The science and management of infectious disease are entering a new stage. Increasingly public policy to manage epidemics focuses on motivating people, through social distancing policies, to alter their behavior to reduce contacts and reduce public disease risk. Person-to-person contacts drive human disease dynamics. People value such contacts and are willing to accept some disease risk to gain contact-related benefits. The cost-benefit trade-offs that shape contact behavior, and hence the course of epidemics, are often only implicitly incorporated in epidemiological models. This approach creates difficulty in parsing out the effects of adaptive behavior. We use an epidemiological-economic model of disease dynamics to explicitly model the trade-offs that drive person-toperson contact decisions. Results indicate that including adaptive human behavior significantly changes the predicted course of epidemics and that this inclusion has implications for parameter estimation and interpretation and for the development of social distancing policies. Acknowledging adaptive behavior requires a shift in thinking about epidemiological processes and parameters.susceptible-infected-recovered model | R 0 | reproductive number | bioeconomics T he science and management of infectious disease is entering a new stage. The increasing focus on incentive structures to motivate people to engage in social distancing-reducing interpersonal contacts and hence public disease risk (1)-changes what health authorities need from epidemiological models. Social distancing is not new-for centuries humans quarantined infected individuals and shunned the obviously ill, but new approaches are being used to deal with modern social interactions. Scientific development of social distancing public policies requires that epidemiological models explicitly address behavioral responses to disease risk and other incentives affecting contact behavior. This paper models the role of adaptive behavior in an epidemiological system. Recognizing adaptive behavior means explicitly incorporating behavioral responses to disease risk and other incentives into epidemiological models (2, 3). The workhorse of modern epidemiology, the compartmental epidemiological model (4, 5), does not explicitly include behavioral responses to disease risk. The transmission factors in these models combine and confound human behavior and biological processes. We develop a simple compartmental model that explicitly incorporates adaptive behavior and show that this modification alters understanding of standard epidemiological metrics. For example, the basic reproductive number, R 0 , is a function of biological processes and human behavior, but R 0 lacks a behavioral interpretation in the existing literature. Biological and behavioral feedbacks muddle R 0 's biological interpretation and confound its estimation.Prior approaches that incorporate behavior into epidemiological models generally fall into three categories: specification of nonlinear contact rate functions, expanded epidemiologi...

Background The coronavirus disease 2019 (COVID-19) pandemic is leading to social (physical) distancing policies worldwide, including in the USA. Some of the first actions taken by governments are the closing of schools. The evidence that mandatory school closures reduce the number of cases and, ultimately, mortality comes from experience with influenza or from models that do not include the effect of school closure on the health-care labour force. The potential benefits from school closures need to be weighed against costs of health-care worker absenteeism associated with additional child-care obligations. In this study, we aimed to measure child-care obligations for US health-care workers arising from school closures when these are used as a social distancing measure. We then assessed how important the contribution of health-care workers would have to be in reducing mortality for their absenteeism due to child-care obligations to undo the benefits of school closures in reducing the number of cases.Methods For this modelling analysis, we used data from the monthly releases of the US Current Population Survey to characterise the family structure and probable within-household child-care options of US health-care workers. We accounted for the occupation within the health-care sector, state, and household structure to identify the segments of the health-care workforce that are most exposed to child-care obligations from school closures. We used these estimates to identify the critical level at which the importance of health-care labour supply in increasing the survival probability of a patient with COVID-19 would undo the benefits of school closures and ultimately increase cumulative mortality.

No abstract

Public policies intended to induce behavioral change, specifically incentives to reduce interpersonal contacts or to “social distance,” increasingly play a prominent role in public disease response strategies as governments plan for and respond to major epidemics. I compare social distancing incentives and outcomes under decentralized, full control social planner, and constrained social planner, without health class specific control, decision making scenarios. Constrained social planner decision making, based on non-health class specific controls, can in some instances make society worse off than decentralized decision making (i.e. no intervention). The oft neglected behavior of recovered and immune individuals is important for welfare and health outcomes.

Many ecosystems appear subject to regime shifts-abrupt changes from one state to another after crossing a threshold or tipping point. Thresholds and their associated stability landscapes are determined within a coupled socioeconomic-ecological system (SES) where human choices, including those of managers, are feedback responses. Prior work has made one of two assumptions about managers: that they face no institutional constraints, in which case the SES may be managed to be fairly robust to shocks and tipping points are of little importance, or that managers are rigidly constrained with no flexibility to adapt, in which case the inferred thresholds may poorly reflect actual managerial flexibility. We model a multidimensional SES to investigate how alternative institutions affect SES stability landscapes and alter tipping points. With institutionally dependent human feedbacks, the stability landscape depends on institutional arrangements. Strong institutions that account for feedback responses create the possibility for desirable states of the world and can cause undesirable states to cease to exist. Intermediate institutions interact with ecological relationships to determine the existence and nature of tipping points. Finally, weak institutions can eliminate tipping points so that only undesirable states of the world remain.alternative stable states | multistability | bioeconomics | invasive species E cological multistability theory describes how distinct ecosystems, identical but for their initial states, can stabilize at very different long-run equilibria. A costly or irreversible "regime shift" can subsequently occur when a multistable (MS) system is moved past a threshold or tipping point and into an alternative equilibrium's basin of attraction (1-3) (Fig. 1). The management of MS systems has become a focal point of the science-policy interface (4-6) in response to fisheries collapse, climate change, vegetation changes, and invasive species, as these phenomena are often deemed causes and effects of regime shifts that reduce ecosystem services (7).Increasingly, human-impacted ecological systems, including MS systems, are regarded as coupled socioeconomic-ecological systems (SES) where human behaviors are feedback responses affecting, and affected by, ecological variables (8) (Fig. 2). The SES perspective means that solving ecological problems requires altering the SES's stability landscape or topology (i.e., the dynamic system's "shape", defining thresholds and basins of attraction) so the system moves to a preferred outcome (8,9). This result is achieved by imposing regulations or altering economic signals that influence human feedback responses (Fig. 2). An ensuing regime shift might be said to result from a shift in regulatory institutions, extending Beisner et al.'s (3) categorization (Fig. 1).SES management is often modeled using a bioeconomic framework that combines economic decision theory with ecological modeling (10). Work on MS-SESs has focused on problems such as rangeland ecosystems (9, 11), cora...