Developing an approach for evaluating epidemic decision-making in low-income and humanitarian settings

Epidemics of infectious disease continue to exact a high public health toll and occur routinely in low-and middle-income countries. Populations experiencing humanitarian crises are at particular risk. In the past 20 years, large scale pandemics have focused attention on a limited number of epidemic pathogens and the preparation for emerging disease threats. However, relatively less attention has been paid to the response to smaller scale epidemics occurring within the contexts of humanitarian crises, most of which continue to be due to well-known pathogens, and as well as to the process of decision-making in these responses. This PhD project aims to contribute to epidemic response in low-income and humanitarian settings by developing an assessment approach and related instruments to evaluate decision-making in epidemic response. Through review of public health evaluation frameworks relevant to epidemic evaluations, this thesis presents a new Adaptive Epidemic Response framework that describes the different phases and components of an epidemic response. A further systematic review of the status of epidemic evaluations in low-income and humanitarian settings demonstrates significant gaps in evaluation coverage, variability in evaluation methods and quality and highlights a pressing need for standardization. In collaboration with the Federal Ministry of Health in Somalia and the Heritage Institute for Policy Studies, a qualitative study of decision-making in the COVID-19 response within Somalia was undertaken to describe the process of decision making and the role of various internal and external factors on decision processes and outcomes. Through further collaboration with WHO Somalia and CARE Somalia, a decision-making audit tool was piloted to assess each organisation’s COVID-19 response. By producing a decision scorecard and identifying shortcomings in decision-making quality, this thesis contributes insight that can improve future decision-making quality in response to epidemics.