Impact of the COVID-19 Pandemic on Antimicrobial Resistance (AMR) Surveillance, Prevention and Control: A Global Survey

Tomczyk, Sara; Taylor, Angelina; Brown, Amanda; Kraker,; Eckmanns, Tim; El-Saed, Aiman; Alshamrani, Majid M.; Hendriksen, René S.; Jacob, Megan E.; Löfmark, Sonja; Perovic, Olga; Shetty, Nandini; Sievert, Dawn M.; Smith, Rachel M.; Stelling, Jörg; Thakur, Siddhartha; Tornimbene, Barbara; Ac, Vietor; Eremin, Sergey

doi:10.1101/2021.03.24.21253807

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…A recent study found that more than 70% of COVID-19 cases in Asia received antimicrobial treatment [7]. Other studies also reported a similar increase in antibiotic usage and AMR rates in several countries and across income levels [15]- [17]. These studies suggest that there is an urgent need for collective efforts to handle and manage AMR, especially in LMICs where resources are very limited.…”

Section: Introductionmentioning

confidence: 90%

Antimicrobial Resistance and Machine Learning: Challenges and Opportunities

et al. 2022

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Antimicrobial Resistance (AMR) has been identified by the World Health Organisation (WHO) as one of the top ten global health threats. Inappropriate use of antibiotics around the world and in particular in Low-to-Middle-Income Countries (LMICs), where antibiotics use and prescription are poorly managed, is considered one of the main reasons for this problem. It is projected that the COVID-19 pandemic will accelerate the threat of AMR due to the increasing use of antibiotics across the world, and especially in countries with limited resources. In recent years, machine learning-based methods showed promising results and proved capable of providing the necessary tools to inform antimicrobial prescription and combat AMR. This timely paper provides a critical and technical review of existing machine learning-based methods for addressing AMR. First, an overview of the AMR problem as a global threat to public health, and its impact on countries with limited resources (LMICs) are presented. Then, a technical review and evaluation of existing literature that utilises machine learning to tackle AMR are provided with emphasis on methods that use readily available demographic and clinical data as well as microbial culture and sensitivity laboratory data of clinical isolates associated with multi-drug resistant infections. This is followed by a discussion of challenges and limitations that are considered barriers to scaling up the use of machine learning to address AMR. Finally, a framework for accelerating the use of AMR data-driven framework, and building a feasible solution that can be realistically implemented in LMICs is presented with a discussion of future directions and recommendations.

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Section: Introductionmentioning

confidence: 90%

Antimicrobial Resistance and Machine Learning: Challenges and Opportunities

et al. 2022

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Assessing Multi-drug resistant Methicillin-Resistant Coagulase-Negative Staphylococci: Comparative Study in the Tertiary Healthcare Settings and Urban Built Environments of Vidarbha Maharashtra, India

Keshri,

Gore,

Kolla

2024

RJPT

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The global challenge of antibiotic resistance is particularly pronounced in India, where hospital and urban built environments (UBE) serve as prominent reservoirs, amplifying the risk of rapid dissemination because of high population growth and inadequate surveillance. With the uncontrolled use of antibiotics and by natural genetic makeup, the resistance in staphylococci species is on the rise. In this study medical hub region Vidarbha, Maharashtra, India investigated for a resistance profile of coagulase-negative staphylococci (CoNS) once identified by a VITEK 2 and coagulase test. The antibiotic profiling was carried out by the Kirby Bauer disk diffusion method and the % resistance was calculated by statistical analysis to record significance change (P<0.05). According to the results UBE and hospital setting are positive for CoNS and prominently deducted as S. cohnii, S. haemolyticus, S. saprophyticus, S. warneri, and S. aureus. The CoNS species were found to be resistant to many antibiotics but sensitive to few, although the statistical difference was found to be non-significant (P>0.05). A study also highlighted that CoNS species in hospital environments were found to be more resistant towards the range of antibiotics compared to UBE isolates in Vidarbha. In conclusion, it has been put forward that five species of staphylococci are dominating in Vidarbha, and all of them are acquired with drug resistance which demands close surveillance in the coming time to reduce the spread of pathogens.

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Impact of the COVID-19 Pandemic on Antimicrobial Resistance (AMR) Surveillance, Prevention and Control: A Global Survey

Cited by 2 publications

References 26 publications

Antimicrobial Resistance and Machine Learning: Challenges and Opportunities

Antimicrobial Resistance and Machine Learning: Challenges and Opportunities

Assessing Multi-drug resistant Methicillin-Resistant Coagulase-Negative Staphylococci: Comparative Study in the Tertiary Healthcare Settings and Urban Built Environments of Vidarbha Maharashtra, India

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