Objective: Food security during public health emergencies relies on situational awareness of needs and resources. Artificial intelligence (AI) has revolutionized situational awareness during crises, allowing the allocation of resources to needs through machine learning algorithms. Limited research exists monitoring Twitter for changes in the food security-related public discourse during the COVID-19 pandemic. We aim to address that gap with AI by classifying food security topics on Twitter and showing topic frequency per day. Methods: Tweets were scraped from Twitter from January 2020 through December 2021 using food security keywords. Latent Dirichlet Allocation (LDA) topic modeling was performed, followed by time-series analyses on topic frequency per day.Results: 237,107 tweets were scraped and classified into topics, including food needs and resources, emergency preparedness and response, and mental/physical health. After the WHO’s pandemic declaration, there were relative increases in topic density per day regarding food pantries, food banks, economic and food security crises, essential services, and emergency preparedness advice. Threats to food security in Tigray emerged in 2021.Conclusions: AI is a powerful yet underused tool to monitor food insecurity on social media. Machine learning tools to improve emergency response should be prioritized, along with measurement of impact. Further food insecurity word patterns testing, as generated by this research, with supervised machine learning models can accelerate the uptake of these tools by policymakers and aid organizations.

The prevalence of stunting in young Indonesian children is the highest among Southeast Asian Nations, and is associated with poor growth during the prenatal and early postnatal periods. A maternal mentoring program was developed for Indonesian women to improve birth outcomes. A cluster randomized control trial (CRCT) was conducted in three sub-districts of the Special Re-gion of Yogyakarta, Indonesia. A total of 384 eligible participants were randomly allocated to either intervention (received maternal mentoring program and standard care; n=189) or control (received standard care only; n=195). The maternal mentoring program provided preconception health education; health monitoring; and text message reminders for preconception women. Fetal growth was measured between gestational weeks 27-30 using estimated fetal weight generated from ultrasonographic measurement. Birth weight was measured within 24 hours of birth. A structured questionnaire captured women’s’ demographics, pregnancy readiness, and body mass index (BMI. After adjustment, fetal weight was 14% (95% CI: 5.1-23.0) higher in the intervention group than in the control group, and the average weight-for-length Z-score at birth was 0.16 (95% CI: 004-0.30) higher in the intervention group than in the control group. A maternal mentoring program was associated with improved fetal growth and birth weight in this population.

Background: Machine learning has revolutionized situational awareness during disaster management by classifying, clustering, and predicting impacted locations and people. Despite its importance, no review has been conducted on machine learning tools for food assistance efforts during emergency or non-emergency situations. The purpose of this scoping review is to address that gap. Methods: Keywords were defined within the concepts of food assistance and machine learning. After the database searches, PRISMA guidelines were followed to perform a partnered, two-round scoping literature review. Text mining and Latent Dirichlet Allocation topic modeling algorithms were used to determine trends. Results: 28 articles met criteria and were included in the analysis. The types of study designs included: model development (42.9%), non-study (i.e., text and opinion) (28.6%), qualitative research (14.3%), case study (10.7%), and meta-analysis (3.6%). There were no quantitative studies. The machine learning tools’ main functions were improving SNAP programs (32.1%), detecting needs and resources (25%), predicting food insecurity (21.4%), and situational awareness of current food insecurity issues (21.4%). None of these studies took place during a disaster or explicitly addressed emergency mitigation, preparedness, or recovery. All of the studies were in early phases of development and implementation. Conclusion: Machine learning tools for improving situational awareness, resource allocation, policymaking, and prediction have the potential to improve food assistance, but there is a lack of implementation and evaluation during all disaster phases. Also needed is more formative work on generating food-related queries and defining variables and features of food security.