Modelling pandemic-induced spatially heterogeneous patterns in income shock, food insecurity and their driving factors: a case of Pakistan
Abstract While pandemic-related income shock (IS) compromises societal well-being by leading to food insecurity (FI) in developing countries, evaluating the spatial inequities of IS, FI, and their significant driving factors is imperative for informed and targeted interventions. In this context, we explore spatial disparities in IS and FI, model significant factors driving IS, and examine the potential association between FI and IS in Pakistan—a nation with ~ 255 million people and vulnerable to food crises—using Covid-19 as a case study. Through the integrated deployment of spatial, econometric, and statistical models, priority intervention areas for IS and FI are identified, enabling efficient resource allocation and informed decision-making. Furthermore, spatial relationships among IS, FI, and significant factors are visualized using bivariate mapping to provide a geographical perspective, supporting the formulation of evidence-based action plans to mitigate their impacts. Our results show that Balochistan, the least developed province of Pakistan, is likely to observe higher levels of IS (95% confidence) during pandemics. Asset ownership, illiteracy, and employment are among the significant factors that drive IS in Pakistan. The geographical evaluation reveals considerable spatial heterogeneity in IS, FI, and several associated factors across the study area. While the higher IS clusters are identified in Khyber Pakhtunkhwa, the hotspots of FI are evident in Sindh province (95% confidence). This study has practical implications for medium- to long-term policy responses to health emergencies, which are essential for building resilience against future pandemics.
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