SPATIAL-TEMPORAL DETERMINANTS OF MDRO TRANSMISSION DYNAMICS: IMPLICATIONS FOR INFECTION CONTROL
Intro: Understanding infectious disease transmission dynamics is key to optimising infection prevention and control (IPC) strategies. Yet, standard epidemiological modelling tools do not fully utilise large datasets of time- evolving spatial interactions, which could lead to improved descriptions of...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-05-01
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| Series: | International Journal of Infectious Diseases |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S120197122300200X |
| _version_ | 1797824613964578816 |
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| author | A. Myall I. Venkatachalam C. Philip M. Yin D. Koon S. Arora Y. Yue R. Peach A. Weiße P. Tambyah A. Chow J. Price A. Cook A. Holmes M. Barahona |
| author_facet | A. Myall I. Venkatachalam C. Philip M. Yin D. Koon S. Arora Y. Yue R. Peach A. Weiße P. Tambyah A. Chow J. Price A. Cook A. Holmes M. Barahona |
| author_sort | A. Myall |
| collection | DOAJ |
| description | Intro: Understanding infectious disease transmission dynamics is key to optimising infection prevention and control (IPC) strategies. Yet, standard epidemiological modelling tools do not fully utilise large datasets of time- evolving spatial interactions, which could lead to improved descriptions of complex transmission dynamics, as exhibited by multi-drug resistant organisms (MDRO). Here, we showcase an approach accounting for spatial-temporal dynamics using machine learning and graph theory to characterize nosocomial MDRO transmission. Methods: We performed a retrospective cohort study using all inpatient admissions to a 1200-bed Singaporean hospital from 2018-01-01 to 2021-12-31. We employ a graph-based machine learning methodology to learn transmission dynamics using spatial-temporal patient interactions. Taking Methicillin-resistant Staphylococcus aureus (MRSA) as a proof-of-principle, we examine the real-world efficacy using routine surveillance and clinical cultures obtained during acute care. Findings: There were 149,352 inpatient admissions and 3.21 million contact-interactions during the study period; 7,753 (5.2%) of these patients tested positive for MRSA, with 1,585 (1.1%;) being likely hospital acquisitions. The probability of onward transmission was largest on the day a culture-positive result was reported (11.2% of all transmissions). However, high onward-transmission persisted beyond (weighted-mean 7.8 days). Through spatial-temporal analsysis, we found hospital rooms with comparatively high acquisition-rates (36/665 wards accounting for 50.2% of hospital-acquisitions); these hotspots were found to be central in network of patient transfers, and interconnected with each other. Conclusion: With extensive and detailed datasets becoming available, new methodologies that leverage their size and detail can provide insights into transmission processes. Here, we demonstrated their use to learn the transmission dynamics of an MDRO and showed their ability to make inferences on spatial-temporal profiles of transmission. Furthering this research, we will engineer spatial-temporal features to predict pathogen transmission dynamics and expand to additional MDROs. |
| first_indexed | 2024-03-13T10:41:35Z |
| format | Article |
| id | doaj.art-fbbb0d2aaaf24da393db790a5fb1f800 |
| institution | Directory Open Access Journal |
| issn | 1201-9712 |
| language | English |
| last_indexed | 2024-03-13T10:41:35Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Infectious Diseases |
| spelling | doaj.art-fbbb0d2aaaf24da393db790a5fb1f8002023-05-18T04:38:09ZengElsevierInternational Journal of Infectious Diseases1201-97122023-05-01130S31SPATIAL-TEMPORAL DETERMINANTS OF MDRO TRANSMISSION DYNAMICS: IMPLICATIONS FOR INFECTION CONTROLA. Myall0I. Venkatachalam1C. Philip2M. Yin3D. Koon4S. Arora5Y. Yue6R. Peach7A. Weiße8P. Tambyah9A. Chow10J. Price11A. Cook12A. Holmes13M. Barahona14Imperial College London, Mathematics, London, United Kingdom; Nanyang Technological University, Lee Kong Chian School of Medicine, Singapore, United Kingdom; Imperial College London, Department of Infectious Disease, London, United KingdomSingapore General Hospital, Infection Prevention and Epidemiology, Singapore, SingaporeSingapore General Hospital, Infection Prevention and Epidemiology, Singapore, SingaporeNational University Hospital, Infection Prevention, Singapore, SingaporeSingapore General Hospital, Infection Prevention and Epidemiology, Singapore, SingaporeSingapore General Hospital, Infection Prevention and Epidemiology, Singapore, SingaporeSingapore General Hospital, Infection Prevention and Epidemiology, Singapore, SingaporeImperial College London, Mathematics, London, United KingdomThe University of Edinburgh, Computing, Edinburgh, United KingdomNational University Hospital, Infection Prevention, Singapore, SingaporeTan Tock Seng Hospital, Department of Preventive and Population Medicine, Singapore, SingaporeImperial College Healthcare NHS Trust, Infection Prevention and Control, London, United KingdomNational University Singapore, Saw Swee Hock School Of Public Health, Singapore, SingaporeImperial College London, Department of Infectious Disease, London, United KingdomImperial College London, Mathematics, London, United KingdomIntro: Understanding infectious disease transmission dynamics is key to optimising infection prevention and control (IPC) strategies. Yet, standard epidemiological modelling tools do not fully utilise large datasets of time- evolving spatial interactions, which could lead to improved descriptions of complex transmission dynamics, as exhibited by multi-drug resistant organisms (MDRO). Here, we showcase an approach accounting for spatial-temporal dynamics using machine learning and graph theory to characterize nosocomial MDRO transmission. Methods: We performed a retrospective cohort study using all inpatient admissions to a 1200-bed Singaporean hospital from 2018-01-01 to 2021-12-31. We employ a graph-based machine learning methodology to learn transmission dynamics using spatial-temporal patient interactions. Taking Methicillin-resistant Staphylococcus aureus (MRSA) as a proof-of-principle, we examine the real-world efficacy using routine surveillance and clinical cultures obtained during acute care. Findings: There were 149,352 inpatient admissions and 3.21 million contact-interactions during the study period; 7,753 (5.2%) of these patients tested positive for MRSA, with 1,585 (1.1%;) being likely hospital acquisitions. The probability of onward transmission was largest on the day a culture-positive result was reported (11.2% of all transmissions). However, high onward-transmission persisted beyond (weighted-mean 7.8 days). Through spatial-temporal analsysis, we found hospital rooms with comparatively high acquisition-rates (36/665 wards accounting for 50.2% of hospital-acquisitions); these hotspots were found to be central in network of patient transfers, and interconnected with each other. Conclusion: With extensive and detailed datasets becoming available, new methodologies that leverage their size and detail can provide insights into transmission processes. Here, we demonstrated their use to learn the transmission dynamics of an MDRO and showed their ability to make inferences on spatial-temporal profiles of transmission. Furthering this research, we will engineer spatial-temporal features to predict pathogen transmission dynamics and expand to additional MDROs.http://www.sciencedirect.com/science/article/pii/S120197122300200X |
| spellingShingle | A. Myall I. Venkatachalam C. Philip M. Yin D. Koon S. Arora Y. Yue R. Peach A. Weiße P. Tambyah A. Chow J. Price A. Cook A. Holmes M. Barahona SPATIAL-TEMPORAL DETERMINANTS OF MDRO TRANSMISSION DYNAMICS: IMPLICATIONS FOR INFECTION CONTROL International Journal of Infectious Diseases |
| title | SPATIAL-TEMPORAL DETERMINANTS OF MDRO TRANSMISSION DYNAMICS: IMPLICATIONS FOR INFECTION CONTROL |
| title_full | SPATIAL-TEMPORAL DETERMINANTS OF MDRO TRANSMISSION DYNAMICS: IMPLICATIONS FOR INFECTION CONTROL |
| title_fullStr | SPATIAL-TEMPORAL DETERMINANTS OF MDRO TRANSMISSION DYNAMICS: IMPLICATIONS FOR INFECTION CONTROL |
| title_full_unstemmed | SPATIAL-TEMPORAL DETERMINANTS OF MDRO TRANSMISSION DYNAMICS: IMPLICATIONS FOR INFECTION CONTROL |
| title_short | SPATIAL-TEMPORAL DETERMINANTS OF MDRO TRANSMISSION DYNAMICS: IMPLICATIONS FOR INFECTION CONTROL |
| title_sort | spatial temporal determinants of mdro transmission dynamics implications for infection control |
| url | http://www.sciencedirect.com/science/article/pii/S120197122300200X |
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