Policy-relevant Models Research Articles

Social contact patterns during the early COVID-19 pandemic in Norway: insights from a panel study, April to September 2020

BackgroundDuring the COVID-19 pandemic, many countries adopted social distance measures and lockdowns of varying strictness. Social contact patterns are essential in driving the spread of respiratory infections, and country-specific measurements are needed. This study aimed to gain insights into changes in social contacts and behaviour during the early pandemic phase in Norway.MethodsWe conducted an online panel study among a nationally representative sample of Norwegian adults by age and gender. The panel study included six data collections waves between April and September 2020, and 2017 survey data from a random sample of the Norwegian population (including children < 18 years old) were used as baseline. The market research company Ipsos was responsible for carrying out the 2020 surveys. We calculated mean daily contacts, and estimated age-stratified contact matrices during the study period employing imputation of child-to-child contacts. We used the next-generation method to assess the relative reduction of R0 and compared the results to reproduction numbers estimated for Norway during the 2020 study period.ResultsOver the six waves in 2020, 5 938 observations/responses were registered from 1 718 individuals who reported data on 22 074 contacts. The mean daily number of contacts among adults varied between 3.2 (95%CI 3.0-3.4) to 3.9 (95%CI 3.6–4.2) across the data collection waves, representing a 67–73% decline compared to pre-pandemic levels (baseline). Fewer contacts in the community setting largely drove the reduction; the drop was most prominent among younger adults. Despite gradual easing of social distance measures during the survey period, the estimated population contact matrices remained relatively stable and displayed more inter-age group mixing than at baseline. Contacts within households and the community outside schools and workplaces contributed most to social encounters. Using the next-generation method R0 was found to be roughly 25% of pre-pandemic levels during the study period, suggesting controlled transmission.ConclusionSocial contacts declined significantly in the months following the March 2020 lockdown, aligning with implementation of stringent social distancing measures. These findings contribute valuable empirical information into the social behaviour in Norway during the early pandemic, which can be used to enhance policy-relevant models for addressing future crises when mitigation measures might be implemented.

FlepiMoP: The evolution of a flexible infectious disease modeling pipeline during the COVID-19 pandemic

The COVID-19 pandemic led to an unprecedented demand for projections of disease burden and healthcare utilization under scenarios ranging from unmitigated spread to strict social distancing policies. In response, members of the Johns Hopkins Infectious Disease Dynamics Group developed flepiMoP (formerly called the COVID Scenario Modeling Pipeline), a comprehensive open-source software pipeline designed for creating and simulating compartmental models of infectious disease transmission and inferring parameters through these models. The framework has been used extensively to produce short-term forecasts and longer-term scenario projections of COVID-19 at the state and county level in the US, for COVID-19 in other countries at various geographic scales, and more recently for seasonal influenza. In this paper, we highlight how the flepiMoP has evolved throughout the COVID-19 pandemic to address changing epidemiological dynamics, new interventions, and shifts in policy-relevant model outputs. As the framework has reached a mature state, we provide a detailed overview of flepiMoP’s key features and remaining limitations, thereby distributing flepiMoP and its documentation as a flexible and powerful tool for researchers and public health professionals to rapidly build and deploy large-scale complex infectious disease models for any pathogen and demographic setup.

The Hidden Side of Electro-Mobility: Modelling Agents’ Financial Statements and Their Interactions with a European Focus

While much attention has been given, to date, to subsidies and taxes, the literature on the topic is yet to address less visible aspects of electro-mobility. These include the interactions among players, including money exchanges, and balance sheet issues. Analysing these is needed, as it helps identify additional mechanisms that may affect electro-mobility. This paper reports a modelling exercise that applies the system dynamics method, with its focus on stock and flow variables. The resulting simulation model captures the financial statements of several macro agents. The results show that the objective of the study is met: the model remains ‘stock-flow consistent’, meaning that assets and equity and liabilities balance out. By attaining this, the model serves as a coherent framework that makes the “hidden” side of electro-mobility visible, for the first time, based on current state-of-the-art, with the implication that it facilitates the analysis of potential financial factors that may either jeopardise or be conducive to faster road electrification. We conclude that the incorporation of the financial statements of key electro-mobility agents and their interlinkages in a simulation model is both a feasible and desired property for policy-relevant models.

Thinking clearly about social aspects of infectious disease transmission.

Social and cultural forces shape almost every aspect of infectious disease transmission in human populations, as well as our ability to measure, understand, and respond to epidemics. For directly transmitted infections, pathogen transmission relies on human-to-human contact, with kinship, household, and societal structures shaping contact patterns that in turn determine epidemic dynamics. Social, economic, and cultural forces also shape patterns of exposure, health-seeking behaviour, infection outcomes, the likelihood of diagnosis and reporting of cases, and the uptake of interventions. Although these social aspects of epidemiology are hard to quantify and have limited the generalizability of modelling frameworks in a policy context, new sources of data on relevant aspects of human behaviour are increasingly available. Researchers have begun to embrace data from mobile devices and other technologies as useful proxies for behavioural drivers of disease transmission, but there is much work to be done to measure and validate these approaches, particularly for policy-making. Here we discuss how integrating local knowledge in the design of model frameworks and the interpretation of new data streams offers the possibility of policy-relevant models for public health decision-making as well as the development of robust, generalizable theories about human behaviour in relation to infectious diseases.

Overview of water quality modeling

Due to population growth, urbanization, and industrialization, water demands have increased, and the quality of water is degraded. Water quality modeling is a significant tool that aids managers and policymakers in multiscale integrated water resources and environmental management. However, water quality modeling is challenging due to several constraints. The modern application of modeling is essentially utilized by the need to comply with rules and regulations. In view of this, water quality modeling requires the standardization of models, identification of common features of models, the hotspots of pollution, and the current state of policy-relevant models. This review presents an overview of water quality modeling and major models frequently applied for water quality assessment at the catchment and at waterbody scales. This review is intended to highlight the applicability of certain water quality models, and the state of water quality modeling, model classification, and uncertainties. Water quality models are described and selected based on their applicability, strengths, weaknesses, and intended use. Some models are applicable for the specific waterbodies, simulate selected water quality parameters, have uncertainties, are not commercially available, require skilled model users, and have huge data requirements. When selecting suitable models, it is recommended to consider the availability of data, model complexity, and type of waterbody, and intended objectives to be modeled.

Towards evidence-based integration of services for HIV, non-communicable diseases and substance use: insights from modelling.

Towards evidence-based integration of services for HIV, non-communicable diseases and substance use: insights from modelling.

Applying Social Network Analysis to Evaluate Implementation of a Multisector Population Health Collaborative That Uses a Bridging Hub Organization.

Background: Multisector collaboratives are increasingly popular strategies for improving population health. To be comprehensive, collaboratives must coordinate the activities of many organizations across a geographic region. Many policy-relevant models encourage creation and use of centralized hub organizations to do this work, yet there is little guidance on how to evaluate implementation of such hubs and track their network reach. We sought to demonstrate how social network analysis (SNA) could be used for this purpose.Methods: Through formative research, we defined and conceptualized key characteristics of a bridging hub network and identified a set of candidate measures—(1) network membership, (2) network interaction, (3) role and reach of the bridging hub, and (4) network collaboration—to evaluate its implementation within a pre-determined geographic region of Southeast Minnesota, USA. We then developed and administered a survey to assess outcomes as part of a SNA. We commented on the feasibility and usefulness of the methods.Results: The initial surveyed network consisted of 50 healthcare organizational sites and 50 community organizations representing sectors of public health, education, research, health promotion, social services, and long-term care and supports. Fifty-three of these organizations responded to the survey. The network's level of collaboration was “Cooperation” (level 2 of 5) and reported levels of collaboration varied by organization. Thirty-eight additional, unsurveyed organizations were identified as collaborators by respondents, pushing the theoretical network denominator up to 138 organizations. These additional organizations included grocery stores, ambulance services, and smaller, independent healthcare and community-based services focused on meeting the needs of underserved populations. The bridging hub organization had the highest betweenness centrality and was in good position to bridge healthcare and the community, although its organizational reach was estimated at only 51%. The SNA methods were feasible and useful for identifying opportunities and guiding implementation.Conclusions: Bridging hub organizations are not likely to link—or even be aware of—all relevant organizations in a geographic region at initial implementation. SNA may be a useful method for evaluating the value and reach of a bridging hub organization and guiding ongoing implementation efforts.Trial registration: http://ClinicalTrials.gov; #NCT03046498

Long-Term Costs and Health Consequences of Issuing Shorter Duration Prescriptions for Patients with Chronic Health Conditions in the English NHS.

The National Health Service (NHS) in England spends over £9 billion on prescription medicines dispensed in primary care, of which over two-thirds is accounted for by repeat prescriptions. Recently, GPs in England have been urged to limit the duration of repeat prescriptions, where clinically appropriate, to 28days to reduce wastage and hence contain costs. However, shorter prescriptions will increase transaction costs and thus may not be cost saving. Furthermore, there is evidence to suggest that shorter prescriptions are associated with lower adherence, which would be expected to lead to lower clinical benefit. The objective of this study is to estimate the cost-effectiveness of 3-month versus 28-day repeat prescriptions from the perspective of the NHS. We adapted three previously developed UK policy-relevant models, incorporating transaction (dispensing fees, prescriber time) and drug wastage costs associated with 3-month and 28-day prescriptions in three case studies: antihypertensive medications for prevention of cardiovascular events; drugs to improve glycaemic control in patients with type 2 diabetes; and treatments for depression. In all cases, 3-month prescriptions were associated with lower costs and higher QALYs than 28-day prescriptions. This is driven by assumptions that higher adherence leads to improved disease control, lower costs and improved QALYs. Longer repeat prescriptions may be cost-effective compared with shorter ones. However, the quality of the evidence base on which this modelling is based is poor. Any policy rollout should be within the context of a trial such as a stepped-wedge cluster design.

Model-Based Policymaking: A Framework to Promote Ethical "Good Practice" in Mathematical Modeling for Public Health Policymaking.

Mathematical models are increasingly relied upon as decision support tools, which estimate risks and generate recommendations to underpin public health policies. However, there are no formal agreements about what constitutes professional competencies or duties in mathematical modeling for public health. In this article, we propose a framework to evaluate whether mathematical models that assess human and animal disease risks and control strategies meet standards consistent with ethical “good practice” and are thus “fit for purpose” as evidence in support of policy. This framework is derived from principles of biomedical ethics: independence, transparency (autonomy), beneficence/non-maleficence, and justice. We identify ethical risks associated with model development and implementation and consider the extent to which scientists are accountable for the translation and communication of model results to policymakers so that the strengths and weaknesses of the scientific evidence base and any socioeconomic and ethical impacts of biased or uncertain predictions are clearly understood. We propose principles to operationalize a framework for ethically sound model development and risk communication between scientists and policymakers. These include the creation of science–policy partnerships to mutually define policy questions and communicate results; development of harmonized international standards for model development; and data stewardship and improvement of the traceability and transparency of models via a searchable archive of policy-relevant models. Finally, we suggest that bespoke ethical advisory groups, with relevant expertise and access to these resources, would be beneficial as a bridge between science and policy, advising modelers of potential ethical risks and providing overview of the translation of modeling advice into policy.

Agent-Based Modeling of Chronic Diseases: A Narrative Review and Future Research Directions

The United States is experiencing an epidemic of chronic disease. As the US population ages, health care providers and policy makers urgently need decision models that provide systematic, credible prediction regarding the prevention and treatment of chronic diseases to improve population health management and medical decision-making. Agent-based modeling is a promising systems science approach that can model complex interactions and processes related to chronic health conditions, such as adaptive behaviors, feedback loops, and contextual effects. This article introduces agent-based modeling by providing a narrative review of agent-based models of chronic disease and identifying the characteristics of various chronic health conditions that must be taken into account to build effective clinical- and policy-relevant models. We also identify barriers to adopting agent-based models to study chronic diseases. Finally, we discuss future research directions of agent-based modeling applied to problems related to specific chronic health conditions.

Literaturbeitrag / Review Paper. Macroeconometric Models – From “Little Science” to “Big Science”

Summary This is a fine, useful book on the history and structure of macroeconometric models. Its perspective is “applied” and has a “positivistic bias”. It gives a good (or not so good) picture of the state of the art. The problems of the now “Big Science” deserve more attention than the modelling community (and Welfe) so far has been willing to pay. The trend towards ever larger policy-relevant models will continue. However, few of them are accessible to third parties. “Transparency”, a major goal models had once started to increase, continues to get out of sight.

Diagnostic indicators for integrated assessment models of climate policy

Integrated assessments of how climate policy interacts with energy-economy systems can be performed by a variety of models with different functional structures. In order to provide insights into why results differ between models, this article proposes a diagnostic scheme that can be applied to a wide range of models. Diagnostics can uncover patterns of model behavior and indicate how results differ between model types. Such insights are informative since model behavior can have a significant impact on projections of climate change mitigation costs and other policy-relevant information. The authors propose diagnostic indicators to characterize model responses to carbon price signals and test these in a diagnostic study of 11 global models. Indicators describe the magnitude of emission abatement and the associated costs relative to a harmonized baseline, the relative changes in carbon intensity and energy intensity, and the extent of transformation in the energy system. This study shows a correlation among indicators suggesting that models can be classified into groups based on common patterns of behavior in response to carbon pricing. Such a classification can help to explain variations among policy-relevant model results.

Non-robust dynamic inferences from macroeconometric models: Bifurcation stratification of confidence regions

Grandmont [J.M. Grandmont, On endogenous competitive business cycles, Econometrica 53 (1985) 995–1045] found that the parameter space of the most classical dynamic models is stratified into an infinite number of subsets supporting an infinite number of different kinds of dynamics, from monotonic stability at one extreme to chaos at the other extreme, and with many forms of multiperiodic dynamics in between. The econometric implications of Grandmont’s findings are particularly important, if bifurcation boundaries cross the confidence regions surrounding parameter estimates in policy-relevant models. Stratification of a confidence region into bifurcated subsets seriously damages robustness of dynamical inferences. Recently, interest in policy in some circles has moved to New-Keynesian models. As a result, in this paper we explore bifurcation within the class of New-Keynesian models. We develop the econometric theory needed to locate bifurcation boundaries in log-linearized New-Keynesian models with Taylor policy rules or inflation-targeting policy rules. Central results needed in this research are our theorems on the existence and location of Hopf bifurcation boundaries in each of the cases that we consider.

Getting By with a Little Help from Your Family: Toward a Policy-Relevant Model of the Household

Recent decades have seen dramatic changes in the ways in which households in developed Western economies gain their livelihoods, with marked elements of a return to old ways of working. There has been a shift from reliance upon one family wage to the need for family employment as well as growing reliance on self-employment and small business. These changes mean that childcare for working parents, and the promotion of new small enterprise, are key areas of policy concern. Drawing on original English empirical research around both these themes, this article shows the ways in which UK households draw on redistribution between the generations as a v - v generally decommodified v - v contribution to livelihoods and "getting by." We argue that these results confound widely utilized models of how people behave, and take particular issue with how economists and policy-makers model the household and its boundaries as the institutional context for individual decisions.

Policy Relevant Modelling: Relationships Between Water, Land Use, and Farmer Decision Processes

This paper presents a policy-relevant model based approach to assessing land-use change that is based upon a number of transdisciplinary mechanisms. We report the development of a modelling platform which supports analysis of the interactions between slope, aquifer, and catchment dynamics, together with the actions of farmers as they change their crop profiles and consequently their water needs. The master-equation based farmer decision making model, driven by rule based decision trees derived within the context of a conceptual framework which accommodates both the human and natural processes and their interdependencies, is central to the method presented in this paper. Our conclusions relate to both the function and process of integrative assessment and we present characteristic model outputs to highlight the interactions between the farmers and their natural environment.

Workplace drug abuse policy: A comment

McGuire and Ruhm (1993) develop an interesting and policy-relevant model of workplace drug abuse policy. They examine workers’ choice to undergo treatment (rehabilitation) for substance abuse and firms’ choice of drug-testing policies, concluding that ‘labor market incentives will generally lead to too little treatment and too much testing’. Drug addicts are assumed to have a ‘high’ probability ( p> of causing a costly workplace accident while non-addicts are assumed to have a ‘low’ probability (a < p) of causing an accident. Another key assumption is that treatment is perfect. That is, any ‘addict’ undergoing treatment changes from a ‘high’ to a ‘low’ risk with probability one. Although the authors acknowledge that actual treatment regimes are imperfect and discuss how imperfect treatment can be incorporated, they do not appear to recognize the ways in which their qualitative results depend on their simplifying assumption. Allowing partially effective treatment by assuming that the accident risk of treated drug users falls to (Y with some

Policy Relevant Models for the 1970s

Policy Relevant Models for the 1970s