristics of low and high pathogenicity avian influenza A virus (H5N2). Epidemiology & Infection 131, 1003–1013.CrossRefGoogle Scholar
Van Kerkhove, MD, Vong, S, Guitian, J, Holl, D, Mangtani, P, San, S and Ghani, AC (2009) Poultry movement networks in Cambodia: implications for surveillance and control of highly pathogenic avian influenza (HPAI/H5N1). Vaccine 27, 6345–6352.CrossRefGoogle Scholar
Wang, RH, Jin, Z, Liu, QX, van de Koppel, J and Alonso, D (2012) A simple stochastic model with environmental transmission explains multi-year periodicity in outbreaks of avian flu. PLoS ONE 7, e28873.CrossRefGoogle ScholarPubMed
Wang, F, Wang, H, Xu, K, Raymond, R, Chon, J, Fuller, S and Debruyn, A (2016) Regional level influenza study with geo-tagged Twitter data. Journal of Medical Systems 40, 189.CrossRefGoogle ScholarPubMed
Wang, X, Wang, Q, Cheng, W, Yu, Z, Ling, F, Mao, H and Chen, E (2017) Risk factors for avian influenza virus contamination of live poultry markets in Zhejiang, China during the 2015–2016 human influenza season. Scientific Reports 7, 42722.CrossRefGoogle ScholarPubMed
Weaver, JT, Malladi, S, Goldsmith, TJ, Hueston, W, Hennessey, M, Lee, B, Voss, S, Funk, J, Der, C, Bjork, KE, Clouse, TL and Halvorson, DA (2012) Impact of virus strain characteristics on early detection of highly pathogenic avian influenza infection in commercial table-egg layer flocks and implications for outbreak control. Avian Diseases 56: 905–912.CrossRefGoogle ScholarPubMed
White, T (2012). Hadoop: The Definitive Guide, 3rd Edn.Sebastopol, CA, USA: O'Reilly Media, Inc.Google Scholar
Wilasang, C, Wiratsudakul, A and Chadsuthi, S (2016) The dynamics of avian influenza: individual-based model with intervention strategies in traditional trade networks in Phitsanulok province, Thailand. Computational and Mathematical Methods in Medicine 2016, 198363. doi: 10.1155/2015/198363CrossRefGoogle ScholarPubMed
Williams, GJ and Huang, Z (1996) A case study in knowledge acquisition for insurance risk assessment using a KDD methodology. In: Proceedings of the Pacific Rim Knowledge Acquisition Workshop, Dept. of AI, Univ. of NSW, Sydney, Australia, pp. 117–129.Google Scholar
Wiratsudakul, A (2014) Mathematical Modelling of the Infectious Spread of Avian Influenza on a Backyard Chicken Production Chain in Thailand (Ph.D. thesis). Université Blaise Pascal Clermont-Ferrand II.Google Scholar
Wiratsudakul, A, Paul, MC, Bicout, DJ, Tiensin, T, Triampo, W and Chalvet-Monfray, K (2014) Modelling the dynamics of backyard chicken flows in traditional trade networks in Thailand: implications for surveillance and control of avian influenza. Tropical Animal Health and Production 46, 845–853.CrossRefGoogle Scholar
Wu, H, Cai, Y, Wu, Y, Zhong, R, Li, Q, Zheng, J, Lin, D and Li, Y (2017) Time series analysis of weekly influenza-like illness rate using a one-year period of factors in random forest regression. Bioscience Trends 11, 292–296.CrossRefGoogle ScholarPubMed
Xu, Z, Lee, J, Park, D and Chung, Y (2017) Multidimensional analysis model for highly pathogenic avian influenza using data cube and data mining techniques. Biosystems Engineering 157, 109–121.CrossRefGoogle Scholar
Zhang, Q, Segall, RS and Cao, M (2010) Visual Analytics and Interactive Technologies: Data, Text and web Mining Applications. Hershey, PA: IGI Global, p. 113.Google Scholar
Zhang, X, Zhang, T, Young, AA and Li, X (2014) Applications and comparisons of four time-series models in epidemiological surveillance data. PLoS One 9, e88075.CrossRefGoogle ScholarPubMed
Zhao, L, Chen, J, Chen, F, Wang, W, Lu, CT and Ramakrishnan, N (2015) Simnest: social media nested epidemic simulation via online semi-supervised deep learning. In: Proceedings of 2015 IEEE International Conference on Data Mining (ICDM), Atlantic City, New Jersey, USA, pp. 639–648.CrossRefGoogle Scholar