An integrated risk and vulnerability assessment framework for climate change and malaria transmission in East Africa

Abstract

BackgroundMalaria is one of the key research concerns in climate change-health relationships. Numerous risk assessments and modelling studies provide evidence that the transmission range of malaria will expand with rising temperatures, adversely impacting on vulnerable communities in the East African highlands. While there exist multiple lines of evidence for the influence of climate change on malaria transmission, there is insufficient understanding of the complex and interdependent factors that determine the risk and vulnerability of human populations at the community level. Moreover, existing studies have had limited focus on the nature of the impacts on vulnerable communities or how well they are prepared to cope. In order to address these gaps, a systems approach was used to present an integrated risk and vulnerability assessment framework for studies of community level risk and vulnerability to malaria due to climate change.ResultsDrawing upon published literature on existing frameworks, a systems approach was applied to characterize the factors influencing the interactions between climate change and malaria transmission. This involved structural analysis to determine influential, relay, dependent and autonomous variables in order to construct a detailed causal loop conceptual model that illustrates the relationships among key variables. An integrated assessment framework that considers indicators of both biophysical and social vulnerability was proposed based on the conceptual model.ConclusionsA major conclusion was that this integrated assessment framework can be implemented using Bayesian Belief Networks, and applied at a community level using both quantitative and qualitative methods with stakeholder engagement. The approach enables a robust assessment of community level risk and vulnerability to malaria, along with contextually relevant and targeted adaptation strategies for dealing with malaria transmission that incorporate both scientific and community perspectives.

Innovation policy analysis and decision making: a systems approach

“…The conceptualisation modelling phase enables a deep and integrated understanding of a system under study through the qualitative exploration and classification of systemic interconnections between the system's components, considering that this process is non-linear and that the policy analysis should be done holistically. In order to build such a comprehensive conceptual model, the structural analysis procedure using MICMAC analysis was enriched (Godet, 2006;Onyango et al, 2016;Suprun et al, 2016). The main purpose of this research stage is to provide a modeller with the detailed understanding of the role of each system's element, which in turn, assists in the further simulation modelling.…”

“…Given the complex nature of the construction innovation system under study, where high uncertainty and lack of data is involved, an integrated systems approach is needed as an appropriate method for modelling the multi-dimensional construction innovation process and studying the dynamic behaviour under different scenarios. A novel holistic modelling procedure ( Figure 3) combines a number of various modelling methods along with qualitative expert inputs under the same framework, which is beneficial compared to traditional computer modelling approaches, taking into account the highly qualitative and complex nature of the system under study (Onyango et al, 2016;Sahin et al, 2017). Moreover, the integrated holistic modelling strategy Figure 3.…”

A Multi-Methodology Approach to Creating a Causal Loop Diagram

“…However, TIA is inappropriate when historical data are unavailable or unreliable [67]. The previous effort to combine MICMAC and CLD can be found in [71][72][73][74]. This present research provides the justification for combining MICMAC and CLD.…”

A Vulnerability Assessment in Scant Data Context: The Case of North Horr Sub-County

“…In particular, there is a lack of quantitative flood-related risk and vulnerability assessment, mainly due to scant data issues [4], namely the absence or the difficulty of getting access to data in African countries [5][6][7] as well as problems with data quality (sustainable, continuous, credible, publicly accessible, quality assured dataset) [8]. The most relevant studies are conducted in the framework of vector-borne infectious diseases [9][10][11][12] or water-borne diseases, especially fecal-oral diseases [13,14].…”