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.

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.

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.

Utilising sigmoid models to predict the spread of antimicrobial resistance at the country level.

BackgroundThe spread of antimicrobial resistance (AMR) is of worldwide concern. Public health policymakers and pharmaceutical companies pursuing antibiotic development require accurate predictions about the future spread of AMR.AimWe aimed to identify and model temporal and geographical patterns of AMR spread and to predict future trends based on a slow, intermediate or rapid rise in resistance.MethodsWe obtained data from five antibiotic resistance surveillance projects spanning the years 1997 to 2015. We aggregated the isolate-level or country-level data by country and year to produce country-bacterium-antibiotic class triads. We fitted both linear and sigmoid models to these triads and chose the one with the better fit. For triads that conformed to a sigmoid model, we classified AMR progression into one of three characterising paces: slow, intermediate or fast, based on the sigmoid slope. Within each pace category, average sigmoid models were calculated and validated.ResultsWe constructed a database with 51,670 country-year-bacterium-antibiotic observations, grouped into 7,440 country-bacterium-antibiotic triads. A total of 1,037 triads (14%) met the inclusion criteria. Of these, 326 (31.4%) followed a sigmoid (logistic) pattern over time. Among 107 triads for which both sigmoid and linear models could be fit, the sigmoid model was a better fit in 84%. The sigmoid model deviated from observed data by a median of 6.5%; the degree of deviation was related to the pace of spread.ConclusionWe present a novel method of describing and predicting the spread of antibiotic-resistant organisms.

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.

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.

The possible association between exposure to air pollution and the risk for congenital malformations.

BACKGROUND: Over the last decade, there is growing evidence that exposure to air pollution may be associated with increased risk for congenital malformations. OBJECTIVES: To evaluate the possible association between exposures to air pollution during pregnancy and congenital malformations among infants born following spontaneously conceived (SC) pregnancies and assisted reproductive technology (ART) pregnancies. METHODS: This is an historical cohort study comprising 216,730 infants: 207,825 SC infants and 8905 ART conceived infants, during the periods 1997-2004. Air pollution data including sulfur dioxide (SO2), particulate matter <10 µm (PM10), nitrogen oxides (NOx) and ozone (O3) were obtained from air monitoring stations database for the study period. Using a geographic information system (GIS) and the Kriging procedure, exposure to air pollution during the first trimester and the entire pregnancy was assessed for each woman according to her residential location. Logistic regression models with generalized estimating equation (GEE) approach were used to evaluate the adjusted risk for congenital malformations. RESULTS: In the study cohort increased concentrations of PM10 and NOx pollutants in the entire pregnancy were associated with slightly increased risk for congenital malformations: OR 1.06(95% CI, 1.01-1.11) for 10 µg/m(3) increase in PM10 and OR 1.03(95% CI, 1.01-1.04) for 10 ppb increase in NOx. Specific malformations were evident in the circulatory system (for PM10 and NOx exposure) and genital organs (for NOx exposure). SO2 and O3 pollutants were not significantly associated with increased risk for congenital malformations. In the ART group higher concentrations of SO2 and O3 in entire pregnancy were associated (although not significantly) with an increased risk for congenital malformations: OR 1.06(95% CI, 0.96-1.17) for 1 ppb increase in SO2 and OR 1.15(95% CI, 0.69-1.91) for 10 ppb increase in O3. CONCLUSIONS: Exposure to higher levels of PM10 and NOx during pregnancy was associated with an increased risk for congenital malformations. Specific malformations were evident in the circulatory system and genital organs. Among ART pregnancies possible adverse association of SO2 and O3 exposure was also observed. Further studies are warranted, including more accurate exposure assessment and a larger sample size for ART pregnancies, in order to confirm these findings.