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1.
Lancet Digital Health ; 4(8):E573-E583, 2022.
Article in English | Web of Science | ID: covidwho-2092794

ABSTRACT

Background Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level. Methods We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020;40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021;43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk. Findings The framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0.89 [95% CI 0.88-0.90]) and similarly predictive using only contact-network variables (0.88 [0.86-0.90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0.82 [95% CI 0.80-0.84]) or patient clinical (0.64 [0.62-0.66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0.85 (95% CI 0.82-0.88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0.84 [95% CI 0.82-0.86] to 0.88 [0.86-0.90];AUC-ROC in the UK post-surge dataset increased from 0.49 [0.46-0.52] to 0.68 [0.64-0.70]). Interpretation Dynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

2.
4th International Conference on Innovative Computing (ICIC) ; : 120-128, 2021.
Article in English | Web of Science | ID: covidwho-1985464

ABSTRACT

The COVID-19 virus spread around the globe very rapidly during early 2020. Identification of the evolution pattern, and genome scale mutations in SARS-CoV-2 is essential to study the dynamics of this disease. The genomic sequences of thousands of SARS-CoV-2 infected patients from different countries are publicly available for sequence based in-depth analysis. In this study, the DNA sequences of SARS-CoV-2 from the COVID-19 infected patients (having or lacking a travel history) from Pakistan and India, the two highest populous neighboring countries in South Asia, have been analyzed by using computational tools of phylogenetics. These analyses revealed that the SARS-CoV-2 strain in Pakistani traveler COVID-19 patients is closely related to Iranian strains, the strain in non-traveler patients is related to the strain of Wuhan, China. Likewise, in India, the SARS-CoV-2 strains in travelers and non-travelers are closely related to Italy, Germany, and Mexico. The selected approach has also been utilized to find out the identical genomic regions and similar strains around the world. Collectively, our study suggested distinct strains and routes of viral transmission in Pakistan and India. These differences may infer partially the reason for the decline phase in viral propagation in Pakistan two months after the peak COVID-19 load, and rapid viral propagation in India making it the second worst-hit country in the world after the USA.

5.
HIV Medicine ; 22(SUPPL 2):39, 2021.
Article in English | EMBASE | ID: covidwho-1409338

ABSTRACT

Background: In recent years there has been increased awareness of Mycoplasma genitalium as a potential sexually-transmitted pathogen and national guidelines now recommend routine testing in a number of clinical scenarios in sexual health clinics, including non-gonococcal urethritis (NGU). Factors including availability and cost of resistance testing, quinolone-associated toxicities and a lack of available alternative treatments, all impact on the feasibility of routine testing. Knowledge of local prevalence and rates of drug resistance can inform the development of patient pathways and management strategies. The aim was to determine the prevalence of Mycoplasma genitalium in men with symptomatic NGU and the proportion of these infections that demonstrated macrolide resistance. Method: Routine testing for Mycoplasma genitalium in symptomatic NGU was introduced in August 2020, during a period when walk-in attendances were limited due to the COVID-19 pandemic. Testing was performed inhouse using the validated Roche Cobas® TV/MG PCR assay and positive samples were tested for the macrolide resistance gene using SpeeDx Resistanceplus® MG. Quinolone resistance testing was not routinely performed. Clinical information including patient demographics, treatments used, test of cure (TOC) results and coding data were reviewed and analysed. Results: 42 symptomatic men presented during August 2020 and were diagnosed with NGU. 34 (81%) had testing performed for Mycoplasma genitalium, 10/34 (29%) tests were positive. Resistance testing was performed on all positive samples, 4 (40%) were positive for macrolide resistance;2 (20%) were indeterminate and 4 (40%) were negative. All patients were treated with regimens according to BASHH guidelines and 1 (10%) patient had a positive TOC. Conclusion: High rates of Mycoplasma genitalium were identified when screening men with symptomatic urethritis in a central London sexual health clinic. In this population, at least 40% of infections had macrolide resistance demonstrated. The majority of individuals cleared the pathogen with symptom resolution and a negative TOC using recommended treatment regimens. Larger surveys in different populations will enhance knowledge of risk factors associated with Mycoplasma genitalium infection and antibiotic resistance. Further study of alternative treatment approaches for drug-resistant organisms are required.

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