Parameter estimation in behavioral epidemic models with endogenous societal risk-response.

Behavioral epidemic models incorporating endogenous societal risk-response, where changes in risk perceptions prompt adjustments in contact rates, are crucial for predicting pandemic trajectories. Accurate parameter estimation in these models is vital for validation and precise projections. However, few studies have examined the problem of identifiability in models where disease and behavior parameters must be jointly estimated. To address this gap, we conduct simulation experiments to assess the effect on parameter estimation accuracy of a) delayed risk response, b) neglecting behavioral response in model structure, and c) integrating disease and public behavior data. Our findings reveal systematic biases in estimating behavior parameters even with comprehensive and accurate disease data and a well-structured simulation model when data are limited to the first wave. This is due to the significant delay between evolving risks and societal reactions, corresponding to the duration of a pandemic wave. Moreover, we demonstrate that conventional SEIR models, which disregard behavioral changes, may fit well in the early stages of a pandemic but exhibit significant errors after the initial peak. Furthermore, early on, relatively small data samples of public behavior, such as mobility, can significantly improve estimation accuracy. However, the marginal benefits decline as the pandemic progresses. These results highlight the challenges associated with the joint estimation of disease and behavior parameters in a behavioral epidemic model.

Why Similar Policies Resulted In Different COVID-19 Outcomes: How Responsiveness And Culture Influenced Mortality Rates.

In the first two years of the COVID-19 pandemic, per capita mortality varied by more than a hundredfold across countries, despite most implementing similar nonpharmaceutical interventions. Factors such as policy stringency, gross domestic product, and age distribution explain only a small fraction of mortality variation. To address this puzzle, we built on a previously validated pandemic model in which perceived risk altered societal responses affecting SARS-CoV-2 transmission. Using data from more than 100 countries, we found that a key factor explaining heterogeneous death rates was not the policy responses themselves but rather variation in responsiveness. Responsiveness measures how sensitive communities are to evolving mortality risks and how readily they adopt nonpharmaceutical interventions in response, to curb transmission. We further found that responsiveness correlated with two cultural constructs across countries: uncertainty avoidance and power distance. Our findings show that more responsive adoption of similar policies saves many lives, with important implications for the design and implementation of responses to future outbreaks.

Effect of mandating vaccination on COVID-19 cases in colleges and universities.

BACKGROUND: With the introduction of COVID-19 vaccines, many colleges and universities decided to mandate vaccination for all students and employees. The objective of this paper is to empirically investigate the effect of the mandate policy on Fall 2021 COVID-19 cases in institutions of higher education. METHOD: We construct a unique dataset of a sample of 94 colleges and universities in the east and southeast regions of the United States, 41 of which required vaccination prior to Fall 2021. A difference-in-differences analysis is conducted, considering vaccine requirement as a policy implemented only in a sub-group of these institutions. We control for several factors, including state-level case per capita and student population. RESULTS: Our analysis shows that mandatory vaccination substantially decreased cases in institutions of higher education by 1,473 cases per 100,000 student population (95 CI: 132, 2813). CONCLUSIONS: The results suggest that a COVID-19 vaccine requirement is an effective policy in decreasing cases in such institutions, leading to a safer educational experience.

Enhancing long-term forecasting: Learning from COVID-19 models.

While much effort has gone into building predictive models of the COVID-19 pandemic, some have argued that early exponential growth combined with the stochastic nature of epidemics make the long-term prediction of contagion trajectories impossible. We conduct two complementary studies to assess model features supporting better long-term predictions. First, we leverage the diverse models contributing to the CDC repository of COVID-19 USA death projections to identify factors associated with prediction accuracy across different projection horizons. We find that better long-term predictions correlate with: (1) capturing the physics of transmission (instead of using black-box models); (2) projecting human behavioral reactions to an evolving pandemic; and (3) resetting state variables to account for randomness not captured in the model before starting projection. Second, we introduce a very simple model, SEIRb, that incorporates these features, and few other nuances, offers informative predictions for as far as 20-weeks ahead, with accuracy comparable with the best models in the CDC set. Key to the long-term predictive power of multi-wave COVID-19 trajectories is capturing behavioral responses endogenously: balancing feedbacks where the perceived risk of death continuously changes transmission rates through the adoption and relaxation of various Non-Pharmaceutical Interventions (NPIs).

A cyclical wildfire pattern as the outcome of a coupled human natural system.

Over the past decades, wildfire has imposed a considerable cost on natural resources and human lives. In many regions, annual wildfire trends show puzzling oscillatory patterns with increasing amplitudes for burned areas over time. This paper aims to examine the potential causes of such patterns by developing and examining a dynamic simulation model that represents interconnected social and natural dynamics in a coupled system. We develop a generic dynamic model and, based on simulation results, postulate that the interconnection between human and natural subsystems is a source of the observed cyclical patterns in wildfires in which risk perception regulates activities that can result in more fire and development of vulnerable properties. Our simulation-based policy analysis points to a non-linear characteristic of the system, which rises due to the interconnections between the human side and the natural side of the system. This has a major policy implication: in contrast to studies that look for the most effective policy to contain wildfires, we show that a long-term solution is not a single action but is a combination of multiple actions that simultaneously target both human and natural sides of the system.

Weather, air pollution, and SARS-CoV-2 transmission: a global analysis.

BACKGROUND: Understanding how environmental factors affect SARS-CoV-2 transmission could inform global containment efforts. Despite high scientific and public interest and multiple research reports, there is currently no consensus on the association of environmental factors and SARS-CoV-2 transmission. To address this research gap, we aimed to assess the relative risk of transmission associated with weather conditions and ambient air pollution. METHODS: In this global analysis, we adjusted for the delay between infection and detection, estimated the daily reproduction number at 3739 global locations during the COVID-19 pandemic up until late April, 2020, and investigated its associations with daily local weather conditions (ie, temperature, humidity, precipitation, snowfall, moon illumination, sunlight hours, ultraviolet index, cloud cover, wind speed and direction, and pressure data) and ambient air pollution (ie, PM2·5, nitrogen dioxide, ozone, and sulphur dioxide). To account for other confounding factors, we included both location-specific fixed effects and trends, controlling for between-location differences and heterogeneities in locations' responses over time. We built confidence in our estimations through synthetic data, robustness, and sensitivity analyses, and provided year-round global projections for weather-related risk of global SARS-CoV-2 transmission. FINDINGS: Our dataset included data collected between Dec 12, 2019, and April 22, 2020. Several weather variables and ambient air pollution were associated with the spread of SARS-CoV-2 across 3739 global locations. We found a moderate, negative relationship between the estimated reproduction number and temperatures warmer than 25°C (a decrease of 3·7% [95% CI 1·9-5·4] per additional degree), a U-shaped relationship with outdoor ultraviolet exposure, and weaker positive associations with air pressure, wind speed, precipitation, diurnal temperature, sulphur dioxide, and ozone. Results were robust to multiple assumptions. Independent research building on our estimates provides strong support for the resulting projections across nations. INTERPRETATION: Warmer temperature and moderate outdoor ultraviolet exposure result in a slight reduction in the transmission of SARS-CoV-2; however, changes in weather or air pollution alone are not enough to contain the spread of SARS-CoV-2 with other factors having greater effects. FUNDING: None.

Simulation-based what-if analysis for controlling the spread of Covid-19 in universities.

A simulation model is developed to analyze the spread of covid-19 in universities. The model can be used to conduct a what-if analysis and estimate infection cases under different policies. For proof-of-concept, the model is simulated for a hypothetical university of 25,000 students and 3,000 faculty/staff in a U.S. college town. Simulation results show that early outbreaks are very likely, and there is no silver bullet to avoid them. Instead, a combination of policies should be carefully implemented. The results suggest (almost) full remote university operations from the beginning of the semester. In a less-preferred alternative, if universities decide to have students attend in person, they should encourage remote operations for high-risk individuals, conduct frequent rapid tests, enforce mask use, communicate with students and employees about the risks, and promote social distancing. Universities should be willing to move to remote operations if cases rise. Under this scenario, and considering implementation challenges, many universities are still likely to experience an early outbreak, and the likelihood of having a case of death is worrisome. In the long run, students and faculty react to the risks, and even if universities decide to continue operations, classes are likely to have very low in-person attendance. Overall, our analysis depicts several sources of system complexities, negative unintended consequences of relying on a single policy, non-linear incremental effects, and positive synergies of implementing multiple policies. A simulation platform for a what-if analysis is offered so marginal effectiveness of different policies and different decision-making thresholds for closure can be tested for universities of varying populations.

Simulation-based estimation of the early spread of COVID-19 in Iran: actual versus confirmed cases.

Understanding the state of the COVID-19 pandemic relies on infection and mortality data. Yet official data may underestimate the actual cases due to limited symptoms and testing capacity. We offer a simulation-based approach which combines various sources of data to estimate the magnitude of outbreak. Early in the epidemic we applied the method to Iran's case, an epicenter of the pandemic in winter 2020. Estimates using data up to March 20th, 2020, point to 916,000 (90% UI: 508 K, 1.5 M) cumulative cases and 15,485 (90% UI: 8.4 K, 25.8 K) total deaths, numbers an order of magnitude higher than official statistics. Our projections suggest that absent strong sustaining of contact reductions the epidemic may resurface. We also use data and studies from the succeeding months to reflect on the quality of original estimates. Our proposed approach can be used for similar cases elsewhere to provide a more accurate, early, estimate of outbreak state. © 2020 System Dynamics Society.

Late retirement, early careers, and the aging of U.S. science and engineering professors.

Studies of rescuing early-career scientists often take narrow approaches and focus on PhD students or postdoc populations. In a multi-method systems approach, we examine the inter-relations between the two ends of the pipeline and ask: what are the effects of late retirement on aging and hiring in academia? With a simulation model, we postulate that the decline in the retirement rate in academia contributes to the aging pattern through two mechanisms: (a) direct effect: longer stay of established professors, and (b) indirect effect: a hiring decline in tenure-track positions. Late retirement explains more than half of the growth in average age and brings about 20% decline in hiring. We provide empirical evidence based on the natural experimental set-up of the removal of mandatory retirement in the 1990s.

SD Meets OR: A New Synergy to Address Policy Problems.

We reflect on our past seven years of collaboration to develop systems models of U.S. higher education and scientific workforce development. Based on three recent modeling examples, we offer a methodological proposition that many traditional Operations Research (OR) models can be improved by including feedback processes as is commonly done in system dynamics (SD) modeling. Such models, even if simple and approximate, can be powerful, insightful, easy to communicate, and effective. While these modeling examples may not follow conventional SD or OR modeling, they benefit from and contribute to both schools of modeling. We argue that to build such synergy, modeling teams should be willing to create models building on the strengths of each school of modeling.

Work-Education Mismatch: An Endogenous Theory of Professionalization.

We model the education-workforce pipeline and offer an endogenous theory of professionalization and ever-higher degree attainment. We introduce two mechanisms that act on the education enterprise, causing the number of educated people to increase dramatically with relatively short-term changes in the job market. Using our illustrative dynamic model, we argue that the system is susceptible to small changes and the introduced self-driving growth engines are adequate to over-incentivize degree attainment. We also show that the mechanisms magnify effects of short-term recessions or technological changes, and create long-term waves of mismatch between workforce and jobs. The implication of the theory is degree inflation, magnified pressures on those with lower degrees, underemployment, and job market mismatch and inefficiency.

Global Trends and Regional Variations in Studies of HIV/AIDS.

We conduct textual analysis of a sample of more than 200,000 papers written on HIV/AIDS during the past three decades. Using the Latent Dirichlet Allocation method, we disentangle studies that address behavioral and social aspects from other studies and measure the trends of different topics as related to HIV/AIDS. We show that there is a regional variation in scientists' approach to the problem of HIV/AIDS. Our results show that controlling for the economy, proximity to the HIV/AIDS problem correlates with the extent to which scientists look at the behavioral and social aspects of the disease rather than biomedical.

Recent trends in the U.S. Behavioral and Social Sciences Research (BSSR) workforce.

While behavioral and social sciences occupations comprise one of the largest portions of the "STEM" workforce, most studies of diversity in STEM overlook this population, focusing instead on fields such as biomedical or physical sciences. This study evaluates major demographic trends and productivity in the behavioral and social sciences research (BSSR) workforce in the United States during the past decade. Our analysis shows that the demographic trends for different BSSR fields vary. In terms of gender balance, there is no single trend across all BSSR fields; rather, the problems are field-specific, and disciplines such as economics and political science continue to have more men than women. We also show that all BSSR fields suffer from a lack of racial and ethnic diversity. The BSSR workforce is, in fact, less representative of racial and ethnic minorities than are biomedical sciences or engineering. Moreover, in many BSSR subfields, minorities are less likely to receive funding. We point to various funding distribution patterns across different demographic groups of BSSR scientists, and discuss several policy implications.

A Dynamic Model of Post-Traumatic Stress Disorder for Military Personnel and Veterans.

Post-traumatic stress disorder (PTSD) stands out as a major mental illness; however, little is known about effective policies for mitigating the problem. The importance and complexity of PTSD raise critical questions: What are the trends in the population of PTSD patients among military personnel and veterans in the postwar era? What policies can help mitigate PTSD? To address these questions, we developed a system dynamics simulation model of the population of military personnel and veterans affected by PTSD. The model includes both military personnel and veterans in a "system of systems." This is a novel aspect of our model, since many policies implemented at the military level will potentially influence (and may have side effects on) veterans and the Department of Veterans Affairs. The model is first validated by replicating the historical data on PTSD prevalence among military personnel and veterans from 2000 to 2014 (datasets from the Department of Defense, the Institute of Medicine, the Department of Veterans Affairs, and other sources). The model is then used for health policy analysis. Our results show that, in an optimistic scenario based on the status quo of deployment to intense/combat zones, estimated PTSD prevalence among veterans will be at least 10% during the next decade. The model postulates that during wars, resiliency-related policies are the most effective for decreasing PTSD. In a postwar period, current health policy interventions (e.g., screening and treatment) have marginal effects on mitigating the problem of PTSD, that is, the current screening and treatment policies must be revolutionized to have any noticeable effect. Furthermore, the simulation results show that it takes a long time, on the order of 40 years, to mitigate the psychiatric consequences of a war. Policy and financial implications of the findings are discussed.

Can a Patient's In-Hospital Length of Stay and Mortality Be Explained by Early-Risk Assessments?

OBJECTIVE: To assess whether a patient's in-hospital length of stay (LOS) and mortality can be explained by early objective and/or physicians' subjective-risk assessments. DATA SOURCES/STUDY SETTING: Analysis of a detailed dataset of 1,021 patients admitted to a large U.S. hospital between January and September 2014. STUDY DESIGN: We empirically test the explanatory power of objective and subjective early-risk assessments using various linear and logistic regression models. PRINCIPAL FINDINGS: The objective measures of early warning can only weakly explain LOS and mortality. When controlled for various vital signs and demographics, objective signs lose their explanatory power. LOS and death are more associated with physicians' early subjective risk assessments than the objective measures. CONCLUSIONS: Explaining LOS and mortality require variables beyond patients' initial medical risk measures. LOS and in-hospital mortality are more associated with the way in which the human element of healthcare service (e.g., physicians) perceives and reacts to the risks.

Posttraumatic Stress Disorder: Five Vicious Cycles that Inhibit Effective Treatment.

Despite a wide range of studies and medical progress, it seems that we are far from significantly mitigating the problem of posttraumatic stress disorder (PTSD). The problem has major social and behavioral components. Developing innovative and effective policies requires a broad scope of analysis and consideration of the highly interconnected social, behavioral, and medical variables. In this article, we take a systems approach and offer an illustrative causal loop diagram which includes individual and social dynamics. Based on the map, we discuss 5 major barriers for effective interventions in PTSD. These barriers work as vicious cycles in the system, reduce effectiveness and therefore value of PTSD treatment. We also discuss policy implications of this perspective.

A Note on PhD Population Growth in Biomedical Sciences.

The explosive increase in the number of postdocs in biomedical fields is puzzling for many science policymakers. We use our previously introduced parameter in this journal, the basic reproductive number in academia (R0 ), to make sense of PhD population growth in biomedical fields. Our analysis shows how R0 in biomedical fields has increased over time, and we estimate that there is approximately only one tenure-track position in the US for every 6.3 PhD graduates, which means the rest need to get jobs outside academia or stay in lower-paid temporary positions. We elaborate on the structural reasons and systemic flaws of science workforce development by discussing feedback loops, especially vicious cycles, which contribute to over-production of PhDs. We argue that the current system is unstable but with no easy solution. A way to mitigate the effects of strong reinforcing loops is full disclosure of the risks of getting PhD.

Effects of government spending on research workforce development: evidence from biomedical postdoctoral researchers.

We examine effects of government spending on postdoctoral researchers' (postdocs) productivity in biomedical sciences, the largest population of postdocs in the US. We analyze changes in the productivity of postdocs before and after the US government's 1997 decision to increase NIH funding. In the first round of analysis, we find that more government spending has resulted in longer postdoc careers. We see no significant changes in researchers' productivity in terms of publication and conference presentations. However, when the population is segmented by citizenship, we find that the effects are heterogeneous; US citizens stay longer in postdoc positions with no change in publications and, in contrast, international permanent residents (green card holders) produce more conference papers and publications without significant changes in postdoc duration. Possible explanations and policy implications of the analysis are discussed.