Forecasting the abundance of disease vectors with deep learning

Arboviral diseases such as dengue, Zika, chikungunya or yellow fever are a worldwide concern. The abundance of vector species plays a key role in the emergence of outbreaks of these diseases, so forecasting these numbers is fundamental in preventive risk assessment. Here we describe and demonstrate a novel approach that uses state-of-the-art deep learning algorithms to forecast disease vector abundances. Unlike classical statistical and machine learning methods, deep learning models use time series data directly as predictors and identify the features that are most relevant from a predictive perspective. We demonstrate for the first time the application of this approach to predict short-term temporal trends in the number of Aedes aegypti mosquito eggs across Madeira Island for the period 2013 to 2019. Specifically, we apply the deep learning models to predict whether, in the following week, the number of Ae. aegypti eggs will remain unchanged, or whether it will increase or decrease, considering different percentages of change. We obtained high predictive performance for all years considered (mean AUC = 0.92 ± 0.05 SD). Our approach performed better than classical machine learning methods. We also found that the preceding numbers of eggs is a highly informative predictor of future trends. Linking our approach to disease transmission or importation models will contribute to operational, early warning systems of arboviral disease risk.info:eu-repo/semantics/publishedVersio

The CLIMALERT project: Climate alert smart system for sustainable water and agriculture

The vulnerability of sensitive European regions to hydro-meteorological extremes has increased dramatically over the past few decades. Extreme weather and climate events are increasingly happening worldwide due to ongoing climate change. As a consequence, hydro- meteorological disaster events are affecting the European economy, environment and society, with impacts on food production, food distribution infrastructure, livelihood assets and human health, in both rural and urban areas. Meanwhile, climate services have started to be developed to further anticipate the impacts of climate variability and to apply climate forecasts in different sectors, such as agriculture and water management. However, connections between climate information users and providers are still weak. The CLIMALERT project emerges to provide climate information in a format that prospective users find it easy to understand and/or incorporate into decision-making. The project main goals are: i) strengthen the link between climate research, water resources and the agriculture sector to assist the management of natural resources, enhance agricultural livelihoods and reduce underlying causes of vulnerability, ii) improve the techniques and tools currently used to incorporate weather and climate information into risk assessment and decision making in agriculture and water sectors, and, iii) contribute to assist decision- makers in the implementation of adaptation and mitigation strategies. In this talk, we will present the project framework, the study areas, the engagement with stakeholders, the selection of climate and hydrological indicators, and the development of an alert system platform that aims to contribute to reduce the risks and vulnerabilities for the agriculture and water management sectors, providing economically valuable services and long-term benefits to farmers and societyERA4CS/0004/2016 - CLIMALER

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Mechanistic species distribution modelling as a link between physiology and conservation

Climate change conservation planning relies heavily on correlative species distribution models that estimate future areas of occupancy based on environmental conditions encountered in present-day ranges. The approach benefits from rapid assessment of vulnerability over a large number of organisms, but can have poor predictive power when transposed to novel environments and reveals little in the way of causal mechanisms that define changes in species distribution or abundance. Having conservation planning rely largely on this single approach also increases the risk of policy failure. Mechanistic models that are parameterized with physiological information are expected to be more robust when extrapolating distributions to future environmental conditions and can identify physiological processes that set range boundaries. Implementation of mechanistic species distribution models requires knowledge of how environmental change influences physiological performance, and because this information is currently restricted to a comparatively small number of well-studied organisms, use of mechanistic modelling in the context of climate change conservation is limited. In this review, we propose that the need to develop mechanistic models that incorporate physiological data presents an opportunity for physiologists to contribute more directly to climate change conservation and advance the field of conservation physiology. We begin by describing the prevalence of species distribution modelling in climate change conservation, highlighting the benefits and drawbacks of both mechanistic and correlative approaches. Next, we emphasize the need to expand mechanistic models and discuss potential metrics of physiological performance suitable for integration into mechanistic models. We conclude by summarizing other factors, such as the need to consider demography, limiting broader application of mechanistic models in climate change conservation. Ideally, modellers, physiologists and conservation practitioners would work collaboratively to build models, interpret results and consider conservation management options, and articulating this need here may help to stimulate collaboration