A socio-ecological System Dynamics model of antimicrobial use and resistance

A socio-ecological System Dynamics model of antimicrobial use and resistance | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results A socio-ecological System Dynamics model of antimicrobial use and resistance View ORCID Profile Carys J. Redman-White , View ORCID Profile Birgit Kopainsky , View ORCID Profile Adrian Muwonge , View ORCID Profile Andrew R. Peters , View ORCID Profile Dominic Moran doi: https://doi.org/10.1101/2025.08.26.672312 Carys J. Redman-White 1 Global Agriculture and Food Systems, Royal (Dick) School of Veterinary Studies, University of Edinburgh 3 Digital One Health Lab, The Roslin Institute, University of Edinburgh Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carys J. Redman-White For correspondence: c.j.redman-white{at}sms.ed.ac.uk Birgit Kopainsky 2 System Dynamics Group, Department of Geography, University of Bergen Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Birgit Kopainsky Adrian Muwonge 3 Digital One Health Lab, The Roslin Institute, University of Edinburgh Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Adrian Muwonge Andrew R. Peters 4 University of Edinburgh Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrew R. Peters Dominic Moran 1 Global Agriculture and Food Systems, Royal (Dick) School of Veterinary Studies, University of Edinburgh Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dominic Moran Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract The concept of resource extraction in the context of antimicrobial resistance (AMR) is rarely explored. In this framework, antimicrobial susceptibility is viewed as a finite resource that is depleted through the use of antimicrobials —thus, AMR represents the exhaustion of this resource. In this work, we examine the system dynamics of AMR using causal loop diagrams to define both the structure and behaviour of two variants of the system. We then evaluate the robustness of the system dynamics models through sensitivity testing. The first model is inspired by a classic “Limits to Growth” structure, in which antimicrobial use practices depend on recent observations of treatment success or failure. The second differs by including AMR surveillance informing antimicrobial prescribing instead of anecdotal experience. The models consider one “bug-drug-mutation-context” combination at a time, but can be applied to different microbes, antimicrobials, and host populations in human or veterinary contexts. Multiparameter sensitivity analyses of relative fitness and timescale parameters were carried out on both model variants. Several key differences in model behaviour over time were observed between the socio-ecological Limits to Growth structure and the modified version in which human judgment, with its associated time lag, is bypassed. The models help explore the effects of human behaviour and associated time lags on patterns of population-level antimicrobial susceptibility over time, applying an established modelling technique in a novel context to generate new insights – notably that plateaux in AMR observed in the field could be in part due to human behaviour rather than purely evolutionary forces. The framing of antimicrobial susceptibility as a variably renewable natural resource is valuable not only as an avenue for alternative modelling approaches, but also as a more collaborative framing for public and policy engagement to promote sustainable management of this common pool resource. Introduction Antimicrobial use (AMU) is vital to modern medicine and has facilitated huge increases in agricultural productivity over the last century, but exposure of human, animal and environmental microbiota to antimicrobial drugs inevitably creates an evolutionary selection pressure for the development and spread of antimicrobial resistance (AMR). The global health and economic consequences of this are well documented ( 1 , 2 ). AMR can be mitigated but not eliminated by careful antimicrobial stewardship, and the reduction of AMR gene prevalence in a microbial population when an antimicrobial drug is withdrawn depends on the fitness cost of resistance, if any. Whilst these general principles apply across One Health (clinical, veterinary and environmental) contexts, there is great variation in epidemiology, microbiology, AMU practices and other key AMR determinants between microbes, antimicrobials, mutations, and One Health settings. This heterogeneity presents a major challenge in understanding the dynamics of AMR, in particular the quantitative relationship between AMU and AMR. Antimicrobial susceptibility as a natural resource Discussion around AMU and antimicrobial efficacy focuses on resistance, the rhetoric frequently framing AMR as an enemy to be eliminated. But as a naturally occurring phenomenon pre-dating the use of antimicrobial drugs by humans ( 3 ), framing AMR as depletion of antimicrobial susceptibility, a natural resource that can be collaboratively exploited by use of antimicrobials, is a potentially more constructive narrative for motivating policy and public engagement. In this framing, susceptibility to a given antimicrobial is depleted as a result of selection pressure when the microbial population is exposed to the drug in question. Depending on selection pressures, the fitness landscape, and particularly the fitness cost (if any) of the mutation conferring resistance in the absence of the antimicrobial, susceptibility may behave as a renewable resource. This conceptual approach has received limited attention, with a small number of economic resource models of AMR published over two decades ago, approaching susceptibility as either a renewable or non-renewable resource ( 4 , 5 ). Conceptual models can be substantiated using different modelling techniques to investigate AMU-AMR dynamics, generating and testing hypotheses to explain phenomena such as plateauing AMR ( 6 , 7 ). In addition to focusing on evolutionary epidemiology within and between hosts ( 6 ), models are useful for exploring policy interventions and their potential impacts at the population scale ( 8 ). This paper further explores the resource extraction framing using System Dynamics (SD) modelling to illustrate the interplay of key variables determining the renewable resource properties of susceptibility. Causal loop diagrams (CLDs) are used here to explore model structures, which form the basis of quantitative SD models. These are investigated using sensitivity testing to assess the behaviour and robustness of the model across a wide parameter space. The next section introduces the SD approach and its use in AMR modelling, as well as examining previous resource extraction modelling approaches to AMR. A methods section describes how this model and its structural variants were developed and the sensitivity analysis carried out to investigate their behaviour. This includes a version in which AMU is determined by anecdotal results of recent treatments and one in which it is determined by current surveillance of AMR. In a results section, the model structures and equations are presented along with the results of the sensitivity analysis. Finally, we discuss the implications of our findings and explore potential next steps. System archetypes The systems thinking approach has been applied to a wide range of problems since its development in the late 20 th century, with recurring problems in systems identified at a structural level. These problematic structures, or system archetypes, are well characterised and modelled ( 9 ). One of the best known, the “Limits to Growth” (LTG) archetype, which lends its name to the title of the seminal 1972 report by the Club of Rome ( 10 ), describes how exponential growth can occur until it is limited by an external constraint. Common applications include harvesting of renewable and non-renewable resources such as fisheries and fossil fuel reserves, respectively. Considering antimicrobial susceptibility as a natural resource “harvested” by use of antimicrobial drugs, this may permit an application of this well-characterised modelling approach to the phenomenon of AMR. In this context, microbial susceptibility to a specific antimicrobial drug would be a partially renewable resource, with development of novel antimicrobial drugs analogous to accessing new “stocks” of the resource. When represented using a CLD, LTG is a simple structure consisting of two feedback loops ( Figure 1 ). In the case of a fishery, increased fishing (efforts) would lead to greater harvests and profit (performance), encouraging more fishing: a positive feedback (reinforcing) loop. However, this would deplete the fish stocks (limiting action/resource), which are replenished dependent on the species reproductive rate (constraint). Depleted stocks reduce harvests: a negative feedback (balancing) loop. Depending on the parameters, including time lags, this system can lead to “boom and bust” oscillations in the resource or may stabilise at an equilibrium ( 9 ). Download figure Open in new tab Figure 1. Causal loop diagram of the “Limits to Growth” archetypal structure. Arrows with a + cause variables to change in the same direction, while arrows with a - cause variables to change in the opposite direction. Feedback loops are labelled R for reinforcing (positive feedback) or B for balancing (negative feedback). Mathematical modelling of AMR Mathematical modelling of AMR at the population level has been approached in a variety of ways. Studies have included deterministic and stochastic models using compartmental and, to a lesser extent, individual based models, with an overall tendency to focus primarily on human health ( 8 ). Compartmental models can be used to represent either populations of hosts colonised by resistant, susceptible and intermediate microbes or the microbe populations themselves across host species, for example to investigate the potential interactions between AMU and AMR in veterinary and human medicine contexts ( 11 ). Several authors have combined compartmental models with economic approaches, modelling AMR as a natural resource. Two related models have emphasised different aspects of the economic and epidemiological challenge ( 4 , 5 ). They combine susceptible-infected-susceptible (SIS) compartmental models ( 12 ) with economic resource extraction modelling, treating susceptibility as either a renewable or a non-renewable resource. The first model considers antimicrobial susceptibility as non-renewable and investigates the optimal usage of antimicrobials from a choice of two drugs with different costs, incorporating other economic parameters such as marginal benefit of successful treatments and discounting of the value of future successful treatments ( 4 ). The authors allude to but do not explore the potential for cyclicity in cases in which susceptibility is renewable. The authors aim to identify the optimal AMU policy rather than investigate the behaviour of the system over time. The second model, based on the first, introduces fitness costs to resistance, modelling susceptibility as a renewable resource to investigate the impacts of strategies involving aggressive use of antimicrobials in comparison to focusing on managing susceptibility prevalence ( 5 ). This paper also focuses on optimisation and steady-state outcomes, and while it does essentially model a LTG system, it does not incorporate any delays or investigate potential for oscillations in susceptibility over time or impacts of human “bounded rationality” on outcomes. System Dynamics modelling Whilst a variety of LTG systems have been quantitatively modelled using system dynamics (SD), this framing has not been applied to AMR. Limited SD modelling of AMR has investigated the evolutionary dynamics of AMR in Streptococcus pneumoniae in humans in response to penicillin prescriptions ( 13 ). This paper maps interlinkages between social, economic and policy influences on AMU and resistance, and discusses microbiological components, such as fitness costs of AMR varying between microbial strains. The authors present a quantitative model of AMU and resistance in S. pneumoniae , with the bacterial population modelled as densities of “susceptible”, “intermediately resistant” and “highly resistant” subpopulations, independent of colonisation of specific hosts. This SD model specifically focuses on the population dynamics of AMR in response to the selection and fitness landscape, with AMU an exogenous variable independent of AMR. Consequently, factors influencing AMU are not investigated, leaving scope for development of models to investigate the behavioural aspects of AMU in the context of AMR. In an LTG model of AMR, specifically, the use of antimicrobials is influenced by observed successful treatment ( Figure 2A ). Download figure Open in new tab Figure 2. CLD representing AMR as a Limits to Growth archetype a) in its basic form and b) with surveillance providing an information connector between susceptibility and AMU Methods Development of model structure CLDs are visual representation of systems that allow us to explain how and why systems behave the way they do over time, by visualising how variables influence each other through reinforcing (positive) or balancing (negative) feedback loops. A CLD of AMR as a LTG system was developed by mapping the archetypal variables ( Figure 1 ) to their corresponding variables for this application, with a single “bug-drug-mutation-context” combination considered. In this case, “efforts” refer to AMU, “performance” to cases successfully treated with this antimicrobial, the “limiting resource/action” to antimicrobial susceptibility, and the “constraint” to the rate at which microbes revert to the susceptible wild-type. A modification was made to the structure to reflect the fact that susceptibility is depleted as a result of selection pressure produced by AMU regardless of whether the case is successfully treated ( Figure 2A ). A variation on the model was created with surveillance bypassing the reinforcing loop ( Figure 2B ) and both versions of the CLD were used as the bases for quantitative SD models, in which the susceptible and resistant fractions of the microbial population are represented as stocks, as are the proportion of the host population currently being treated with the antimicrobial ( Figure 3 ). All CLD and SD modelling was carried out using Stella Architect software ( 14 ). Download figure Open in new tab Sensitivity testing Multiparameter sensitivity testing was carried out on both structural variants of the model in order to assess the robustness, reliability and behaviour of the model under different conditions and to explore the influences of different parameter values on behaviour. Testing addressed both modelled time parameters for response to AMU (microbial evolution and clinician judgment) and relative fitness of AMR mutation in both presence and absence of the antimicrobial. Heterogeneity among microbes in rate of evolution and among AMR mutations in relative fitness ( 15 ) suggest that these parameters could vary substantially under field conditions. The evolutionary fitness landscape and the timeframe of clinician judgment may also be modifiable, with potential implications for mitigation of AMR depending on their effects on system behaviour. In the analysis of response timescale parameters, the timescale of microbial evolution was investigated for values of 0.1-26 weeks, while the timescale of clinician judgment, a parameter present only in the “anecdotal prescribing” model variant, covered a range of 1-26 weeks. The relative fitness of the resistant form was set to 0.95 in the presence of the antimicrobial, and 0.05 in its absence. In the analysis exploring relative fitness, the relative fitness of the resistant form was explored from 1×10 −14 to 1 in both the presence and absence of the antimicrobial. The time horizon of clinician judgment was set to 6 weeks and the timescale of microbial evolution to 5 weeks. In all analyses, the initial susceptible fraction was set to 1 (a naïve and completely susceptible population), time unit of prescribing to 1 week and mean antimicrobial course length to 2 weeks. Uniform distributions of parameter values were explored over runs using Sobol sequencing ( 16 ). However, for the analysis of timescale of microbial evolution under surveillance-informed prescribing ( Figure 5 ), the initial 50 runs indicated that the model behaved differently for parameter values of less than 2 weeks. An additional 25 runs were carried out with the timescale of microbial evolution varying from 0.1-2 weeks, with all other parameters the same, in order to clarify model behaviour over this range. Runs of 2080 weeks (40 years) were carried out in order to assess time for equilibration in the “anecdotal” variant of the model, while 520 weeks (10 years) was more than sufficient for equilibration in the surveillance-informed version. Model behaviour was examined for each sensitivity analysis, in particular the time taken to reach equilibrium, oscillations in susceptibility prior to stabilisation, and final susceptible fraction of the microbe population. Results Model structure The initial iteration of this model closely followed the Limits to Growth archetype, with one reinforcing loop and one balancing loop ( Figures 1 and 2a ). In this model, use of an antimicrobial to treat a susceptible infection results in successful treatment of cases, increasing confidence in the drug (reinforcing loop R1). This encourages further use of the drug, but AMU creates selection pressure for resistance – essentially “harvesting” the natural resource of antimicrobial susceptibility. With depletion of susceptibility, the number of cases successfully treated with the drug decreases, reducing confidence in the drug and thus driving use of antimicrobials other than the one considered in the model (balancing loop B1). The extent and rate of reversion of the microbial population to susceptibility as a result of curtailed AMU depends on the fitness cost of the resistance mutation in question. There are two sources of delay in this model structure: the time taken for the susceptible fraction of the microbe population to change in response to changes in selection pressure, and the time horizon over which confidence in the antimicrobial is determined. In a departure from the archetypal structure, an additional information connector may be added to represent surveillance of AMR, bypassing the reinforcing loop R1 ( Figure 2b ) so that use of the drug in question is determined by susceptibility rather than the anecdotal confidence in the drug driven by the number of cases successfully treated recently. This eliminates the delay created by anecdotal judgment of antimicrobial efficacy. From these two CLDs, a pair of quantitative system dynamics models were developed, with and without surveillance of AMR bypassing the reinforcing loop R1 ( Figure 3 ). Microbial reversion to the susceptible wild-type was represented with changes in resistant and susceptible fractions of the population dependent on their current values (see Equations 1 and 2 ) to reflect the fact that in a population with a resistance mutation present at a very low gene frequency, only a small proportion of the population is capable of reverting to wild-type. The effect of this is to create two additional balancing loops, B2 and B3, with the potential to stabilise the system and reduce the presence of oscillations in antimicrobial susceptibility in comparison to many LTG systems. Model equations Equations 1 - 7 . Model equations. Note that equations 5a and 7 are present in the anecdotal prescribing version of the model only, while in the surveillance-based variant, 5a is replaced by 5b . where: S : Susceptible fraction of microbe population, which in this model is assumed to be equal to the antimicrobial course success rate (0-1; dimensionless) R : Susceptible fraction of microbe population (0-1; dimensionless) F P : Relative fitness of resistant form in presence of antimicrobial (0-1; dimensionless) F A : Relative fitness of resistant form in absence of antimicrobial (0-1; dimensionless) E : Fraction of infected host population exposed to antimicrobial, which in this model equals fraction of microbe population exposed to antimicrobial (0-1; dimensionless) P S : Selection pressure for reversion to susceptibility (0-1; dimensionless) P R : Selection pressure for development of resistance (0-1; dimensionless) J : Clinician/farmer/veterinarian [judgement of] confidence in antimicrobial (0-1; dimensionless) C : Cases successfully treated with antimicrobial (0-1; dimensionless) H J : Time horizon of clinician judgment (1-52; weeks) H E : Time unit of prescribing (1-52; weeks) M : Timescale of microbial evolution (1-52; weeks) L : Mean antimicrobial course length (0.1-52; weeks) Sensitivity analysis Sensitivity runs for both variants of the model, investigating changing timescale and microbial relative fitness parameters, showed impacts on oscillatory behaviour, time to stabilise, and final susceptible fraction ( Figure 4 ). Sensitivity analysis results are summarised in Table 1 . Download figure Open in new tab Figure 4. Sensitivity analysis runs, varying timescale parameters (time horizon of clinician judgment and timescale of microbial evolution) and relative fitness of the resistant form in presence and absence of the antimicrobial, for both variants of the model structure. View this table: View inline View popup Table 1. Summary of sensitivity analysis results Under anecdotal prescribing, both timescale of microbial evolution and time horizon of clinician judgment affected the time taken to stabilise, although the timescale of microbial evolution had a clearer effect (Figure S1). Both contributed similarly to oscillatory behaviour, with the highest amplitude of oscillations occurring when both timescale parameters were equal. Frequency of oscillations was highest when time parameters were shorter, particularly the timescale of microbial evolution. Under surveillance-informed prescribing, the parameter reflecting clinician judgment was removed, leaving only microbial evolution ( Figure 5 ). Oscillations occurred only with a timescale of microbial evolution shorter than 1.65 weeks, with stabilisation time increasing linearly with parameter values above this. As the timescale of microbial evolution decreased below this threshold, the number and maximum amplitude of oscillations increased. Download figure Open in new tab Figure 5. Sensitivity analysis results, varying timescale of microbial evolution for the surveillance-based prescribing variant of the model: a) sensitivity runs with microbial evolution timescale ranging 0.1-26 weeks, b) effect of parameter values ranging 0.1-2 weeks on number and amplitude of oscillations prior to stabilisation, and effect of parameter value on maximum amplitude of oscillations and time for susceptible fraction to stabilise with parameter ranges of c) 0.1-26 weeks and d) 0.1-2 weeks. Model structure influenced stabilisation time, presence of oscillations and final susceptible fraction. Under anecdotal prescribing, the time to stabilise was observed to range from 105.5 to 2061.75 weeks (approximately 2 to 40 years). Under surveillance-informed prescribing, stabilisation time was much reduced, ranging from 10.5 weeks to 325.25 weeks (<1 year to approximately 6 years), and the final susceptible fraction of the microbe population was typically lower. With anecdotal prescribing, presence, number, frequency and amplitude of oscillations varied with timescale parameters and relative fitness of the resistant form in the absence of the antimicrobial, while in the surveillance-informed prescribing variant, oscillations were largely eliminated, only occurring at the lowest values of timescale of microbial evolution. Discussion Considering antimicrobial susceptibility as a natural resource, and specifically the application of LTG modelling, offers a useful framing of the global challenge of AMR. AMR, a natural phenomenon occurring as part of the evolutionary arms race between microbes, existed long before humans started to use antimicrobials ( 3 ). Representing susceptibility as a common property resource to be conserved and sustainably managed has great potential for engagement with the public and policymakers and avoids widely-used war metaphors for AMR that risk miscommunicating the problem and its potential solutions ( 17 ). A particular advantage of the LTG model is that it explicitly incorporates both human behaviour and evolutionary dynamics, allowing an exploration of the role of AMR surveillance and shedding light on the phenomenon of plateauing AMR observed in clinical surveillance ( 7 ). A number of evolutionary explanations have been proposed ( 6 ), but to our knowledge, this is the first model to suggest a behavioural explanation where those using the antimicrobial in question lose confidence in its efficacy and therefore decrease their use of the drug. The use of SD modelling is of additional value in that it incorporates time lags and allows comparison of model behaviour over time under different conditions. The simplicity of this model has both advantages and disadvantages: it can potentially be adapted to different microbes, antimicrobials, host species and One Health settings. However, it necessarily simplifies the evolutionary dynamics of AMR, focusing on the population-level effects of gene frequency of a single AMR mutation rather than considering different dynamics of de novo mutations versus vertical and horizontal gene transfer, and focuses on a single resistance mutation. The model also assumes that all infected hosts are symptomatic and receive treatment with this antimicrobial or another, and that all microbes are in the host population with no reservoirs of infection. Selection pressure for resistance is modelled as depending solely on the binary of exposure/non-exposure to the antimicrobial, with no effect of co-selection for AMR or inappropriate course lengths or doses, or indeed total microbial population size. The applicability and relative importance of these phenomena, as well as parameter values, are likely to vary substantially between applications of the model, so we ensured that our sensitivity analysis explored a wide parameter space for both model variants. Future iterations of this model could address some of these assumptions, and a partial model calibration could be used to test the microbial evolution component of the model using population level data on AMU and AMR ( 13 ). In the sensitivity analysis, oscillations in susceptibility were generally restricted to the version of the model with anecdotally-informed AMU. In this variant of the model, the relative fitness of the resistant form in the absence of antimicrobials (that is, the fitness cost of resistance) showed a much greater effect on oscillatory behaviour than the degree of advantage conferred by the resistance mutation on exposure to the antimicrobial. Notably, oscillations occurred when the former was less than approximately 0.5, representing a fitness cost of the AMR mutation. Timescale parameters had comparable levels of impact on oscillatory behaviour, with oscillation frequency decreasing with increasing time parameter values. When oscillations in susceptibility did occur, the system reached a steady state eventually, with relative fitness in the presence and absence of the antimicrobial showing similar degrees of effect on the final susceptible fraction and both timescale parameters, particularly microbial evolution, influencing the time taken to stabilise. It is possible that the deterministic nature of this model is responsible for the dampening of oscillations; in other compartmental models, stochastic perturbations have been shown to counteract this ( 18 ). The surveillance-informed AMU variant of the model largely eliminated oscillations, which only occurred in this variant under conditions of rapid microbial evolution. In this version of the model structure, antimicrobial susceptibility reached a steady state much sooner, and typically with a lower final susceptible fraction. This could be viewed as a net positive outcome: the fraction of infections susceptible to the antimicrobial is lower, but the antimicrobial is being used as much as possible on the susceptible cases, while leaving a “reserve” of susceptibility in the microbial population. In order for the correct patients (those with infections susceptible to the antimicrobial) to receive the drug in question, individual sensitivity testing would be optimal rather than population-level surveillance. Nevertheless, this illustrates the utility of population-level AMR surveillance and implies a greater-still value of diagnostics for individual patients. Conclusion The model presented here applies a well-characterised modelling approach in a novel context, using a system archetype commonly explored in SD to model both behavioural and ecological aspects of AMR in both human and veterinary contexts. The model variants, one following the classic LTG structure and one eliminating the human decision-making component, allows for analysis of two scenarios over a wide parameter space. Multiparameter sensitivity analysis of both variants revealed a dramatic decrease in oscillations and time taken to stabilise when surveillance, rather than recent experience of treatment success, determined AMU. Surveillance-informed AMU was also associated with a lower final susceptible fraction of the microbe population, although the final fraction was also influenced by the relative fitness of the resistant form of the microbe in the presence and absence of the antimicrobial in question. Under conditions of anecdotally-informed prescribing, the fitness cost of the mutation in the absence of the antimicrobial had a greater effect on oscillatory behaviour than the evolutionary advantage conferred by resistance in the presence of the antimicrobial, with this oscillations and stabilisation time also influenced by the timescales of microbial evolution and of human judgment of confidence in the antimicrobial. The socio-ecological model structure allows us to explore several hypotheses, including the possibility that AMR could plateau as a result of human behaviour as opposed to solely evolutionary forces ( 6 ), and a revival of an underappreciated framing of antimicrobial susceptibility as a renewable natural resource to be conserved, recognising AMR as a natural evolutionary phenomenon. This framing is valuable not only as an avenue for alternative modelling approaches, but also as an alternative, more collaborative, framing for public and policy engagement to promote sustainable management of this common pool resource. Acknowledgements CRW acknowledges support for a studentship part-funded by Zoetis and the UKRI Biotechnology and Biological Sciences Research Council (BBSRC) under grant number BB/T00875X/1. DM also acknowledges support under UKRI awards UKRI (BB/Z515644/1, BB/T004436/1 and BB/T004452/1, the latter of which is [co] funded by the UK Department of Health and Social Care as part of the Global AMR Innovation Fund (GAMRIF). This is a UK aid programme that supports early-stage innovative research in underfunded areas of antimicrobial resistance (AMR) research and development for the benefit of those in low- and middle-income countries (LMICs), who bear the greatest burden of AMR. The views expressed in this publication are those of the author(s) and not necessarily those of the UK Department of Health and Social Care. AM is funded by the University of Edinburgh Chancellor’s Fellowship and BBSRC core funding for the Roslin Institute. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. Funder Information Declared UK Research and Innovation, https://ror.org/001aqnf71 , BB/T00875X/1 , BB/Z515644/1 , BB/T004436/1 , BB/T004452/1 , Roslin Institute core funding University of Edinburgh , Chancellor's Fellowship References 1. ↵ Murray CJ , Ikuta KS , Sharara F , Swetschinski L , Robles Aguilar G , Gray A , et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis . The Lancet . 2022 . 2. ↵ Jonas OB , Irwin A , Berthe FCJ , Le Gall FG , Marquez PV . 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Be Mindful of Your Metaphors about Microbes . mSphere . 2021 ; 6 ( 3 ). 18. ↵ Greer M , Saha R , Gogliettino A , Yu C , Zollo-Venecek K . Emergence of oscillations in a simple epidemic model with demographic data . Royal Society Open Science . 2020 ; 7 ( 1 ): 191187 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted August 26, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following A socio-ecological System Dynamics model of antimicrobial use and resistance Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. 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