The impact of antibiotic use on transmission of resistant bacteria in hospitals: Insights from an agent-based model

Extensive antibiotic use over the years has led to the emergence and spread of antibiotic resistant bacteria (ARB). Antibiotic resistance poses a major threat to public health since for many infections antibiotic treatment is no longer effective. Hospitals are focal points for ARB spread. Antibiotic use in hospitals exerts selective pressure, accelerating the spread of ARB. We used an agent-based model to explore the impact of antibiotics on the transmission dynamics and to examine the potential of stewardship interventions in limiting ARB spread in a hospital. Agents in the model consist of patients and health care workers (HCW). The transmission of ARB occurs through contacts between patients and HCW and between adjacent patients. In the model, antibiotic use affects the risk of transmission by increasing the vulnerability of susceptible patients and the contagiousness of colonized patients who are treated with antibiotics. The model shows that increasing the proportion of patients receiving antibiotics increases the rate of acquisition non-linearly. The effect of antibiotics on the spread of resistance depends on characteristics of the antibiotic agent and the density of antibiotic use. Antibiotic's impact on the spread increases when the bacterial strain is more transmissible, and decreases as resistance prevalence rises. The individual risk for acquiring ARB increases in parallel with antibiotic density both for patients treated and not treated with antibiotics. Antibiotic treatment in the hospital setting plays an important role in determining the spread of resistance. Interventions to limit antibiotic use have the potential to reduce the spread of resistance, mainly by choosing an agent with a favorable profile in terms of its impact on patient's vulnerability and contagiousness. Methods to measure these impacts of antibiotics should be developed, standardized, and incorporated into drug development programs and approval packages

Estimating the number of infections caused by antibiotic-resistant Escherichia coli and Klebsiella pneumoniae in 2014: a modelling study

Background: The number of infections caused by resistant organisms is largely unknown. We estimated the number of infections worldwide that are caused by the WHO priority pathogens third-generation cephalosporin-resistant and carbapenem-resistant Escherichia coli and Klebsiella pneumoniae. Methods: We calculated a uniform weighted mean incidence of serious infections caused by antibiotic-susceptible E coli and K pneumoniae using data from 17 countries. Using this uniform incidence, as well as population sizes and country-specific resistance levels, we estimated the number of infections caused by third-generation cephalosporin-resistant and carbapenem-resistant E coli and K pneumoniae in 193 countries in 2014. We also calculated interval estimates derived from changing the fixed incidence of susceptible infections to 1 SD below and above the weighted mean. We compared an additive model with combination models in which resistant infections were replaced by susceptible infections. We distinguished between higher-certainty regions (those with good-quality data sources for resistance levels and resistance ≤30%), moderate-certainty regions (those with good-quality data sources for resistance levels and including some countries with resistance >30%), and low-certainty regions (those in which good-quality data sources for resistance levels were unavailable for countries comprising at least 20% of the region's population, regardless of resistance level). Findings: Using the additive model, we estimated that third-generation cephalosporin-resistant E coli and K pneumoniae caused 6·4 million (interval estimate 3·5–9·2) bloodstream infections and 50·1 million (27·5–72·8) serious infections in 2014; estimates were 5·5 million (3·0–7·9) bloodstream infections and 43·1 million (23·6–62·2) serious infections in the 25% replacement model, 4·6 million (2·5–6·6) bloodstream infections and 36·0 million (19·7–52·2) serious infections in the 50% replacement model, and 3·7 million (2·0–5·3) bloodstream infections and 28·9 million (15·8–41·9) serious infections in the 75% replacement model. Carbapenem-resistant strains caused 0·5 million (0·3–0·7) bloodstream infections and 3·1 million (1·8–4·5) serious infections based on the additive model, 0·5 million (0·3–0·7) bloodstream infections and 3·0 million (1·7–4·3) serious infections based on the 25% replacement model, 0·4 million (0·2–0·6) bloodstream infections and 2·8 million (1·6–4·1) serious infections based on the 50% replacement model, and 0·4 million (0·2–0·6) bloodstream infections and 2·7 million (1·5–3·8) serious infections based on the 75% replacement model. Interpretation: To our knowledge, this study is the first to report estimates of the global number of infections caused by antibiotic-resistant priority pathogens. Uncertainty stems from scant data on resistance levels from low-income and middle-income countries and insufficient knowledge regarding resistance dynamics when resistance is high. Funding: Innovative Medicines Initiative

Exploring the effectiveness of a COVID-19 contact tracing app using an agent-based model

A contact-tracing strategy has been deemed necessary to contain the spread of COVID-19 following the relaxation of lockdown measures. Using an agent-based model, we explore one of the technology-based strategies proposed, a contact-tracing smartphone app. The model simulates the spread of COVID-19 in a population of agents on an urban scale. Agents are heterogeneous in their characteristics and are linked in a multi-layered network representing the social structure—including households, friendships, employment and schools. We explore the interplay of various adoption rates of the contact-tracing app, different levels of testing capacity, and behavioural factors to assess the impact on the epidemic. Results suggest that a contact tracing app can contribute substantially to reducing infection rates in the population when accompanied by a sufficient testing capacity or when the testing policy prioritises symptomatic cases. As user rate increases, prevalence of infection decreases. With that, when symptomatic cases are not prioritised for testing, a high rate of app users can generate an extensive increase in the demand for testing, which, if not met with adequate supply, may render the app counterproductive. This points to the crucial role of an efficient testing policy and the necessity to upscale testing capacity

How can an agent-based model explore the impact of interventions on children’s physical activity in an urban environment?

Insufficient physical activity (PA) among most children and adolescents is a global problem that is undermining the realisation of numerous developmental and health benefits. The aim of this study was to explore the potential impact of interventions on PA by using an agent-based model (ABM) simulating children's daily activities in an urban environment. Three domains for interventions were explored: outdoor play, school physical education and active travel. Simulated interventions increased children's average daily moderate-to-vigorous PA by 2–13 min and reduced the percentage of children not meeting PA guidelines, from 34% to 10%–29%, depending on the intervention. Promotion of active travel and outdoor play benefited more those in a higher socio-economic position. Agents' interactions suggested that: encouraging activity in diverse groups will reduce percentage of the least active in the population; and initiating outdoor events in neighbourhoods can generate an enhancing effect on children's engagement in PA. The ABM provided measurable outcomes for interventions that are difficult to estimate using reductionist methods. We suggest that ABMs should be used more commonly to explore the complexity of the social-environmental PA system

Situating Agent-Based Modelling in Population Health Research

Abstract Today’s most troublesome population health challenges are often driven by social and environmental determinants, which are difficult to model using traditional epidemiological methods. We agree with those who have argued for the wider adoption of agent-based modelling (ABM) in taking on these challenges. However, while ABM has been used occasionally in population health, we argue that for ABM to be most effective in the field it should be used as a means for answering questions normally inaccessible to the traditional epidemiological toolkit. In an effort to clearly illustrate the utility of ABM for population health research, and to clear up persistent misunderstandings regarding the method’s conceptual underpinnings, we offer a detailed presentation of the core concepts of complex systems theory, and summarise why simulations are essential to the study of complex systems. We then examine the current state of the art in ABM for population health, and propose they are well-suited for the study of the ‘wicked’ problems in population health, and could make significant contributions to theory and intervention development in these areas

The actual impact of comprehensive land-use plans: Insights from high resolution observations

Like most EU and US planning systems, planning in Israel aims to promote certainty regarding future development by employing statutory land-use plans for stabilizing and binding the development of land use. In Israel, district planning from the 1980s onwards took place in the form of long-term land-use plans. However, in practice, Israeli planning witnessed a movement toward discretionary-oriented decision-making, providing for revisions of the land-use plans and subsequently diminishing its efficacy. A pending reform suggests eliminating district land-use plans and absorbing them into national and local plans. Concerning the debate on the future of the Israeli planning system, this research aims to assess the gap between certainty-oriented regulation and actual development, often occurring on a case-by-case basis. Our aim is to evaluate the actual performance of a district land-use plan, focusing particularly on aspects of land-use. Remote Sensing and GIS-based Plan Implementation Evaluation (PIE) analysis was used to test the impact of a comprehensive outline plan for Israel's Central District on the actual development of the built environment. The results show fundamental gaps between the original land-use assignments of the district plan and actual development. The limited effectiveness of regulatory land-use planning for complex, densely populated districts is then discussed in line with the certainty–flexibility dilemma in land-use planning and the structure of planning decision-making in Israel

Assessing innovation: Dynamics of high-rise development in an Israeli city

Urban models serve as laboratories, providing researchers with the opportunity to assess the impact of a wide range of social and economic processes on the development of a built environment. Novelty and unexpected changes play an essential role in this development, but these are difficult to formalize and imitate. Typically, urban models simulate innovation by introducing stochastic fluctuations of the pre-established development rules. This research offers a methodology for assessing innovation in a developing city and examining its impact on urban development. The methodology is implemented in the Israeli city of Netanya, where urban development is analyzed at a resolution of single buildings over a period of three decades. We recognize two types of innovation: spatial innovation, manifested by leapfrogging residential clusters that establish new development areas; and contextual innovation, manifested by residential clusters that include buildings that are substantially higher than their surroundings. We demonstrate the impact of few innovative residential clusters on urban development in the following decades and highlight the diffusion of innovation in the city

The evolution of the land development industry: an agent-based simulation model

Urban spatial structure is shaped by decisions of land developers that both react to and influence urban plans. The paper presents an agent-based model of the evolution of the land development industry in a city regulated by a land-use plan that is modified from time to time by the planner. At the heart of the model are investment decisions of developers that generate profits and accumulated assets, which in turn affect investment decisions. In the model, the economic state of the developers is initially equal. Over time, certain developers accumulate wealth that enables them to make larger investments and take higher risks by investing in low priced lands that are not zoned for urban development. These risky investments are motivated by the prospect of obtaining land-use variance. We demonstrate that when the land market favors large developers who are more likely to obtain construction permits from the planner, a positive feedback effect is created, which leads to an oligopolistic market, controlled by a few large developers. We also demonstrate that the interaction between risk-taking developers and a flexible planner who approves incremental amendments and periodic updates to the land-use plan may result in bifurcations of the city structure, which leads to a polycentric city

Hospital parameters used in the simulations.

<p>Hospital parameters used in the simulations.</p