Dataset Information

Quantifying Proximity, Confinement, and Interventions in Disease Outbreaks: A Decision Support Framework for Air-Transported Pathogens.

ABSTRACT: The inability to communicate how infectious diseases are transmitted in human environments has triggered avoidance of interactions during the COVID-19 pandemic. We define a metric, Effective ReBreathed Volume (ERBV), that encapsulates how infectious pathogens, including SARS-CoV-2, transport in air. ERBV separates environmental transport from other factors in the chain of infection, allowing quantitative comparisons among situations. Particle size affects transport, removal onto surfaces, and elimination by mitigation measures, so ERBV is presented for a range of exhaled particle diameters: 1, 10, and 100 ?m. Pathogen transport depends on both proximity and confinement. If interpersonal distancing of 2 m is maintained, then confinement, not proximity, dominates rebreathing after 10-15 min in enclosed spaces for all but 100 ?m particles. We analyze strategies to reduce this confinement effect. Ventilation and filtration reduce person-to-person transport of 1 ?m particles (ERBV₁) by 13-85% in residential and office situations. Deposition to surfaces competes with intentional removal for 10 and 100 ?m particles, so the same interventions reduce ERBV₁₀ by only 3-50%, and ERBV₁₀₀ is unaffected. Prior knowledge of size-dependent ERBV would help identify transmission modes and effective interventions. This framework supports mitigation decisions in emerging situations, even before other infectious parameters are known.

SUBMITTER: Bond TC

PROVIDER: S-EPMC7927283 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quantifying Proximity, Confinement, and Interventions in Disease Outbreaks: A Decision Support Framework for Air-Transported Pathogens.

Bond Tami C TC Bosco-Lauth Angela A Farmer Delphine K DK Francisco Paul W PW Pierce Jeffrey R JR Fedak Kristen M KM Ham Jay M JM Jathar Shantanu H SH VandeWoude Sue S

Environmental science & technology 20210219 5

The inability to communicate how infectious diseases are transmitted in human environments has triggered avoidance of interactions during the COVID-19 pandemic. We define a metric, Effective ReBreathed Volume (ERBV), that encapsulates how infectious pathogens, including SARS-CoV-2, transport in air. ERBV separates environmental transport from other factors in the chain of infection, allowing quantitative comparisons among situations. Particle size affects transport, removal onto surfaces, and el ...[more]

PMID: 33605140

Similar Datasets

Project description:BackgroundAustralian governments are increasingly mandating the use of cost-benefit analysis (CBA) to inform the efficient allocation of government resources. CBA is likely to be useful when evaluating preventive health interventions that are often cross-sectoral in nature and require Cabinet approval prior to implementation. This study outlines a CBA framework for the evaluation of preventive health interventions that balances the need for consistency with other agency guidelines whilst adhering to guidelines and conventions for health economic evaluations.MethodsWe analysed CBA and other evaluation guidance documents published by Australian federal and New South Wales (NSW) government departments. Data extraction compared the recommendations made by different agencies and the impact on the analysis of preventive health interventions. The framework specifies a reference case and sensitivity analyses based on the following considerations: (1) applied economic evaluation theory; (2) consistency between CBA across different government departments; (3) the ease of moving from a CBA to a more conventional cost-effectiveness/cost-utility analysis framework often used for health interventions; (4) the practicalities of application; and (5) the needs of end users being both Cabinet decision-makers and health policy-makers.ResultsNine documents provided CBA or relevant economic evaluation guidance. There were differences in terminology and areas of agreement and disagreement between the guidelines. Disagreement between guidelines involved (1) the community included in the societal perspective; (2) the number of options that should be appraised in ex ante analyses; (3) the appropriate time horizon for interventions with longer economic lives; (4) the theoretical basis and value of the discount rate; (5) parameter values for variables such as the value of a statistical life; and (6) the summary measure for decision-making.ConclusionsThis paper addresses some of the methodological challenges that have hindered the use of CBA in prevention by outlining a framework that is consistent with treasury department guidelines whilst considering the unique features of prevention policies. The effective use and implementation of a preventive health CBA framework is likely to require considerable investment of time and resources from state and federal government departments of health and treasury but has the potential to improve decision-making related to preventive health policies and programmes.

Project description:In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect. Our results show that the control policy that would have been chosen using all the data is also identified from an early stage in an outbreak using only the available data, despite high variability in projections of epidemic size. Critically, we find that it is an improved understanding of the locations of infected farms, rather than improved estimates of transmission parameters, that drives improved prediction of the relative performance of control interventions. However, the ability to estimate undetected infectious premises is a function of uncertainty in the transmission parameters. Here, we demonstrate the need for both real-time model fitting and generating projections to evaluate alternative control interventions throughout an outbreak. Our results highlight the use of using models at outbreak onset to inform policy and the importance of state-dependent interventions that adapt in response to additional information throughout an outbreak.

Project description:BackgroundThe severity of COVID-19, as well as the speed and scale of its spread, has posed a global challenge. Countries around the world have implemented stringent non-pharmaceutical interventions (NPI) to control transmission and prevent health systems from being overwhelmed. These NPI have had profound negative social and economic impacts. With the timeline to worldwide vaccine roll-out being uncertain, governments need to consider to what extent they need to implement and how to de-escalate these NPI. This rapid review collates de-escalation criteria reported in the literature to provide a guide to criteria that could be used as part of de-escalation strategies globally.MethodsWe reviewed literature published since 2000 relating to pandemics and infectious disease outbreaks. The searches included Embase.com (includes Embase and Medline), LitCovid, grey literature searching, reference harvesting and citation tracking. Over 1,700 documents were reviewed, with 39 documents reporting de-escalation criteria included in the final analysis. Concepts retrieved through a thematic analysis of the included documents were interlinked to build a conceptual dynamic de-escalation framework.ResultsWe identified 52 de-escalation criteria, the most common of which were clustered under surveillance (cited by 43 documents, 10 criteria e.g. ability to actively monitor confirmed cases and contact tracing), health system capacity (cited by 30 documents, 11 criteria, e.g. ability to treat all patients within normal capacity) and epidemiology (cited by 28 documents, 7 criteria, e.g. number or changes in case numbers). De-escalation is a gradual and bi-directional process, and resurgence of infections or emergence of variants of concerns can lead to partial or full re-escalation(s) of response and control measures in place. Hence, it is crucial to rely on a robust public health surveillance system.ConclusionsThis rapid review focusing on de-escalation within the context of COVID-19 provides a conceptual framework and a guide to criteria that countries can use to formulate de-escalation plans.

Dataset Information

Quantifying Proximity, Confinement, and Interventions in Disease Outbreaks: A Decision Support Framework for Air-Transported Pathogens.

Publications

Quantifying Proximity, Confinement, and Interventions in Disease Outbreaks: A Decision Support Framework for Air-Transported Pathogens.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets