Displacement continues to increase at a global scale and is increasingly happening in complex, multicrisis settings, leading to more complex and deeper humanitarian needs. Humanitarian needs are therefore increasingly outgrowing the available humanitarian funding. Thus, responding to vulnerabilities before disaster strikes is crucial but anticipatory action is contingent on the ability to accurately forecast what will happen in the future. Forecasting and contingency planning are not new in the humanitarian sector, where scenario-building continues to be an exercise conducted in most humanitarian operations to strategically plan for coming events. However, the accuracy of these exercises remains limited. To address this challenge and work with the objective of providing the humanitarian sector with more accurate forecasts to enhance the protection of vulnerable groups, the Danish Refugee Council has already developed several machine learning models. The Anticipatory Humanitarian Action for Displacement uses machine learning to forecast displacement in subdistricts in the Liptako-Gourma region in Sahel, covering Burkina Faso, Mali, and Niger. The model is mainly built on data related to conflict, food insecurity, vegetation health, and the prevalence of underweight to forecast displacement. In this article, we will detail how the model works, the accuracy and limitations of the model, and how we are translating the forecasts into action by using them for anticipatory action in South Sudan and Burkina Faso, including concrete examples of activities that can be implemented ahead of displacement in the place of origin, along routes and in place of destination.

The discovery of more than 600 whole and fragmentary engraved stone plaques in the early third millennium BC infill from the ditches of a causewayed enclosure at Vasagård, on the Danish island of Bornholm, represents a unique find in Neolithic miniature art. Termed ‘sun stones’ in reference to the rayed images that characterise many of the plaques, the stones were deposited en masse over a short period. This article offers a fundamental classification of the rich imagery captured in the engravings and examines its potential function at a time of possible climatic crisis that impacted not just Bornholm but the wider northern hemisphere.

Galaxy Zoo is an online project to classify morphological features in extra-galactic imaging surveys with public voting. In this paper, we compare the classifications made for two different surveys, the Dark Energy Spectroscopic Instrument (DESI) imaging survey and a part of the Kilo-Degree Survey (KiDS), in the equatorial fields of the Galaxy And Mass Assembly (GAMA) survey. Our aim is to cross-validate and compare the classifications based on different imaging quality and depth. We find that generally the voting agrees globally but with substantial scatter, that is, substantial differences for individual galaxies. There is a notable higher voting fraction in favour of ‘smooth’ galaxies in the DESI+zoobot classifications, most likely due to the difference between imaging depth. DESI imaging is shallower and slightly lower resolution than KiDS and the Galaxy Zoo images do not reveal details such as disc features and thus are missed in the zoobot training sample. We check against expert visual classifications and find good agreement with KiDS-based Galaxy Zoo voting. We reproduce the results from Porter-Temple+ (2022), on the dependence of stellar mass, star formation, and specific star formation on the number of spiral arms. This shows that once corrected for redshift, the DESI Galaxy Zoo and KiDS Galaxy Zoo classifications agree well on population properties. The zoobot cross-validation increases confidence in its ability to compliment Galaxy Zoo classifications and its ability for transfer learning across surveys.

Echinococcosis is a parasitic invasion caused by a cestode of the genus Echinococcus. Kyrgyzstan is a country in Central Asia known for an extremely high incidence of echinococcosis. A total of 10 093 subjects were screened in the Osh, Naryn and Batken regions of Kyrgyzstan in 2015–2017 by ultrasound and questioned for potential risk factors. Cystic echinococcosis (CE) prevalence (combined newly diagnosed and post-surgery cases) ranged between 0.2 and 25.2% across the study regions. Typical factors, such as dog or livestock ownership, weakly affected CE risk (odds ratio [OR] = 1.18–1.83). Use of water from a well and owning a cat had a greater effect on CE risk (OR = 2.02–2.28). The risk factors of CE were highly dissimilar among the study regions, with patterns not always compatible with classical biohelminthosis transmission routes (no risk from livestock in certain areas, significant risk from using well water, owning cats). Therefore, the CE epidemic in Kyrgyzstan is not holistic in terms of potential mechanisms and risk factors, and certain areas can greatly benefit from preventive measures that will have limited efficiency elsewhere.