Spatial-Temporal Model for Emerging Disease Surveillance: Case Study of Nodding Syndrome

Abstract

Nodding syndrome is an illness that has eluded surveillance models in Africa for over six decades, since its identification in the 1960s in South Sudan, Southern Tanzania and later Uganda. The purpose of the study was to develop a spatial-temporal model for emerging disease surveillance (nodding syndrome). The specific objectives of the study were; to identify the factors that explains the spatial-temporal distribution of nodding syndrome, to determine the relationship between the spatial-temporal distribution of nodding syndrome and the factors influencing the distribution, to design a model for surveillance of nodding syndrome from the spatial-temporal relationships established, and finally to evaluate the disease surveillance model. The research adopted positivist philosophical and deductive epistemological positions in its methodological enquiry. For the positivist view, it assumed that a spatial and temporal reality of nodding syndrome can be identified, investigated, understood, and measured. For those reasons, the research began with a theory and views associated with nodding syndrome, developed hypotheses from that theory, and then collected and analyzed data to test the hypotheses to derive generalized conclusions. Design science method was preferred because of its rigour by using existing knowledge of epidemiologic triangle to inform conceptual model and later used it for building disease surveillance process models as the final artefact. Ethical clearance was sought from Institutional Review Board and administrative clearance from districts under study for the research. Spatial and temporal data of vector, climate and environment were collected to provide empirical evidence for triangulation relationship model before being used for building surveillance model for nodding syndrome. The findings were that blackfly (simulium damnosium) seasonal abundance was found to be influenced by rainfall seasons, and are closely linked to with the seasonal onsets of nodding syndrome. Indigenous knowledge and terrain analysis agree with this fact. Among the factors considered for analysis, Nodding-Syndrome-Associated-Epilepsy was found to influence nodding syndrome significantly closely followed by rainfall seasons. The research concludes that in a situation of the outbreak of nodding syndrome, associated epilepsy data to be used as an alternative data source for surveillance. Based on Geographic Information System triangulation (comparing results) of environment, vector and the scaffolding patterns, a surveillance model for nodding syndrome was proposed using Unified Modelling Language activity flow chart diagram. The model was evaluated using Delphi technique and experts found the model processes relevant for surveillance of nodding syndrome and emerging diseases with unknown cause.