Antimicrobial Resistance Forecasting requires urgent attention

Antimicrobial Resistance Forecasting requires urgent attention image

In this article

What is antimicrobial resistance? The growing threat Current forecasting approaches Challenges in AMRO forecasting Future directions

Antimicrobial Resistance Forecasting requires urgent attention

April 21, 2023

What is antimicrobial resistance?


Did you know, according to WHO, at least 1.27 million people die every year due to drug-resistant infections. A record number of countries are now monitoring and reporting on antibiotic resistance – marking a major step forward in the global fight against drug resistance. But the data they provide reveals that a worrying number of bacterial infections are increasingly resistant to the only medicines at hand to treat them. So what’s leading to this resistance and how can we reduce it?


Antimicrobial resistance happens when microorganisms (such as bacteria, fungi, viruses, and parasites) develop the ability to continue to grow, even when they are exposed to antimicrobial medicines that are meant to kill them or limit their growth (such as antibiotics, antifungals, antivirals, antimalarials, and anthelmintics). As a result, the medicines become ineffective and infections persist in the body, increasing the risk of spread to others. While antimicrobial resistance refers to all microbes that resist treatments designed to destroy them, antibiotic resistance specifically deals with bacteria that are resistant to antibiotics.


Usually, the more often antibiotics are used, the more bacteria adapt and find new ways to survive, which means a small portion of bacteria in the body become resistant to antibiotics. Instead of being killed by the antibiotics, some bacteria survive and continue to multiply, causing more harm. Antibiotics are used in the treatment of many diseases and surgical procedure. Examples include organ transplants, blood infections, complicated deliveries, pneumonia and in cancer care (see ReAct Group). Therefore, patients with infections caused by these drug-resistant bacteria are at an increased risk of poorer clinical outcomes, including death.

The growing threat


In a recent article published in the United States Centers for Disease Control and Prevention (CDC) Emerging Infectious Diseases, researchers highlight the need for prioritising research on antimicrobial-resistant organism (AMRO) forecasting. Moreover, they discuss current challenges in AMRO forecasting and the potential of forecasting tools at the population and facility levels. 


In 2019, it was estimated that around 4.95 million deaths worldwide were attributed to bacterial antimicrobial resistance, with most of these deaths due to 6 key pathogens: Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa. Despite the numerous advancements that have been made in developing predictive models for both viral and acute infectious diseases such as influenza, dengue, and the coronavirus disease 2019 (COVID-19), there remains a lack of forecasting models that have been implemented for predicting the severity of future AMROs. Improving the predictive intelligence on AMROs will allow researchers and public health officials to gain important insights into the potential emergence and spread of antimicrobial resistance within populations and healthcare facilities.

Current forecasting approaches


Various mathematical and statistical models have been used to combat antimicrobial resistance, with some of these models having potential applications as forecasting tools. Time series analyses, for example, have previously been used to determine the association between antibiotic use and the prevalence of antimicrobial resistance at the population level; however, this type of analysis is insufficient in its ability to predict antimicrobial prevalence.


Process-based mathematical models have also been used to study AMROs by stimulating competition between resistant and sensitive strains. Additionally, individual-level models have been developed by incorporating information from previous patients or contact with healthcare workers in order to devise potential transmission networks in healthcare facilities.


When developing antimicrobial resistance forecasting models, it is essential first to determine whether these tools will be applied to population – or facility-level scales. When applied at the population level, AMRO forecasting can predict how the infection will affect the general population for extended periods ranging from months to years.


At the population level, forecast targets may include the number of antimicrobial-resistant infections or the proportion of isolates that exhibit resistance. Collectively, this information could be used to determine the future burden of antimicrobial resistance within the population, including its impact on deaths, hospitalisation, days of work lost, or direct and indirect economic costs.


Comparatively, at the facility level, the forecast target may instead be the number of antimicrobial infections detected within a single hospital or hospital system. With this information, healthcare facilities can preemptively allocate resources, including equipment, medications, staffing, and hospital space, if a surge in these infections arises.

Challenges in AMRO forecasting


Lacking a scientific understanding of the reasons for the spread of antimicrobial-resistant pathogens is the first and foremost issue with designing accurate forecasting models. To date, there remains a lack of understanding of how antibiotic use contributes to the development of antibiotic resistance and whether a single antibiotic drug has a more significant impact on the emergence of resistant species.


The extent to which competition between susceptible bacterial strains might impact the incidence of resistant strains also remains poorly understood. The coexistence of these strains over extended periods of time also remains unclear.


In acute viral infections, viral load can generally be linked to infectivity and disease phenotype, which can subsequently allow researchers to predict the severity of these infections. However, the correlation between pathogen load and clinical outcomes is less clear for bacterial or fungal infections. This is mainly due to the vast commensal bacterial population that resides within humans, many of which can be found in varying levels throughout the human body.  


In some countries, surveillance systems gather data on changes in antimicrobial susceptibility; however, these systems do not track pathogens responsible for healthcare-related infections. Moreover, antimicrobial-resistant pathogen profiling is much more limited in low- and middle-income countries. To date, despite surveillance at the population level, data to inform operationally beneficial forecasts of antimicrobial resistance remain inadequate.


In healthcare settings, since the surveillance for asymptomatic AMRO carriage is not of immediate clinical interest, it has hindered the estimation of overall AMRO prevalence, thereby leading to biassed targets for AMRO prediction models. It is also impractical to gather data on non biologic processes driving the transmission of resistant pathogens, such as patient interactions with healthcare staff. Overall, antimicrobial-resistant data from the facility level is scarce, missing, or of poor quality.

Future directions


In the future, all stakeholders, such as healthcare practitioners, public health officials, and healthcare organisations, must identify specific requirements from antimicrobial resistance modelling to facilitate the generation of operational forecasts in real-world settings. In addition, our analysis procedures require more efficient computational algorithms to calibrate antimicrobial resistance models to varied, multiscale data and more interpretable models to ensure that clinicians are more confident about these tools.


In healthcare settings, consistently collecting data on the testing and reporting of antimicrobial-resistant infections would standardise training and forecasting targets, the scale of the forecast horizon, and proper scoring rules, which, in turn, would ease evaluating forecast performance. This is a difficult task and one that requires a significant increase in funding, research, national and international resources, as well as of course public health cooperation at all levels. 


In summary, despite lessons learned from recent advances in forecasting for other acute infectious diseases, AMRO prediction has its own set of challenges, including wide and prolonged asymptomatic carriage, longer time scales, continuing evolution due to strain competition and antimicrobial drug use, and poorly observed disease burden. It will be critical to set appropriate expectations for the performance of AMRO predictions and establish sensible criteria for successful forecasting. However, with the improving collaborative efforts of all stakeholders, antimicrobial-resistant forecasting can certainly successfully address real-world issues in public health and patient care.


Pei, S., Blumberg, S., Vega, J. C., et al. (2023). Challenges in Forecasting Antimicrobial Resistance. Emerging Infectious Diseases. DOI:10.3201/eid2904.221552


World Health Organization. Antimicrobial resistance. 2021 [cited 2023 April 2].


Murray  CJL, Ikuta  KS, Sharara  F, Swetschinski  L, Robles Aguilar  G, Gray  A, et al.; Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022; DOI: 399:629–55

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