Vulnerability is a complex concept that is relevant to many fields, whether it is economics, health, education or engineering. It involves understanding the correlations of risk and response, which can leave people disadvantaged due to factors beyond their control. In this article, we delve into the multifaceted nature of vulnerability and explore how predictive modelling can assist with identifying those most in need. By understanding the complexities of vulnerability better, we can more accurately locate those in need and provide them with the assistance and support they require.
Understanding Vulnerability: Who is Vulnerable and Why?
Hoddinott and Quisumbing (2003), in the journal Risks, Shocks, and Human Development, note the difficulty with defining vulnerability. They say: “Vulnerability - like risk and love - means different things to different people; there are many definitions of vulnerability and, seemingly, no consensus on its definition or measurement.”
In most communities around the world women, children, elderly people, those fleeing war and disaster, ethnic minorities and socio economically weaker ones are considered to be vulnerable. For instance, women might lack the same level of autonomy and rights as men in some communities and thus be unable to seek education or health services for themselves. Similarly children and elderly people could be dependent on others to make decisions on their behalf which could put them under socioeconomic disadvantages. In essence, vulnerability prevents certain groups having equal access to basic needs such as housing, healthcare, education, and nutrition.
AI and Predictive Modelling: How can it help?
Identifying vulnerable groups is crucial, but it can be difficult without direct information, however, Big data and predictive modelling can help. By using contextual information, such as housing quality, distribution of basic facilities such as educational institutes, health facilities, night-time lights, road networks and even risk levels for natural disasters like floods or droughts, vulnerable groups can be located - for example children at risk of malnutrition or girls who may not complete their schooling.
EPCON used predictive modelling to find vulnerable population groups across Nigeria. By cross-referencing simple data sources such as literacy levels, sanitation, and access to clean water, vulnerable groups in this region could be located, and then supported. With AI and predictive modelling, knowledge of one location can be tweaked and extrapolated to a national level to better ensure targeted interventions.
In conclusion, predictive modelling is a powerful tool that can greatly assist local programs in the domain of health and social welfare with locating those most in need. Finding such groups helps develop a better understanding of vulnerability and aids in the creation of a more equitable society, where everyone has appropriate access to the available resources.