Bacterial meningitis is a significant public health concern, with over 1.2 million cases reported globally each year. Rwanda is at increased risk of meningitis outbreaks due to its proximity to countries that lie in the meningitis belt. Rwanda has been conducting surveillance and recording meningitis outbreak cases across the country since 2012. We evaluated the meningitis surveillance system at Kibogora Level Two Teaching hospital, Nyamasheke district of Rwanda to assess whether the surveillance objectives were being met. The study was cross-sectional, using purposive sampling to select healthcare providers participating in the meningitis surveillance. Rwanda's bacterial meningitis data from 2017 to 2021 was collected from clinical registers and Rwanda's electronic integrated disease surveillance system (eIDSR) from Kibogora Level Two Teaching Hospital catchment area, Nyamasheke district, Rwanda. The study area was chosen because a meningitis outbreak was recorded in the area and its bordering country namely Democratic of Republic of Congo (DRC) prior to the current study period. Information on the participant's demographics, occupation, training, professional experience, and their perception on the surveillance system were gathered using a structured questionnaire. Meningitis surveillance systems attributes including usefulness, acceptability, and flexibility were assessed and categorized as poor (< 50% score), reasonable (50-69%), good (70-90%), or excellent (> 90%) in reference to the study conducted on the evaluation of the meningitis surveillance system in Luanda Province, Angola in March 2017. Data collected from clinical registers and eIDSR were used to assess core functions of the meningitis surveillance system including accuracy in detection of cases, laboratory confirmation of cases, and availability of evaluation reports. Descriptive statistics were analyzed using Microsoft Office Excel. Thirty-one healthcare providers working on meningitis surveillance in the Kibogora Level Two Teaching Hospital were interviewed. During the period under evaluation, 48 suspected cases of meningitis were identified; 43 (90%) met the surveillance case definition, and only 10 (21%) were reported to eIDSR (completeness). Attributes such as flexibility scored good while stability and acceptability scored reasonable. Out of 48 suspected meningitis cases, only 2 (4%) samples were collected from patients and sent to the hospital laboratory for analysis. This study found a good knowledge level of the meningitis surveillance system among healthcare workers; however, the system's core functions, such as notification rate and laboratory confirmation were found to have gaps. The notification rate could be improved by conducting regular refresher courses for healthcare workers supporting surveillance system. Moreover, MoH could enhance the implementation of a national policy requiring mandatory CSF sample testing to confirm pathogens for all suspected cases. Future studies should explore performance-based incentives to improve reporting completeness. Rwanda's experience could provide insights for other low-resource settings facing similar surveillance challenges.

BACKGROUND: Indoor residual spraying (IRS) has been implemented in Rwanda in districts with high malaria transmission, including Ngoma District. The first IRS campaign (IRS-1) was conducted in March 2019, ahead of the peak malaria season, followed by a second campaign (IRS-2) in August 2020, targeting 89,331 structures. This study assessed factors influencing IRS uptake and evaluated the impact of IRS interventions on malaria morbidity in Ngoma District, Eastern Province, Rwanda. METHODS: A household survey employing multistage cluster sampling design was conducted in May 2021 to randomly select households. A structured questionnaire was administered to the head of household or a designated representative. Logistic regression, adjusted for the complex survey design and weighted for sampling, was used to identify factors associated with IRS uptake. Additionally, secondary data on malaria cases registered in the Rwanda Health Management Information System (RHMIS) from January 2015 to December 2022 were analyzed using interrupted time series analysis to evaluate the effect of IRS on malaria morbidity. RESULTS: A total of 636 households participated in the survey. Households headed by self-employed individuals (aOR = 0.07; 95% CI 0.01-0.55) and unemployed individuals (aOR = 0.18; 95% CI 0.03-0.99) were less likely to take up IRS compared to those headed by farmers. Households receiving IRS information through media channels (aOR = 0.01; 95% CI 0.00-0.17) were less likely to participate compared to those informed by community health workers. From the RHMIS data, 919,843 malaria cases were identified from January 2015 to December 2022. Interrupted time series analysis revealed that the baseline number of adjusted malaria cases was approximately 16,920. The first IRS intervention in March 2019 resulted in a significant reduction of 14,380 cases (p < 0.001), while the second intervention in August 2020 led to a reduction of 2495 cases, though this was not statistically significant (p = 0.098). CONCLUSION: This study demonstrates the effectiveness of IRS in reducing malaria incidence in Ngoma District and highlights the role of socioeconomic factors and sources of information in influencing IRS uptake. To maximize the impact of IRS and ensure equitable benefits, targeted strategies, enhanced IRS education, and integrated malaria control approaches, including the use of bed nets, are crucial.

The emerging field of outbreak analytics calls attention to the need for data from multiple sources to inform evidence-based decision making in managing infectious diseases outbreaks. To date, these approaches have not systematically integrated evidence from social and behavioural sciences. During the 2018-2020 Ebola outbreak in Eastern Democratic Republic of the Congo, an innovative solution to systematic and timely generation of integrated and actionable social science evidence emerged in the form of the Cellulle d'Analyse en Sciences Sociales (Social Sciences Analytics Cell) (CASS), a social science analytical cell. CASS worked closely with data scientists and epidemiologists operating under the Epidemiological Cell to produce integrated outbreak analytics (IOA), where quantitative epidemiological analyses were complemented by behavioural field studies and social science analyses to help better explain and understand drivers and barriers to outbreak dynamics. The primary activity of the CASS was to conduct operational social science analyses that were useful to decision makers. This included ensuring that research questions were relevant, driven by epidemiological data from the field, that research could be conducted rapidly (ie, often within days), that findings were regularly and systematically presented to partners and that recommendations were co-developed with response actors. The implementation of the recommendations based on CASS analytics was also monitored over time, to measure their impact on response operations. This practice paper presents the CASS logic model, developed through a field-based externally led consultation, and documents key factors contributing to the usefulness and adaption of CASS and IOA to guide replication for future outbreaks.

On August 1, 2018, the Democratic Republic of the Congo (DRC) declared its 10th Ebola virus disease (Ebola) outbreak in an area with a high volume of cross-border population movement to and from neighboring countries. The World Health Organization (WHO) designated Rwanda, South Sudan, and Uganda as the highest priority countries for Ebola preparedness because of the high risk for cross-border spread from DRC (1). Countries might base their disease case definitions on global standards; however, historical context and perceived risk often affect why countries modify and adapt definitions over time, moving toward or away from regional harmonization. Discordance in case definitions among countries might reduce the effectiveness of cross-border initiatives during outbreaks with high risk for regional spread. CDC worked with the ministries of health (MOHs) in DRC, Rwanda, South Sudan, and Uganda to collect MOH-approved Ebola case definitions used during the first 6 months of the outbreak to assess concordance (i.e., commonality in category case definitions) among countries. Changes in MOH-approved Ebola case definitions were analyzed, referencing the WHO standard case definition, and concordance among the four countries for Ebola case categories (i.e., community alert, suspected, probable, confirmed, and case contact) was assessed at three dates (2). The number of country-level revisions ranged from two to four, with all countries revising Ebola definitions by February 2019 after a December 2018 peak in incidence in DRC. Case definition complexity increased over time; all countries included more criteria per category than the WHO standard definition did, except for the "case contact" and "confirmed" categories. Low case definition concordance and lack of awareness of regional differences by national-level health officials could reduce effectiveness of cross-border communication and collaboration. Working toward regional harmonization or considering systematic approaches to addressing country-level differences might increase efficiency in cross-border information sharing.