Public Health and Epidemiology Informatics: Can Artificial Intelligence Help Future Global Challenges? An Overview of Antimicrobial Resistance and Impact of Climate Change in Disease Epidemiology

 

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Summary

Objectives: To provide an oveiview of the current application of artificial intelligence (AI) in the field of public health and epidemiology, with a special focus on antimicrobial resistance and the impact of climate change in disease epidemiology. Both topics are of vital importance and were included in the “Ten threats to global health in 2019“ report published by the World Health Organization.

Methods: We analysed publications that appeared in the last two years, between January 2017 and October 2018. Papers were searched using Google Scholar with the following keywords: public health, epidemiology, machine learning, data analytics, artificial intelligence, disease surveillance, climate change, antimicrobial resistance, and combinations thereof. Selected articles were organised by theme.

Results: In spite of a large interest in AI generated both within and outside the scientific community, and of the many opinions pointing towards the importance of a better use of data in public health, few papers have been published on the selected topics in the last two years. We identify several potential reasons, including the complexity of the integration of heterogeneous data, and the lack of sound and unbiased validation procedures.

Conclusions: As there is a better comprehension of AI and more funding available, artificial intelligence will become not only the centre of attention in informatics, but more importantly the source of innovative solutions for public health.

• References

• 1 Russell S, Norvig P. Artificial Intelligence: A Modern Approach. 3rd ed. Prentice Hall; 2009
  PubMed
• 2 Patel VL, Shortliffe EH, Stefanelli M, Szolovits P, Berthold MR, Bellazzi R. , et al. The coming of age of artificial intelligence in medicine. Artif Intell Med 2009; 46 (01) 5-17
  CrossrefPubMedGoogle Scholar
• 3 Ramesh AN, Kambhampati C, Monson JRT, Drew PJ. Artificial intelligence in medicine. Ann R Coll Surg Engl 2004; 86 (05) 334-8
  PubMedGoogle Scholar
• 4 Artificial Intelligence in Medicine | NEJM [Internet]. N Engl J Med [cited 2018 Dec 2]. Available from: https://www.nejm.org/doi/pdf/10.1056/NEJM198703123161109
  PubMed
• 5 Horvitz EJ, Breese JS, Henrion M. Decision theory in expert systems and artificial intelligence. Int J Approx Reason 1988; 2 (03) 247-302
  CrossrefPubMedGoogle Scholar
• 6 Miller PL. The evaluation of artificial intelligence systems in medicine. Comput Methods Programs Biomed 1986; 22 (01) 3-11
  CrossrefPubMedGoogle Scholar
• 7 Bini SA. Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care?. J Arthroplasty 2018; 33 (08) 2358-61
  CrossrefPubMedGoogle Scholar
• 8 Du XL, Li WB, Hu BJ. Application of artificial intelligence in ophthalmology. Int J Ophthalmol 2018; 11 (09) 1555-61
  PubMedGoogle Scholar
• 9 Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial Intelligence in Surgery: Promises and Perils. Ann Surg 2018; 268 (01) 70-6
  CrossrefPubMedGoogle Scholar
• 10 Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S. , et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017; 2 (04) 230-43
  CrossrefPubMedGoogle Scholar
• 11 Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial Intelligence in Precision Cardiovascular Medicine. J Am Coll Cardiol 2017; 69 (21) 2657-64
  CrossrefPubMedGoogle Scholar
• 12 Miller DD, Brown EW. Artificial Intelligence in Medical Practice: The Question to the Answer?. Am J Med 2018; 131 (02) 129-33
  CrossrefPubMedGoogle Scholar
• 13 Sniecinski I, Seghatchian J. Artificial intelligence: A joint narrative on potential use in pediatric stem and immune cell therapies and regenerative medicine. Transfus Apher Sci Off J World Apher Assoc Off J Eur Soc Haemapheresis 2018; 57 (03) 422-4
  PubMedGoogle Scholar
• 14 Stewart J, Sprivulis P, Dwivedi G. Artificial intelligence and machine learning in emergency medicine. Emerg Med Australas EMA 2018; 30 (06) 870-4
  PubMedGoogle Scholar
• 15 Chen JH, Asch SM. Machine Learning and Prediction in Medicine – Beyond the Peak of Inflated Expectations. N Engl J Med 2017; 376 (26) 2507-9
  CrossrefPubMedGoogle Scholar
• 16 Fogel AL, Kvedar JC. Artificial intelligence powers digital medicine. Npj Digit Med 2018; 1 (01) 5
  CrossrefPubMedGoogle Scholar
• 17 Naughton J. Magical thinking about machine learning won’t bring the reality of AI any closer | John Naughton. The Guardian [Internet] 2018 Aug 5 [cited 2018 Nov 21]; Available from: https://www.theguardian.com/commentisfree/2018/aug/05/magical-thinking-about-machine-learning-will-not-bring-artificial-intelligence-any-closer
  PubMed
• 18 Schwartz O. “The discourse is unhinged”: how the media gets AI alarmingly wrong. The Guardian [Internet] 2018 Jul 25 [cited 2018 Nov 21]; Available from: https://www.theguardian.com/technology/2018/jul/25/ai-artificial-intelligence-social-media-bots-wrong
  PubMed
• 19 Lynch SM, Moore JH. A call for biological data mining approaches in epidemiology. BioData Min [Internet] 2016 Jan 4 [cited 2018 Nov 21];9. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4700596/
  PubMed
• 20 Jones N. How machine learning could help to improve climate forecasts. Nat News 2017; 548 (7668): 379
  PubMedGoogle Scholar
• 21 McGovern A, Elmore KL, Gagne DJ, Haupt SE, Karstens CD, Lagerquist R. , et al. Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather. Bull Am Meteorol Soc 2017; 98 (10) 2073-90
  CrossrefPubMedGoogle Scholar
• 22 Flahault A, Bar-Hen A, Paragios N. Public Health and Epidemiology Informatics. Yearb Med Inform 2016; 1: 240-6
  Article in Thieme ConnectPubMedGoogle Scholar
• 23 Thiebaut R, Thiessard F. Public Health and Epidemiology Informatics. Yearb Med Inform 2017; 26 (01) 248-51
  Article in Thieme ConnectPubMedGoogle Scholar
• 24 Levy SB. Microbial resistance to antibiotics. An evolving and persistent problem. Lancet Lond Engl 1982; 2 (8289): 83-8
  PubMedGoogle Scholar
• 25 Barber M, Rozwadowska-Dowzenko M. Infection by penicillin-resistant staphylococci. Lancet Lond Engl 1948; 2 (6530): 641-4
  PubMedGoogle Scholar
• 26 WHO | Antimicrobial resistance: global report on surveillance 2014 [Internet]. WHO. 2014 [cited 2018 Nov 26]. Available from: http://www.who.int/drugresistance/documents/surveillancereport/en/
  PubMed
• 27 Zhanel GG, Wiebe R, Dilay L, Thomson K, Rubinstein E, Hoban DJ. , et al. Comparative review of the carbapenems. Drugs 2007; 67 (07) 1027-52
  CrossrefPubMedGoogle Scholar
• 28 Cassini A, Högberg LD, Plachouras D, Quattrocchi A, Hoxha A, Simonsen GS. , et al. Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis. Lancet Infect Dis 2019; 19 (01) 56-66
  CrossrefPubMedGoogle Scholar
• 29 Stemming the Superbug Tide [Internet]. [cited 2019 Apr 10]. Available from: https://www.oecd-ilibrary.org/social-issues-migration-health/stemming-the-superbug-tide_9789264307599-en
  PubMed
• 30 Levy SB. Balancing the drug-resistance equation. Trends Microbiol 1994; 2 (10) 341-2
  CrossrefPubMedGoogle Scholar
• 31 Wormser GP, Bergman MM. The Antibiotic Paradox: How the Misuse of Antibiotics Destroys Their Curative Powers, 2nd Edition By Stuart B. Levy Cambridge, Massachusetts: Perseus Publishing; 2002. 376 pp., illustrated. $17.50 (paper). Clin Infect Dis 2003; 36 (02) 238
  PubMedGoogle Scholar
• 32 Milkman R. Gene Transfer in the Environment. Stuart B. Levy and Robert V Miller. McGraw-Hill, New York, 1989. x, 434 pp., illus. $54.95. Environmental Biotechnology. Science 1990; 247 (4940): 350-1
  CrossrefPubMedGoogle Scholar
• 33 Levy SB, Marshall B. Antibacterial resistance worldwide: causes, challenges and responses. Nat Med 2004; 10 (12 Suppl): S122-129
  CrossrefPubMedGoogle Scholar
• 34 Giuliani A, Rinaldi AC. editors. Antimicrobial Peptides: Methods and Protocols [Internet]. Humana Press; 2010 [cited 2018 Nov 26]. (Methods in Molecular Biology). Available from: http://www.springer.com/la/book/9781607615934
  PubMed
• 35 Fjell CD, Hancock REW, Cherkasov A. AMPer: a database and an automated discovery tool for antimicrobial peptides. Bioinforma Oxf Engl 2007; 23 (09) 1148-55
  CrossrefPubMedGoogle Scholar
• 36 Lee EY, Lee MW, Fulan BM, Ferguson AL, Wong GCL. What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning?. Interface Focus 2017; 7 (06) 20160153
  CrossrefPubMedGoogle Scholar
• 37 Mousavizadegan M, Mohabatkar H. Computational prediction of antifungal peptides via Chou’s PseAAC and SVM. J Bioinform Comput Biol 2018; 29: 1850016
  PubMedGoogle Scholar
• 38 Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by Nello Cristianini [Internet]. Cambridge Core 2000 [cited 2019 Apr 10]. Available from: http://core/books/an-introduction-to-support-vector-machines-and-other-kernelbased-learning-methods/A6A6F4084056A4B23F88648DDBFDD6FC
  PubMed
• 39 Vishnepolsky B, Gabrielian A, Rosenthal A, Hurt DE, Tartakovsky M, Managadze G. , et al. Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria. J Chem Inf Model 2018; 58 (05) 1141-51
  CrossrefPubMedGoogle Scholar
• 40 Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Comput 1997; 9 (08) 1735-80
  CrossrefPubMedGoogle Scholar
• 41 Youmans M, Spainhour C, Qiu P. Long short-term memory recurrent neural networks for antibacterial peptide identification. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2017. p. 498-502
  PubMed
• 42 Porto WF, Pires AS, Franco OL. Antimicrobial activity predictors benchmarking analysis using shuffled and designed synthetic peptides. J Theor Biol 2017; 426: 96-103
  CrossrefPubMedGoogle Scholar
• 43 Müller AT, Hiss JA, Schneider G. Recurrent Neural Network Model for Constructive Peptide Design. J Chem Inf Model 2018; 58 (02) 472-9
  CrossrefPubMedGoogle Scholar
• 44 Schneider P, Müller AT, Gabernet G, Button AL, Posselt G, Wessler S. , et al. Hybrid Network Model for “Deep Learning” of Chemical Data: Application to Antimicrobial Peptides. Mol Inform 2017; 36 (1-2): 1600011
  CrossrefPubMedGoogle Scholar
• 45 Yoshida M, Hinkley T, Tsuda S, Abul-Haija YM, McBurney RT, Kulikov V. , et al. Using Evolutionary Algorithms and Machine Learning to Explore Sequence Space for the Discovery of Antimicrobial Peptides. Chem 2018; 4 (03) 533-43
  PubMedGoogle Scholar
• 46 Veltri D, Kamath U, Shehu A. Improving Recognition of Antimicrobial Peptides and Target Selectivity through Machine Learning and Genetic Programming. IEEE/ACM Trans Comput Biol Bioinform 2017; 14 (02) 300-13
  CrossrefPubMedGoogle Scholar
• 47 Wernli D, Jorgensen PS, Harbarth S, Carroll SP, Laxminarayan R, Levrat N. , et al. Antimicrobial resistance: The complex challenge of measurement to inform policy and the public. PLoS Med 2017; 14 (08) e1002378
  CrossrefPubMedGoogle Scholar
• 48 Safdari R, Ghazi Saeedi M, Masoumi-Asl H, Rezaei-Hachesu P, Mirnia K, Samad-Soltani T. Knowledge discovery and visualization in antimicrobial resistance surveillance systems: a scoping review. Artif Intell Rev [Internet] 2018 Sep 25 [cited 2018 Nov 26]; Available from: https://doi.org/10.1007/s10462-018-9659-6
  CrossrefPubMed
• 49 Rezaei-Hachesu P, Samad-Soltani T, Yaghoubi S, Ghazi Saeedi M, Mirnia K, Masoumi-Asl H. , et al. The design and evaluation of an antimicrobial resistance surveillance system for neonatal intensive care units in Iran. Int J Med Inf 2018; 115: 24-34
  CrossrefPubMedGoogle Scholar
• 50 Schouten J, De Waele J. Antimicrobial Stewardship in ICU. Academic Press; 2017
  Google Scholar
• 51 Barlow G. Clinical challenges in antimicrobial resistance. Nat Microbiol 2018; 3 (03) 258
  CrossrefPubMedGoogle Scholar
• 52 Kim SW. Antimicrobial Stewardship with Intravenous to Oral Conversion and Future Directions of Antimicrobial Stewardship. Infect Chemother. 2017; 49 (01) 87-9
  CrossrefPubMedGoogle Scholar
• 53 Beaudoin M, Kabanza F, Nault V, Valiquette L. Evaluation of a machine learning capability for a clinical decision support system to enhance antimicrobial stewardship programs. Artif Intell Med 2016; 68: 29-36
  CrossrefPubMedGoogle Scholar
• 54 Cánovas-Segura B, Campos M, Morales A, Juarez JM, Palacios F. Development of a clinical decision support system for antibiotic management in a hospital environment. Prog Artif Intell 2016; 5 (03) 181-97
  CrossrefPubMedGoogle Scholar
• 55 Evans RS, Pestotnik SL, Classen DC, Clemmer TP, Weaver LK, Orme JF. , et al. A computer-assisted management program for antibiotics and other antiinfective agents. N Engl J Med 1998; 338 (04) 232-8
  CrossrefPubMedGoogle Scholar
• 56 Leonard H, Colodner R, Halachmi S, Segal E. Recent Advances in the Race to Design a Rapid Diagnostic Test for Antimicrobial Resistance. ACS Sens 2018; 3 (11) 2202-17
  CrossrefPubMedGoogle Scholar
• 57 Yu H, Jing W, Iriya R, Yang Y, Syal K, Mo M. , et al. Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy. Anal Chem 2017; 90 (10) 6314-22
  PubMedGoogle Scholar
• 58 Wu TF, Chen YC, Wang WC, Fang YC, Fukuoka S, Pride DT. , et al. A Rapid and Low-Cost Pathogen
  PubMed
• 59 Athamanolap P, Hsieh K, Chen L, Yang S, Wang TH. Integrated Bacterial Identification and Antimicrobial Susceptibility Testing Using PCR and High-Resolution Melt. Anal Chem 2017; 89 (21) 11529-36
  CrossrefPubMedGoogle Scholar
• 60 Hernandez B, Herrero P, Rawson TM, Moore LSP, Evans B, Toumazou C. , et al. Supervised learning for infection risk inference using pathology data. BMC Med Inform Decis Mak 2017; 17 (01) 168
  CrossrefPubMedGoogle Scholar
• 61 Zhao J. Study on the application of classification tree model in building the risk model for nosocomial infection in critically ill patients of emergency department. Chin J Postgrad Med 2017; 40 (09) 791-4
  PubMedGoogle Scholar
• 62 Quinlan JR. Induction of decision trees. Mach Learn 1986; 1 (01) 81-106
  CrossrefPubMedGoogle Scholar
• 63 Breiman L. Random Forests. Mach Learn 2001; 45 (01) 5-32
  CrossrefPubMedGoogle Scholar
• 64 Hartvigsen T, Sen C, Brownell S, Teeple E, Kong X, Rundensteiner E. Early Prediction of MRSA Infections using Electronic Health Records. In 2018 [cited 2018 Nov 26]. p. 156-67. Available from: http://www.scitepress.org/PublicationsDetail.aspx?ID=wFHke4fgXY4=&t=1
  PubMed
• 65 Ling CX, Huang J, Zhang H. AUC: A Statistically Consistent and More Discriminating Measure Than Accuracy. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence [Internet]. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.; 2003 [cited 2019 Apr 10]. p. 519-24. (IJCAI’03). Available from: http://dl.acm.org/citation.cfm?id=1630659.1630736
  PubMed
• 66 Estrada F, Botzen WJW, Tol RSJ. A global economic assessment of city policies to reduce climate change impacts. Nat Clim Change 2017; 7 (06) 403-6
  CrossrefPubMedGoogle Scholar
• 67 Hsiang S, Kopp R, Jina A, Rising J, Delgado M, Mohan S. , et al. Estimating economic damage from climate change in the United States. Science 2017; 356 (6345): 1362-9
  CrossrefPubMedGoogle Scholar
• 68 Tol RSJ. The Economic Impacts of Climate Change. Rev Environ Econ Policy 2018; 12 (01) 4-25
  CrossrefPubMedGoogle Scholar
• 69 Zhang P, Zhang J, Chen M. Economic impacts of climate change on agriculture: The importance of additional climatic variables other than temperature and precipitation. J Environ Econ Manag 2017; 83: 8-31
  CrossrefPubMedGoogle Scholar
• 70 Pecl GT, Araujo MB, Bell JD, Blanchard J, Bonebrake TC, Chen IC. , et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 2017; 31 ;355(6332): eaai9214
  PubMedGoogle Scholar
• 71 Glaciers and climate change | National Snow and Ice Data Center [Internet]. [cited 2018 Nov 25]. Available from: https://nsidc.org/cryosphere/glaciers/questions/climate.html
  PubMed
• 72 Seneviratne SI, Donat MG, Mueller B, Alexander LV. No pause in the increase of hot temperature extremes. Nat Clim Change 2014; 4: 161-3
  CrossrefPubMedGoogle Scholar
• 73 Ghini R, Bettiol W, Hamada E. Diseases in tropical and plantation crops as affected by climate changes: current knowledge and perspectives. Plant Pathol 2011; 60 (01) 122-32
  CrossrefPubMedGoogle Scholar
• 74 Hoegh-Guldberg O, Bruno JF. The Impact of Climate Change on the World’s Marine Ecosystems. Science 2010; 328 (5985): 1523-8
  CrossrefPubMedGoogle Scholar
• 75 Climate Change | Turning Up the Heat [Internet]. Taylor & Francis. [cited 2018 Nov 25]. Available from: https://www.taylorfrancis.com/books/9781134035588
  PubMed
• 76 Lafferty KD. The ecology of climate change and infectious diseases. Ecology 2009; 90 (04) 888-900
  CrossrefPubMedGoogle Scholar
• 77 Patz JA, Epstein PR, Burke TA, Balbus JM. Global Climate Change and Emerging Infectious Diseases. JAMA 1996; 275 (03) 217-23
  CrossrefPubMedGoogle Scholar
• 78 Waits A, Emelyanova A, Oksanen A, Abass K, Rautio A. Human infectious diseases and the changing climate in the Arctic. Environ Int 2018; 121: 703-13
  CrossrefPubMedGoogle Scholar
• 79 Booth M. Chapter Three - Climate Change and the Neglected Tropical Diseases. In: Rollinson D, Stothard JR. , editors. Advances in Parasitology [Internet]. Academic Press; 2018. [cited 2018 Nov 25]. p. 39-126. Available from: http://www.sciencedirect.com/science/article/pii/S0065308X18300046
  Google Scholar
• 80 Manogaran G, Lopez D. Spatial cumulative sum algorithm with big data analytics for climate change detection. Comput Electr Eng 2018; 65: 207-21
  CrossrefPubMedGoogle Scholar
• 81 Faghmous JH, Kumar V. A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science. Big Data 2014; 2 (03) 155-63
  CrossrefPubMedGoogle Scholar
• 82 Jang SM, Hart PS. Polarized frames on “climate change” and “global warming” across countries and states: Evidence from Twitter big data. Glob Environ Change 2015; 32: 11-7
  CrossrefPubMedGoogle Scholar
• 83 Hampton SE, Strasser CA, Tewksbury JJ, Gram WK, Budden AE, Batcheller AL. , et al. Big data and the future of ecology. Front Ecol Environ 2013; 11 (03) 156-62
  CrossrefPubMedGoogle Scholar
• 84 O’Gorman PA, Dwyer JG. Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events. J Adv Model Earth Syst 2018; 10 (10) 2548-63
  CrossrefPubMedGoogle Scholar
• 85 McMichael AJ, Woodruff RE, Hales S. Climate change and human health: present and future risks. The Lancet 2006; 367 (9513): 859-69
  CrossrefPubMedGoogle Scholar
• 86 Altizer S, Ostfeld RS, Johnson PTJ, Kutz S, Harvell CD. Climate Change and Infectious Diseases: From Evidence to a Predictive Framework. Science 2013; 341 (6145): 514-9
  CrossrefPubMedGoogle Scholar
• 87 WHO | Leishmaniasis [Internet]. WHO. [cited 2018 Nov 27]. Available from: http://www.who.int/gho/neglected_diseases/leishmaniasis/en/
  PubMed
• 88 González C, Wang O, Strutz SE, González-Salazar C, Sánchez-Cordero V, Sarkar S. Climate Change and Risk of Leishmaniasis in North America: Predictions from Ecological Niche Models of Vector and Reservoir Species. PLoS Negl Trop Dis 2010; 4 (01) e585
  CrossrefPubMedGoogle Scholar
• 89 Johansson MA, Reich NG, Hota A, Brownstein JS, Santillana M. Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico. Sci Rep 2016; 6: 33707
  CrossrefPubMedGoogle Scholar
• 90 Wang Y, Li J, Gu J, Zhou Z, Wang Z. Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (China). Appl Soft Comput 2015; 35: 280-90
  CrossrefPubMedGoogle Scholar
• 91 Haykin S. Neural Networks: A Comprehensive Foundation. 2nd ed. Prentice Hall; 1998
  Google Scholar
• 92 Smola AJ, Scholkopf B. A tutorial on support vector regression. Stat Comput 2004; 14 (03) 199-222
  CrossrefPubMedGoogle Scholar
• 93 Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL. , et al. Global trends in emerging infectious diseases. Nature 2008; 451 (7181): 990-3
  CrossrefPubMedGoogle Scholar
• 94 Kovats RS, Campbell-Lendrum DH, McMichel AJ, Woodward A, Cox JSH. Early effects of climate change: do they include changes in vector-borne disease?. Philos Trans R Soc Lond B Biol Sci 2001; 356 (1411): 1057-68
  CrossrefPubMedGoogle Scholar
• 95 Manogaran G, Lopez D. Disease Surveillance System for Big Climate Data Processing and Dengue Transmission. Clim Change Environ Concerns Breakthr Res Pract 2018;427-46
  PubMed
• 96 Traore BB, Kamsu-Foguem B, Tangara F. Data mining techniques on satellite images for discovery of risk areas. Expert Syst Appl 2017; 72: 443-56
  CrossrefPubMedGoogle Scholar
• 97 Xu L, Stige LC, Chan KS, Zhou J, Yang J, Sang S. , et al. Climate variation drives dengue dynamics. Proc Natl Acad Sci 2017; 114 (01) 113-8
  CrossrefPubMedGoogle Scholar
• 98 Semenza JC, Hoeser C, Herbst S, Rechenburg A, Suk JE, Frechen T. , et al. Knowledge Mapping for Climate Change and Food and Waterborne Diseases. Crit Rev Environ Sci Technol 2012; 42 (04) 378-411
  CrossrefPubMedGoogle Scholar
• 99 Aramaki E, Maskawa S, Morita M. Twitter Catches the Flu: Detecting Influenza Epidemics Using Twitter. In: Proceedings ofthe Conference on Empirical Methods in Natural Language Processing [Internet]. Stroudsburg, PA, USA: Association for Computational Linguistics; 2011 [cited 2019 Mar 30]. p. 1568-76. (EMNLP ’11). Available from: http://dl.acm.org/citation.cfm?id=2145432.2145600
  PubMed
• 100 Charles-Smith LE, Reynolds TL, Cameron MA, Conway M, Lau EHY, Olsen JM. , et al. Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review. PLoS One 2015; 10 (10) e0139701
  CrossrefPubMedGoogle Scholar
• 101 Paul MJ, Dredze M, Broniatowski D. Twitter Improves Influenza Forecasting. PLoS Curr [Internet] 2014 Oct 28 [cited 2018 Nov 28];6. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4234396/
  PubMed
• 102 Signorini A, Segre AM, Polgreen PM. The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U. . during the Influenza A H1N1 Pandemic. PLoS One 2011; 6 (05) e19467
  PubMedGoogle Scholar
• 103 Macesic N, Polubriaginof F, Tatonetti NP. Machine learning: novel bioinformatics approaches for combating antimicrobial resistance. Curr Opin Infect Dis 2017; 30 (06) 511-7
  CrossrefPubMedGoogle Scholar