# The environmental drivers of bacterial meningitis epidemics in the Democratic Republic of Congo, central Africa

Serge Mazamay, Hélène Broutin, Didier Bompangue, Jean-Jacques Muyembe, Jean-François Guégan

► To cite this version:

Serge Mazamay, Hélène Broutin, Didier Bompangue, Jean-Jacques Muyembe, Jean-François Guégan. The environmental drivers of bacterial meningitis epidemics in the Democratic Republic of Congo, central Africa. PLoS Neglected Tropical Diseases, 2020, 14 (10), 10.1371/journal.pntd.0008634. hal-03014262

HAL Id: hal-03014262

https://hal.inrae.fr/hal-03014262

Submitted on 19 Nov 2020

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

RESEARCH ARTICLE

# The environmental drivers of bacterial meningitis epidemics in the Democratic Republic of Congo, central Africa

Serge Mazamay<sup>1,2*</sup>, Hélène Broutin<sup>2,3,4</sup>, Didier Bompangue<sup>1,5</sup>, Jean-Jacques Muyembe<sup>1</sup>, Jean-François Guégan<sup>2,6,7</sup>

1 Département de Microbiologie, Faculté de Médecine, Université de Kinshasa, Kinshasa, Democratic Republic of Congo, 2 MIVEGEC, IRD, CNRS, Université de Montpellier, Montpellier, France, 3 Département de Parasitologie-Mycologie, Faculté de Médecine, Université Cheikh Anta Diop (UCAD), Dakar, Sénégal, 4 Centre de Recherche en Evolution et Ecologie de la Santé (CREES), Montpellier, France, 5 Chrono-Environnement, UMR CNRS 6249 Université de Franche-Comté, Besançon, France, 6 ASTRE, INRAE, Cirad, Université de Montpellier, Campus International de Baillarguet, Montpellier, France, 7 oneHEALTH Global Research Programme, FutureEarth programme, Paris, France

* sergemazamayiko@gmail.com

<figure><img src="image_3.png" /></figure>

OPEN ACCESS

**Citation:** Mazamay S, Broutin H, Bompangue D, Muyembe J-J, Guégan J-F (2020) The environmental drivers of bacterial meningitis epidemics in the Democratic Republic of Congo, central Africa. PLoS Negl Trop Dis 14(10): e0008634. https://doi.org/10.1371/journal.pntd.0008634

**Editor:** Joseph Raymond Zunt, University of Washington, UNITED STATES

**Received:** October 18, 2019

**Accepted:** July 23, 2020

**Published:** October 7, 2020

**Copyright:** © 2020 Mazamay et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

**Data Availability Statement:** All relevant data are within the manuscript and its Supporting Information files.

**Funding:** The NGO « Organisation pour le Développement Durable de Mai-ndombe » (NGO ODDM), the URF-ECMI (Training and Research Unit in Ecology of infectious diseases, Medicine Faculty, University of Kinshasa, DRC) and IRD/ MIVEGEC, UMR CNRS IRD UM, Montpellier, France. Jean-François Guégan is supported by an

## Abstract

### 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 ($\chi^2 = 6.910, p$-value = 0.009), in areas dominated by
"Investissement d'Avenir" grant managed by
Agence Nationale de la Recherche (LABEX CEBA:
ANR-10-LABX-25-01), and is also supported by
Institut de recherche pour le développement (IRD),
Institut national de recherche pour l'agriculture,
l'alimentation et l'environnement (INRAE),
Université de Montpellier and Ecole des Hautes
Etudes en Santé Publique (EHESP). The funders
had no role in study design, data collection and
analysis, decision to publish, or preparation of the
manuscript.

**Competing interests:** The authors have declared
that no competing interests exist.

savannah landscape than in areas with dense forest or forest in mountainous areas ($\chi^2$ =
15.185, *p*-value = 0.001), and with no significant difference between climate types ($\chi^2$ =
1.211, *p*-value = 0.449). Additionally, no significant difference was observed for attack rate
between the two types of heath zones ($\chi^2$ = 0.982, *p*-value = 0.322). Conversely, strong dif-
ferences in attack rate values were obtained for vegetation types ($\chi^2$ = 13.627, *p*-value =
0.001) and climate types ($\chi^2$ = 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 men-
ingitis. 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 impor-
tant to consider in the epidemiology of bacterial meningitis epidemics in DRC. This informa-
tion 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 differ-
ent 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.

**Introduction**

Global burden of bacterial meningitis was estimated to 5 million of new cases and 290,000
deaths globally in 2017 [1], mainly due to three major bacteria: *Neisseria meningitidis* (Nm),
*Streptococcus pneumoniae* (Sp) and *Haemophilus influenzae* type b (Hib) [2]. Bacterial menin-
gitis remains a major public health in Africa, and in particular in the Sub-Saharan region
stretching from Senegal to Ethiopia, called the « Meningitis Belt » [3, 4].
In the African meningitis belt, bacterial meningitis incidence exhibits a seasonal trend with peaks during the dry season which then subsides with the onset of the rainy season [5, 6].

The massive introduction of the affordable conjugate vaccine against meningococcal serogroup A started in December 2010, the MenAfriVac [7, 8] in addition to the recent introduction of the 10-13 pneumococcal serotypes (from 2013) vaccines in infants through the Expanded Programme of Immunisation (EPI) in many countries in the meningitis belt [9], induced a strong decrease of meningitis incidence in this part of the world. However, bacterial meningitis remains a public health threat in the African continent mainly due to the persistence of other meningococcal *Neisseria* serogroups principally Nm C, Nm W, to *Haemophilus influenzae* (Hi) infection and high incidence of pneumococcal (Sp) meningitis in adults. This is strongly observed in the meningitis belt, while the ecology of meningitis disease in Africa but outside the belt is still poorly understood.

A better understanding of the mechanisms and factors which intervene behind this epidemiology is therefore needed, along with appropriate statistical models allowing the identification of environmental, demographic and socio-economic factors of meningitis epidemics not only in the meningitis belt but also outside this belt. This is highly relevant for better preventive strategies against this infection and the adjustment of disease surveillance. Both the epidemiological and ecological landscapes represent appropriate tools to merge for analyzing the occurrence and geographical distribution of infectious diseases and their interactions with environmental parameters in both space and time [10, 11].

Many studies at different spatial scales and in different countries of the meningitis belt have been carried out, highlighting the role of dust, relative humidity and Harmattan winds in the different phases of meningitis epidemics [5, 12, 13, 14]. The link between climate-environment and meningitis is becoming more and more evident through many scientific works and some environmental factors or proxies are also considered in early warning systems and prevention [5, 15, 16]. Through the countries of the meningitis belt, previous modeling efforts relied on a wide range of unknown parameter values [17] given the lack of surveillance data from which parameters could be estimated. Others have used incidence data for model fitting at low spatial resolution, mainly data aggregated at district level [18, 19]. This does not allow for the differentiation between dry seasons with localized epidemics and dry seasons without localized epidemics, as localized epidemics usually can be seen at the health center level only [20, 21]. Other studies are underway in the meningitis belt in order to highlight other drivers that may modify the epidemiology of this deadly and debilitating disease. However, the upsurge of meningitis epidemic outbreaks outside the Sahelo-Sudanian belt, which is at the origin of the Southern meningitis epidemics' hypothesis [22, 23], is still of little interest and remains poorly studied despite the negative impact of this disease from a public health point of view in those regions. The evidence of meningitis seasonality and environmental, demographic and socio-economic variables that can influence the incidence of disease cases remains also poorly studied.

This article sought to evaluate the effect of environmental, demographic and socio-economic drivers on meningitis diseases risk in the Democratic Republic of Congo (DRC) where bacterial epidemics are recurrent and present a high diversity in meningitis incidences.

# Methods

## Study area

The DRC is an African country crossed by the Equator line, located at the 5° North latitude and 13° South Latitude, with a 2,345,409 km² surface area. The population size is estimated at 85,026 million inhabitants, a low density of 21 inhabitants per km², and with about 50% of
under 15 years-old, and 55-60% of the total population living in rural areas. The country shares limits with 9 distinct countries (to the north by the Central African Republic and South Sudan, to the west by the Republic of Congo, to the east by Uganda, Rwanda, Burundi and Tanzania, to the south-east, by Zambia and in the south-west by Angola).

DRC presents diverse geography including a basin in the center of the country (48% of the total surface), with a mean altitude of 350 m and covered by dense tropical and equatorial forests. Uplands surround this part of the country up to the borders except in the eastern part where uplands end with mountains with a mean altitude higher than 1000 m. This geographic mosaic also includes 4 different climates across the country: an equatorial climate in the center of the country (the basin or pool), a tropical and humid climate in the north and south, a temperate climate in the east and a mountain climate in the extreme east; and this conditions the existence of different biomes made of dense forest, moist forest, dry forest, meadow, savannah, bush in the north and south and mountain forests in the east [24].

Additionally to the dense tropical forest in the basin, the important river network (i.e., the Congo river of 4700 km and its numerous tributaries and lakes) constitutes an important geographic characteristic of the country. The socio-economic situation of the population is very poor, especially in rural areas where populations live from traditional agriculture, fishing and bushmeat hunting.

The health system is organized into 3 administrative levels, i.e., central, intermediary and peripheric. As of 2015, the country is divided into 26 sanitary provinces [25], 515 health zones (a geographical entity of an average of 6000 to 10,000 km² including a population of at least 100,000 inhabitants) and 8504 health areas (a geographical entity of an average of 300 to 500km² including a population of at least 10,000 inhabitants) [26].

In connection with the global fight against meningitis, a standard definition of bacterial meningitis suspected case and epidemic disclosed by the General Directorate for the fight against the disease (DGLM), is used through the various health structures (General Hospitals, health centers and others) of the 515 health zones [27]. A suspected case of acute meningitis is a patient with a sudden onset of fever (>38.5°C rectal or 38.0°C axillary) with stiff neck. In patients under 1 year of age, a suspected case of bacterial meningitis occurs when fever is accompanied by a bulging fontanelle.

In the DRC the epidemic threshold is defined by 15 cases per 100,000 inhabitants per week for one week in a district with a population between 30,000 and 100,000 inhabitants, or by 5 cases in one week or doubling of cases during 3 weeks for a population of less than 30,000 inhabitants. However, when the epidemic risk is low (for example, no epidemic for 3 years or warning threshold exceeded at the beginning of the dry season), the recommended epidemic threshold is 10 cases per 100,000 people per week [27].

**Epidemiological data.** We collected epidemiological data from the weekly numbers of suspected cases and deaths reported in the DRC national Integrated Diseases Surveillance System (IDSR) collected by the national health system from the Ministry of Health, between 2000 and 2018 (S1 Data), at the health zone area scale. Meningitis suspected cases were defined according to the WHO guidelines [27].

The cases were notified to the hierarchic level by the health structures (hospital and health centers) in the 515 different health zones and from sentinel health zone sites, attached to health areas in the provinces of Haut Katanga (Lubumbashi) and the city-province of Kinshasa (Lingwala, Kalembelembe and Kingasani).

Patients with suspected meningitis who were evaluated at reference centers or at sentinel sites structures had a lumbar puncture performed to collect cerebrospinal (CSF) fluid for laboratory testing by Gram stain and culture or latex agglutination, when available, or by Polymerase Chain Reaction (PCR) at the Institut National de Recherche Biomédicale (INRB),
Kinshasa. Between 2008 to 2012, patients were primarily infected by *S. pneumoniae* (50%), *N. meningitidis* (13%), *H. influenzae* (2%) or other pathogens (35%) identified by CSF culture or PCRs performed. 486 CSF samples were tested via culture (100%) and 267 via PCR (54.93%) [28].

**Population data.** Population sizes of health areas were collected from the Expanded Programme on Immunization (EPI), program of Public Health Ministry. They correspond to estimations based on the last census performed in 1984 with a calculated annual growth rate of 1.03% applied for the following years.

**Shapefile data.** Issued from WHO Healthmapper (a WHO information and mapping application for public health).

**Socio-environmental data.** Climate data has been downloaded from WorldClim— Global Climate Data [29]. They concern 30-years average monthly rainfall and temperature data (1970–2000). The variables are collected at the scale of 30 arc seconds (~1km²). Each zip file contains 12 GeoTiff (.tif) files, 1 per month (January = 1, December = 12). The raster image was superimposed on the DRC 515 health zones Shapefile, and we extracted the average, minimum, maximum, and mean temperature (in °C) and precipitation data (in mm) for each health zones using ArcGIS (ver 10.6.1). For all these data we used the average values.

**The altitude data** for health zones are from the Shuttle Radar Topography Mission (SRTM) in the European soil data center (ESDAC) [30]. Outside the USA and their associated territories, an altitude measurement is taken every 90 m. This makes it possible to draw quite high accuracy isolines of altitudes throughout the DRC. Information is available in the form of rasters, and backgrounds were obtained from SRTM 90m Digital Elevation Database v4.1 site h [31].

In order to cover the entire territory of the DRC, 19 raster images have been downloaded. These were subsequently mosaic into a single image. The raster image was superimposed on the 515 health zones shapefile of the DRC, and the average altitude data extraction for each health zone was performed using ArcGIS.

**The latitude and longitude data** of the health zones were extracted from the shapefile of the 515 health zones using the *R* software.

**Bushfire** data by health zone are dated and geo-localized from MODIS images provided by NASA site [32, 33].

**Livestock** rearing activity in the health zone (1 = Health zone without livestock breeding activities, 2 = HZ with small-scale livestock activities, 3 = HZ with large livestock activities).

**The Index of Economic Well-being (IEW)** was estimated during the second Demographic and Health Survey conducted (DHS) in the DRC between 2013 and 2014 [34]. The IEW is built on information on household ownership of certain durable goods (television, radio, etc.) and on certain housing characteristics (availability of electricity, type of drinking water supply, type of toilets, flooring materials, number of rooms used for sleeping, type of fuel for cooking, etc.). The index is constructed as follows: 1) assigning a weight or a score to each household property or characteristic (score or coefficient) generated from a principal component analysis; 2) standardization of scores according to a standard normal distribution of mean 0 and standard deviation of 1; 3) allocation and summation of all scores for each household; 4) ranking of households in ascending order of total score according to five equal size categories called quintiles, ranging from 1 (the lowest quintile) to 5 (the highest quintile). We used the classification of different districts according to this index found in the above national survey. We summed the lowest quintile with the second quintile to correspond to a poverty proxy that we assigned to the health zones corresponding to the district.

All of these parameters were used as independent variables in the different statistical models.
**Statistical methods.** Non-parametric Kruskal-Wallis' tests were used to establish the difference between the different types of health zones, climate, vegetation in relation to the number of cases and attack rates for the period of study.

The relationships between the number of meningitis cases in the 515 health zones and environmental, demographic and socio-economic parameters (population size, population density, type of health zone, type of climate, type of vegetation, altitude, latitude, longitude, precipitation, minimum temperature, mean temperature, maximum temperature, large livestock existence, proportion of bushfires and index of economic well-being) were modeled using different generalized- (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 types, climate types) [35, 36].

Comparing the different statistical models, the models with the smallest AIC (Akaike's Information Criterion) were selected as the best ones. 515 different health zones from 26 provinces were considered for the construction of the different GLM and GLMM models.

Taking into account the data type of the parameters mentioned above in our possession, we developed GLMs and GLMMs models. In particular, GLMMs are generalized linear models with mixed effects. They are used to analyze non-numerical continuous—such as counting data, binary responses, or proportions (for the GLM portion) -, and when the data are not independent (for the mixed part) [36]. The count type data are not distributed according to a normal parametric distribution, but according to a Poisson or Poisson-like distribution. Given this distribution law, the variance of the residuals is not constant but proportional to the mean counts predicted by the model. We thus used GLMM with a logit link and a Poisson error distribution, then a binomial negative error distribution, and finally a zero-inflated binomial negative error distribution in turn. Since we generated many different statistical models in the present work, models with Poisson structure which never succeeded to converge are not illustrated in the present work.

Using these different parameters, the number of expected meningitis cases and or the attack rate is given by the formula of the following structure:

$$ \text{Number of meningitis cases or mean attack rate} = f(\text{PopDensity} + \text{Poptize} + \text{Type of climate} + \\ \text{Type of vegetation} + \text{Altitude} + \text{Latitude} + \text{Longitude} + \text{mean Precipitation} + \text{Tempmin} + \text{Tempmax} + (\text{Eq1}) \\ \text{Tempmoy} + \text{BushFire} + \text{livestock} + \text{IEW, random} \sim \text{1|health zone, method = "ML"}). $$

With:

Number of cases = number of meningitis cases expected or mean attack rate

PopDensity = Population density of the health zone and Poptize = Population size in number of inhabitants

TypeZS = Type of health zone (1 = urban; 2 = rural and urban-rural)

Typeclimate = Climate type (1 = Montagnard, 2 = Equatorial; 3 = Tropical humid)

Vegetation type = type of vegetation (1 = forest in mountain area, 2 = dense forest; 3 = savannah)

Altitude = Altitude of the health zone

Longitude = Longitude of the health zone

Precipitation = mean amount of precipitation in millimeters

Tempmin = minimal Temperature in Celsius degree

Tempmax = maximum temperature in Celsius degree

Tempmoy = Mean temperature in Celsius degree

Bushfire = bushfire existence in the health zone
Livestock = livestock rearing activity in the health zone

IEW = index of economic well-being.

In Eq 1 above, health zone was used as a random variable. In subsequent GLMMs, we also introduced the vegetation type and climate type as random variables in turn, and then com-plexified the models in considering the hierarchical nature of several independent variables (e.g., health zones nested within vegetation types) in the random sub-equation of Eq 1.

Since we generated many different statistical models in this work with different types of error structure, we decided to present only GLMM results and not GLM ones, to not illustrate Poisson error structure models, and to present only the best-fitted minimum models with the smallest AIC values. Only the statistically significant dependent variables, derived from the negative binomial, zero-inflated negative binomial and multi-hierarchical zero-inflated nega-tive binomial models were then retained as the best determinants of meningitis case incidence and attack rate in the DRC. The analyses were performed with the software Stata ver. 16 (Stata-Corp, LLC, 2019).

**Results**

Between 2000 and 2018, there were 118,378 cases reported by the Ministry of Health through the DGLM with a lethality of 11.5% across the 515 health zones. The incidence of cases is more marked in the regions located in the North and South-East of the country and in the provincial city of Kinshasa. [28].

Non-parametric Kruskal-Wallis' tests show that more meningitis cases occur on average in urban health zones than in rural ones ($\chi^2 = 6.910, p$-value = 0.009), with more cases observed in dominant savannah vegetation areas than in dense forests or mountainous forest zones ($\chi^2 = 15.185, p$-value = 0.001; all pairwise comparisons were also significant, $p$-value = 0.001). No difference in disease incidence was observed across the different types of climates ($\chi^2 = 1.221, p$-value = 0.499). Concerning attack rate values, no difference was observed between the two health zones, i.e., urban versus rural/urbano-rural ($\chi^2 = 0.982, p$-value = 0.322; all pairwise comparisons were also significant, $p$-value = 0.001). Important differences were obtained for climate ($\chi^2 = 13.627, p$-value = 0.001) and vegetation ($\chi^2 = 13.627, p$-value = 0.001; all pairwise comparisons were also significant, $p$-value = 0.001 except between equatorial climate and humid tropical, where $p$-value = 0.027) types (see figures in Fig 1).

From the different GLMM models we generated, the total number of meningitis cases across the different health zones in the DRC is best explained by a consistent set of explanatory environmental, demographic and socio-economic variables, and this whatever the statistical models developed and its error structure (see Table 1).

Controlling for the effect of population size and population density in the different models, latitude was positively associated with meningitis cases with higher latitudinal areas in the DRC exhibiting more meningitis cases than lower latitudinal areas. For longitude, we also observed a spatial trend of more meningitis cases in Eastern regions in the DRC than in the Western part of the country. Altitude was also associated negatively with disease incidence indicating that mountainous and hilly regions were less affected by meningitis cases than low altitude areas like valleys and river catchment areas. Maximum temperature was correlated negatively with the number of meningitis cases with upper temperatures acting probably as a limit for the development of the disease agent life-cycle. Mean temperature was positively associated to the number of disease cases with an optimal temperature range being also important for the circulation of the different disease agents causing bacterial meningitis in the region. IEW was negatively associated to disease incidence with higher values for this index of well-being favoring less cases of meningitis in the country.
<figure><img src="image_4.png" /><figcaption>Fig 1. Figures issued to non-parametric test analysis (Kruskal-Wallis). File in. TIFF extension.</figcaption></figure>

https://doi.org/10.1371/journal.pntd.0008634.g001

We observed that minimum temperature was also retained in minimal models, acting negatively on the number of meningitis cases, which tends to indicate the same process of a lower temperature limit for the development of this disease in the DRC (Table 1). The adjusted coefficients of determination (adjusted-R²) for the different GLM and GLMM models to explain the number of total meningitis cases across health zones fluctuated between adjusted-R² = 0.109 (p<0.0001, corrected-AIC = 7268.998, first-order autocorrelation = 0.065) for minimum negative binomial models, adjusted-R² = 0.267 (p<0.0001, corrected-AIC = 5634.442, first-order autocorrelation = 0.035) for minimum negative binomial zero-inflated models and adjusted-R² = 0.322 (p<0.0001, corrected-AIC = 4821.318, first-order autocorrelation = 0.026) for the most elaborated minimal multi-hierarchical zero-inflated binomial negative models (see also Table 1).

Concerning the attack rates, we also observed the same set of coherent environmental and socio-economic variables as previously described for meningitis incidence in the DRC (see Table 2). The adjusted-R² values ranged from 0.152 (p<0.001, corrected-AIC = 4964.880, first autocorrelation = 0.019) for minimum negative binomial models, 0.196 (p<0.001, corrected-AIC = 3865.422, first-order autocorrelation = 0.017) for minimum negative binomial zero-inflated models and 0.256 (p<0.001, corrected-AIC = 3257.754, first-order autocorrelation = 0.016) for the multi-hierarchical zero-inflated negative binomial models (see Table 2).

## Discussion

Despite the existence of vaccination and the effectiveness of antibiotic therapy, bacterial meningitis remains an important source of morbidity and mortality among populations, especially in Sub-Saharan Africa. Studies conducted in the African meningitis belt countries have shown a link between several environmental factors and the incidence of cases. These studies have focused on the meningitis belt, with few studies conducted outside this belt, such as in the DRC, where meningitis cases are officially registered with the government.

The present study conducted in the DRC, a country that reported nearly 118,378 cases of meningitis between 2000 and 2018 with a lethality of 11.5%, close to the lethality found in countries of the meningeal belt [37] aimed to understand for the first time the main
<table><caption>Table 1. Summary of generalized linear mixed models (GLMM) to explain the total number of meningitis cases across the 515 health districts in the Democratic Republic of Congo (DRC).</caption><thead><tr><th rowspan="3">Error structure<br>Determinant variables</th><th colspan="3">Negative binomial</th><th colspan="3">Zero-inflated negative binomial (with<br>Population density and Population size zero-<br>inflated)</th><th colspan="3">Multi-hierarchical zero-inflated negative<br>binomial</th></tr><tr><th colspan="3">Healthzone as random variable</th><th colspan="3">HealthZones included in Climatetypes, as<br>random variables</th><th colspan="3">HealthZones included in Vegetationtypes,<br>as random variables</th></tr><tr><th>Coef.</th><th>(P>|z|)</th><th>[95% Conf.<br>Interval]</th><th>Coef.</th><th>(P>|z|)</th><th>[95% Conf. Interval]</th><th>Coef.</th><th>(P>|z|)</th><th>[95% Conf. Interval]</th></tr></thead><tbody><tr><td>Popdensity</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Altitude</td><td>-.0006</td><td>(0.005)</td><td>[-.0009 -.0002]</td><td>-.0005</td><td>(0.013)</td><td>[-.0009 -.0001]</td><td>-.0005</td><td>(0.008)</td><td>[-.0009 -.0001]</td></tr><tr><td>Longitude</td><td>.0794</td><td>(0.000)</td><td>[.0531 .1057]</td><td>.0623</td><td>(0.000)</td><td>[.0344 .0902]</td><td>.0742</td><td>(0.000)</td><td>[.0471 .1012]</td></tr><tr><td>Latitude</td><td></td><td></td><td></td><td>.0224</td><td>(0.045)</td><td>[.0005 .0444]</td><td>.0179</td><td>(0.102)</td><td>[-.0035 .0395]</td></tr><tr><td>Maxtemp</td><td>-.1599</td><td>(0.008)</td><td>[-.2781 -.0419]</td><td>-.2046</td><td>(0.001)</td><td>[-.3239 -.0852]</td><td>-.1735</td><td>(0.004)</td><td>[-.2926 -.0543]</td></tr><tr><td>MeanTemp</td><td>.2488</td><td>(0.000)</td><td>[.1473 .3503]</td><td>.2948</td><td>(0.000)</td><td>[.1917 .3978]</td><td>.2532</td><td>(0.000)</td><td>[.1518 .3546]</td></tr><tr><td>IEW</td><td>-.0107</td><td>(0.000)</td><td>[-.0150 -.0064]</td><td>-.0182</td><td>(0.000)</td><td>[-.0225 -.0138]</td><td>-.0116</td><td>(0.000)</td><td>[-.0161 -.0072]</td></tr><tr><td></td><td colspan="3">Climate type as random variable</td><td colspan="3">HealthZones included in Vegetationtypes,<br>as random variables</td><td colspan="3">HealthZones included in Vegetationtypes,<br>as random variables</td></tr><tr><td>Popdensity</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Altitude</td><td>-.0006</td><td>(0.005)</td><td>[-.0009 -.0002]</td><td></td><td></td><td></td><td>-.0006</td><td>(0.005)</td><td>[-.0009 -.0002]</td></tr><tr><td>Longitude</td><td>.0796</td><td>(0.000)</td><td>[.0532 .1061]</td><td>.0332</td><td>(0.011)</td><td>[.0077 .0587]</td><td>.0794</td><td>(0.000)</td><td>[.0531 .1057]</td></tr><tr><td>Latitude</td><td></td><td></td><td></td><td>.0697</td><td>(0.000)</td><td>[.0476 .0918]</td><td></td><td></td><td></td></tr><tr><td>Mintemp</td><td></td><td></td><td></td><td>-.1099</td><td>(0.006)</td><td>[-.1878 -.0319]</td><td></td><td></td><td></td></tr><tr><td>Maxtemp</td><td>-.1600</td><td>(0.008)</td><td>[-.2783 -.0418]</td><td>-.2874</td><td>(0.000)</td><td>[-.4420 -.1327]</td><td>-.1599</td><td>(0.008)</td><td>[-.2781 -.0419]</td></tr><tr><td>MeanTemp</td><td>.2514</td><td>(0.000)</td><td>[.1488 .3541]</td><td>.4151</td><td>(0.000)</td><td>[.2266 .6036]</td><td>.2488</td><td>(0.000)</td><td>[.1473 .3503]</td></tr><tr><td>IEW</td><td>-.0116</td><td>(0.000)</td><td>[-.0176 -.0057]</td><td>-.0085</td><td>(0.000)</td><td>[-.0129 -.0041]</td><td>-.0107</td><td>(0.000)</td><td>[-.0151 -.0064]</td></tr><tr><td></td><td colspan="3">Vegetation type as random variable</td><td colspan="3"></td><td colspan="3"></td></tr><tr><td>Altitude</td><td>-.0006</td><td>(0.005)</td><td>[-.0009-.0002]</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Longitude</td><td>.0794</td><td>(0.000)</td><td>[.0531 .1057]</td><td>.0472</td><td>(0.000)</td><td>[.0212 .0731]</td><td></td><td></td><td></td></tr><tr><td>Latitude</td><td></td><td></td><td></td><td>.0661</td><td>(0.000)</td><td>[.0438 .0884]</td><td></td><td></td><td></td></tr><tr><td>Mintemp</td><td></td><td></td><td></td><td>-.1078</td><td>(0.006)</td><td>[-.1849 -.0307]</td><td></td><td></td><td></td></tr><tr><td>Maxtemp</td><td>-.1599</td><td>(0.008)</td><td>[-.2781 -.0419]</td><td>-.2518</td><td>(0.001)</td><td>[-.4018 -.1018]</td><td></td><td></td><td></td></tr><tr><td>MeanTemp</td><td>.2488</td><td>(0.000)</td><td>[.1473 .3503]</td><td>.4021</td><td>(0.000)</td><td>[.2198 .5845]</td><td></td><td></td><td></td></tr><tr><td>IEW</td><td>-.0107</td><td>(0.000)</td><td>[-.0151 -.0064]</td><td>-.0106</td><td>(0.000)</td><td>[-.0149 -.0061]</td><td></td><td></td><td></td></tr></tbody></table>

Note: Only minimal models based on their AIC values are illustrated in this table.

https://doi.org/10.1371/journal.pntd.0008634.t001

environmental, demographic and socio-economic drivers of meningitis cases and attack rates over this large country of central Africa.

We previously demonstrated a spatial distribution of cases in 8 clusters at the spatial scale [28]. These meningitis clusters are distributed in health provinces with a high average attack rate, particularly in the North-East and South-East provinces and in the Kinshasa province in the south-west of the country.

This study allowed us to provide the first explanations on the spatial heterogeneities observed at the level of health zones taken into account several important environmental, demographic and socio-economic variables that could have a link with the incidence of meningitis cases.

Indeed, we showed a significant trend between the number of bacterial meningitis cases and several environmental and socio-economic factors, including: type of health zone, altitude,
<table><caption>Table 2. Summary of generalized linear mixed models (GLMM) to explain the mean attack rate of meningitis across the 515 health districts in the Democratic Republic of Congo (DRC).</caption><thead><tr><th rowspan="3">Error structure<br>Determinant variables</th><th colspan="3">Negative binomial</th><th colspan="3">Zero-inflated negative binomial (with<br>Population density and Population size<br>zero-inflated)</th><th colspan="3">Multi-hierarchical zero-inflated negative<br>binomial</th></tr><tr><th colspan="3">Healthzone as random variable</th><th colspan="3">HealthZones included in Climatetypes, as<br>random variables</th><th colspan="3">HealthZones included in Vegetationtypes,<br>as random variables</th></tr><tr><th>Coef.</th><th>(P>|z|)</th><th>[95% Conf. Interval]</th><th>Coef.</th><th>(P>|z|)</th><th>[95% Conf. Interval]</th><th>Coef.</th><th>(P>|z|)</th><th>[95% Conf. Interval]</th></tr></thead><tbody><tr><td>Popdensity</td><td>-.0002</td><td>(0.000)</td><td>[-.0004 -.00001]</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Altitude</td><td>-.0005</td><td>(0.007)</td><td>[-.0009 -.0001]</td><td>-.0005</td><td>(0.011)</td><td>[-.0009 -.0001]</td><td>-.0005</td><td>(0.007)</td><td>[-.0009 -.0001]</td></tr><tr><td>Longitude</td><td>.0692</td><td>(0.000)</td><td>[.0421 .0963]</td><td>.0529</td><td>(0.000)</td><td>[.0248 .0811]</td><td>.0642</td><td>(0.000)</td><td>[.0372 .0912]</td></tr><tr><td>Latitude</td><td>.0264</td><td>(0.014)</td><td>[.0054 .0475]</td><td>.0318</td><td>(0.004)</td><td>[.0102 .0535]</td><td>.0279</td><td>(0.009)</td><td>[.0069 .0489]</td></tr><tr><td>Maxtemp</td><td>-.1815</td><td>(0.003)</td><td>[-.3013 -.0617]</td><td>-.2131</td><td>(0.001)</td><td>[-.3355 -.0907]</td><td>-.1874</td><td>(0.002)</td><td>[-.3081 -.0667]</td></tr><tr><td>MeanTemp</td><td>.2583</td><td>(0.000)</td><td>[.1540 .3625]</td><td>.3046</td><td>(0.000)</td><td>[.1976 .4116]</td><td>.2654</td><td>(0.000)</td><td>[.1611 .3697]</td></tr><tr><td>IEW</td><td>-.0117</td><td>(0.000)</td><td>[-.0174 -.0059]</td><td>-.0210</td><td>(0.000)</td><td>[-.0254 -.0167]</td><td>-.0143</td><td>(0.000)</td><td>[-.0187 -.0099]</td></tr><tr><th colspan="10">Climate type as random variable</th></tr><tr><td>Popdensity</td><td>-.0001</td><td>(0.017)</td><td>[-.0003–20.0000]</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Altitude</td><td>-.0005</td><td>(0.009)</td><td>[-.0009 -.0001]</td><td></td><td></td><td></td><td>-.0005</td><td>(0.007)</td><td>[-.0009 -.0001]</td></tr><tr><td>Longitude</td><td>.0603</td><td>(0.000)</td><td>[.0331 .0875]</td><td>.0272</td><td>(0.037)</td><td>[.0016 .0528]</td><td>.0642</td><td>(0.000)</td><td>[.0372 .0912]</td></tr><tr><td>Latitude</td><td>.0300</td><td>(0.005)</td><td>[.0089 .0511]</td><td>.0778</td><td>(0.000)</td><td>[.0561 .0995]</td><td>.0279</td><td>(0.009)</td><td>[.0069 .0489]</td></tr><tr><td>Mintemp</td><td></td><td></td><td></td><td>-.0903</td><td>(0.020)</td><td>[-.1663 -.0142]</td><td></td><td></td><td></td></tr><tr><td>Maxtemp</td><td>-.1939</td><td>(0.002)</td><td>[-.3138 -.0739]</td><td>-.2757</td><td>(0.001)</td><td>[-.4313 -.1201]</td><td>-.1874</td><td>(0.002)</td><td>[-.3081 -.0667]</td></tr><tr><td>MeanTemp</td><td>.2819</td><td>(0.000)</td><td>[.1773 .3865]</td><td>.3933</td><td>(0.000)</td><td>[.2044 .5821]</td><td>.2654</td><td>(0.000)</td><td>[.1611 .3697]</td></tr><tr><td>IEW</td><td>-.0167</td><td>(0.000)</td><td>[-.0216 -.0119]</td><td>-.0112</td><td>(0.000)</td><td>[-.0156 -.0068]</td><td>-.0143</td><td>(0.000)</td><td>[-.0187 -.0099]</td></tr><tr><th colspan="10">Vegetation type as random variable</th></tr><tr><td>Altitude</td><td>-.0005</td><td>(0.007)</td><td>[-.0009 -.0001]</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Longitude</td><td>.0642</td><td>(0.000)</td><td>[.0372 .0912]</td><td>.0396</td><td>(0.003)</td><td>[.0137 .0654]</td><td></td><td></td><td></td></tr><tr><td>Latitude</td><td>.0279</td><td>(0.009)</td><td>[.0069 .0489]</td><td>.0767</td><td>(0.000)</td><td>[.0549 .0985]</td><td></td><td></td><td></td></tr><tr><td>Mintemp</td><td></td><td></td><td></td><td>-.0869</td><td>(0.027)</td><td>[-.1638 .0101]</td><td></td><td></td><td></td></tr><tr><td>Maxtemp</td><td>-.1874</td><td>(0.002)</td><td>[-.3081 -.0667]</td><td>-.2414</td><td>(0.002)</td><td>[-.3969 .0858]</td><td></td><td></td><td></td></tr><tr><td>MeanTemp</td><td>.2654</td><td>(0.000)</td><td>[.1611 .3697]</td><td>.3743</td><td>(0.000)</td><td>[.1852 .5635]</td><td></td><td></td><td></td></tr><tr><td>IEW</td><td>-.0143</td><td>(0.000)</td><td>[-.0187 -.0099]</td><td>-.0133</td><td>(0.000)</td><td>[-.0177 -.0089]</td><td></td><td></td><td></td></tr><tr><th colspan="10">Livestock, as random variable</th></tr><tr><td>Altitude</td><td></td><td></td><td></td><td>-.0006</td><td>(0.005)</td><td>[-.0010 -.0002]</td><td></td><td></td><td></td></tr><tr><td>Longitude</td><td></td><td></td><td></td><td>.1123</td><td>(0.000)</td><td>[.0826 .1420]</td><td></td><td></td><td></td></tr><tr><td>Maxtemp</td><td></td><td></td><td></td><td>-.2518</td><td>(0.000)</td><td>[-.3805 -.1232]</td><td></td><td></td><td></td></tr><tr><td>MeanTemp</td><td></td><td></td><td></td><td>.4495</td><td>(0.000)</td><td>[.3361 .5629]</td><td></td><td></td><td></td></tr><tr><td>IEW</td><td></td><td></td><td></td><td>-.0187</td><td>(0.000)</td><td>[-.0232 .0142]</td><td></td><td></td><td></td></tr></tbody></table>

Note: Only minimal models based on their AIC values are illustrated in this table.

https://doi.org/10.1371/journal.pntd.0008634.t002

latitude, longitude, temperature, vegetation, type of climate and the economic well-being index across the different health zones.

The DRC constitutes a very large country harboring different climatic and biome regions. This important spatial heterogeneity in environmental conditions across the country also mirrors a wide variability in administrative, socio-economic, educational or health systems in the
515 districts surveyed during this work. All these conditions may predispose some particular areas in DRC to be more prone to the development of these bacterial infections.

We show here that a set of drivers of meningitis cases and attack rates is retained by the dif-
ferent statistical models. First, obviously the population size or the population density of the
different health districts are positively associated with the development of meningitis out-
breaks in DRC, and we took into consideration these two demographic parameters to analyze
the effects of other environmental or socio-economic factors, the two demographic parameters
being kept constant into analysis. Second, both latitudinal and longitudinal gradients are
important in the development of meningitis in this African country. We observe, in general,
more meningitis cases and severe attack rates at higher latitude and longitude eastwards in
spite of the fact that Kinshasa and its different districts represent an important place for the
development of meningitis outbreaks. Then, altitude constitutes another important environ-
mental parameter in the development of meningitis in the DRC, with lower altitude areas like
large plains and river catchment areas favoring the development of the bacterial disease. The
role of altitude could be explained by long-distance transport of dust in upper layers of the
atmosphere that may expose microbes to challenging conditions [38]. For example in the epi-
demiology of malaria, the incidence of cases decreases when altitude increases [39, 40]. Then
higher uplands and mountains do not favor the development of meningitis probably due to
the prevailing climatic conditions [41] and barriers that prevent unsurmountable barriers to
human presence and migration. Indeed, temperature and notably its maximum and mean val-
ues appear as two important parameters to explain meningitis cases and attack rates. Regions
in DRC with the warmest temperature values tend to see less or no epidemic outbreaks of
meningitis, which represents a physiological limit for the development of the disease bacterial
agent life-cycle favoring its autolysis [42]. In addition, mean temperature is important for the
development of meningitis, which corresponds to a range of optimal temperature values for
the circulation of the different bacterial strains causing meningitis [41]. In some statistical
models, we also obtained minimum temperature as an explanatory variable, which is entirely
consistent with the explanations given above of a minimal temperature limit to the expansion
of bacteria. It is important to note here that precipitation was never retained in the many dif-
ferent statistical models we produced; this can be explained by important collinearity that may
exist with other dependent variables retained by our models but also by the fact that at the
scale of the country the range in rainfall values is too low to be retained in generalized linear
models, that which introduced statistical consequences in the models, i.e., Elton's sound
hypothesis [43]. Livestock condition was also not retained in the present results, suggesting
that areas exhibiting large herds, and so plausible flows of pastoralists, may not influence statis-
tically the development of meningitis epidemics.

The role of the index of economic well-being could be explained by the fact that a popula-
tion often living in poverty is prone to a difficulty of social life in general including undernour-
ishment that would expose it to malnutrition. Among the consequences of malnutrition, we
have a decrease of immunity and therefore susceptibility to catch infections including bacteria
[15, 44, 45].

Regarding the type of health zones, the most affected in our study were the urban areas,
e.g., Kinshasa, Lubumbashi and Kisangani. This could be explained by the poverty that one
encounters in urban settlements due to a large demography of multifactorial origin including
the rural exodus and immigration of populations from neighboring countries (e.g., Aru,
Ariwara, Mahagi), which can be healthy carriers of bacteria, and which fuels bacterial importa-
tion [2, 46]. This is more evident in some studies carried out in the countries of the meningitis
belt, where the poverty and the incidence of meningitis have been correlated, for example with
epidemic outbreaks after large mass gathering pilgrimages [47, 48]. Therefore, it will be
necessary to conduct more detailed investigations for the districts that are the more influenced
by the introduction of the disease from surrounding countries in order to reinforce this
hypothesis.

Surveillance data for meningitis in the DRC have some limitations that should not bias our analyses. This is confirmed by a recent publication on the evaluation of IDSR in Africa with more than 85% data completeness and implementation of IDSR in 50 to 85% of structures at the peripheral level in 2017 [49]. First, these data correspond to suspected cases and a low proportion is actually tested for biological confirmation. Similarly to the studies performed in the African meningitis belt, the surveillance data do not reflect the exact number of meningitis cases with potential under estimations of cases, mainly during epidemics. Meanwhile, under-reporting of cases can occur because of the poor training of care providers and sometimes due to a lack of data transmission tools to the hierarchy. Biological confirmations of routine suspect cases are almost non-existent especially in rural areas. The biological confirmation protocol of the cases defined by the WHO requires a respect of norms and a technical platform not always accessible in the structures of care (reagents, materials for cerebrospinal fluid transport, culture and others). Difficulties related to the collection of cerebrospinal fluid (CSF) are transport and the use of certain methods of laboratory diagnosis (e.g., microscopic exam, culture, biochemical analyses useful for diagnosis, soluble antigens search, Gene amplification...). In DRC, data quality control is performed internally within the Directorate for Disease Control at a quarterly pace. And this direction is also assessed by the DRC General Secretariat for Health at a quarterly rate and during these evaluations; special attention is paid to the quality of surveillance data. Also, annually, the Directorate for Disease Control in particular and the Ministry of Health in general are subject to external quality control of surveillance data (by South Africa national laboratory and CDC Atlanta for Laboratory Data). Another limitation is the population data, which correspond to estimates based on a 1984 census with application of a calculated annual growth rate of 1.03%. That is the only one available and accepted by all actors in this country. The biological confirmation data are useful for determining the case confirmation rate that remains low for several reasons, including the low availability of diagnostic materials and inputs in provinces, but also the transport of CSF samples (transport medium Trans-isolate from cerebrospinal fluid) [28].

Conclusion

Our results show for the first time that the incidence of meningitis cases and attack rates in the DRC are strongly associated with some key-environmental drivers such as altitude, latitude, longitude, mean and maximum temperature, vegetation biomes and climate categories. Obviously also, important demographic parameters like population size and density in the different health districts and the index of national well-being developed for DRC are important to consider in the development of meningitis epidemic outbreaks over the country. However, when these demographic and socio-economic factors are considered in our models, important bio-geographical patterns of meningitis are also revealed.

Our study 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-type landscape, with a humid tropical climate and a mean range temperature, with a population with a low IEW constitutes the perfect conditions for the development of meningitis. Interestingly, some of the explanatory parameters in the development of meningitis disease in DRC, i.e., bioclimatic factors, are highly sensitive to global environmental changes, and particularly climate change. We strongly recommend to better survey meningitis in the DRC and more generally in countries located outside the
meningitis belt because several epidemiological patterns could be altered by global environ-
mental changes, and particularly within the current context of global warming.

**Ethics approval and consent to participate:** This research was not considered human sub-
jects research. Therefore, consultation of an ethics committee and consent to participate is not
required.

**Supporting information**

S1 Data. Data base that allowed modeling. File in.xlsx extension.
(XLSX)

**Acknowledgments**

Authors thank members of URF-ECMI (Training and Research Unit in Ecology of infectious diseases, Medicine Faculty, University of Kinshasa, DRC) for data entry and librarian at the WHO office in Brazzaville.

**Author Contributions**

**Conceptualization:** Serge Mazamay, Hélène Broutin, Didier Bompangue, Jean-Jacques Muyembe, Jean-François Guégan.

**Data curation:** Didier Bompangue, Jean-François Guégan.

**Formal analysis:** Serge Mazamay, Jean-François Guégan.

**Funding acquisition:** Jean-François Guégan.

**Methodology:** Serge Mazamay, Didier Bompangue, Jean-François Guégan.

**Software:** Serge Mazamay, Jean-François Guégan.

**Supervision:** Jean-François Guégan.

**Validation:** Serge Mazamay, Jean-François Guégan.

**Visualization:** Serge Mazamay.

**Writing – original draft:** Hélène Broutin, Jean-François Guégan.

**Writing – review & editing:** Serge Mazamay, Hélène Broutin, Didier Bompangue, Jean-François Guégan.

**References**

1. GBD Results Tool, 2019. GBD Results Tool | GHDx [WWW document]. URL http://ghdx.healthdata.org/gbd-results-tool (Accessed 28 October 2019).

2. World Health Organization. Guidelines on the control of epidemic meningococcal disease. 2nd ed. Geneva: WHO. Report Number WHO/EMC/BAC/98.3.84.1998.

3. Lapeyssonnie L, Organization WH. La méningite cérébrospinale en Afrique. Bull World Health Org. 1963; v. 28. Supplement.

4. Agier L, Martiny N, Thiongane O, Mueller JE, Paireau J, Watkins ER, et al. Towards understanding the epidemiology of Neisseria meningitidis in the African meningitis belt: a multi-disciplinary overview. Int J Infect Dis. 2017; 54:103–12. https://doi.org/10.1016/j.ijid.2016.10.032 PMID: 27826113

5. Sultan B, Labadi K, Guégan J-F, Janicot S. Climate Drives the Meningitis Epidemics Onset in West Africa. Hales S, editor. PLoS Med. 2005 Jan 25; 2(1):e6. https://doi.org/10.1371/journal.pmed.0020006 PMID: 15696216
6. Mueller JE, Yaro S, Ouédraogo MS, Levina N, Njanpop-Lafourcade B-M, Tall H, et al. Pneumococci in the African meningitis belt: meningitis incidence and carriage prevalence in children and adults. PloS One. 2012; 7(12):e52464. https://doi.org/10.1371/journal.pone.0052464 PMID: 23285051

7. Daugla DM, Gami JP, Gamougam K, Naibei N, Mbainadji L, Narbé M, et al. Effect of a serogroup A meningococcal conjugate vaccine (PsA-TT) on serogroup A meningococcal meningitis and carriage in Chad: a community study [corrected]. Lancet Lond Engl. 2014 Jan 4; 383(9911):40–7.

8. Trotter CL, Lingani C, Fernandez K, Cooper LV, Bita A, Tevi-Benissan C, et al. Impact of MenAfriVac in nine countries of the African meningitis belt, 2010–15: an analysis of surveillance data. Lancet Infect Dis. 2017 Aug; 17(8):867–72. https://doi.org/10.1016/S1473-3099(17)30301-8 PMID: 28545721

9. WHO. WHO | Pneumococcal conjugate vaccines [Internet]. WHO. 2019 [cited 2019 Sep 9]. Available from: https://www.who.int/biologicals/areas/vaccines/pneumo/en/

10. Pavlovskiï EN (Evgenï N, 1884–1965, Levine ND, Pavlovskiï EN (Evgenï N, 1884–1965. Natural nidality of transmissible diseases, with special reference to the landscape epidemiology of zooanthroposores. 1966 [cited 2019 Sep 12]; Available from: http://agris.fao.org/agris-search/search.do?recordID=US201300601889

11. Meentemeyer RK, Haas SE, Václavík T. Landscape epidemiology of emerging infectious diseases in natural and human-altered ecosystems. Annu Rev Phytopathol. 2012; 50:379–402. https://doi.org/10.1146/annurev-phyto-081211-172938 PMID: 22681449

12. Agier L, Deroubaix A, Martiny N, Yaka P, Djibo A, Broutin H. Seasonality of meningitis in Africa and climate forcing: aerosols stand out. J R Soc Interface. 2012 Dec 5; 10(79):20120814–20120814. https://doi.org/10.1098/rsif.2012.0814 PMID: 23221989

13. Martiny N, Chiapello I. Assessments for the impact of mineral dust on the meningitis incidence in West Africa. Atmos Environ. 2013 May; 70:245–53.

14. Paireau J, Ma?nassara HB, Jusot J-F, Collard J-M, Idi I, Moulia-Pelat J-P, et al. Spatio-Temporal Factors Associated with Meningococcal Meningitis Annual Incidence at the Health Centre Level in Niger, 2004?2010. Ribeiro GS, editor. PLoS Negl Trop Dis. 2014 May 22; 8(5):e2899. https://doi.org/10.1371/journal.pntd.0002899 PMID: 24852960

15. Lipsitch M, Viboud C. Influenza seasonality: Lifting the fog. Proc Natl Acad Sci U S A. 2009 Mar 10; 106(10):3645–6. https://doi.org/10.1073/pnas.0900933106 PMID: 19276125

16. Deroubaix A, Martiny N, Chiapello I, Marticorena B. Suitability of OMI aerosol index to reflect mineral dust surface conditions: Preliminary application for studying the link with meningitis epidemics in the Sahel. Remote Sens Environ. 2013 Jun; 133:116–27.

17. Irving TJ, Blyuss KB, Colijn C, Trotter CL. Modelling meningococcal meningitis in the African meningitis belt. Epidemiol Infect. 2012 May; 140(5):897–905. https://doi.org/10.1017/S0950268811001385 PMID: 21781369

18. Tartof S, Cohn A, Tarbangdo F, Djingarey MH, Messonnier N, Clark TA, et al. Identifying optimal vaccination strategies for serogroup A Neisseria meningitidis conjugate vaccine in the African meningitis belt. PloS One. 2013; 8(5):e63605. https://doi.org/10.1371/journal.pone.0063605 PMID: 23671685

19. Karachaliou A, Conlan AJK, Preziosi M-P, Trotter CL. Modeling Long-term Vaccination Strategies With MenAfriVac in the African Meningitis Belt. Clin Infect Dis Off Publ Infect Dis Soc Am. 2015 Nov 15; 61(Suppl 5):S594–600.

20. Paireau J, Girond F, Collard J-M, Maïnassara HB, Jusot J-F. Analysing Spatio-Temporal Clustering of Meningococcal Meningitis Outbreaks in Niger Reveals Opportunities for Improved Disease Control. PLoS Negl Trop Dis [Internet]. 2012 Mar 20 [cited 2019 Sep 2]; 6(3). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3308932/

21. Tall H, Hugonnet S, Donnen P, Dramaix-Wilmet M, Kambou L, Drabo F, et al. Definition and characterization of localised meningitis epidemics in Burkina Faso: a longitudinal retrospective study. BMC Infect Dis. 2012 Jan 5; 12:2. https://doi.org/10.1186/1471-2334-12-2 PMID: 22221583

22. Chippaux J-P, Debois H, Saliou P. Revue critique des stratégies de contrôle des épidémies de méningite à méningocoque en Afrique subsaharienne. Bull Société Pathol Exot. 2002; 94(1):37–44.

23. Greenwood B. Editorial: 100 years of epidemic meningitis in West Africa–has anything changed? Trop Med Int Health. 2006 Jun 1; 11(6):773–80. https://doi.org/10.1111/j.1365-3156.2006.01639.x PMID: 16771997

24. Azzali S, Menenti M. Mapping vegetation-soil-climate complexes in southern Africa using temporal Fourier analysis of NOAA-AVHRR NDVI data. Int J Remote Sens. 2000 Jan 1; 21(5):973–96.

25. Mandja B.-A.M., Bompangue D., Handschumacher P., Gonzalez J.-P., Salem G., Muyembe J.-J., Mauny F., 2019a. The score of integrated disease surveillance and response adequacy (SIA): a pragmatic score for comparing weekly reported diseases based on a systematic review. BMC Public Health 19. https://doi.org/10.1186/s12889-019-6954-3
26. Ministère de la Santé Publique de la RDC. Plan National de Développement Sanitaire (PNDS 2011–2015). Kinshasa: 2010.

27. Ministère de la Santé publique. Guide pour la surveillance intégrée de la maladie et riposte en République Démocratique du Congo. Kinshasa: 2011, 262p.

28. Mazamay S., Bompangue D., Guégan J. F., Muyembe J. J., Raoul F., & Broutin H. (2020). Understanding the spatio-temporal dynamics of meningitis epidemics outside the belt: the case of the Democratic Republic of Congo (DRC). BMC Infectious Diseases, 20(1), 291. https://doi.org/10.1186/s12879-020-04996-7 PMID: 32312246

29. WordClim. WorldClim Version2 | WorldClim—Global Climate Data [Internet]. 2016 [cited 2019 Sep 9].
Available from: http://worldclim.org/version2

30. SRTM. SRTM(Shuttle Radar Topogr.)—ESDAC—European Commission [Internet]. 2007 [cited 2019 Sep 9]. Available from: https://esdac.jrc.ec.europa.eu/projects/srtmshuttle-radar-topogr

31. SRTM. SRTM 90m Digital Elevation Database v4.1 [Internet]. 2012 [cited 2019 Sep 9]. Available from: https://www.arcgis.com/home/item.html?id=d863d51225b94c948bd7cf85e4c432b6

32. NASA. LANCE: NASA Near Real-Time Data and Imagery | Earthdata [Internet]. 2019 [cited 2019 Sep 9]. Available from: https://earthdata.nasa.gov/earth-observation-data/near-real-time

33. NASA. Active Fire Data | Earthdata [Internet]. 2019 [cited 2019 Sep 9]. Available from: https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/active-fire-data

34. Ministère du Plan et Suivi de la Mise en œuvre de la Révolution de la Modernité (MPSMRM), Ministère de la Santé Publique (MSP) et ICF International. Enquête Démographique et de Santé en République Démocratique du Congo 2013–2014. 2014;

35. Müller M. Generalized Linear Models. 2004 Feb 6;

36. Kain MP, Bolker BM, McCoy MW. A practical guide and power analysis for GLMMs: detecting among treatment variation in random effects. PeerJ [Internet]. 2015 Sep 17 [cited 2019 Sep 9]; 3. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4579019/

37. Who « on line ». https://www.who.int/news-room/fact-sheets/detail/meningococcal-meningitis. Accessed 2 September 2019.

38. Yahya RZ, Arrieta JM, Cusack M, Duarte CM. Airborne Prokaryote and Virus Abundance Over the Red Sea. Front Microbiol [Internet]. 2019 [cited 2019 Aug 1]; 10. Available from: https://www.frontiersin.org/articles/10.3389/fmicb.2019.01112/full

39. Delacollette C, Barutwanayo M, Mpitabakana P. EPIDEMIOLOGIE DU PALUDISME AU BURUNDI. Médecine Afr Noire. 1990; 4.

40. Bødker R, Akida J, Shayo D, Kisinza W, Msangeni HA, Pedersen EM, et al. Relationship between altitude and intensity of malaria transmission in the Usambara Mountains, Tanzania. J Med Entomol. 2003 Sep; 40(5):706–17. https://doi.org/10.1603/0022-2585.40.5.706 PMID: 14596287

41. Wu X, Lu Y, Zhou S, Chen L, Xu B. Impact of climate change on human infectious diseases: Empirical evidence and human adaptation. Environ Int. 2016 Jan 1; 86:14–23. https://doi.org/10.1016/j.envint.2015.09.007 PMID: 26479830

42. Jusot J-F, Neill DR, Waters EM, Bangert M, Collins M, Bricio Moreno L, et al. Airborne dust and high temperatures are risk factors for invasive bacterial disease. J Allergy Clin Immunol. 2017 Mar; 139 (3):977–986.e2. https://doi.org/10.1016/j.jaci.2016.04.062 PMID: 27523432

43. Chavy A, Ferreira Dales Nava A, Luz SLB, Ramírez JD, Herrera G, Vasconcelos Dos Santos T, et al. Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome. PLoS Negl Trop Dis. 2019 Aug; 13(8):e0007629. https://doi.org/10.1371/journal.pntd.0007629 PMID: 31412022

44. Karacabey K. The Effect of Nutritional Elements on the Immune System. J Obes Weight Loss Ther [Internet]. 2012 [cited 2019 Sep 2]; 02(09). Available from: https://www.omicsonline.org/open-access/the-effect-of-nutritional-elements-on-the-immune-system-2165-7904.1000152.php?aid=10186

45. Cooper EL, Ma MJ. Understanding nutrition and immunity in disease management. J Tradit Complement Med. 2017 Jan 16; 7(4):386–91. https://doi.org/10.1016/j.jtcme.2016.12.002 PMID: 29034184

46. Serdeczny O, Adams S, Baarsch F, Coumou D, Robinson A, Hare W, et al. Climate change impacts in Sub-Saharan Africa: from physical changes to their social repercussions. Reg Environ Change. 2017 Aug; 17(6):1585–600.

47. Mosavi-Jarrahi A, Esteghamati A, Asgari F, Heidarnia M, Mousavi-Jarrahi Y, Goya M. Temporal analysis of the incidence of meningitis in the Tehran metropolitan area, 1999–2005. Popul Health Metr. 2009 Dec 23; 7:19.
48. Yezli S. The threat of meningococcal disease during the Hajj and Umrah mass gatherings: A comprehensive review. Travel Med Infect Dis. 2018 Jul; 24:51–8. https://doi.org/10.1016/j.tmaid.2018.05.003 PMID: 29751133

49. Fall I.S., Rajatonirina S., Yahaya A.A., Zabulon Y., Nsubuga P., Nanyunja M., Wamala J., Njuguna C., Lukoya C.O., Alemu W., Kasolo F.C., Talisuna A.O., 2019. Integrated Disease Surveillance and Response (IDSR) strategy: current status, challenges and perspectives for the future in Africa. BMJ Glob Health 4, e001427. https://doi.org/10.1136/bmjgh-2019-001427 PMID: 31354972