Why should we apply ABM for decision analysis for infectious diseases?-An example for dengue interventions

Modelling HIV/AIDS epidemiological complexity: A scoping review of Agent-Based Models and their application

OBJECTIVE

To end the AIDS epidemic by 2030, despite the increasing poverty and inequalities, policies should be designed to deal with population heterogeneity and environmental changes. Bottom-up designs, such as the Agent-Based Model (ABM), can model these features, dealing with such complexity. HIV/AIDS has a complex dynamic of structural factors, risk behaviors, biomedical characteristics and interventions. All embedded in unequal, stigmatized and heterogeneous social structure. To understand how ABMs can model this complexity, we performed a scoping review of HIV applications, highlighting their potentialities.

METHODS

We searched on PubMed, Web of Science, and Scopus repositories following the PRISMA extension for scoping reviews. Our inclusion criteria were HIV/AIDS studies with an ABM application. We identified the main articles using a local co-citation analysis and categorized the overall literature aims, (sub)populations, regions, and if the papers declared the use of ODD protocol and limitations.

RESULTS

We found 154 articles. We identified eleven main papers, and discussed them using the overall category results. Most studies model Transmission Dynamics (37/154), about Men who have sex with Men (MSM) (41/154), or individuals living in the US or South Africa (84/154). Recent studies applied ABM to model PrEP interventions (17/154) and Racial Disparities (12/154). Only six papers declared the use of ODD Protocol (6/154), and 34/154 didn't mention the study limitations.

CONCLUSIONS

While ABM is among the most sophisticated techniques available to model HIV/AIDS complexity. Their applications are still restricted to some realities. However, researchers are challenged to think about social structure due model characteristics, the inclusion of these features is still restricted to case-specific. Data and computational power availability can enhance this feature providing insightful results.

Agent-based simulation and child protection systems: Rationale, implementation, and verification

BACKGROUND AND OBJECTIVE

Simulation models are an important tool used in health care and other disciplines to support operational research and decision-making. In the child protection literature, simulation models are an under-utilized source of research evidence.

PARTICIPANTS AND SETTING

In this paper, we describe the rationale for and the development of an agent-based simulation of a child protection system in the US. Using the investigation, prevention service, and placement histories of 600,000 children served in an urban child welfare system, we walk the reader through the development of a prototype known as OSPEDALE.

METHODS

The governing equations built into OSPEDALE probabilistically simulate the onset of investigations. Then, drawing from empirical survival distributions, the governing equations trace the probability of subsequent interactions with the system (recurrence of maltreatment, service referrals, and placement) conditional on the characteristics of children, their assessed risk level, and prior child protection system involvement.

RESULTS

As an initial test of OSPEDALE's utility, we compare empirical admission counts with counts generated from OSPEDALE. Though the verification step is admittedly simple, the comparison shows that OSPEDALE replicates the empirical count of new admissions closely enough to justify further investment in OSPEDALE.

CONCLUSIONS

Management of public child protection systems is increasingly research evidence-dependent. The emphasis on research evidence as a decision-support tool has elevated evidence acquired through randomized clinical trials. Though important, the evidence from clinical trials represents only one type of research evidence. Properly specified, simulation models are another source of evidence with real-world relevance.

Causal evidence in health decision making: methodological approaches of causal inference and health decision science

Objectives

Public health decision making is a complex process based on thorough and comprehensive health technology assessments involving the comparison of different strategies, values and tradeoffs under uncertainty. This process must be based on best available evidence and plausible assumptions. Causal inference and health decision science are two methodological approaches providing information to help guide decision making in health care. Both approaches are quantitative methods that use statistical and modeling techniques and simplifying assumptions to mimic the complexity of the real world. We intend to review and lay out both disciplines with their aims, strengths and limitations based on a combination of textbook knowledge and expert experience.

Methods

To help understanding and differentiating the methodological approaches of causal inference and health decision science, we reviewed both methods with the focus on aims, research questions, methods, assumptions, limitations and challenges, and software. For each methodological approach, we established a group of four experts from our own working group to carefully review and summarize each method, followed by structured discussion rounds and written reviews, in which the experts from all disciplines including HTA and medicine were involved. The entire expert group discussed objectives, strengths and limitations of both methodological areas, and potential synergies. Finally, we derived recommendations for further research and provide a brief outlook on future trends.

Results

Causal inference methods aim for drawing causal conclusions from empirical data on the relationship of pre-specified interventions on a specific target outcome and apply a counterfactual framework and statistical techniques to derive causal effects of exposures or interventions from these data. Causal inference is based on a causal diagram, more specifically, a directed acyclic graph (DAG), which encodes the assumptions regarding the causal relations between variables. Depending on the type of confounding and selection bias, traditional statistical methods or more complex g-methods are needed to derive valid causal effects. Besides the correct specification of the DAG and the statistical model, assumptions such as consistency, positivity, and exchangeability must be checked when aiming at causal inference. Health decision science aims for guiding policy decision making regarding health interventions considering and balancing multiple competing objectives of a decision based on data from multiple sources and studies, for example prevalence studies, clinical trials and long-term observational routine effectiveness studies, and studies on preferences and costs. It involves decision analysis, a systematic, explicit and quantitative framework to guide decisions under uncertainty. Decision analyses are based on decision-analytic models to mimic the course of disease as well as aspects and consequences of the intervention in order to quantitatively optimize the decision. Depending on the type of decision problem, decision trees, state-transition models, discrete event simulation models, dynamic transmission models, or other model types are applied. Models must be validated against observed data, and comprehensive sensitivity analyses must be performed to assess uncertainty. Besides the appropriate choice of the model type and the valid specification of the model structure, it must be checked if input parameters of effects can be interpreted as causal parameters in the model. Otherwise results will be biased.

Conclusions

Both causal inference and health decision science aim for providing best causal evidence for informed health decision making. The strengths and limitations of both methods differ and a good understanding of both methods is essential for correct application but also for correct interpretation of findings from the described methods. Importantly, decision-analytic modeling should be combined with causal inference when developing guidance and recommendations regarding decisions on health care interventions.

The CP-ABM approach for modelling COVID-19 infection dynamics and quantifying the effects of non-pharmaceutical interventions

The motivation for this research is to develop an approach that reliably captures the disease dynamics of COVID-19 for an entire population in order to identify the key events driving change in the epidemic through accurate estimation of daily COVID-19 cases. This has been achieved through the new CP-ABM approach which uniquely incorporates Change Point detection into an Agent Based Model taking advantage of genetic algorithms for calibration and an efficient infection centric procedure for computational efficiency. The CP-ABM is applied to the Northern Ireland population where it successfully captures patterns in COVID-19 infection dynamics over both waves of the pandemic and quantifies the significant effects of non-pharmaceutical interventions (NPI) on a national level for lockdowns and mask wearing. To our knowledge, there is no other approach to date that has captured NPI effectiveness and infection spreading dynamics for both waves of the COVID-19 pandemic for an entire country population.

On the role of data, statistics and decisions in a pandemic

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

Identifying a Single Optimal Integrated Cervical Cancer Prevention Policy in Norway: A Cost-Effectiveness Analysis

BACKGROUND

Interventions targeting the same disease but at different points along the disease continuum (e.g., screening and vaccination to prevent cervical cancer [CC]) are often evaluated in isolation, which can affect cost-effectiveness profiles and policy conclusions. We evaluated nonavalent human papillomavirus (HPV) vaccine (9vHPV) compared with bivalent HPV vaccine (2vHPV) alongside deintensified screening intervals for a vaccinated birth cohort to inform a single optimal integrated CC prevention policy.

METHODS

Using a multimodeling approach, we evaluated the health and economic impacts of alternative CC screening strategies for a Norwegian birth cohort eligible for HPV vaccination in 2021 assuming they received 1) 2vHPV or 2) 9vHPV. We conducted 1) a restricted analysis that evaluated the optimal HPV vaccine under current screening guidelines; and 2) a comprehensive analysis including alternative screening and vaccination strategy combinations. We calculated incremental cost-effectiveness ratios (ICERs) and evaluated them according to different cost-effectiveness thresholds.

RESULTS

Assuming a cost-effectiveness threshold of $40,000 per quality-adjusted life year (QALY) gained, we found that, while holding screening intensity fixed, switching the routine vaccination program in Norway from 2vHPV to 9vHPV would not be considered cost-effective (ICER of $132,700 per QALY gained). However, when allowing for varying intensities of CC screening, we found that switching to 9vHPV would be cost-effective compared with 2vHPV under an alternative threshold of $55,000 per QALY gained, if coupled with reductions in the number of lifetime screens.

CONCLUSIONS

Our analysis highlights the importance of evaluating the full potential policy landscape for country-level decision makers considering policy adoption, including nonindependent primary and secondary prevention efforts, to draw appropriate conclusions and avoid sub-optimal outcomes.

HIGHLIGHTS

Without evaluating the full potential policy landscape, including primary and secondary prevention efforts, country-level decision makers may not be able to draw appropriate policy conclusions, resulting in suboptimal outcomes.An applied example from cervical cancer prevention in Norway compared a restricted analysis of current screening guidelines to a comprehensive analysis including alternative screening and vaccination strategy combinations.We found that a switch from bivalent to nonavalent human papillomavirus vaccine would be considered cost-effective in Norway if coupled with reductions in the number of lifetime screens compared with the current screening strategy.A comprehensive analysis that considers how different types of interventions along the disease continuum affect each other will be critical for decision makers interpreting cost-effectiveness analysis results.

Application of Gaussian process regression as a surrogate modeling method to assess the dynamics of COVID-19 propagation

In this research, we aimed to assess the possibility of using surrogate modeling methods to replace time-consuming calculations related to modeling of COVID-19 dynamics. The Gaussian process regression (GPR) was used as a surrogate to replace detailed simulations by a COVID-19 multiagent model. Experiments were conducted with various kernels, as a result, in accordance with the quality metrics of the models, kernels were identified in which the surrogate gives the most accurate result (Rational Quadratic kernel and Additive kernel). It was demonstrated that by smoothing the dynamics of COVID-19 propagation, it is possible to achieve greater accuracy in GPR training. The obtained results prove the potential possibility of using surrogate modeling methods to conduct an uncertainty quantification of the multiagent model of COVID-19 propagation.

University Operations During a Pandemic: A Flexible Decision Analysis Toolkit

Modeling infection spread during pandemics is not new, with models using past data to tune simulation parameters for predictions. These help in understanding of the healthcare burden posed by a pandemic and responding accordingly. However, the problem of how college/university campuses should function during a pandemic is new for the following reasons: (i) social contact in colleges are structured and can be engineered for chosen objectives; (ii) the last pandemic to cause such societal disruption was more than 100 years ago, when higher education was not a critical part of society; (iii) not much was known about causes of pandemics, and hence effective ways of safe operations were not known; and (iv) today with distance learning, remote operation of an academic institution is possible. As one of the first to address this problem, our approach is unique in presenting a flexible simulation system, containing a suite of model libraries, one for each major component. The system integrates agent-based modeling and the stochastic network approach, and models the interactions among individual entities (e.g., students, instructors, classrooms, residences) in great detail. For each decision to be made, the system can be used to predict the impact of various choices, and thus enables the administrator to make informed decisions. Although current approaches are good for infection modeling, they lack accuracy in social contact modeling. Our agent-based modeling approach, combined with ideas from Network Science, presents a novel approach to contact modeling. A detailed case study of the University of Minnesota’s Sunrise Plan is presented. For each decision made, its impact was assessed, and results were used to get a measure of confidence. We believe that this flexible tool can be a valuable asset for various kinds of organizations to assess their infection risks in pandemic-time operations, including middle and high schools, factories, warehouses, and small/medium-sized businesses.

How an election can be safely planned and conducted during a pandemic: Decision support based on a discrete event model

In 2020, the ongoing COVID-19 pandemic caused major limitations for any aspect of social life and in specific for all events that require a gathering of people. While most events of this kind can be postponed or cancelled, democratic elections are key elements of any democratic regime and should be upheld if at all possible. Consequently, proper planning is required to establish the highest possible level of safety to both voters and scrutineers. In this paper, we present the novel and innovative way how the municipal council and district council elections in Vienna were planned and conducted using an discrete event simulation model. Key target of this process was to avoid queues in front of polling stations to reduce the risk of related infection clusters. In cooperation with a hygiene expert, we defined necessary precautions that should be met during the election in order to avoid the spread of COVID-19. In a next step, a simulation model was established and parametrized and validated using data from previous elections. Furthermore, the planned conditions were simulated to see whether excessive queues in front of any polling stations could form, as these could on the one hand act as an infection herd, and on the other hand, turn voters away. Our simulation identified some polling stations where long queues could emerge. However, splitting up these electoral branches resulted in a smooth election across all of Vienna. Looking back, the election did not lead to a significant increase of COVID-19 incidences. Therefore, it can be concluded that careful planning led to a safe election, despite the pandemic.

Scoping review of Neglected Tropical Disease Interventions and Health Promotion: A framework for successful NTD interventions as evidenced by the literature

Background

Neglected Tropical Diseases (NTDs) affect more than one billion people globally. A Public Library of Science (PLOS) journal dedicated to NTDs lists almost forty NTDs, while the WHO prioritises twenty NTDs. A person can be affected by more than one disease at the same time from a range of infectious and non-infectious agents. Many of these diseases are preventable, and could be eliminated with various public health, health promotion and medical interventions. This scoping review aims to determine the extent of the body of literature on NTD interventions and health promotion activities, and to provide an overview of their focus while providing recommendations for best practice going forward. This scoping review includes both the identification of relevant articles through the snowball method and an electronic database using key search terms. A two-phased screening process was used to assess the relevance of studies identified in the search–an initial screening review followed by data characterization using the Critical Appraisal Skills Program (CASP). Studies were eligible for inclusion if they broadly described the characteristics, methods, and approaches of (1) NTD interventions and/or (2) community health promotion.

Principal findings

90 articles met the CASP criteria partially or fully and then underwent a qualitative synthesis to be included in the review. 75 articles specifically focus on NTD interventions and approaches to their control, treatment, and elimination, while 15 focus specifically on health promotion and provide a grounding in health promotion theories and perspectives. 29 of the articles provided a global perspective to control, treatment, or elimination of NTDs through policy briefs or literature reviews. 19 of the articles focused on providing strategies for NTDs more generally while 12 addressed multiple NTDs or their interaction with other infectious diseases. Of the 20 NTDs categorized by the WHO and the expanded NTD list identified by PLOS NTDs, several NTDs did not appear in the database search on NTD interventions and health promotion, including yaws, fascioliasis, and chromoblastomycosis.

Conclusions

Based on the literature we have identified the four core components of best practices including programmatic interventions, multi sectoral and multi-level interventions, adopting a social and ecological model and clearly defining ‘community.’ NTD interventions tend to centre on mass drug administration (MDA), particularly because NTDs were branded as such based on their being amenable to MDA. However, there remains a need for intervention approaches that also include multiple strategies that inform a larger multi-disease and multi-sectoral programme. Many NTD strategies include a focus on WASH and should also incorporate the social and ecological determinants of NTDs, suggesting a preventative and systems approach to health, not just a treatment-based approach. Developing strong communities and incorporating social rehabilitation at the sublocation level (e.g. hospital) could benefit several NTDs and infectious diseases through a multi-disease, multi-sectoral, and multi-lateral approach. Finally, it is important the ‘community’ is clearly defined in each intervention, and that community members are included in intervention activities and viewed as assets to interventions.

Neglected Tropical Diseases (NTDs) affect more than one billion people globally. A person can be affected by more than one disease at the same time. Many of these diseases are preventable, and could be eliminated with various public health, health promotion and medical interventions. This scoping review aims to determine the extent of the body of literature on NTD interventions and health promotion activities, and to provide an overview of their focus while providing recommendations for best practice going forward. Through a database search and by identifying appropriate literature 75 articles were identified that specifically focus on NTD interventions and approaches to their control, treatment, and elimination, while 15 focus specifically on health promotion and provide a grounding in health promotion theories and perspectives. Based on the literature we have identified the four core components of best practices including programmatic interventions, multi sectoral and multi-level interventions, adopting a social and ecological model and clearly defining ‘community.’

Cost-Effectiveness of Quadrivalent Versus Trivalent Influenza Vaccination in the Dutch National Influenza Prevention Program

OBJECTIVES

As of 2019, quadrivalent influenza vaccine (QIV) has replaced trivalent influenza vaccine (TIV) in the national immunization program in The Netherlands. Target groups are individuals of 60+ years of age and those with chronic diseases. The objective was to estimate the incremental break-even price of QIV over TIV at a threshold of €20 000 per quality-adjusted life-year (QALY).

METHODS

An age-structured compartmental dynamic model was adapted for The Netherlands to assess health outcomes and associated costs of vaccinating all individuals at higher risk for influenza with QIV instead of TIV over the seasons 2010 to 2018. Influenza incidence rates were derived from a global database. Other parameters (probabilities, QALYs and costs) were extracted from the literature and applied according to Dutch guidelines. A threshold of €20 000 per QALY was applied to estimate the incremental break-even prices of QIV versus TIV. Sensitivity analyses were performed to test the robustness of the model outcomes.

RESULTS

Retrospectively, vaccination with QIV instead of TIV could have prevented on average 9500 symptomatic influenza cases, 2130 outpatient visits, 84 hospitalizations, and 38 deaths per year over the seasons 2010 to 2018. This translates into 385 QALYs and 398 life-years potentially gained. On average, totals of €431 527 direct and €2 388 810 indirect costs could have been saved each year.

CONCLUSION

Using QIV over TIV during the influenza seasons 2010 to 2018 would have been cost-effective at an incremental price of maximally €3.81 (95% confidence interval, €3.26-4.31). Sensitivity analysis showed consistent findings on the incremental break-even price in the same range.