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Introduction Bacterial meningitis still constitutes an important threat in Africa. In the meningitis belt, a clear seasonal pattern in the incidence of meningococcal disease during the dry season has been previously correlated with several environmental parameters like dust and sand particles as well as the Harmattan winds. In parallel, the evidence of seasonality in meningitis dynamics and its environmental variables remain poorly studied outside the meningitis belt. This study explores several environmental factors associated with meningitis cases in the Democratic Republic of Congo (DRC), central Africa, outside the meningitis belt area. Methods Non-parametric Kruskal-Wallis’ tests were used to establish the difference between the different health zones, climate and vegetation types in relation to both the number of cases and attack rates for the period 2000–2018. The relationships between the number of meningitis cases for the different health zones and environmental and socio-economical parameters collected were modeled using different generalized linear (GLMs) and generalized linear mixed models (GLMMs), and different error structure in the different models, i.e., Poisson, binomial negative, zero-inflated binomial negative and more elaborated multi-hierarchical zero-inflated binomial negative models, with randomization of certain parameters or factors (health zones, vegetation and climate types). Comparing the different statistical models, the model with the smallest Akaike’s information criterion (AIC) were selected as the best ones. 515 different health zones from 26 distinct provinces were considered for the construction of the different GLM and GLMM models. Results Non-parametric bivariate statistics showed that there were more meningitis cases in urban health zones than in rural conditions (χ2 = 6.910, p-value = 0.009), in areas dominated by savannah landscape than in areas with dense forest or forest in mountainous areas (χ2 = 15.185, p-value = 0.001), and with no significant difference between climate types (χ2 = 1.211, p-value = 0,449). Additionally, no significant difference was observed for attack rate between the two types of heath zones (χ2 = 0.982, p-value = 0.322). Conversely, strong differences in attack rate values were obtained for vegetation types (χ2 = 13.627, p-value = 0,001) and climate types (χ2 = 13.627, p-value = 0,001). This work demonstrates that, all other parameters kept constant, an urban health zone located at high latitude and longitude eastwards, located at low-altitude like in valley ecosystems predominantly covered by savannah biome, with a humid tropical climate are at higher risk for the development of meningitis. In addition, the regions with mean range temperature and a population with a low index of economic well-being (IEW) constitute the perfect conditions for the development of meningitis in DRC. Conclusion In a context of global environmental change, particularly climate change, our findings tend to show that an interplay of different environmental and socio-economic drivers are important to consider in the epidemiology of bacterial meningitis epidemics in DRC. This information is important to help improving meningitis control strategies in a large country located outside of the so-called meningitis belt., Author summary Bacterial meningitis remains an international public health threat. The most affected area worldwide is the Sahelian region between Senegal and Ethiopia called the meningitis belt. In parallel recurrent epidemics have also occurred out of the belt in Africa for the last twenty years. While environmental, socioeconomic and demographic factors are well described to explain the epidemiology of meningitis in the belt, very few studies have focused on the role of these factors to understand meningitis epidemics outside of the belt. Based on epidemiological, socio-economic, demographic and environmental data collected for the period 2000–2018 in the Democratic Republic of Congo (DRC), we used different statistical methods to explore the links between meningitis cases and the different explored factors. Our results showed that urbanized areas where populations have a low economic index of well-being, high latitude and longitude eastwards, medium to low altitude level, savannah-type vegetation, medium temperature value, are risk factors of meningitis outbreaks from North to South in the DRC. This information is important to help improving meningitis control strategies in a large country located outside the so-called meningitis belt.

Monkeypox is a viral disease with a clinical presentation resembling that of smallpox. Although monkeypox is considered to be an important zoonotic viral disease, its epidemiology remains poorly understood, especially the spatial and temporal distribution of the disease. The present study examined weekly reports of monkeypox cases collected from 2000 to 2015 at the health zone scale in the Democratic Republic of Congo. SaTScan was performed to identify spatial and temporal clusters of monkeypox cases. Significant primary spatial clusters were detected in the districts of Sankuru and Tshuapa. A centrifugal pattern was found, with significant primary spatial clusters extending over time from Sankuru and Tshuapa to several neighboring districts. Peaks of cases occurred from July to September for the 2000-2002 and 2003-2009 sub-periods and from January to March for the 2010-2015 sub-period. Despite the lack of additional data for confirmation, the increasing of monkeypox reported incidence was observed in the Democratic Republic of Congo during 2000-2015 period and this increase cannot be explain only by the improvements of surveillance systems. The detected spatial clusters were located in the dense rainforest of the Congo basin. The reasons for the excess incidence of monkeypox cases in the central region of the country are unknown, and the relative influence of ecological, environmental, and human factors on the mechanism of emergence of monkeypox has yet to be identified.

BackgroundThe Integrated Disease Surveillance and Response (IDSR) strategy implemented by the World Health Organization (WHO) in Africa has produced a large amount of data on participating countries, and in particular on the Democratic Republic of Congo (DRC). These data are increasingly considered as unevaluable and, therefore, as requiring a rigorous process of validation before they can be used for research or public health purposes. The aim of this study was to propose a method to assess the level of adequacy of IDSR morbidity data in reflecting actual morbidity.MethodsA systematic search of English- and French-language articles was performed in Scopus, Medline, Science Direct, Springer Link, Cochrane, Cairn, Persee, and Erudit databases. Other types of documents were identified through manual searches. Selected articles focused on the determinants of the discrepancies (differences) between reported morbidity and actual morbidity. An adequacy score was constructed using some of the identified determinants. This score was applied to the 15 weekly reported diseases monitored by IDSR surveillance in the DRC. A classification was established using the Jenks method and a sensitivity analysis was performed. Twenty-three classes of determinants were identified in 35 IDSR technical guides and reports of outbreak investigations and in 71 out of 2254 researched articles. For each of the 15 weekly reported diseases, the SIA was composed of 12 items grouped in 6 dimensions.ResultsThe SIA classified the 15 weekly reported diseases into 3 categories or types: high score or good adequacy (value >=14), moderate score or fair adequacy (value >=8 and