Data-based, synthesis-driven: Setting the agenda for computational ecology

Computational thinking is the integration of algorithms, software, and data, tosolve general questions in a field. Computation ecology has the potential totransform the way ecologists think about the integration of data and models. Asthe practice is gaining prominence as a way to conduct ecological research, itis important to reflect on what its agenda could be, and how it fits within thebroader landscape of ecological research. In this contribution, we suggest areasin which empirical ecologists, modellers, and the emerging community ofcomputational ecologists could engage in a constructive dialogue to build on oneanother's expertise; specifically, about the need to make predictions frommodels actionable, about the best standards to represent ecological data, andabout the proper ways to credit data collection and data reuse. We discuss howtraining can be amended to improve computational literacy.TP thanks the Canadian Institute for Ecology and Evolution for financial support. BIS is supported by the Natural Environment Research Council as part of the Cambridge Earth System Science NERC DTP (NE/L002507/1)

Multilevel optimization for policy design with agent-based epidemic models

Epidemiological modeling has a long history and is often used to forecast the course of infectious diseases or pandemics. These models come in different complexities, ranging from systems of simple ordinary differential equations (ODEs) to complex agent-based models (ABMs). The former allow a fast and straightforward optimization, but are limited in accuracy, detail, and parameterization, while the latter can resolve spreading processes in detail, but are extremely expensive to optimize. Epidemiological modeling can also be used to propose and design non-pharmaceutical interventions such as lockdowns. In general, their optimal design often leads to nonlinear optimization problems. We consider policy optimization in a prototypical situation modeled as both ODE and ABM, review numerical optimization approaches, and propose a heterogeneous multilevel approach based on combining a fine-resolution ABM and a coarse ODE model. Numerical experiments, in particular with respect to convergence speed, are given for illustrative examples

Tracking U.S. Pertussis Incidence: Correlation of Public Health Surveillance and Google Search Data Varies by State

The Morbidity and Mortality Weekly Reports of the U.S. Centers for Disease Control and Prevention document a raw proxy for counts of pertussis cases in the U.S., and the Project Tycho (PT) database provides an improved source of these weekly data. These data are limited because of reporting delays, variation in state-level surveillance practices, and changes over time in diagnosis methods. We aim to assess whether Google Trends (GT) search data track pertussis incidence relative to PT data and if sociodemographic characteristics explain some variation in the accuracy of state-level models. GT and PT data were used to construct auto-correlation corrected linear models for pertussis incidence in 2004&ndash;2011 for the entire U.S. and each individual state. The national model resulted in a moderate correlation (adjusted R2&thinsp;=&thinsp;0.2369, p&thinsp;&lt;&thinsp;0.05), and state models tracked PT data for some but not all states. Sociodemographic variables explained approximately 30% of the variation in performance of individual state-level models. The significant correlation between GT models and public health data suggests that GT is a potentially useful pertussis surveillance tool. However, the variable accuracy of this tool by state suggests GT surveillance cannot be applied in a uniform manner across geographic sub-regions.</p