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The recent global expansion of dengue has been facilitated by changes in urbanisation, mobility, and climate. In this work, we project future changes in dengue incidence and case burden to 2099 under the latest climate change scenarios. We fit a statistical model to province-level monthly dengue case counts from eight countries across Southeast Asia, one of the worst affected regions. We project that dengue incidence will peak this century before declining to lower levels with large variations between and within countries. Our findings reveal that northern Thailand and Cambodia will show the biggest decreases and equatorial areas will show the biggest increases. The impact of climate change will be counterbalanced by income growth, with population growth having the biggest influence on increasing burden. These findings can be used for formulating mitigation and adaptation interventions to reduce the immediate growing impact of dengue virus in the region., F.J.C.-G., R.G., R.L. and O.J.B. were supported by the UK Space Agency Dengue Forecasting Model Satellite-based System (D-MOSS). R.L. was supported by a Royal Society Dorothy Hodgkin Fellowship., Peer Reviewed, Postprint (published version)

Dengue is expanding globally, but how dengue emergence is shaped locally by interactions between climatic and socio-environmental factors is not well understood. Here, we investigate the drivers of dengue incidence and emergence in Vietnam, through analysing 23 years of district-level case data spanning a period of significant socioeconomic change (1998-2020). We show that urban infrastructure factors (sanitation, water supply, long-term urban growth) predict local spatial patterns of dengue incidence, while human mobility is a more influential driver in subtropical northern regions than the endemic south. Temperature is the dominant factor shaping dengue’s distribution and dynamics, and using long-term reanalysis temperature data we show that warming since 1950 has expanded transmission risk throughout Vietnam, and most strongly in current dengue emergence hotspots (e.g., southern central regions, Ha Noi). In contrast, effects of hydrometeorology are complex, multi-scalar and dependent on local context: risk increases under either short-term precipitation excess or long-term drought, but improvements in water supply mitigate drought-associated risks except under extreme conditions. Our findings challenge the assumption that dengue is an urban disease, instead suggesting that incidence peaks in transitional landscapes with intermediate infrastructure provision, and provide evidence that interactions between recent climate change and mobility are contributing to dengue’s expansion throughout Vietnam., The authors thank Colin Carlson for the insightful discussion of early results. This research was supported by the UK Space Agency-funded International Partnership Programme 2 D-MOSS project “An integrated dengue early warning system driven by Earth Observations in Vietnam” (all authors), as well as a Royal Society Dorothy Hodgkin Fellowship (R.L.), a Wellcome Trust Sir Henry Wellcome Fellowship (206471/Z/17/Z) and a UK Medical Research Council Career Development Award (MR/V031112/1, both O.B.)., Peer Reviewed, "Article signat per 21 autors/es: Rory Gibb, Felipe J. Colón-González, Phan Trong Lan, Phan Thi Huong, Vu Sinh Nam, Vu Trong Duoc, Hung Do Thai, Nguyễn Thanh Dong, Vien Chinh Chien, Ly Thi Thuy Trang, Do Kien Quoc, Tran Minh Hoa, Nguyen Hữu Tai, Tran Thi Hang, Gina Tsarouchi, Eleanor Ainscoe, Quillon Harpham, Barbara Hofmann, Darren Lumbroso, Oliver J. Brady & Rachel Lowe ", Postprint (published version)

Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from www.batdetective.org. When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio.