On the forecastability of food insecurity

Foini, Pietro; Tizzoni, Michele; Martini, Giulia; Paolotti, Daniela; Omodei, Elisa

doi:10.1038/s41598-023-29700-y

Cited by 15 publications

(6 citation statements)

References 63 publications

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“…Martini et al (2022) employed XGBoost, explaining up to 81% of the variation in insufficient food consumption and up to 73% of the variation in crisis or above food-based coping levels. The research by Foini et al (2023) demonstrated that precise forecasts of insufficient food consumption levels could be made up to 30 days into the future, thereby informing decisions regarding the allocation of need-based humanitarian assistance. However, Deléglise et al (2020) discovered that predicting food security indices is a challenging issue; their models did not exceed R 2 = 0.38 for the Household Dietary Diversity Score (HDDS) and R 2 = 0.35 for the Food Consumption Score (FCS).…”

Section: Discussionmentioning

confidence: 99%

Predicting Food Consumption to Reduce the Risk of Food Insecurity in Kazakhstan

Duisenbekova,

Kulisz,

Danilowska

et al. 2023

Economies

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In modern times, the risk of food insecurity is a concern for policymakers at the global and national levels, as the issue of hunger and malnutrition still exists. Food security is vulnerable to any crises. The main goal of this paper is to create a neural-network-based predictive model to forecast food consumption trends in Kazakhstan, aiming to reduce the risk of food insecurity. The initial phase of this study involved identifying socioeconomic factors that significantly influence food consumption behaviors in Kazakhstan. Principal component analysis was used to identify key variables, which became the basis for modelling artificial neural networks. It was revealed that the poverty rate, GDP per capita, and food price index are pivotal determinants of food consumption in Kazakhstan. Two models were prepared: to predict food consumption on a national scale per capita per month, and to predict the percentage distribution of various food categories. The prediction of the percentage distribution of various food categories in Kazakhstan demonstrates the positive modelling quality indicators and strengthens the assumption that network modelling can be used. Predictions for total food consumption over the next three years indicate declining metrics, raising concerns about the potential food insecurity risk in Kazakhstan.

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Section: Discussionmentioning

confidence: 99%

Predicting Food Consumption to Reduce the Risk of Food Insecurity in Kazakhstan

Duisenbekova,

Kulisz,

Danilowska

et al. 2023

Economies

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“…Socio-economic variables, which are crucial for understanding vulnerabilities and impacts, are often limited to coarse administrative levels and infrequent sampling intervals. Nevertheless, ML can successfully leverage them to predict drought impacts in the Horn of Africa: the WFP HungerMap utilizes XGBoost regression-tree models 74 to nowcast 75 and forecast 76 food insecurity. In socioeconomic models, interpretability is crucial to generate trust from decision makers.…”

Section: Addressing the Current Limitations With Existing Ml: Opportu...mentioning

confidence: 99%

Early warning of complex climate risk with integrated artificial intelligence

Reichstein,

Frank,

Benson

et al. 2024

Preprint

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As our climate undergoes significant shifts, both the Earth and human societies are increasingly exposed to disasters and stress. This situation underscores the critical need for robust Early Warning Systems (EWS), which are intricately designed to monitor, assess, and relay information about impending risks and hazards. EWS are vital in promoting resilient and sustainable development, yet they encounter substantial challenges in forecasting hazards and impacts, communicating risks, and in the efficiency of decision-making processes. In this perspective, we examine these challenges and explore the transformative role of integrated Artificial Intelligence Foundation Models (AI FMs), especially focusing on the capabilities of Large Multi-Modal Models (LMMs). We discuss the power of these models in developing a Multi-Hazard Early Warning System (MHEWS), combining Meteorological and Geospatial FMs for impact prediction in a comprehensive approach. Emphasizing a user-centric strategy, this paper highlights the importance of intuitive interfaces and incorporating community feedback to enhance crisis management. Given the complex nature of climate risks, we emphasize the need for causal representations in AI models, to avoid conclusions and predictions based on spurious correlations. Additionally, we introduce the concept of decadal EWSs, which aims to provide longer term, yet spatially resolved forecasts. By leveraging climate ensembles and generative approaches, these advancements aim to provide proactive solutions for evolving climate dynamics and societal responses.

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“…Near real-time data on the food insecurity situation collected by international organizations such as the World Food Programme can be crucial to monitor and forecast time trends of insufficient food consumption levels in countries at risk. 28 Another study found that food insecurity has been…”

Section: Food Insecuritymentioning

confidence: 99%

The One Health approach for allergic diseases and asthma

Jutel

Mosnaim

Bernstein

et al. 2023

Allergy

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The One Health approach is a collaborative and interdisciplinary strategy with focal point on human, animal, and environmental health interconnections. One Health can support the advanced management of allergic diseases and asthma, as complex, multifactorial diseases driven by interactions between the resilience response to the exposome. According to the One Health concept allergic diseases and asthma arising from exposures to a wide range of allergens, infectious agents and irritants (such as pollutants) occurring indoors and outdoors can be heavily influenced by environmental health (air, water, and soil quality) intermingled with animal health. These are currently heavily impacted by climate change, land use, urbanization, migration, overpopulation, and many more. Thus, a coordinated response to address the underlying factors that contribute to the development of allergic diseases and asthma needs to focus on the environment, human, and animal health altogether. Collaborative efforts across multiple sectors, including public health, veterinary medicine, environmental science, and community engagement are thus needed. A wide range of activities, including monitoring and surveillance of environmental and health data, targeted interventions to reduce exposures to allergens and irritants, and research on the underlying mechanisms that drive the development of allergic diseases and asthma are needed to move the field forward. In this consensus document elaborated by the European Academy of Allergy and Clinical Immunology (EAACI) and American Academy of Allergy, Asthma, and Immunology (AAAAI) under the practical allergy (PRACTALL) series, we provide insights into the One Heath approach aiming to provide a framework for addressing the complex and multifactorial nature of allergic diseases and asthma.

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On the forecastability of food insecurity

Cited by 15 publications

References 63 publications

Predicting Food Consumption to Reduce the Risk of Food Insecurity in Kazakhstan

Predicting Food Consumption to Reduce the Risk of Food Insecurity in Kazakhstan

Early warning of complex climate risk with integrated artificial intelligence

The One Health approach for allergic diseases and asthma

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