Abstract
Multridrug-resistant (MDR) bacteria are currently a serious threat to public health. They are primarily associated with hospital-acquired infections and their spread is related to an increase in the morbidity, mortality and healthcare cost. Therefore, their control and prevention is a priority problem today. In this control, the need of detecting MDR-bacteria outbreaks inside a hospital stands out. This complex process requires interweaving reports with temporal and spatial information. In this matter, computer-aided visualization techniques might play an important role, by helping explain and understand data-driven decision making. Thus, the hypothesis of this PhD thesis is that spatial-temporal modeling and visualization techniques allow clinicians to increase their confidence and comprehension of AI-based epidemiological analysis and prediction models. During the first phase of this PhD project, we have carried out a detailed analysis of what has been done on the application of spatial-temporal visualization techniques on epidemiological data. The results of this investigation have helped us identify the current trends and gaps in this field. Following this, we have implemented a simulation model and its simulator, with the objective of generating clinically realistic spatial-temporal data of infection outbreaks within hospitals.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abat, C., Chaudet, H., Rolain, J.M., Colson, P., Raoult, D.: Traditional and syndromic surveillance of infectious diseases and pathogens. Int. J. Infect. Dis.: IJID: Official Publ. Int. Soc. Infect. Dis. 48, 22–28 (2016). https://doi.org/10.1016/j.ijid.2016.04.021
Arantes, A., Carvalho, E.d.S., Medeiros, E.A.S., Farhat, C.K., Mantese, O.C.: Use of statistical process control charts in the epidemiological surveillance of nosocomial infections. Revista De Saude Publica 37(6), 768–774 (2003). https://doi.org/10.1590/s0034-89102003000600012
Baumgartl, T., Petzold, M., Wunderlich, M., Höhn, M., Archambault, D., Lieser, M., Dalpke, A., Scheithauer, S., Marschollek, M., Eichel, V.M., Mutters, N.T., Consortium, H., von Landesberger, T.: In search of patient zero: visual analytics of pathogen transmission pathways in hospitals. IEEE Trans. Vis. Comput. Graph. 27(2), 711–721 (2021). https://doi.org/10.1109/TVCG.2020.3030437,http://arxiv.org/abs/2008.09552, arXiv: 2008.09552
Centers for Disease Control and Prevention: General Recommendations for Routine Prevention and Control of mdros in Healthcare Settings (2019). https://www.cdc.gov/infectioncontrol/guidelines/mdro/table3-1-routine-prevention.html. Last access 9 May 2022
Clabots, C.R., Johnson, S., Olson, M.M., Peterson, L.R., Gerding, D.N.: Acquisition of Clostridium difficile by hospitalized patients: evidence for colonized new admissions as a source of infection. J. Infect. Dis. 166(3), 561–567 (1992). https://doi.org/10.1093/infdis/166.3.561
Codella, J., Safdar, N., Heffernan, R., Alagoz, O.: An agent-based simulation model for Clostridium difficile infection control. Med. Decis. Making: Int. J. Soc. Med. Decis. Making 35(2), 211–229 (2015). https://doi.org/10.1177/0272989X14545788
van Duin, D., Paterson, D.L.: Multidrug-resistant bacteria in the community. Infect. Dis. Clin. North Am. 30(2), 377–390 (2016). https://doi.org/10.1016/j.idc.2016.02.004
Kim, D., Campos, M., Juarez, J.M., Canovas-Segura, B.: Visualization of spatial-temporal epidemiological data: a systematic review. J. Med. Internet Res. (2022). (under review)
Lital Meyer, S., Ricardo Espinoza, A., Rodrigo Quera, P.: Infección por clostridium difficile: epidemiología, diagnóstico y estrategias terapéuticas. Revista Médica Clínica Las Condes 25(3), 473–484 (2014)
Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G.: PRISMA Group: preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 6(7), e1000097 (2009). https://doi.org/10.1371/journal.pmed.1000097
Myall, A.C., Peach, R.L., Weiße, A.Y., Davies, F., Mookerjee, S., Holmes, A., Barahona, M.: Network Memory in the Movement of Hospital Patients Carrying Drug-Resistant Bacteria. arXiv preprint arXiv:2009.14480 (2020)
Olson, M.M., Shanholtzer, C.J., Lee, J.T., Gerding, D.N.: Ten years of prospective Clostridium difficile-associated disease surveillance and treatment at the Minneapolis VA Medical Center, 1982–1991. Infection Control Hosp. Epidemiol. 15(6), 371–381 (1994). https://doi.org/10.1086/646934
Tseng, Y.J., Wu, J.H., Ping, X.O., Lin, H.C., Chen, Y.Y., Shang, R.J., Chen, M.Y., Lai, F., Chen, Y.C.: A web-based multidrug-resistant organisms surveillance and outbreak detection system with rule-based classification and clustering. J. Med. Internet Res. 14(5), e131 (2012). https://doi.org/10.2196/jmir.2056
World Health Organization: Antimicrobial Resistance (2021). https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance. Last access 9 May 2022
Acknowledgements
This work was partially funded by the SITSUS project (Ref: RTI2018-094832-B-I00), the CONFAINCE project (Ref: PID2021-122194OB-I00), supported by the Spanish Ministry of Science and Innovation, the Spanish Agency for Research (MCIN/AEI/10.13039/501100011033) and, as appropriate, by ERDF A way of making Europe. This research is also partially funded by the FPI program grant (Ref:PRE2019-089806).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kim, D., Juarez, J.M., Campos, M., Canovas-Segura, B. (2023). Visualization for Infection Analysis and Decision Support in Hospitals. In: Machado, J.M., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference. DCAI 2022. Lecture Notes in Networks and Systems, vol 585. Springer, Cham. https://doi.org/10.1007/978-3-031-23210-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-23210-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23209-1
Online ISBN: 978-3-031-23210-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)