Mapping the Structure of Social Vulnerability Systems for Malaria in East Africa

Abstract

Millions of people fall ill with malaria every year—most of them are located in sub-Saharan Africa. The weight of the burden of malaria on a society is determined by a complex interplay of environmental and social factors, including poverty, awareness and education, among others. A substantial share of the affected population is characterized by a general lack of anticipation and coping capacities, which renders them particularly vulnerable to the disease and its adverse side effects. This work aims at identifying interdependencies and feedback mechanisms in the malaria social vulnerability system and their variations in space by combining concepts, methods and tools from Climate Change Adaptation, Spatial Analysis, and Statistics and System Dynamics. The developed workflow is applied to a selected set of social, economic and biological vulnerability indicators covering five East-African Nations. As the study areas’ local conditions vary in a multitude of aspects, the social vulnerability system is assumed to vary accordingly throughout space. The study areas’ spatial entities were therefore aggregated into three system-regions using correlation-based clustering. Their respective correlation structures are displayed as Causal Loop Diagrams (CLDs). While the three resulting CLDs do not necessarily display causal relations (as the set of social vulnerability indicators are likely linked through third variables and parts of the data are proxies), they give a good overview of the data, can be used as basis for discussions in participatory settings and can potentially enhance the understanding the malaria vulnerability system.

Dynamic modelling of coastal aquaculture systems: A Review

“…SDM methodology has been applied in different areas, including manufacturing, energy, climate, population, ecology and economics (Sterman et al, 2012;Adane et al, 2019;Harvey, 2021;Moallemi et al, 2021). The SDM was developed to improve industrial business processes (Forrester, 1971), successfully applied to model human and natural resources interactions (Meadows et al, 1974), climate change risk assessments (Duran-Encalada et al, 2017;Gohari et al, 2017;Masia et al, 2018), water management issues (Davies and Simonovic, 2011;Gohari et al, 2017), disasters studies (Simonovic, 2010;Menk et al, 2020), waterenergy-food nexus studies (Davies and Simonovic, 2008;Sušnik et al, 2018) and applications fostering participatory modelling (Stave, 2010;Malard et al, 2017). It is an ideal tool to study complex interactions and dynamic behaviour in a wide variety of complex systems (Ford, 2020), though may not be ideal for all applications.…”