UC Berkeley

Climate change has been deemed the greatest threat to global public health. And yet, there is a yawning research gap surrounding the impact that this anthropogenic phenomenon may have on health-relevant infrastructure and decisionmaking. This dissertation is structured around a theoretical framework and four lead author papers that employ computational methods to interrogate the potential implications that one facet of environmental change – temperature – may have for designing and deploying vector-borne disease prevention. The points raised in this body of work contribute to the model-based acceleration of adaptive public health management as the planet continues to warm and new options for biocontrol are advanced. A substantive Appendix grounds considerations for one such innovation, gene drive technology, in the context of past field trials for genetic-based tools.

Chapter 1 asks: having characterized a temperature-driven shift in the risk profile for infectious diseases transmitted by mosquitoes, what science is needed to operationalize responsive biocontrol? I propose a framework for designing application-ready research, instigated by climate concerns and extensible to be broadly relevant to the public health arena. The proposed TEchno-STrategic (TEST) framework distills vector-borne disease prevention into two broad areas: the technological and the strategic. TEST maps onto the methodological field of sequential decision analytics, wherein decisions are optimized by defining performance indicators and problem uncertainties, then making several successive observations of a dynamic process.

Chapter 2 acknowledges that the evidence-based evaluation of intervention options requires baseline performance criteria that enable direct comparisons across both technologies and models; this necessity is underscored by the scientific advances that are expanding biocontrol options to include genetically engineered organisms. Climate change – which extrinsically affects mosquito thermal biology and stands to confound the epidemiological metrics unique to vector-borne parasitic species and viruses – presents an argument for measurements that are suitable in the presence of environmental variability. Therefore, I propose Standard Entomological Metrics (SEMs) to facilitate the model-based comparison of vector control tools.

Chapter 3 observes that, like the biological vectors they are developed to manage, new genetic-based intervention technologies may also be subject to the complexities of global warming. No research had yet examined the effect of climate change on Wolbachia-based biocontrol methods, nor employed empirical data on the thermal sensitivity of Wolbachia to model the dynamics of this tool. Further, published modeling work that explored the influence of temperature on mosquito-borne disease focused on trends (increasing average temperatures) not variability (the magnitude, duration, and frequency of heat waves). I simulated future interventions in two locations with historically successful field trials to conclude that this technology is generally robust to near-term (2030s) climate change.

Chapter 4 demonstrates how nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) can advance the state of the art in designing the operational implementation of genetic biocontrol technologies. I review existing approaches to optimizing the deployment of these interventions, then formulate a mathematical program that enables the incorporation of crucial ecological and logistical details. I use the model to optimize the application of three distinct transgenic tools -- two of which are presently in active use around the world -- given alternative policy goals and diverse conditions of temperature and geography.

Chapter 5 highlights the utility of open-source software for addressing potential equity and efficiency gaps in existing approaches to public health decision-making, which presently omit the optimization methods that can accommodate local-level operational objectives and limitations. It outlines GeneDrive.jl, a Julia package tailored for planning the genetic-based interventions that offer timely solutions to mosquitoes’ evolving insecticide resistance. The scalable software accounts for biological, climatological, geographic, and budgetary context to optimize customized goals given uncertain daily weather variability.

Chapter 6 concludes, summarizing takeaways from past work and outlining potential future efforts. Finally, Appendix A reviews lessons learned from existing field trials of genetic-based biocontrol for public health, including Wolbachia-transfected mosquitoes, RIDL (Release of Insects carrying a Dominant Lethal gene), and irradiation. From these precedents, it distills implications for a phased exploration of gene drive technology -- including homing-based gene drive, chromosomal translocation, and split gene drive systems -- as interventions that are potentially suitable for intermediate release.