Erratum: Publisher Correction: wMel replacement of dengue-competent mosquitoes is robust to near-term climate change.

[This corrects the article DOI: 10.1038/s41558-023-01746-w.].

wMel replacement of dengue-competent mosquitoes is robust to near-term change.

Rising temperatures are impacting the range and prevalence of mosquito-borne diseases. A promising biocontrol technology replaces wild mosquitoes with those carrying the virus-blocking Wolbachia bacterium. Because the most widely used strain, wMel, is adversely affected by heat stress, we examined how global warming may influence wMel-based replacement. We simulated interventions in two locations with successful field trials using Coupled Model Intercomparison Project Phase 5 climate projections and historical temperature records, integrating empirical data on wMel's thermal sensitivity into a model of Aedes aegypti population dynamics to evaluate introgression and persistence over one year. We show that in Cairns, Australia, climatic futures necessitate operational adaptations for heatwaves exceeding two weeks. In Nha Trang, Vietnam, projected heatwaves of three weeks and longer eliminate wMel under the most stringent assumptions of that symbiont's thermal limits. We conclude that this technology is generally robust to near-term (2030s) climate change. Accelerated warming may challenge this in the 2050s and beyond.

Ten simple rules on writing clean and reliable open-source scientific software.

Functional, usable, and maintainable open-source software is increasingly essential to scientific research, but there is a large variation in formal training for software development and maintainability. Here, we propose 10 "rules" centered on 2 best practice components: clean code and testing. These 2 areas are relatively straightforward and provide substantial utility relative to the learning investment. Adopting clean code practices helps to standardize and organize software code in order to enhance readability and reduce cognitive load for both the initial developer and subsequent contributors; this allows developers to concentrate on core functionality and reduce errors. Clean coding styles make software code more amenable to testing, including unit tests that work best with modular and consistent software code. Unit tests interrogate specific and isolated coding behavior to reduce coding errors and ensure intended functionality, especially as code increases in complexity; unit tests also implicitly provide example usages of code. Other forms of testing are geared to discover erroneous behavior arising from unexpected inputs or emerging from the interaction of complex codebases. Although conforming to coding styles and designing tests can add time to the software development project in the short term, these foundational tools can help to improve the correctness, quality, usability, and maintainability of open-source scientific software code. They also advance the principal point of scientific research: producing accurate results in a reproducible way. In addition to suggesting several tips for getting started with clean code and testing practices, we recommend numerous tools for the popular open-source scientific software languages Python, R, and Julia.

Health Impacts of Climate Change as Contained in Economic Models Estimating the Social Cost of Carbon Dioxide.

The health impacts of climate change are substantial and represent a primary motivating factor to mitigate climate change. However, the health impacts in economic models that estimate the social cost of carbon dioxide (SC-CO2) have generally been made in isolation from health experts and have never been rigorously evaluated. Version 3.10 of the Framework for Uncertainty, Negotiation and Distribution (FUND) model was used to estimate the health-based portion of current SC-CO2 estimates across low-, middle-, and high-income regions. In addition to the base model, three additional experiments assessed the sensitivity of these estimates to changes in the socio-economic assumptions in the model. Economic impacts from adverse health outcomes represent ∼8.7% of current SC-CO2 estimates. The majority of these health impacts (74%) were attributable to diarrhea mortality (from both low- and high-income regions) followed by diarrhea morbidity (12%) and malaria mortality (11%); no other health impact makes a meaningful contribution to SC-CO2 estimates in current economic models. The results of the socio-economic experiments show that the health-based portion of SC-CO2 estimates are highly sensitive to assumptions regarding income elasticity of health effects, income growth, and use of equity weights. Improving the health-based portion of SC-CO2 estimates could have substantial impacts on magnitude of the SC-CO2. Incorporating additional health impacts not previously included in estimates of SC-CO2 will be a critical component of model updates. This effort will be most successful through coordination between economists and health researchers and should focus on updating the form and function of concentration-response functions.

Principles for data analysis workflows.

A systematic and reproducible "workflow"-the process that moves a scientific investigation from raw data to coherent research question to insightful contribution-should be a fundamental part of academic data-intensive research practice. In this paper, we elaborate basic principles of a reproducible data analysis workflow by defining 3 phases: the Explore, Refine, and Produce Phases. Each phase is roughly centered around the audience to whom research decisions, methodologies, and results are being immediately communicated. Importantly, each phase can also give rise to a number of research products beyond traditional academic publications. Where relevant, we draw analogies between design principles and established practice in software development. The guidance provided here is not intended to be a strict rulebook; rather, the suggestions for practices and tools to advance reproducible, sound data-intensive analysis may furnish support for both students new to research and current researchers who are new to data-intensive work.

Translating gene drive science to promote linguistic diversity in community and stakeholder engagement.

Information about genetic engineering (GE) for vector control in the United States is disseminated primarily in English, though non-English speakers are equally, and in some geographic regions even more affected by such technologies. Non-English-speaking publics should have equal access to such information, which is especially critical when the technology in question may impact whole communities. We convened an interdisciplinary workgroup to translate previously developed narrated slideshows on gene drive mosquitoes from English into Spanish, reviewing each iteration for scientific accuracy and accessibility to laypeople. Using the finalised stimuli, we conducted five online, chat-based focus groups with Spanish-speaking adults from California. Overall, participants expressed interest in the topic and were able to summarise the information presented in their own words. Importantly, participants asked for clarification and expressed scepticism about the information presented, indicating critical engagement with the material. Through collaboration with Spanish-speaking scientists engaged in the development of GE methods of vector control, we translated highly technical scientific information into Spanish that successfully engaged Spanish-speaking participants in conversations about this topic. In this manuscript, we document the feasibility of consulting Spanish-speaking publics about a complex emerging technology by drawing on the linguistic diversity of the scientific teams developing the technology.