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The increased availability of datasets during the COVID-19 pandemic enabled machine-learning approaches for modeling and forecasting infectious diseases. However, such approaches are known to amplify the bias in the data they are trained on. Bias in such input data like clinical case data for COVID-19 is difficult to measure due to disparities in testing availability, reporting standards, and healthcare access among different populations and regions. Furthermore, the way such biases may propagate through the modeling pipeline to decision-making is relatively unknown. Therefore, we present a system that leverages a highly detailed agent-based model (ABM) of infectious disease spread in a city to simulate the collection of biased clinical case data where the bias is known. Our system allows users to load either a pre-selected region or select their own (using OpenStreetMap data for the environment and census data for the population), specify population and infectious disease parameters, and the degree(s) to which different populations will be overrepresented or underrepresented in the case data. In addition to the system, we provide a large number of benchmark datasets that produce case data at different levels of bias for different regions. Wehope that infectious disease modelers will use these datasets to investigate how well their models are robust to data bias or whether their model is overfit to biased data. 

The spread of infectious diseases is a highly complex spatiotemporal process, difficult to understand, predict, and effectively respond to. Machine learning and artificial intelligence (AI) have achieved impressive results in other learning and prediction tasks; however, while many AI solutions are developed for disease prediction, only a few of them are adopted by decision-makers to support policy interventions. Among several issues preventing their uptake, AI methods are known to amplify the bias in the data they are trained on. This is especially problematic for infectious disease models that typically leverage large, open, and inherently biased spatiotemporal data. These biases may propagate through the modeling pipeline to decision-making, resulting in inequitable policy interventions. Therefore, there is a need to gain an understanding of how the AI disease modeling pipeline can mitigate biased input data, in-processing models, and biased outputs. Specifically, our vision is to develop a large-scale micro-simulation of individuals from which human mobility, population, and disease ground-truth data can be obtained. From this complete dataset—which may not reflect the real world—we can sample and inject different types of bias. By using the sampled data in which bias is known (as it is given as the simulation parameter), we can explore how existing solutions for fairness in AI can mitigate and correct these biases and investigate novel AI fairness solutions. Achieving this vision would result in improved trust in such models for informing fair and equitable policy interventions. 

SUMMARY Greigite is a sensitive environmental indicator and occurs commonly in nature as magnetostatically interacting framboids. Until now only the magnetic response of isolated non-interacting greigite particles have been modelled micromagnetically. We present here hysteresis and first-order reversal curve (FORC) simulations for framboidal greigite (Fe3S4), and compare results to those for isolated particles of a similar size. We demonstrate that these magnetostatic interactions alter significantly the framboid FORC response compared to isolated particles, which makes the magnetic response similar to that of much larger (multidomain) grains. We also demonstrate that framboidal signals plot in different regions of a FORC diagram, which facilitates differentiation between framboidal and isolated grain signals. Given that large greigite crystals are rarely observed in microscopy studies of natural samples, we suggest that identification of multidomain-like FORC signals in samples known to contain abundant greigite could be interpreted as evidence for framboidal greigite. 

SUMMARY Quasi-linear field-dependence of remanence provides the foundation for sedimentary relative palaeointensity studies that have been widely used to understand past geomagnetic field behaviour and to date sedimentary sequences. Flocculation models are often called upon to explain this field dependence and the lower palaeomagnetic recording efficiency of sediments. Several recent studies have demonstrated that magnetic-mineral inclusions embedded within larger non-magnetic host silicates are abundant in sedimentary records, and that they can potentially provide another simple explanation for the quasi-linear field dependence. In order to understand how magnetic inclusion-rich detrital particles acquire sedimentary remanence, we carried out depositional remanent magnetization (DRM) experiments on controlled magnetic inclusion-bearing silicate particles (10–50 μm in size) prepared from gabbro and mid-ocean ridge basalt samples. Deposition experiments confirm that the studied large silicate host particles with magnetic mineral inclusions can acquire a DRM with accurate recording of declination. We observe a silicate size-dependent inclination shallowing, whereby larger silicate grains exhibit less inclination shallowing. The studied sized silicate samples do not have distinct populations of spherical or platy particles, so the observed size-dependence inclination shallowing could be explained by a ‘rolling ball’ model whereby larger silicate particles rotate less after depositional settling. We also observe non-linear field-dependent DRM acquisition in Earth-like magnetic fields with DRM behaviour depending strongly on silicate particle size, which could be explained by variable magnetic moments and silicate sizes. Our results provide direct evidence for a potentially widespread mechanism that could contribute to the observed variable recording efficiency and inclination shallowing of sedimentary remanences.