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Using real-time data from the University of Luxembourg’s COME-HERE nationally representative panel survey, covering more than 8,000 individuals across France, Germany, Italy, Spain, and Sweden, the author investigates how income distributions and poverty rates have changed from January to September 2020. The author finds that poverty rates increased on average in all countries from January to May and partially recovered in September. The increase in poverty is heterogeneous across countries, with Italy being the most affected and France the least; within countries, COVID-19 contributed to exacerbating poverty differences across regions in Italy and Spain. With a set of poverty measures from the Foster–Greer–Thorbecke family, the author then explores the role of individual characteristics in shaping different poverty profiles across countries. Results suggest that poverty increased disproportionately more for young individuals, women, and respondents who had a job in January 2020 – with different intensities across countries.

Purpose – The purpose of this study is to explore the concept of spatial modeling in adolescent and under-five children’s nutritional status.

Design/Methodology/Approach – The indicator used to identify spatial autocorrelation is the Local Indicator of Spatial Association (LISA). LISA is a method of exploratory analysis of spatial data capable of detecting spatial relationships at the local level and its effects globally. Aplication of stochastic modeling in spatial nutrition identification mapping can be categorized into two cases based on spatial autocorrelation and non-spatial autocorrelation.

Findings – This results of this study indicate that there is no spatial autocorrelation in the adolescent nutritional dataset. The thematic map for anemia showed that that the highest number of anemia in adolescents was in KutaAlam sub-districts (48 people). Sub-districts that were second most common were Meuraxa, Jaya Baru, and Baiturrahman sub-districts. The fewest cases were found in Lueng Bata sub-district (12 people). There were no sub-districts affected by neighboring areas, in the case of adolescents’ anemia in Banda Aceh. For the under-five nutritional data set, it shows that there are four factors that significantly affect spatial influence, which are malnutrition, chronic energy deficiency, woman of child-bearing age, proportion of family planning, percentage of households with PHBS and coverage of access to clean water.

Research Limitations/Implications – Anemia data were obtained with a school-based survey. Household survey would be better to implement in spatial analysis.

Practical Implications – The comparison of the dataset with the two methods provides a simple example to implement special autocorrelation in practice.

Social Implications – The results contribute to a much better comparison in many cases in the nutritional field.

Originality/Value – This is the initial nutritional status of adolescents in Banda Aceh.