Using a Cluster Survey to Reconstruct and Estimate the Magnitude of a Major Dengue Outbreak in El-Obeid, Sudan

Introduction: Dengue fever surveillance in El-Obeid, Sudan, identified approximately 572 cases. To estimate the outbreak's true magnitude, a community-wide cluster survey was conducted. Methodology: Eighty clusters, each comprising ten households, were chosen via systematic random sampling. Rapid diagnostic tests were used on blood samples from symptomatic and asymptomatic individuals within each household. Results: There were discrepancies between the official surveillance system and the cluster survey. The cluster survey revealed a delayed detection, a wider spread of the virus, and a significantly higher number of cases compared to the official data. The cluster survey of 1285 participants residing in approximately 4,800 households identified a high prevalence of dengue markers (35.3%). This translates to an estimated 323,604 individuals (extrapolated from total population) with potential past or present infection. About 24.7% of the sample were positive for IgM, 25.1% for IgG, and 1.7% for NS-1, indicating an estimated 14,930 individuals with potentially infectious cases. Investigations in El-Obeid City health facilities revealed critical shortcomings in the dengue outbreak response. These included severely limited communication between preventive and curative healthcare, a barely functional public health laboratory hindering diagnostics, and a shortage of trained staff, highlighting broader resource constraints. Additionally, inadequate public awareness campaigns failed to effectively reach the population. Conclusions: Combining data from both surveillance and the cluster survey provided valuable insights into the El-Obeid dengue outbreak. The survey revealed a delay in detection and underreporting by the existing system. It also likely increased awareness among healthcare workers and the public, potentially improving case detection and control measures. This integrated approach highlights the importance of using multiple data sources for a more comprehensive understanding of outbreaks.