Kevin Berry

Lyme disease transmission dynamics in the northeastern United States vary by context. Periurban regions, including Block Island, RI, have experienced decades of endemic transmission. In urban areas, including Staten Island, a borough in New York City, NY, Lyme disease is an emerging issue. Knowledge, attitudes, and practices around Lyme disease evolve as an area progresses from emergent to endemic. We conducted focus group discussions and household surveys within Staten Island, NY and Block Island, RI to compare knowledge, attitudes, and practices surrounding Lyme disease and other tick-borne diseases. Qualitative data were coded into themes, and survey data were used to provide more general context of the focus group discussions. Using item response theory, we developed an index of knowledge from relevant factors within the household survey. We identified a significant difference in knowledge scores between the two study areas. Additionally, we identified key differences across multiple domains. Participants from Block Island were more likely to report prior tick exposure and Lyme disease in themselves or household members and were more likely to express concerns about the environmental impact of mitigation strategies. Individuals on Staten Island were more likely to assign responsibility of prevention and control to local, state, and federal government than to take personal prevention measures. Prevention of Lyme disease and other tick-borne diseases must be tailored to the community context and monitored over time as perceptions and priorities may evolve as transmission dynamics transition from emergent to endemic.

Climate warming, sea level rise, and extreme weather events are creating intensifying and more frequent hazards for human populations inhabiting the coast. In Alaska’s remote coastal communities, flooding and erosion are rapidly increasing due to the combined effect of sea level rise, more frequent storm surges, and increasingly powerful wave action from lack of sea ice. This paper presents survey results documenting socio-economic and psychological livelihood impacts and relocation preferences as reported by residents of a remote coastal Indigenous community. We quantified direct costs of lost or damaged private property, affected community infrastructure, and interruption of public services and found that the resulting financial hardship adds to existing economic challenges and climate stressors. Findings underline a community-level preoccupation with coastal climate threats that manifests primarily in intrusive and distressing thoughts of consequences from storms and other destructive climatic events. We highlight the need to monitor more broadly livelihood impacts to inform the design of innovative risk management tools to moderate financial hardship and strengthen community-driven action. We conclude that new policy responding to the needs of remote Indigenous communities affected by repetitive environmental disasters needs to account for a complex array of community and culture-specific socio-economic, health, and biophysical factors that require frequent co-produced assessments to capture rapidly changing conditions at the local scale.

This study investigated the relationship between food neophobia, the tendency to avoid new foods, and cultural intelligence (CQ). A questionnaire was administered to undergraduate students at a university in the Northwestern United States. Survey administration occurred during the 2018-2019 academic year. The questionnaire included a combination of demographic questions along with two validated survey instruments, the Cultural Intelligence Scale (CQS) (Ang & Dyne, 2008) and the Food Neophobia Scale (FNS) (Pliner & Hobden, 1992). CQS was used to measure cultural intelligence and FNS gauged food neophobia. Results indicated a strong correlation between CQ and food neophobia. An analysis of CQ’s components showed that the significant drivers of the results were metacognitive/strategy and motivational/drive CQ subconstructs.

Sustainability is often framed as achieving prosperity, equality, and environmental integrity: in other words, high living standards, evenly-distributed, and low-impact. But what if there is a trilemma that inhibits the joint achievement of these three key goals? We analyzed synergies and tradeoffs between these dimensions of sustainability by investigating historical data on per-capita gross national income, the Gini coefficient for income distribution, and per-capita ecological footprint. We found that no country in the world successfully solved this trilemma, but there are typologies of countries that can help identify common barriers, and strategies to overcome them. Countries typically reduce inequality and then increase prosperity and environmental impact. Some countries are trapped in a high inequality space, while prosperous countries have reduced inequality only at the expense of increasing environmental impact. Escaping this trilemma to achieve ambitious sustainability goals will likely require economic redistribution and more urgent collective action for planetary stewardship.

One cause of the high rate of COVID-19 cases in the USA is thought to be insufficient prior capital investment in national health programs to preemptively reduce the likelihood of an outbreak and in national capacity to reduce the severity of any outbreak that does occur. We analyze the choice of capital investments (e.g. testing capacity, stockpiles of PPE, and information sharing capacity) and find the economically efficient capital stock associated with mitigating pandemic risk should be dramatically expanded. Policymakers who fail to invest in public health forgo significant expected cost savings from being prepared.

Transmission of the SAR-COV-2 virus that causes COVID-19 is largely driven by human behavior and person-to-person contact. By staying home, people reduce the probability of contacting an infectious individual, becoming infected, and passing on the virus. One of the most promising sources of data on time use is smartphone location data. We develop a time use driven proportional mixing SEIR model that naturally incorporates time spent at home measured using smartphone location data and allows people of different health statuses to behave differently. We simulate epidemics in almost every county in the United States. The model suggests that Americans’ behavioral shifts have reduced cases in 55%-86% of counties and for 71%-91% of the population, depending on modeling assumptions. Resuming pre-epidemic behavior would lead to a rapid rise in cases in most counties. Spatial patterns of bending and flattening the curve are robust to modeling assumptions. Depending on epidemic history, county demographics, and behavior within a county, returning those with acquired immunity (assuming it exists) to regular schedules generally helps reduce cumulative COVID-19 cases. The model robustly identifies which counties would experience the greatest share of case reduction relative to continued distancing behavior. The model occasionally mischaracterizes epidemic patterns in counties tightly connected to larger counties that are experiencing large epidemics. Understanding these patterns is critical for prioritizing testing resources and back-to-work planning for the United States.