# Modeling the transmission of antibiotic-resistant Enterobacterales in the community: A systematic review

Eve Rahbé, Philippe Glaser, Lulla Opatowski

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Eve Rahbé, Philippe Glaser, Lulla Opatowski. Modeling the transmission of antibiotic-resistant Enterobacterales in the community: A systematic review. Epidemics, 2024, 48, pp.100783. 10.1016/j.epidem.2024.100783. hal-04684903

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# Modeling the transmission of antibiotic-resistant Enterobacterales in the community: A systematic review

Eve Rahbé<sup>a, b, *, 2</sup>, Philippe Glaser<sup>c, 1</sup>, Lulla Opatowski<sup>a, b, *, 1</sup>

## ARTICLE INFO

**Keywords:**

Mathematical models

Epidemiology

Community transmission

E. coli

K. pneumoniae

Antibiotic resistance

Carriage

Public health

## ABSTRACT

**Background:** Antibiotic-resistant Enterobacterales (ARE) are a public health threat worldwide. Dissemination of these opportunistic pathogens has been largely studied in hospitals. Despite high prevalence of asymptomatic colonization in the community in some regions of the world, less is known about ARE acquisition and spread in this setting. As explaining the community ARE dynamics has not been straightforward, mathematical models can be key to explore underlying phenomena and further evaluate the impact of interventions to curb ARE circulation outside of hospitals.

**Methods:** We conducted a systematic review of mathematical modeling studies focusing on the transmission of AR-E in the community, excluding models only specific to hospitals. We extracted model features (population, setting), formalism (compartmental, individual-based), biological hypotheses (transmission, infection, antibiotic impact, resistant strain specificities) and main findings. We discussed additional mechanisms to be considered, open scientific questions, and most pressing data needs.

**Results:** We identified 18 modeling studies focusing on the human transmission of ARE in the community (n=11) or in both community and hospital (n=7). Models aimed at (i) understanding mechanisms driving resistance dynamics; (ii) identifying and quantifying transmission routes; or (iii) evaluating public health interventions to reduce resistance. To overcome the difficulty of reproducing observed ARE dynamics in the community using the classical two-strains competition model, studies proposed to include mechanisms such as within-host strain competition or a strong host population structure. Studies inferring model parameters from longitudinal carriage data were mostly based on models considering the ARE strain only. They showed differences in ARE carriage duration depending on the acquisition mode: returning travelers have a significantly shorter carriage duration than discharged hospitalized patient or healthy individuals. Interestingly, predictions across models regarding the success of public health interventions to reduce ARE rates depended on pathogens, settings, and antibiotic resistance mechanisms. For *E. coli*, reducing person-to-person transmission in the community had a stronger effect than reducing antibiotic use in the community. For *Klebsiella pneumoniae*, reducing antibiotic use in hospitals was more efficient than reducing community use.

**Conclusions:** This study raises the limited number of modeling studies specifically addressing the transmission of ARE in the community. It highlights the need for model development and community-based data collection especially in low- and middle-income countries to better understand acquisition routes and their relative contribution to observed ARE levels. Such modeling will be critical to correctly design and evaluate public health interventions to control ARE transmission in the community and further reduce the associated infection burden.

<footnote><sup>a</sup> Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Antimicrobials Evasion research unit, Paris, France</footnote>

<footnote><sup>b</sup> Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology research team, Montigny-Le-Bretonneux, France</footnote>

<footnote><sup>c</sup> Institut Pasteur, Ecology and Evolution of Antibiotic Resistance research unit, Université Paris Cité, Paris, France</footnote>

<footnote>* Corresponding authors at: Institut Pasteur, Epidemiology and Modelling of Antimicrobials Evasion research unit, Paris, France.
E-mail addresses: eve.rahbe@orange.fr (E. Rahbé), lulla.opatowski@pasteur.fr (L. Opatowski).</footnote>

<footnote n="1">¹ Contributed equally to this work.</footnote>

<footnote n="2">² 0000-0002-3828-3910</footnote>
# 1. Introduction

Antibiotic resistance in clinically relevant bacteria is a major threat for global health (WHO. Global action plan on antimicrobial resistance, 2015). Yet its control, both at local and global scales, remains a challenge. Antibiotic resistance epidemiology is highly heterogeneous, as the emergence, selection and spread of resistance in human populations differs across pathogens, antibiotic resistance mechanisms and transmission settings. As a consequence, observed resistance rates vary greatly depending on the antibiotic and bacterial species under study. Rahbe et al. (2023) *Escherichia coli* and *Klebsiella pneumoniae*, two species of the order Enterobacteriales, are estimated to be among the bacteria associated with the greatest burden of antibiotic resistance worldwide (Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in, 2019). Enterobacterales are frequent opportunistic pathogens, predominantly carried asymptomatically in the human gut but commonly responsible for extra-intestinal diseases, including potentially severe infections of the urinary tract, respiratory tract and bloodstream (Jenkins et al., 2017). Since the 1980s, they have been increasingly associated with resistance to successive generations of extended-spectrum β-lactams, later followed by carbapenem resistance (Schwaber and Carmeli, 2008). For *E. coli* alone, antibiotic resistance was associated with an estimated 829,000 deaths in 2019 (95 % confidence interval: 601,000–1120,000) (Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in, 2019). For decades, antibiotic-resistant Enterobacteriales infections were mostly reported as hospital-acquired, causing a large nosocomial burden (Abban et al., 2023). At present, a growing number of antibiotic-resistant Enterobacterales infections, especially those caused by extended spectrum β-lactamase (ESBL)-producing *E. coli*, are now reported as being community-acquired (Bonten et al., 2021; Pitout et al., 2005).

Mathematical models of infectious disease transmission (“models”) are increasingly used in the field of antibiotic resistance epidemiology, to better characterize the emergence and transmission routes of antibiotic-resistant bacteria (ARB) in human populations, and to support public health decisions regarding ARB control (Opatowski et al., 2011). As they rely on mechanistic hypotheses, models have generally focused on the transmission of specific antibiotic-bacteria combinations within specific environments. On one hand, “hospital” models formalize ARB selection and transmission within healthcare settings. Such models have been widely used to evaluate impacts of interventions such as antibiotic stewardship and patient isolation, and to assess transmission in different medical environments such as intensive care units and long-term care facilities (Bonten et al., 2001). The most studied ARB in hospital models are those historically at highest risk of nosocomial spread, including methicillin-resistant *Staphylococcus aureus* and to a lesser extent ESBL-producing Enterobacterales (Niewiadomska et al., 2019). On the other hand, “community” models address ARB spread within human populations outside healthcare settings. Most published community models have focused on antibiotic-resistant *Streptococcus pneumoniae* or *Neisseria gonorrhoeae* (Ramsay et al., 2018).

Antibiotic-resistant Enterobacterales (ARE) are also frequently carried in the community (Bonten et al., 2021). Enterobacterales are intestinal commensals that face constant and diverse exposures through their host life and can shed into the environment through the orofecal route. Apart from the human microbiota, where microbial interactions such as competition or horizontal gene transfer (HGT) can take place, they can inhabit other ecological niches such as the microbiota of livestock or domestic animals, the soil or water (Hernando-Amado et al., 2019). Understanding how they spread in the community and in these different reservoirs is of high importance for public health control, but has been rarely investigated and quantified by models (Niewiadomska et al., 2019; Ramsay et al., 2018). For instance, the contribution of HGT of antibiotic resistance genes within or between different reservoirs in driving the population-level epidemiology of ARE is still not properly

known, (Despotovic et al., 2023) as is the process leading from asymptomatic carriage of ARE to infection in healthy individuals (Ny et al., 2017; Raffelsberger et al., 2023) (note: we use the terms carriage and colonization interchangeably). It is also unclear how ARE persistence in the environment contributes to maintain community transmission and resistance levels in humans (Chatterjee et al., 2018; Godijk et al., 2022a). Difficulties lie in the fact that the “community” is a heterogeneous setting in which ARE transmission levels might depend on population density, inhabitants characteristics, contacts with the environment, the country’s resources or other factors. Despite scarce antibiotic resistance data available in the community, mathematical modeling can help make sense of it and disentangle the non-observable phenomena contributing to ARE dynamics in different communities. It can also help to evaluate the impact of public health interventions to curb ARE circulation outside of hospitals.

Here, we systematically review published mathematical models addressing antibiotic-resistant Enterobacteriales transmission in the community. We summarize models’ characteristics and biological hypotheses and highlight their contributions and limitations. Finally, we discuss assumptions made by these models and the data needed to support the development of future studies tailored to this growing global health problem.

# 2. Methods

## 2.1. Database and search terms

We searched PubMed for published mathematical modeling studies describing the population-level human-to-human transmission of antibiotic-resistant Enterobacterales (*E. coli* and/or *K. pneumoniae*) in the community. The search included all studies presenting original results (excluding review articles) published in English between 1 January 1992 and 1 July 2023. We used the following search query:

"transmission model*"[Title/Abstract] OR "dynamic model*"[Title/Abstract] OR "mechanistic model*"[Title/Abstract] OR "mathematical model*"[Title/Abstract] OR "compartment model*"[Title/Abstract] OR "compartmental model*"[Title/Abstract])

AND

"(commensal*"[All Fields] OR "Escherichia coli"[All Fields] OR "coli"[All Fields] OR "e coli"[All Fields] OR "Klebsiella pneumoniae"[All Fields] OR "klebsiella"[All Fields] OR "k pneumoniae"[All Fields] OR "enterobacteriales"[All Fields] OR "enterobacteriaceae"[All Fields] OR "gram-negative"[All Fields])

AND

"(antibiotic resistance"[Title/Abstract] OR "antimicrobial resistance"[Title/Abstract] OR "multidrug resistance"[Title/Abstract] OR "beta lactam resistance"[Title/Abstract] OR "carbapenem resistance"[Title/Abstract] OR "antibiotic-resistant"[Title/Abstract] OR "multidrug-resistant"[Title/Abstract] OR "beta lactam-resistant"[Title/Abstract] OR "carbapenem-resistant"[Title/Abstract] OR "ESBL"[Title/Abstract])

We included modeling studies on commensal bacteria in addition to Enterobacterales, to also retrieve studies that focused on generic intestinal commensals. We did not explicitly add search terms such as “community” or “general population” in case these were too restrictive.

## 2.2. Model selection: inclusion and exclusion criteria

Studies reporting a mathematical model formalizing the transmission of antibiotic-resistant Enterobacteriales between human hosts in the community were eligible. We excluded studies which were in the following categories:
- different methods: studies not presenting a mechanistic transmission model, but rather statistical models, time-series analysis, or cost-effectiveness analysis;

- different host population: transmission models not focusing on humans, but rather on animals, livestock or pets;

- different pathogens: transmission models specifically designed for bacteria other than Enterobacterales, like Helicobacter pylori or S. pneumoniae;

- different settings: models only describing transmission in healthcare settings (hospitals, long-term care homes, nursing homes), without including the community;

- different scales: microscopic-level models (pharmaco-dynamic or pharmaco-kinetic models, microbial population models, within-host models, ...) not also including populational-level transmission.

## 2.3. Data extracted and qualitative analysis

In a first step, we extracted key model features including: studied antibiotic-bacteria combinations, specificities of the host population (general population, high-risk groups), data sources used for estimation of antibiotic use parameter values (country, region, theoretical), and studied transmission settings (households, community, community and hospital). We summarized model hypotheses regarding bacterial transmission, the infection process, and effects of antibiotic exposure on carriage. The potential inclusion of any additional mechanisms was also summarized, such as potential resistance-associated fitness costs, multiple resistance acquisition routes and host population structure (Table 1). Finally, models were characterized according to the type of epidemiological antibiotic resistance data used (none, aggregated or individual-level) to perform parameter estimation (Table 2 and Table 3).

In a second step, we summarized each study's major findings. Reviewed articles were categorized according to their main scientific goals: (i) analyzing the impact of biological and/or populational mechanisms on antibiotic resistance dynamics; (ii) identifying routes of transmission and/or quantifying transmission; and (iii) evaluating the impact of public health interventions on antibiotic resistance trends.

# 3. Results

Our search returned 192 publications, of which 18 met inclusion criteria and were included in the review (see PRISMA flowchart in Fig. 1) (MacFadden et al., 2019; Mughini-Gras et al., 2019; Kachalov et al., 2021; Haverkate et al., 2017; Davies et al., 2019; Salazar-Vizcaya et al., 2022; Talaminos et al., 2016; Olesen et al., 2020; Kardaś-Sloma et al., 2020; Eisenberg et al., 2012; Godijk et al., 2022b; van den Bunt et al., 2020; Sundqvist et al., 2010; Blanquart et al., 2018; Austin et al., 1997; Arcilla et al., 2017; Knight et al., 2018; Allel et al., 2022).

## 3.1. Model features

The included modeling studies are listed in Table 1. They predominantly described *E. coli* transmission (9/18, Table 1). The others modeled transmission of generic commensal bacteria (3/18), generic Enterobacterales (3/18), *K. pneumoniae* (2/18) or generic Gram-negative bacteria (1/18). ESBL production was the most frequent type of antibiotic resistance mechanism modeled (10/18), while just one model focused on carbapenem resistance in *K. pneumoniae* (1/18, Table 1) (Kachalov et al., 2021). Host population was mainly representative of the general human population. We use the term “general population” in this study to refer to individuals sampled from the community without any selective criteria. In contrast, two studies specifically focused on high-risk groups from high-income countries, either returning travelers or returning hospitalized patients (Haverkate et al., 2017; Arcilla et al., 2017). Concerning antibiotic use, ten studies explicitly used parameter values obtained from high income countries (France, Sweden, Switzerland, United Kingdom, United States, or European countries), two studies focused on low-resource villages in South

America (Ecuador and Chile), (Eisenberg et al., 2012; Allel et al., 2022) while two studies used theoretical parameters, and the remaining four did not specify antibiotic use parameters. While eleven models focused on community settings only, seven explicitly modeled both community and healthcare settings, considering setting-specific transmission parameters and antibiotic use rates. Compartmental modeling was the most frequent formalism used (n=15), while two models were individual-based, and one study included both a compartmental and individual-based model.

## 3.2. Biological hypotheses

**Between-human bacterial transmission.** The between-human transmission of antibiotic-resistant Enterobacterales was modeled in three different ways in the included studies (Fig. 2): (i) models formalizing the transmission of resistant strains only (“R” models); (ii) models formalizing the transmission of sensitive or resistant strains (“R-S” models); and (iii) models focusing on the transmission of sensitive or resistant strains and allowing for single or multi-carriage of strains (“R-S-multi” models). Multi-carriage models have been proposed in the field of antibiotic resistance mathematical modeling to reproduce the observed coexistence of resistant and sensitive strains across different levels of antibiotic use, both at the individual- and population-level (Spicknall et al., 2013).

**Bacterial infection.** While six models differentiated between asymptomatic carriage and symptomatic infection (“carriage & infection” model, Fig. 2), most models (14/18, Table 1) did not (“infection-only” model, Fig. 2). Although carriage is a precursor for many infections, (Johnson et al., 2008; Leimbach et al., 2013) its exclusion from most modelling studies likely stems from a lack of robust epidemiological data describing asymptomatic carriage of antibiotic-resistant Enterobacterales in healthy populations.

**Antibiotic selection for resistance.** Most models assumed that antibiotic exposure exerts selection pressure advantaging antibiotic-resistant bacteria (n=14). This was modelled according to three distinct mechanisms: (i) *S clearance*. the effective clearance of sensitive bacteria compared with no or less efficient clearance of resistant ones, giving the latter a relative transmission advantage; (ii) *R acquisition*. an increased probability of resistant bacteria acquisition in antibiotic exposed individuals, for instance due to loss of colonization resistance following microbiome dysbiosis; and (iii) *R emergence*, an increased probability of within-host populations of sensitive bacteria becoming resistant subsequent to antibiotic exposure, through antibiotics favoring the horizontal transfer of resistance genes from co-colonizing microbiota, or again through antibiotics killing sensitive microbiota, favoring the survival and outgrowth of resistant bacteria initially present within the host in undetectably small quantities, a phenomenon known as endogenous resistance acquisition (Fig. 2 C). Antibiotic use was formalized either explicitly, i.e. considering distinct compartments for treated and untreated individuals, or through multiplicative coefficients increasing rates at which sensitive strains are cleared or resistant strains are acquired.

## 3.3. Main findings

The 18 reviewed modeling studies were classified according to their main scientific objectives (Table 1).

**Analyzing the impact of biological and/or populational mechanisms on antibiotic resistance dynamics**

Six modeling studies aimed to analyze how biological or population mechanisms, such as within-host dynamics and host population structure, impact antibiotic-resistant Enterobacterales transmission dynamics (Table 1). Five out of these six studies used the “R-S” or “R-S-multi” model structures as presented in Fig. 2.

Several modeling studies formalized within-host mechanisms to investigate their impact on host-level antibiotic resistance dynamics.
<table><caption>**Table 1**
Summary of the 18 modeling studies included in the review.</caption><thead><tr><th rowspan="2">Objectives</th><th rowspan="2">Study</th><th colspan="4">Model features</th><th rowspan="2">Model formalism</th><th colspan="3">Biological hypotheses</th><th colspan="3">Additional mechanisms included</th><th rowspan="2">Data for inference (details in Table 3)</th></tr><tr><th>Drug-bug pair *</th><th>Type of hosts</th><th>Antibiotic use data</th><th>Setting **</th><th>Transmission</th><th>I vs C ***</th><th>Antibiotic impact</th><th>R strain specificities</th><th>Bacterial acquisition §</th><th>Host population structure</th></tr></thead><tbody><tr><td rowspan="6">1. Impact of mechanisms on dynamics</td><td>Austin et al. 1997 (Austin et al., 1997)</td><td>G</td><td>General population</td><td>Theoretical</td><td>C</td><td>Deterministic compartmental</td><td>R-S</td><td>-</td><td>R emergence</td><td>Reduced transmission</td><td>Human-to-human + background</td><td>-</td><td>-</td></tr><tr><td>Eisenberg et al. 2012 (Eisenberg et al., 2012)</td><td>APR-EC</td><td>General population</td><td>Village, Ecuador</td><td>C</td><td>Deterministic compartmental</td><td>R</td><td>-</td><td>R emergence</td><td>-</td><td>Human-to-human + background</td><td>-</td><td>-</td></tr><tr><td>Blanquart et al. 2018 (Blanquart et al., 2018)</td><td>G</td><td>General population</td><td>Theoretical</td><td>C</td><td>Deterministic compartmental</td><td>R-S</td><td>-</td><td>Increased S clearance depending on susceptibility</td><td>Reduced transmission</td><td>Human-to-human</td><td>Yes</td><td>-</td></tr><tr><td>Davies et al. 2019 (Davies et al., 2019)</td><td>APR-EC FQR-EC</td><td>General population</td><td>European countries</td><td>C</td><td>Stochastic individual-based approximated by a deterministic compartmental</td><td>R-S-multi</td><td>-</td><td>Increased S clearance & R emergence</td><td>Reduced transmission</td><td>Human-to-human</td><td>-</td><td>Aggregated</td></tr><tr><td>Olesen et al. 2020 (Olesen et al., 2020)</td><td>G</td><td>General population</td><td>European countries</td><td>C</td><td>Deterministic compartmental</td><td>R-S-multi</td><td>-</td><td>Increased S clearance</td><td>Reduced transmission</td><td>Human-to-human</td><td>Yes</td><td>-</td></tr><tr><td>Godijk et al. 2022 (Godijk et al., 2022b)</td><td>ESBL-EC</td><td>General population</td><td>Netherlands</td><td>C + H</td><td>Deterministic compartmental</td><td>R-S-multi</td><td>Yes</td><td>Increased S clearance & R emergence</td><td>Different resistance-associated fitness costs and benefits</td><td>Human-to-human + background</td><td>-</td><td>-</td></tr><tr><td rowspan="6">2. Transmission route identification & quantification</td><td>Arcilla et al. 2017 (Arcilla et al., 2017)</td><td>ESBL-E</td><td>High-risk group in household</td><td>-</td><td>C</td><td>Markov</td><td>R</td><td>-</td><td>No effect</td><td>-</td><td>Human-to-human + background</td><td>-</td><td>Individual</td></tr><tr><td>Haverkate et al. 2017 (x2 models) (Haverkate et al., 2017)</td><td>ESBL-E</td><td>High-risk group in household</td><td>-</td><td>C</td><td>Markov / Deterministic compartmental</td><td>R</td><td>-</td><td>No effect</td><td>-</td><td>Human-to-human + background</td><td>-</td><td>Individual</td></tr><tr><td>Knight et al. 2018 (Knight et al., 2018)</td><td>ESBL-EC</td><td>General population</td><td>United Kingdom</td><td>C + H</td><td>Deterministic compartmental</td><td>R</td><td>Yes</td><td>Increased R acquisition</td><td>Reduced infection risk</td><td>Human-to-human + background</td><td>/</td><td>Yes</td></tr><tr><td>Mughini-Gras et al. 2019 (Mughini-Gras et al., 2019)</td><td>ESBL-EC</td><td>General population</td><td>-</td><td>C</td><td>Deterministic compartmental</td><td>R</td><td>-</td><td>No effect</td><td>-</td><td>Human-to-human + background</td><td>-</td><td>-</td></tr><tr><td>Van den Bunt et al. 2020 (van den Bunt et al., 2020)</td><td>ESBL-E</td><td>General population</td><td>-</td><td>C</td><td>Deterministic compartmental</td><td>R</td><td>-</td><td>No effect</td><td>-</td><td>Human-to-human</td><td>-</td><td>Individual</td></tr><tr><td>Allel et al. 2022 (Allel et al., 2022)</td><td>GN</td><td>General population</td><td>Village, Chile</td><td>C + H</td><td>Deterministic compartmental</td><td>R</td><td>Yes</td><td>Increased R acquisition</td><td>-</td><td>Human-to-human + background</td><td>-</td><td>Aggregated</td></tr><tr><td rowspan="2">3. Intervention evaluation</td><td>Sundqvist et al. 2010 (Sundqvist et al., 2010)</td><td>TrimR-EC</td><td>General population</td><td>Kronoberg, Sweden</td><td>C</td><td>Deterministic compartmental</td><td>R-S</td><td>-</td><td>Increased S clearance</td><td>Increased clearance</td><td>Human-to-human</td><td>-</td><td>Aggregated</td></tr><tr><td>Talaminos et al. 2016 (Talaminos et al., 2016)</td><td>ESBL-EC</td><td>General population</td><td>Spain</td><td>C + H</td><td>Deterministic + stochastic compartmental</td><td>R-S</td><td>Yes</td><td>Increased R infection risk</td><td>-</td><td>Human-to-human</td><td>-</td><td>-</td></tr></tbody></table>

(continued on next page)
<table><caption>Table 1 (continued)</caption><thead><tr><th rowspan="2">Objectives</th><th rowspan="2">Study</th><th rowspan="2">Model features</th><th rowspan="2">Drug bug pair *</th><th rowspan="2">Type of hosts</th><th rowspan="2">Antibiotic use data</th><th rowspan="2">Setting **</th><th rowspan="2">Model formalism</th><th colspan="2">Biological hypotheses</th><th colspan="2">Additional mechanisms included</th><th rowspan="2">Data for inference (details in Table 3)</th></tr><tr><th>Transmission</th><th>I vs C</th><th>Antibiotic impact</th><th>R strain specificities</th><th>Bacterial acquisition §</th><th>Host population structure</th></tr></thead><tbody><tr><td></td><td>Macfadden et al. 2019 (MacFadden et al., 2019)</td><td>ESBL-EC</td><td>General population</td><td></td><td>Sweden</td><td>C + H</td><td>Deterministic compartmental</td><td>R-S</td><td>-</td><td>Increased S clearance & R acquisition</td><td>Reduced transmission</td><td>Human-to-human</td><td>-</td><td>Aggregated</td></tr><tr><td></td><td>Kardas-Sloma et al. 2020 (Kardaś-Sloma et al., 2020)</td><td>ESBL-EC</td><td>General population</td><td></td><td>France</td><td>C</td><td>Stochastic individual-based</td><td>R</td><td>-</td><td>Increased R acquisition</td><td>-</td><td>Human-to-human + background</td><td>-</td><td>-</td></tr><tr><td></td><td>Kachalov et al., 2021 (Kachalov et al., 2021)</td><td>3GCR-KP</td><td>General population</td><td></td><td>European countries</td><td>C + H</td><td>Deterministic compartmental</td><td>R-S</td><td>Yes</td><td>Increased S clearance & R emergence</td><td>Reduced transmission</td><td>Human-to-human + background</td><td>-</td><td>Aggregated</td></tr><tr><td></td><td>Salazar-Vizcaya et al. 2022 (Salazar-Vizcaya et al., 2022)</td><td>ESBL-KP</td><td>General population</td><td></td><td>Switzerland</td><td>C + H</td><td>Deterministic compartmental</td><td>R</td><td>Yes</td><td>Increased R acquisition</td><td>-</td><td>Human-to-human + background</td><td>-</td><td>Aggregated</td></tr></tbody></table>

-: not included in the model

* G: generic bacteria; EC: E. coli; KP: K. pneumoniae; ESBL: extended-spectrum beta-lactamase producing bacteria; TrimR: trimethoprim resistant; 3GCR: third-generation cephalosporin resistant; CR: carbapenem resistant; APR: aminopenicillins resistant; FQR: fluoroquinolone resistant ** C: community; H: hospital *** I vs C: infection versus carriage differentiation; S: sensitive bacteria; R: resistant bacteria; § Bacterial acquisition: "background" acquisition reflects acquisition from non-human sources, such as environmental or animals ones, or within-host acquisition by mutations or acquisition of antibiotic resistance genes; as opposed to "human-to-human" that reflects inter-individual transmission.

Austin et al. first explored how resistance-associated fitness costs in bacterial transmission could explain coexistence of sensitive and resistant strains of commensal bacteria in the intestinal microbiota, after individuals are given antibiotics following infection (Austin et al., 1997). In this study, high fitness cost values were considered, assuming that resistant strains were 50% less transmissible than sensitive ones (c = 0.50, with parameter c defined in Fig. 2 A). Later, Davies et al. explicitly formalized the role of strain competition within the host, introducing multiple sub-compartments of resistant strain carriage and differential intra-host growth between resistant and sensitive strains (Davies et al., 2019). Including such within-host competition mechanisms successfully reproduced rates of aminopenicillin and fluoroquinolone resistance estimated in E. coli across a large range of antibiotic use rates for European countries, and allowed for more flexible co-existence than the commonly used multi-carriage model ("R-S-multi" model defined in Fig. 2 A) (Davies et al., 2019). Here, they estimated lower resistance-associated transmission costs (c < 0.20 for both resistances in E. coli) (Davies et al., 2019). One model also formalized plasmid-mediated acquisition of ESBL production in E. coli (Godijk et al., 2022b).

Other modeling studies evaluated whether structure in the host population could impact and explain antibiotic resistance dynamics at the population level (e.g., by considering that different host groups were characterized by distinct antibiotic use rates and inter-groups connectivity). They explored different structures, including geographic regions, (Olesen et al., 2020) where spatial spillover led to weak observed antibiotic use-resistance associations in some regions, or host classes such as age groups (Blanquart et al., 2018). Overall, considering highly structured populations allowed for the coexistence of sensitive and resistant bacteria over variable antibiotic use rates in these two studies (Olesen et al., 2020; Blanquart et al., 2018). Interestingly, results from a modeling study analyzing data from Ecuador suggested that persistence of antibiotic-resistant E. coli in communities inter-connected through travel was explained either by high within-community transmission or high interconnectedness, but not by differences in antibiotic consumption (Eisenberg et al., 2012). While these results might not be generalizable to other settings, they confirm the importance of inter-individual interactions in the observed resistance levels.

### 3.4. Identifying and quantifying transmission routes

We identified six studies using models to characterize routes of transmission and/or quantify transmission parameters for antibiotic-resistant Enterobacterales. All these studies used the "R" model structure, considering only transmission of resistant bacteria (Table 1).

Knight et al. and Allel et al. explored the relative contribution of community versus healthcare settings in the overall transmission of antibiotic resistance (Knight et al., 2018; Allel et al., 2022). Their results both suggested that individuals from the general population mostly acquire resistant E. coli or other Gram-negative bacteria in the community, despite higher levels of antibiotic use and transmission in the hospital (Knight et al., 2018; Allel et al., 2022). Within the community, multiple transmission routes were investigated in Mughini-Gras et al., such as human, food, environment, farm and companion animals (Mughini-Gras et al., 2019). Analyzing antibiotic resistance gene occurrences from the Netherlands through a source-attribution model, they estimated that 60% of community carriage of ESBL-producing Enterobacterales originated from human-to-human transmission. Nevertheless, their results also suggested that, while non-human sources do not represent the main source of acquisition events, their contribution is required to maintain observed levels of colonization in the community (Mughini-Gras et al., 2019).

In three studies, model fitting to individual antibiotic resistance carriage data provided parameter estimates for the transmission rate and carriage duration of ESBL-producing Enterobacterales (Haverkate et al., 2017; van den Bunt et al., 2020; Arcilla et al., 2017). Results were
<caption>**Table 2**</caption>

Transmission model parameter estimates for ESBL-producing Enterobacterales, obtained from individual-based models informed by longitudinal antibiotic resistance carriage data.

<table><caption>**Table 3**</caption><thead><tr><th>Study</th><th>Host population</th><th>Country</th><th>Human-to-human transmission rate (95% confidence interval)</th><th>Background environmental acquisition rate (95% confidence interval)</th><th>Median carriage duration (95% confidence interval)</th></tr></thead><tbody><tr><td>Arcilla et al. 2017 (Arcilla et al., 2017)</td><td>Returning travelers, within households</td><td>The Netherlands</td><td>1.3x10<sup>-3</sup> / colonized individual/day (0.5x10<sup>-3</sup> - 2.4x10<sup>-3</sup>)</td><td>7.3x10<sup>-4</sup> / day (5.4x10<sup>-4</sup> - 9x10<sup>-4</sup>)</td><td>30 days (29-33) for returning travelers</td></tr><tr><td>Haverkate et al. 2017 (x2) (Haverkate et al., 2017)</td><td>Discharged patients, within households</td><td>The Netherlands</td><td>5.3x10<sup>-3</sup> / colonized individual/day (2.5x10<sup>-3</sup> - 11x10<sup>-3</sup>)</td><td>1.5x10<sup>-4</sup> / day (0.2x10<sup>-4</sup> - 3.9x10<sup>-4</sup>)</td><td>267 days (173-433) for discharged hospital patients 111 days (56-217) for household members 128 days (106-157) for healthy individuals</td></tr><tr><td>Van den Bunt et al. 2020 (van den Bunt et al., 2020)</td><td>Individuals sampled from the general population</td><td>The Netherlands</td><td>9x10<sup>-4</sup> / colonized individual/day (6x10<sup>-4</sup> - 10x10<sup>-4</sup>)</td><td>-</td><td></td></tr></tbody></table>

Details on empirical data used for inference in the reviewed modeling studies.

<table><thead><tr><th rowspan="2">Objective</th><th rowspan="2">Study</th><th colspan="5">Empirical data used for inference</th></tr><tr><th>Drug-bug pair</th><th>Isolates sampling</th><th>Collection scale</th><th>Resistance data type</th><th>Data source</th></tr></thead><tbody><tr><td>1. Impact of mechanisms on dynamics</td><td>Davies et al. 2019 ( Davies et al., 2019)</td><td>Aminopenicillin and fluoroquinolone resistances in E. coli</td><td><strong>Infection</strong><br>Invasive isolates extracted from blood and cerebrospinal fluid</td><td>Country-level (aggregated)</td><td><strong>One-year proportion data</strong><br>2015</td><td><strong>Surveillance data</strong><br>30 countries; European Center for Disease Control - ECDC</td></tr><tr><td rowspan="3">2. Transmission routes identification & quantification</td><td>Arcilla et al. 2017 ( Arcilla et al., 2017)</td><td>ESBL-producing Enterobacteriaceae</td><td><strong>Carriage</strong><br>Fecal samples</td><td>Individual-level</td><td><strong>Longitudinal count data</strong><br>12 months follow-up with 5 time points, 2012-2013</td><td><strong>Cohort study</strong><br>Multicenter, longitudinal, prospective cohort of travelers and non-traveling household members followed up before and after travel; Netherlands</td></tr><tr><td>Haverkate et al. 2017 (x2) ( Haverkate et al., 2017)</td><td>ESBL-producing Enterobacteriaceae</td><td><strong>Carriage</strong><br>Fecal samples</td><td>Individual-level</td><td><strong>Longitudinal count data</strong><br>18 months follow-up with 5 time points, 2010-2013</td><td><strong>Cohort study</strong><br>Multicenter, longitudinal, prospective cohort of hospitalized patients and non-hospitalized household members followed up during and after hospitalization; Netherlands</td></tr><tr><td>Van den Bunt et al. 2020 (van den Bunt et al., 2020)</td><td>ESBL-producing Enterobacteriaceae</td><td><strong>Carriage</strong><br>Fecal samples</td><td>Individual-level</td><td><strong>Longitudinal count data</strong><br>6 months follow-up with 3 time points, 2014-2016</td><td><strong>Cohort study</strong><br>Longitudinal cohort of community individuals selected from a nationwide cross-sectional study; Netherlands</td></tr><tr><td rowspan="5">3. Intervention evaluation</td><td>Allel et al. 2022 ( Allel et al., 2022)</td><td>Fluoroquinolone, cephalosporin and carbapenem resistances in Gram-negative bacteria</td><td><strong>Carriage</strong><br>Fecal samples</td><td>Region-level (aggregated)</td><td><strong>Yearly incidence data</strong><br>2018-2019</td><td><strong>Cohort study</strong><br>Longitudinal cohort study sampling households in Chile (MAUCO and MDRGN studies)</td></tr><tr><td>Sundqvist et al. 2010 (Sundqvist et al., 2010)</td><td>Multiple resistance in E. coli</td><td><strong>Infection</strong><br>Isolates extracted from urine</td><td>Region-level (aggregated)</td><td><strong>Monthly proportion data</strong><br>1991-2008</td><td><strong>Intervention study</strong><br>Intervention for 24 months from 2004 to 2006; inclusion of all isolates tested by 2 hospitals; Kronoberg county, Sweden</td></tr><tr><td>MacFadden et al. 2019 (MacFadden et al., 2019)</td><td>Third-generation cephalosporin resistance in E. coli</td><td><strong>Infection</strong><br>Isolates from both outpatient (urine) and inpatient (blood) settings</td><td>Country-level (aggregated)</td><td><strong>Yearly proportion data</strong><br>2001-2016</td><td><strong>Surveillance data</strong><br>Surveillance of antibiotic resistance from hospital and community settings; Sweden</td></tr><tr><td>Kachalov et al., 2021 (Kachalov et al., 2021)</td><td>Third-generation cephalosporin and carbapenem resistance in K. pneumoniae</td><td><strong>Infection</strong><br>Invasive isolates extracted from blood and cerebrospinal fluid</td><td>Country-level (aggregated)</td><td><strong>Yearly proportion data</strong><br>2005-2015</td><td><strong>Surveillance data</strong><br>11 countries; European Center for Disease Control - ECDC</td></tr><tr><td>Salazar-Vizcaya et al. 2022 ( Salazar-Vizcaya et al., 2022)</td><td>ESBL-producing K. pneumoniae</td><td><strong>Infection</strong><br>Invasive and non-invasive isolates</td><td>Region-level (aggregated)</td><td><strong>Yearly proportion data</strong><br>2004-2017</td><td><strong>Surveillance data</strong><br>Routine and patient-specific collection of antibiotic resistance data; data from Valais; Swiss Centre for Antibiotic Resistance database - ANRESIS</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>Fig. 1. PRISMA flowchart representing screening and selection of publications included in the reviewing process.</figcaption></figure>

highly dependent on the population considered in the models and are summarized in Table 2. Within households, probability of transmission from a colonized index case to other household members was estimated to be 12% from a returning traveler (95% confidence interval CI 5–18) (Arcilla et al., 2017) and 67% from a discharged hospital patient (95% CI 38–88) (Haverkate et al., 2017). In both studies, the estimated human-to-human transmission rate was in the order of $10^{-3}$ per colonized individual per day, whereas the estimated background environmental acquisition rate was in the order of $10^{-4}$ per day (Table 2). Interestingly, longer median carriage duration was estimated for discharged hospital patients (267 days, 95% CI 173–433) and household members (111 days, 95% CI 56–217), (Haverkate et al., 2017) than for returning travelers (30 days, 95% CI 29–33) (Arcilla et al., 2017). Within the general population, Van Den Bunt et al. estimated the human-to-human transmission rate to be $9 \times 10^{-4}$ events per colonized individual per day (95% CI $6 \times 10^{-4}$ – $10 \times 10^{-4}$) (van den Bunt et al., 2020). In agreement with estimates from Haverkate et al., (Haverkate et al., 2017) the median duration of carriage was estimated to be 128 days (95% CI 106–157).

### 3.5. Evaluating the impact of population-level interventions on antibiotic resistance dynamics in the community

Six studies evaluated the effectiveness of public health interventions at reducing antibiotic resistance rates in Enterobacterales in the community (Table 1). Interventions included: antibiotic use reduction in the hospital, in the community, or both; (MacFadden et al., 2019; Kachalov et al., 2021; Salazar-Vizcaya et al., 2022; Talaminos et al., 2016; Kardaś-Sloma et al., 2020) antibiotic molecule replacement in the community; (Sundqvist et al., 2010) and person-to-person transmission reduction in the hospital, (Kachalov et al., 2021; Salazar-Vizcaya et al., 2022) or in the community through hand hygiene (Talaminos et al., 2016; Kardaś-Sloma et al., 2020). Importantly, the afore-mentioned interventions were only investigated in the context of high-income

countries, and mostly in Europe.

Among those, two modeling studies tested the theoretical impact of community-focused interventions. At both the household and population levels, modeling results suggested that hand hygiene was more effective than antibiotic use restriction to reduce ESBL-producing *E. coli* transmission (Talaminos et al., 2016; Kardaś-Sloma et al., 2020). In another study, Sundqvist et al. analyzed the impact of a 2-year substitution of trimethoprim with other antibiotics on resistance rates in *E. coli* urinary isolates, in a county in Sweden (Sundqvist et al., 2010). They found a low reversibility of resistance, both from time-series analyses and a population-level mathematical model. They also estimated a relatively low fitness cost associated with trimethoprim resistance ($c = 0.01–0.02$, with parameter $c$ defined in Fig. 2 A), likely contributing to this low reversibility.

Three models combining nosocomial and community transmission were used to compare the impact of interventions across these two settings (MacFadden et al., 2019; Kachalov et al., 2021; Salazar-Vizcaya et al., 2022). MacFadden et al. fitted a transmission model to observed proportions of ESBL production among *E. coli* over time in Sweden (MacFadden et al., 2019). They predicted that a 20% decrease in antibiotic use in the community led to greater reductions in ESBL prevalence in both settings (community and hospital) than a same 20% decrease in hospital antibiotic use. Concerning *K. pneumoniae*, two studies fitted their model to surveillance data of ESBL and carbapenem resistance rates over time in several European countries. In contrast to above estimates for *E. coli*, results from Kachalov et al. suggested that reducing nosocomial transmission and antibiotic consumption in hospitals had the strongest impact on *K. pneumoniae* resistance prevalence overall (Kachalov et al., 2021). Salazar-Vizcaya et al. predicted that a 50% decrease in consumption of all antibiotic classes in both hospitals and the community would lead to a small but significant decline in the prevalence of resistant *K. pneumoniae* in upcoming years in both settings, whereas a 50% decrease in the in-hospital transmission rate would lead to a decline only in hospital (Salazar-Vizcaya et al., 2022).
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Hypotheses of mathematical models regarding between-human bacterial transmission, bacterial carriage and infection, and impacts of antibiotic use on the population-level dynamics of antibiotic resistant Enterobacterales. (A) Transmission. 3 mechanisms considered for between-human bacterial transmission: transmission of resistant strains only (1, “R”); transmission of both sensitive and resistant strains (2, “R-S”); and transmission of sensitive and resistant strains with both single and multi-carriage compartments (3, “R-S-multi”). (B) Infection. 2 mechanisms considered for bacterial carriage and infection: model only considering infection compartments (1, “infection-only”); and model accounting for both carriage and infection compartments, using a progression-to-disease rate between the two (2, “carriage & infection”) – in both cases, sampling is often performed on infections. (C) Antibiotic selection for resistance. three mechanisms were considered – highlighted by the red arrows – to formalize the selective impact of antibiotic use on resistant bacteria, based either on compartments representing specifically antibiotic exposed individuals (1,2,3) or relying only on antibiotic associated parameters affecting the global carriage or transmission of strains (4,5): S clearance. the clearance of sensitive bacteria compared to no clearance or slower clearance of resistant ones (1,4); R acquisition. the increased probability of resistant bacteria acquisition (2,5); and R emergence. within the host, populations of sensitive bacteria become resistant (3). The latter can be interpreted as representing the acquisition of antibiotic resistance genes from co-colonizing microbiota through HGT; and/or endogenous acquisition, which could for example represent the outgrowth of sub-dominant resistant bacteria present, prior to antibiotic exposure, in small quantities among individuals primarily carrying sensitive strains; and/or de novo mutation selection during antibiotic treatment. It is important to note that the formalisms shown here are not exhaustive. (A-B-C) Parameter description. λ: force of colonization = β × $\frac{R}{S+R}$ + b; β: between-human transmission rate in [/ day] or [/ carriers / day]; b: constant background rate that captures transmission from non-human sources, horizontal transmission, or de novo emergence in [/ day]; c: resistance-associated fitness cost; 1/γ: duration of carriage (1/γS vs 1/γR if sensitive and resistant bacteria have different durations) in [day]); σS,σR: progression-to-disease rate in [/ carriers / day]; a: multiplicative factor to increase clearance of sensitive bacteria, when individuals are under antibiotic use (a > 1); A: multiplicative factor to increase transmission of resistant bacteria, when individuals are under antibiotic use (A > 1).</figcaption></figure>

### 3.6. Empirical data for statistical inference of parameters

Of the 18 reviewed studies, half inferred model parameters by fitting model simulations to antibiotic resistance data collected in human populations (Table 3). Among those, data either described individual-level carriage or aggregated region- or country-level cases of infection. Antibiotic resistance carriage data were obtained from microbial analysis of fecal samples taken from individuals. Depending on the study, stool was sampled from healthy volunteers or from high-risk groups at regular intervals (days or months) to detect intestinal carriage of ESBL-producing Enterobacterales. The analysis of consecutive individual samples provides temporal information on the presence or absence of resistant bacteria, which can then be used to track bacterial acquisition or clearance. In three studies, longitudinal individual-level carriage data were accessed from cohort studies (Haverkate et al., 2017; van den Bunt

et al., 2020; Arcilla et al., 2017). These models were formalized using the “R” structure, where the acquisition and transmission of only resistant bacteria is described (Table 1, Fig. 2). This is probably due to a lack of information on non-resistant bacteria carriage in the available data.

The other main type of data considered was aggregated counts of infections sensitive or resistant to a particular antibiotic, by unit of time at the region or country level. These data predominantly came from national surveillance systems, such as in Sweden or Switzerland, (MacFadden et al., 2019; Salazar-Vizcaya et al., 2022) or at the European level (Kachalov et al., 2021; Davies et al., 2019). One study used aggregated infection cases from an intervention study (Sundqvist et al., 2010). In these studies, the “R-S” or “R-S-multi” structures (Fig. 2) were usually chosen, considering the circulation of both resistant and non-resistant bacteria in the population. However, most of them considered only an infection stage, i.e. not considering carriage
(Table 1).

The nine remaining studies did not estimate transmission parameters from data, relying instead on existing estimates from studies published in the literature. In one study, assumed parameters values were specific to Enterobacterales, (Knight et al., 2018) while, for two others, values estimated for *S. pneumoniae* were considered (Olesen et al., 2020; Blanquart et al., 2018). While these latter models were explicitly proposed to describe antibiotic resistance in generic commensal bacteria (as labeled “G” in Table 1), assumed resistance-associated fitness cost values were derived from *S. pneumoniae* and fixed to $c = 0.168$, (Olesen et al., 2020) or $c = 0.04$ (Blanquart et al., 2018).

# 4. Discussion

In this systematic review, we identified and analyzed 18 mathematical modeling studies published between 1992 and 2023 that describe the transmission of antibiotic-resistant Enterobacterales (ARE) in the community. Overall, models varied substantially in their structure, assumptions and focus, but nonetheless provided consistent insights by (i) confirming the significant role of community transmission in the overall burden of ARE, (ii) investigating how various candidate mechanisms explained observed epidemiological trends, (iii) providing species-specific estimates of key parameters related to unobserved processes such as transmission and clearance and (iv) projecting how the effectiveness of public health interventions depended on settings, bacterial species, and types of resistance. Despite important contributions in the last decade, we identified a lack of modeling studies specifically designed for ARE in the community, likely linked to a paucity of epidemiological knowledge and standardized data in this setting.

## 4.1. Modeling ARE transmission: how and why?

This review first illustrated the two main model structures currently used to describe ARE transmission. Studies aiming at quantifying transmission routes of ARE all used the “R” model structure, while those aimed at assessing the impact of different mechanisms or of public health interventions on ARE dynamics frequently used the “R-S” and “R-S-multi” structures (Table 1). Such structural choices may have important consequences for model predictions. On one hand, the “R-S” structure, which assumes a resistance-associated fitness cost, predicts a key impact of antibiotic use on the invasion, maintenance, and transmission of competing sensitive and resistant strains in populations. On the other hand, “R” structure models do not account for such ecological competition but instead consider ARE as a pathogen *per se*. While many knowledge gaps still exist on the epidemiology of ARE, several assumptions made by these commonly used models could be challenged by current observations. As opposed to other antibiotic-resistant pathogens, some successfully worldwide-disseminated ARE strains might be associated with low to no resistance-associated fitness costs compared to their sensitive counterparts, (D'Andrea et al., 2013) challenging the resistance-associated fitness cost parameter of the “R-S” model structure. Moreover, as opposed to other commensals, some Enterobacterales species such as *E. coli* are carried by almost all human adults throughout their entire life, (Tenaillon et al., 2010) giving them more frequent opportunities to evolve or acquire antibiotic resistance. Here, the basic “R” structure might fail to capture these within-host dynamics.

Such specificities suggest that commonly used models tailored to other antibiotic-resistant pathogens may have limited relevance for the study of ARE (Lipsitch et al., 2009). Here, four of the reviewed studies were not species-specific nor resistance-specific but rather considered generic intestinal antibiotic-resistant commensals (Olesen et al., 2020; Blanquart et al., 2018; Austin et al., 1997; Allel et al., 2022). For example, two of them used epidemiological parameters previously estimated for *S. pneumoniae* (Olesen et al., 2020; Blanquart et al., 2018). However, Enterobacterales and *S. pneumoniae* do not share the same ecological niche within the host (mainly gut vs upper respiratory tract)

and differ in primary transmission routes (direct or indirect orofecal vs airborne transmission), antibiotic resistance mechanisms (e.g., efflux pumps removing antibiotics from the cell in some strains of macrolide-resistant *S. pneumoniae* versus the production of antibiotic-degrading enzymes among ESBL-producing Enterobacterales) or in the fraction of the human population that are carriers (Tenaillon et al., 2010; Bogaert et al., 2004). In addition, most pneumococcus models estimated strong serotype-dependent fitness costs associated with resistance, resulting in reduced transmission capacity of resistant strains compared with sensitive ones (Maher et al., 2012; de Cellès et al., 2015). While there is evidence of resistance-associated fitness costs in Enterobacterales *in vitro*, (Melnyk et al., 2015; Vanacker et al., 2023) these estimates at the epidemiological level are rare. Such costs may be expected to vary not only across *E. coli*, *K. pneumoniae* and other Enterobacterales, but also depending on whether a specific resistance mechanism is mutation- or horizontally-acquired (Enne et al., 2005; MacLean and San Millan, 2019). It might also be partially alleviated if compensatory mutations have arisen. Conversely, antibiotic resistance in *E. coli* have also been reported to confer fitness advantages in a mouse model, with multi-drug resistant strains displaying colonization advantages over non-resistant strains in absence of antibiotics (Connor et al., 2023). Improving our knowledge on such key parameters across bacterial species and resistance mechanisms, from *in vivo* and epidemiological studies, will be needed to develop more biologically relevant models. This will help to more consistently reproduce ARE dynamics and better assess the expected impact of public health interventions on antibiotic resistance levels in these bacteria (Sundqvist et al., 2010).

As commensals mostly colonizing the human gut, Enterobacterales face a myriad of ecological phenomena, such as bacteria-bacteria interactions, niche competition and HGT. Such phenomena have been suggested to influence the acquisition, carriage, and pathogenesis of ARE (Lamberte and van Schaik, 2022). For example, dysbiosis following antibiotic use has been shown to reduce colonization resistance to the invasion of new and potentially antibiotic-resistant strains. It might also promote the overgrowth of pre-existing antibiotic-resistant strains (Pamer, 2016). Regardless of potential impacts of antibiotics, host heterogeneity in microbiota composition likely also modulates carriage risk and duration (Chang et al., 2016). Importantly, even within Enterobacterales, strong heterogeneity exists between species: while most individuals commensally carry *E. coli*, *K. pneumoniae* is carried in less than 50% of the general population, (Chang et al., 2021) and ESBL-producing *K. pneumoniae* have been associated with greater transmissibility and pathogenicity than ESBL-producing *E. coli* (Duval et al., 2019; Denkel et al., 2020). Further, HGT is known to contribute to the endogenous acquisition of resistance genes among resident bacteria, in part driving the observed prevalence of ARE carriage (MacLean and San Millan, 2019). Many of the models included in this review have used structures that implicitly assume some degree of within-host ecological interaction between Enterobacterales and other microbiota. However, explicit data-driven quantification of such interactions has been scarce, and a previous review has highlighted that impacts of microbiota and HGT have been, until now, only marginally included in population-level models of ARE epidemiology (Leclerc et al., 2019).

## 4.2. Modeling ARE transmission in the specific context of the community

While the emergence of antibiotic resistance for many pathogenic species is frequently associated with hospitals, which are environments with high rates of antibiotic use and reservoirs of multidrug-resistant organisms, results from the reviewed modeling studies highlighted the significant role of the community in driving the overall spread of ARE (MacFadden et al., 2019; Knight et al., 2018; Allel et al., 2022). The challenge of studying the community lies in the fact that this setting exhibit a lower frequency of observable infections and has a much larger population compared to hospitals. Mathematical models can be used to analyze available antibiotic resistance data while integrating
non-observable phenomena such as transmission from asymptomatic carriers, and the role of microbiota dysbiosis or ecological competition in influencing such transmission. In this context, they can be used to support the investigation of ARE transmission in the community. Epidemiological data from the literature also supported the significance of the community, showing that asymptomatic carriage of ARE resulted mostly from frequent community-based acquisition events, at least for *E. coli* (Herindrainy et al., 2018; Madigan et al., 2015). Consequently, the community and hospitals likely both play significant but distinct roles in the global emergence and dissemination of ARE. Modeling their transmission within and between these two settings is thus highly relevant, and several of the reviewed studies did so (MacFadden et al., 2019; Kachalov et al., 2021; Salazar-Vizcaya et al., 2022; Talaminos et al., 2016; Godijk et al., 2022b; Knight et al., 2018; Allel et al., 2022). However, only two studies used detailed antibiotic resistance infection data specific to the two environments (MacFadden et al., 2019; Salazar-Vizcaya et al., 2022). In the future, extensive antibiotic resistance and antibiotic use data collection, through general practitioners or outpatient microbiological laboratories, should enable setting-specific model parametrization and therefore help to better characterize the contribution of each setting to transmission.

Robust modelling of ARE transmission in the community requires accounting for heterogeneity in risks of transmission or acquisition, and carriage duration. Modeling studies have quantified the high risks of transmission of ARE from specific groups such as discharged hospital patients and colonized infants to healthy members of their households (Haverkate et al., 2017; Kardaś-Słoma et al., 2020). From epidemiological studies, it has been shown that travel to high-prevalence areas, including certain countries in Africa and Asia, was an important risk factor for ARE acquisition (Raffelsberger et al., 2023; Otter et al., 2019). However, despite this high acquisition risk, both model and epidemiological studies suggested that returning travelers might be associated with a low subsequent transmission risk in households, (Arcilla et al., 2017) possibly due to a short carriage duration (Arcilla et al., 2017; Ling et al., 2020; Ruppé et al., 2015). Future research should further investigate the traveler's role in spreading ARE locally. Indeed, while the rapid global spread of high-risk multidrug-resistant clones is clearly linked to travel, (van der Bij and Pitout, 2012) it is unclear to what extent population movement contributes to community transmission dynamics once a clone has become established in a given region. Metapopulation models can be used to investigate impacts of such demographic and spatial heterogeneity on transmission (Krieger et al., 2020). Their development requires access to fine-grained epidemiological data stratified by age or location that are not accessible yet in most part of the world. However, such models could be harnessed in the future to assess the global impact of public health interventions targeting specific sub-regions or sub-populations.

It is important to stress that the existing modeling literature on ARE transmission in the community mostly based their assumptions on the context of high-income countries (HIC), especially Europe. Resulting transmission risk estimates or interventions outcomes are therefore not generalizable to low- or middle-income countries (LMIC). LMIC face their own challenges that might strongly affect the epidemiology and ecology of ARE transmission, such as access to clean water, safe sanitation, or vaccination (Okeke et al., 1999). The organization of “households” or “community” also differ between countries, in terms of population density, frequency of contacts, or exposure to different reservoirs of antibiotic-resistant bacteria, like healthcare settings, water, soil, and livestock or domestic animals (Ikhimiukor et al., 2022). Moreover, hospital infection reporting is done routinely in many HIC, providing extensive antibiotic susceptibility data on invasive isolates, (Johnson, 2015) but this is not the case in most parts of the world (Ashley et al., 2018). On the contrary, antibiotic resistance carriage surveillance outside of hospitals is more difficult to put in place both in HIC and LMIC (Johnson, 2015). Such data are crucial for ARE: as carriage has been shown to be associated with an increased risk of infection,

(Isendahl et al., 2019) estimation of carriage prevalence in different populations might help tailor public health interventions to reduce associated infection burden. To fill this gap, epidemiological cohorts are implemented to estimate the prevalence of ARE carriage and further evaluate transmission rates in the community. In the modeling literature reviewed here, these estimates are exclusively inferred from data collected in a few HIC (Table 2) (Haverkate et al., 2017; van den Bunt et al., 2020; Arcilla et al., 2017). Future availability of robust carriage data in the community, through cohorts or cross-sectional studies especially focusing on LMIC, will be key to better inform models and further disentangle the role of asymptomatic colonization in ARE spread.

To maintain sustained ARE transmission in the community, reviewed models also supported the hypothesis that non-human-source transmission events are relevant, (Mughini-Gras et al., 2019) including contact with animals or contaminated water, especially in low-income contexts (Eisenberg et al., 2012; Allel et al., 2022). Frequent contacts with animals and further bacterial transmission have been described in rapidly urbanizing communities in several LMIC (Muloi et al., 2022; Rwego et al., 2008). Moreover, antibiotics are still used for growth promotion in livestock in some countries, (Van Boeckel et al., 2015) enhancing selection pressures on antibiotic-resistant bacterial commensals, notably Enterobacterales. However, identifying and quantifying non-human-to-human transmission routes heavily depends on input data (Mughini-Gras et al., 2018). The appropriate design of community cohorts where humans, animals and the environment could be sampled in a controlled manner, collecting both genomic and epidemiological data, would help better infer the relative input of potential environmental transmission routes. Methodological developments will also be essential to better incorporate such genomic data within epidemiological transmission models, for example using phylodynamic approaches.

## 5. Conclusion

To conclude, our study provided a detailed summary of epidemiological insights provided by published modeling studies of ARE transmission in the community setting. We highlighted that the available modeling literature is still scarce compared to the one focusing on other antibiotic-resistant bacterial species or on hospital transmission, especially for LMIC contexts (Niewiadomska et al., 2019; Assab et al., 2017). The continued integration of hypothesis driven mechanisms and relevant epidemiological data into transmission models will be necessary to better formalize the acquisition, colonization, and transmission of these intestinal commensals within the community. Open challenges in the field are, among others, to include with more precision the within-host processes in population-level models, to better integrate genomic and epidemiological data and to explore One Health transmission routes. Moreover, we advocate for extensive collection of antibiotic resistance carriage data in the community, and not only in Europe, to better understand its relationship with infection by these opportunistic pathogens. Such considerations will be instrumental in improving our understanding of the ecology and epidemiology of antibiotic-resistant Enterobacterales. In turn, this will be key to support the design of public health interventions and further evaluate their efficacy (hygiene measures, antibiotic prescribing reductions, wastewater treatment, vaccines, etc.), and identifying optimal target populations (high-risk individuals, households, communities, etc.) to reduce antibiotic resistance levels in Enterobacterales.

## Funding

ER's work was funded by an independent research Pfizer Global Medical Grant (number 57504809). LO and PG's work was funded by the French Government's "Investissement d'Avenir" program, Laboratoire d'Excellence "Integrative Biology of Emerging Infectious
Diseases" (Grant ANR-10-LABX-62-IBEID).

## Authors' contribution

ER, PG and LO designed the study. ER identified and analyzed the data. ER, PG and LO reviewed the data and wrote the manuscript. All authors read and approved the final manuscript.

## CRediT authorship contribution statement

Eve Rahbe: Writing – review & editing, Writing – original draft, Formal analysis, Data curation, Conceptualization. Philippe Glaser: Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. Lulla Opatowski: Writing – review & editing, Validation, Supervision, Methodology, Conceptualization.

## Declaration of Competing Interest

None.

## Acknowledgments

We thank David RM Smith for critical feedback on the manuscript.

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