Behavioral planning: Improving behavioral design with “roughly right” foresight

Many challenges emerging from the current COVID-19 pandemic are behavioral in nature, which has prompted the field of behavioral design to propose solutions for issues as wide-ranging as hand-washing, wearing masks, and the adoption of new norms for staying and working from home. On the whole, however, these behavioral interventions have been somewhat underwhelming, exposing an inherent brittleness that comes from three common “errors of projection” in current behavioral design methodology: projected stability, which insufficiently plans for the fact that interventions often function within inherently unstable systems; projected persistence, which neglects to account for changes in those system conditions over time; and projected value, which assumes that definitions of success are universally shared across contexts. Borrowing from strategic design and futures thinking, a new proposed strategic foresight model—behavioral planning—can help practitioners better address these system-level, anticipatory, and contextual weaknesses by more systematically identifying potential forces that may impact behavioral interventions before they have been implemented. Behavioral planning will help designers more effectively elicit signals indicating the emergence of forces that may deform behavioral interventions in emergent COVID-19 contexts, and promote “roughly right” directional solutions at earlier stages in solution development to better address system shifts

Math Biosci Eng

: Zoonotic pathogens on dairy farms are a known risk for people who work and live there. Exposure and/or transmission of | serovars, | (O157; H7), |, and | have been documented to occur in the dairy farm environment. Social ecological factors have been identified as determinants of preventive behaviors of people at risk of infectious diseases.|: This study described the effect of socio-ecological factors on selected zoonotic bacterial and protozoal diseases in 42 workers of two dairy farms.|: Occupational exposure to | ser. Dublin, |, and |. was confirmed. Self-efficacy and negative workplace perceptions were risk factors for | Dublin exposure (OR\ua0=\ua01.43[95% CI 1.11-2.22] & 1.22 [95% CI 1.02-1.53] respectively,). Additionally, safety knowledge and risk perceptions were protective factors of exposure (OR\ua0=\ua00.90 [95% CI 0.79-1.00]). Positive perceptions of supervisors and coworkers was a protective factor of | exposure (OR\ua0=\ua00.89 [95% CI 0.79-0.98]).|: Results indicated that the presence of a supporting organizational environment, good communication with supervisors and coworkers, and training on prevention of zoonotic diseases would potentially reduce occupational exposures to zoonotic diseases on these farms.U54 OH008085/OH/NIOSH CDC HHSUnited States/2022-10-11T00:00:00Z35603374PMC955332411998vault:4337

Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil

This study was funded by the Brazilian National Council for Scientific and Technological Development (CNPq) [grant number 402834/2020-8]. MEB received a technological and industrial scholarship from CNPq [grant number 315854/2020-0]. LSF received a master's scholarship from Coordination for the Improvement of Higher Education Personnel (CAPES) [finance code 001]. SP was supported by São Paulo Research Foundation (FAPESP) [grant number 2018/24037-4]. AMB received a technological and industrial scholarship from CNPq [grant number 402834/2020-8]. CF was supported by FAPESP [grant numbers 2019/26310-2 and 2017/26770-8]. MQMR received a postdoctoral scholarship from CAPES [grant number 305269/2020-8]. LMS received a technological and industrial scholarship from CNPq [grant number 315866/2020-9]. RSK has been supported by CNPq [grant number 312378/2019-0]. PIP has been supported by CNPq [grant number 313055/2020-3]. JAFD-F has been supported by CNPq productivity fellowship and the National Institutes for Science and Technology in Ecology, Evolution and Biodiversity Conservation (INCT-EEC), supported by MCTIC/CNPq [grant number 465610/2014-5] and Goiás Research Foundation (FAPEG) [grant number 201810267000023]. RAK has been supported by CNPq [grant number 311832/2017-2] and FAPESP [grant number 2016/01343-7]. CMT has been supported by CNPq productivity fellowship and the National Institute for Health Technology Assessment (IATS) [grant number 465518/2014-1].Peer reviewedPublisher PD

Modelling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil

We simulate the impact of school reopening during the COVID-19 pandemic in three major urban centers in Brazil to identify the epidemiological indicators and the best timing for the return of in-school activities and the effect of contact tracing as a mitigation measure. Our goal is to offer guidelines for evidence-based policymaking. We implement an extended SEIR model stratified by age and considering contact networks in different settings – school, home, work, and community, in which the infection transmission rate is affected by various intervention measures. After fitting epidemiological and demographic data, we simulate scenarios with increasing school transmission due to school reopening, and also estimate the number of hospitalization and deaths averted by the implementation of contact tracing. Reopening schools results in a non-linear increase in reported COVID-19 cases and deaths, which is highly dependent on infection and disease incidence at the time of reopening. When contact tracing and quarantining are restricted to school and home settings, a large number of daily tests is required to produce significant effects in reducing the total number of hospitalizations and deaths. Policymakers should carefully consider the epidemiological context and timing regarding the implementation of school closure and return of in-person school activities. While contact tracing strategies prevent new infections within school en- vironments, they alone are not sufficient to avoid significant impacts on community transmission

Effects of Population Co-location Reduction on Cross-county Transmission Risk of COVID-19 in the United States

The rapid spread of COVID-19 in the United States has imposed a major threat to public health, the real economy, and human well-being. With the absence of effective vaccines, the preventive actions of social distancing and travel reduction are recognized as essential non-pharmacologic approaches to control the spread of COVID-19. Prior studies demonstrated that human movement and mobility drove the spatiotemporal distribution of COVID-19 in China. Little is known, however, about the patterns and effects of co-location reduction on cross-county transmission risk of COVID-19. This study utilizes Facebook co-location data for all counties in the United States from March to early May 2020. The analysis examines the synchronicity and time lag between travel reduction and pandemic growth trajectory to evaluate the efficacy of social distancing in ceasing the population co-location probabilities, and subsequently the growth in weekly new cases. The results show that the mitigation effects of co-location reduction appear in the growth of weekly new cases with one week of delay. Furthermore, significant segregation is found among different county groups which are categorized based on numbers of cases. The results suggest that within-group co-location probabilities remain stable, and social distancing policies primarily resulted in reduced cross-group co-location probabilities (due to travel reduction from counties with large number of cases to counties with low numbers of cases). These findings could have important practical implications for local governments to inform their intervention measures for monitoring and reducing the spread of COVID-19, as well as for adoption in future pandemics. Public policy, economic forecasting, and epidemic modeling need to account for population co-location patterns in evaluating transmission risk of COVID-19 across counties.Comment: 12 pages, 7 figure