Analysing Spatio-Temporal Clustering of Meningococcal
Meningitis Outbreaks in Niger Reveals Opportunities for
Improved Disease Control
Juliette Paireau1*, Florian Girond2, Jean-Marc Collard3, Halima B. Maı ¨nassara2, Jean-Franc ¸ois Jusot2
1Unite ´ d’Epide ´miologie des Maladies Emergentes, Institut Pasteur, Paris, France, 2Unite ´ Epide ´miologie/Sante ´-Environnement-Climat, Centre de Recherche Me ´dicale et
Sanitaire (CERMES)/Re ´seau International des Instituts Pasteur, Niamey, Niger, 3Unite ´ de Biologie, Centre de Recherche Me ´dicale et Sanitaire (CERMES)/Re ´seau
International des Instituts Pasteur, Niamey, Niger
Background: Meningococcal meningitis is a major health problem in the ‘‘African Meningitis Belt’’ where recurrent
epidemics occur during the hot, dry season. In Niger, a central country belonging to the Meningitis Belt, reported meningitis
cases varied between 1,000 and 13,000 from 2003 to 2009, with a case-fatality rate of 5–15%.
Methodology/Principal Findings: In order to gain insight in the epidemiology of meningococcal meningitis in Niger and to
improve control strategies, the emergence of the epidemics and their diffusion patterns at a fine spatial scale have been
investigated. A statistical analysis of the spatio-temporal distribution of confirmed meningococcal meningitis cases was
performed between 2002 and 2009, based on health centre catchment areas (HCCAs) as spatial units. Anselin’s local Moran’s
I test for spatial autocorrelation and Kulldorff’s spatial scan statistic were used to identify spatial and spatio-temporal
clusters of cases. Spatial clusters were detected every year and most frequently occurred within nine southern districts.
Clusters most often encompassed few HCCAs within a district, without expanding to the entire district. Besides, strong intra-
district heterogeneity and inter-annual variability in the spatio-temporal epidemic patterns were observed. To further
investigate the benefit of using a finer spatial scale for surveillance and disease control, we compared timeliness of epidemic
detection at the HCCA level versus district level and showed that a decision based on threshold estimated at the HCCA level
may lead to earlier detection of outbreaks.
Conclusions/Significance: Our findings provide an evidence-based approach to improve control of meningitis in sub-
Saharan Africa. First, they can assist public health authorities in Niger to better adjust allocation of resources (antibiotics,
rapid diagnostic tests and medical staff). Then, this spatio-temporal analysis showed that surveillance at a finer spatial scale
(HCCA) would be more efficient for public health response: outbreaks would be detected earlier and reactive vaccination
would be better targeted.
Citation: Paireau J, Girond F, Collard J-M, Maı ¨nassara HB, Jusot J-F (2012) Analysing Spatio-Temporal Clustering of Meningococcal Meningitis Outbreaks in Niger
Reveals Opportunities for Improved Disease Control. PLoS Negl Trop Dis 6(3): e1577. doi:10.1371/journal.pntd.0001577
Editor: Joseph M. Vinetz, University of California San Diego School of Medicine, United States of America
Received July 1, 2011; Accepted February 9, 2012; Published March 20, 2012
Copyright: ? 2012 Paireau 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.
Funding: This work was financially supported by French Ministry of Foreign Affairs (FSP 2005-174), Sanofi Pasteur (contract Men07), the Belgian Technical
Cooperation and the Office of International Cooperation of the Principality of Monaco. The internship of JP in CERMES was financially supported by the Fondation
Pierre-Ledoux Jeunesse Internationale. 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.
* E-mail: email@example.com
Meningococcal meningitis (MM), caused by the bacterium
Neisseria meningitidis (Nm), is a major health problem in sub-
Saharan Africa. The highest incidences of the disease are observed
in the so-called ‘‘African Meningitis Belt’’ where annual recurrent
epidemics occur during the very hot, dry season . In Niger,
reported meningitis cases varied between 1,000 and 13,000 from
2003 to 2009, with case-fatality rates of 5–15%. The factors
involved in the spatio-temporal occurrence of meningococcal
epidemics are only suspected and still poorly understood.
Surveillance and reactive vaccination are the predominant
strategies for managing meningococcal meningitis outbreaks in the
Belt, recently completed with a conjugate vaccine to prevent the
carriage of Nm serogroup A. In Niger like in most sub-Saharan
countries, surveillance is performed at the district level. Quanti-
tative morbidity and mortality data on meningitis are collected
within a reporting network managed by the Direction for
Statistics, Surveillance and Response to Epidemics (DSSRE) from
the Ministry of Public Health. Data from all health care facilities
covering the entire Niger population are collected on a weekly
basis by the district health authorities, which aggregate and
forward their data to the regions and subsequently to the DSSRE.
These reported data include all suspected and probable cases,
according to the standard clinical definition of meningococcal
meningitis : A suspected case is any person with sudden onset of
fever (.38.5uC rectal or 38.0uC axillary) and one or more of the
following signs: stiff neck, altered consciousness or other meningeal
sign; in patients under one year of age, a suspected case occurs
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when fever is accompanied by a bulging fontanelle. A probable
case is defined as a suspected case with turbid CSF or Gram stain
showing Gram-negative diplococcus or petechial/purpural rash or
ongoing epidemic. Laboratory confirmation of meningitis is not
required to report a case. In parallel to this epidemiologic
surveillance and in close collaboration with DSSRE, the Centre de
Recherche Me ´dicale et Sanitaire (CERMES) is in charge of the
national microbiological surveillance of meningitis. The CERMES
collects the cerebrospinal fluid (CSF) samples taken from suspected
cases of meningitis by health care workers or physicians and
carries out the etiological diagnosis (see Methods).
Based on this national surveillance system, the strategy applied
in Niger to respond to meningitis outbreaks with limited amounts
of available vaccines, is to initiate reactive vaccination in a district
once weekly incidence exceeds the epidemic threshold defined by
WHO (see  and Methods for definitions). Thus, early detection
of epidemics is essential for an effective operational response.
Analysing spatio-temporal patterns of epidemics at a fine
geographic scale could lead to a better understanding of the
underlying causes of the disease and potential future prediction of
outbreaks . One of the techniques to uncover spatial patterns of
disease is cluster detection. In epidemiology, a cluster is a number
of health events situated close together in space and/or time .
Identifying spatial and spatio-temporal clusters of cases could help:
(i) to generate new information for further etiologic studies; (ii) to
identify risk areas where to focus the surveillance and allocate the
resources (antibiotics, rapid diagnostic tests…); (iii) to develop cost-
efficient vaccination strategies.
In sub-Saharan Africa, data from national disease notification
have already been used at country, regional or district levels to
study the geographical and temporal dynamics of epidemics and
their correlation with environmental factors [6–12]. However,
little is known about MM emergence and distribution at a sub-
district level. Using a finer spatial scale such as health centre
catchment areas (HCCAs) would have several advantages: (i) it
would capture heterogeneity in MM incidence at sub-district level;
(ii) epidemic thresholds would be studied at a more accurate scale,
allowing for a more rapid and targeted public health response; (iii)
monitoring of the impact of the intervention would be performed
at the same level as the intervention itself.
Therefore, we aimed to investigate the spatio-temporal
distribution of MM epidemics in Niger at the health centre
catchment area level, to identify the most frequently affected
HCCAs requiring a particular attention from public health
authorities. The national microbiological surveillance database
was used to perform two cluster detection methods in order to
uncover spatial and spatio-temporal clustering of MM incidence
from July 2002 to June 2009. Then, as a preliminary analysis to a
more thorough etiologic study, we searched for ecologic
correlation of MM incidence with human density and roads at
the HCCA level. Finally, to further investigate the benefit of using
a finer spatial scale for surveillance and disease control, we
compared timeliness of epidemic detection at the HCCA level
versus district level. This paper provides new insights into the
spatio-temporal dynamics of MM epidemics and discusses the
potential implications of our findings for meningitis control in sub-
Data collection and laboratory analyses
The CERMES is the national laboratory in charge of the
microbiological surveillance of meningitis in Niger. This surveil-
lance has been reinforced since 2002 [13,14] by its extension to the
whole country (it was only effective in the capital city before 2002)
and by the inclusion of a Polymerase Chain Reaction (PCR) assay
for etiological diagnosis of meningitis to the DSSRE routine
surveillance. CSF samples were collected by health care workers or
physicians from suspected cases of acute meningitis. Each CSF was
documented with an epidemiological form that included date of
sample collection, clinical information and general characteristics
about the patient (age, sex, geographic origin such as region,
district, HCCA and village). The samples were kept either
refrigerated or frozen in health facilities, or inoculated into a
trans-Isolate (TI) medium. The more remote health centres sent
CSF samples (frozen in a cool box) on a voluntary basis to
CERMES by mandated transport companies. Additionally,
CERMES carried out active collection of samples twice a day in
Niamey, so that the samples remained suitable for culture, and
every month within a radius of about 300 kilometres around
Niamey, in the regions of Tillabery and Dosso. Etiological
diagnosis of MM was carried out by PCR for all CSF as described
in  and  and by culture  for suitable CSF received
promptly at CERMES (fresh CSF and CSF inoculated into TI
medium). Questionnaire data and microbiological results were
entered in a database managed by CERMES. The data were used
for a retrospective study on meningococcal meningitis cases
between July 1, 2002 and June 30, 2009.
All data were collected through the national routine surveillance
system. Therefore, written consent was not asked and approval
from the national ethics committee was not needed. However,
patients were informed of the reason why their cerebrospinal fluid
was sampled and confidentiality on patients’ identity was
Geographic and demographic data
In 2008, in order to create a digitized National Health Map of
Niger, CERMES mapped the country’s HCCAs, each of which
Meningococcal meningitis (MM) is an infection of the
meninges caused by a bacterium, Neisseria meningitidis,
transmitted through respiratory and throat secretions. It
can cause brain damage and results in death in 5–15% of
cases. Large epidemics of MM occur almost every year in
sub-Saharan Africa during the hot, dry season. Under-
standing how epidemics emerge and spread in time and
space would help public health authorities to develop
more efficient strategies for the prevention and the control
of meningitis. We studied the spatio-temporal distribution
of MM cases in Niger from 2002 to 2009 at the scale of the
health centre catchment areas (HCCAs). We found that
spatial clusters of cases most frequently occurred within
nine districts out of 42, which can assist public health
authorities to better adjust allocation of resources such as
antibiotics or rapid diagnostic tests. We also showed that
the epidemics break out in different HCCAs from year to
year and did not follow a systematic geographical
direction. Finally, this analysis showed that surveillance at
a finer spatial scale (health centre catchment area rather
than district) would be more efficient for public health
response: outbreaks would be detected earlier and
reactive vaccination would be better targeted.
Spatio-Temporal Clustering of Meningitis in Niger
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included all villages served by the same health centre. As projected
data were required for the spatial statistics, all analyses were
carried out with a projected version of the National Health Map,
using the WGS84 – UTM32N projection. The number of
inhabitants per village was extracted from the 2001 census
database of the Institut National de la Statistique (INS) and an
annual population growth rate of 3% was applied. A shapefile of
primary roads was retrieved from the HealthMapper application
of the World Health Organization (WHO).
concentrate therapeutic and preventive efforts, we searched for
annual spatial clusters, defined as groups of MM cases occurring
during the same epidemiological year and situated closer together
in space than would be expected from the variation in population
density and chance fluctuations. Two cluster detection methods
were used to uncover spatial clustering of MM incidence at the
HCCA level in Niger for each epidemiological year between 2003
and 2009. An epidemiological year n was defined as running from
July 1 of the year n21 to June 30 of the year n. The first method
was the Anselin’s Local Moran’s I test [4,16], implemented in the
ArcGIS software (version 9.3, ESRI Inc. Redlands, CA), which
provided a measure of the spatial autocorrelation for a given
HCCA with its neighbours. Spatial clusters of MM cases were
identified by mapping the significant high-high HCCAs (i.e. high
incidence rate HCCA surrounded by high incidence rate HCCAs)
and high-low HCCAs (i.e. high incidence rate HCCA surrounded
by low incidence rate HCCAs). The second method was the
Kulldorff’s spatial scan statistic [4,17] implemented in the
SaTScan software (version 8.0, Kulldorff and Information
Management Services, Inc.), which used a circular moving
window to identify single HCCAs or groups of HCCAs of
significantly high risk. To determine beforehand the optimal scale
for cluster detection by both methods, i.e. the distance where
spatial effects were maximized , the Global Moran’s I test was
used in ArcGIS and correlograms of I against different threshold
distances were plotted in R (version 2.9.1, R Development Core
Team, R Foundation for Statistical Computing, Vienna, Austria).
More details regarding the cluster detection methods can be found
in Supporting information (Text S1).
The frequency of spatial cluster occurrence was calculated for
each HCCA and each health district, respectively defined as the
number of years during which the HCCA contributed to a cluster,
and as the number of years during which the district contained at
least one HCCA contributing to a cluster. The frequencies were
calculated taking into account either (i) clusters detected by one of
the two methods (weaker evidence of clustering) or (ii) clusters
detected by both methods (stronger evidence of clustering).
Detection of spatio-temporal clusters.
emergence and diffusion patterns of MM cases within each
epidemic season, we then searched for spatio-temporal clusters,
groups of MM cases situated close together in space and time. The
Kulldorff’s space-time scan  implemented in SaTScan was
performed to identify spatio-temporal clusters of maximum one-
week duration. More details regarding the method can be found in
Supporting information (Text S1).
To investigate if the highlighted
spatio-temporal patterns could be related to host density and
movement, correlations of MM incidence at the HCCA level with
human density and distance to primary road were explored using
Pearson correlation coefficient.
to risk which
To analyse the
Timeliness of epidemic detection.
potential gain in timeliness of epidemic detection at the HCCA
level compared to the district-level surveillance, we applied to our
HCCA-level data the two thresholds currently used for detection
of outbreaks . The alert threshold was defined as 5 cases per
100,000 inhabitants per week for population .30,000 inhabitants
or 2 cases in one week for population ,30,000 inhabitants. The
epidemic threshold was defined as 10 cases per 100,000
appropriate conditions , otherwise 15 per 100,000) or 5 cases
in one week for population ,30,000 inhabitants. If there was an
epidemic in a neighbouring area, the alert threshold became the
Finally, to evaluate the
Description of the data
From July 1, 2002 to June 30, 2009, a total of 15 801 CSF
specimens from meningitis suspected cases were analysed at the
CERMES laboratory (table 1). 112 CSF (0.7%) could not be tested
(depleted, broken tubes…) and 79 (0.5%) did not give conclusive
results because of contamination. Overall, biological specimens
originated from 416 (61%) of the 682 HCCAs mapped in 2009.
Among these CSF, 6556 (41.5%) were confirmed as bacterial
meningitis cases, 82.2% of which were positive for Neisseria
meningitidis. Serogroup A was the predominant serogroup every
year, except in 2006. The mean (SD) age of the MM cases was 9.6
(7.5) years and 58.8% were male. Over the study period, MM
cases were detected in 349 HCCAs (51.2%) in all regions of Niger
(figure 1), with contrasting incidence rates within districts. The
highest incidence rates were found in HCCAs of Niamey,
Tillabery, Dosso, Tahoua, Maradi and Zinder regions. As for
the temporal distribution, 82.5% of the MM cases occurred from
February to April.
Detection of spatial clusters and frequency of occurrence
Figure 2 depicts for each year the Kulldorff’s spatial scan
statistic results overlain on the Anselin’s Local Moran’s I results.
Over the seven years, the Local Moran’s I method identified 140
high-risk HCCAs (130 high-high and 10 high-low), with an annual
number ranging from 11 (in 2003 and 2007) to 31 (in 2008 and
2009). The spatial scan method identified 58 significant spatial
clusters altogether, with an annual number ranging from 3 (in
2003) to 16 (in 2009). The median number of HCCAs per cluster
was 2 (IQ range=1–5) and the median annual incidence rate of
the clusters was 34.9 (IQ range=20.5–72.3) cases per 100,000.
Almost 80% of the high-risk HCCAs identified with the Local
Moran’s I were included in clusters detected by SaTScan and 62%
of the SaTScan clusters encompassed high-high or high-low HCCAs.
Spatial clusters generally occurred in different HCCAs from
year to year over the study period, as shown by the low frequencies
observed at the HCCA level (figure 3). Among the HCCAs
contributing to a cluster at least once over the study period, the
median frequency was 1 (range=1–4) for clusters detected by at
least one method, and 1 (range=1–3) for clusters detected by both
methods. Only four HCCAs were detected three or more times by
at least one method and two or more times by both methods. They
were: Chare Zamna (in Zinder urban community), Gazaoua,
Doumega and Loudou.
Spatial clusters most frequently occurred within nine districts
out of 42, containing three or more times a cluster detected by at
least one method, and two or more times a cluster detected by
both methods. These districts were: Tera and Say (bordering
Burkina Faso), Keita, Zinder and five districts bordering Nigeria,
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Doutchi, Madaoua, Guidan Roumji, Madarounfa and Aguie. The
median time interval between two clusters occurring in the same
district was one year. When a district contained a cluster detected
by at least one method, only 13.3% (median) of its HCCAs
contributed to that cluster, and 9.7% when a district contained a
cluster detected by both methods.
Detection of spatio-temporal clusters
No systematic spatio-temporal pattern for cluster emergence and
epidemic spread was observed within the seven years of the study
period. Figure 4 shows the 66 significant spatio-temporal clusters
cluster inDirkou,Bilma district,which is outsidethe displayedzone)
and the incidence rate observed for each HCCA of a spatio-
temporal cluster during the time period associated to that cluster.
They essentially occurred between February and April, with an
additional few at the beginning (November–January) and the end
(May) of the epidemics. In 2003, the epidemic could be summarized
in two western and eastern poles, with the western pole occurring
before the eastern one. In 2004, the first cluster was detected in the
west; then all clusters appeared in the east, ending with the
northernmost oneinTanoutdistrict.In 2005,clustersweredetected
only in the eastern part. In 2006, two spatio-temporal poles were
clearly distinguished, first in the east and then in the west. In 2007,
the first three clusters were detected in the west, followed by one in
theeast,stillanotheroneinthewestand a finalnorthernmost onein
Keita district. In 2008, between the eastern clusters at the beginning
and the end of the epidemic season, other clusters essentially
appeared in the centre (Tahoua region) and the west (Tillabery and
Dosso regions) without a clear order, concluding again the
northernmost cluster in Keita district. In 2009, from the first cluster
in the east to the final one in the west, clusters appeared in all
regions in between, but followed no clear geographical direction.
Correlation with population density and roads
No significant correlation was found between MM incidence at
the HCCA level and human density (r=0.02) or distance to
primary roads (r=20.07).
Timeliness of epidemic detection
Between 2003 and 2009, 88 districts crossed the alert threshold.
For 42 (47.7%) of them, the alert threshold was crossed earlier (4
weeks early in median) in at least one HCCA of these districts.
Between 2003 and 2009, 46 districts crossed the epidemic
threshold. For 15 (32.6%) of them, the epidemic threshold was
crossed earlier (3 weeks early in median) in at least one HCCA of
To our knowledge, this is the first study using health centre
catchment areas as spatial units for the spatio-temporal analysis of
MM over a whole sub-Saharan country. The study’s first finding was
the more frequent detection of spatial clusters within nine southern
most often encompassed only a few HCCAs within a district, without
expanding to the entire district. In addition, no consistent annual
spatio-temporal pattern for cluster emergence and epidemic spread
could be observed, thus precluding the capacity to predict where the
next epidemic would break out, and what geographical direction it
would follow. These findings rely on laboratory-based data and have
important public health implications as discussed hereafter.
The first asset of this study was the quality of the microbiological
data. We used laboratory-confirmed N. meningitidis cases data,
coming from a surveillance system managed by CERMES and
DSSRE throughout the country. Most other spatio-temporal
studies on meningitis epidemics in sub-Saharan Africa [6–12,19]
are based instead on suspected cases reported in the framework of
the national surveillance systems. In our dataset, none of the three
typical bacterial aetiologies (N. meningitidis, S. pneumoniae and H.
influenzae) could be identified in almost 60% of the CSF analysed
by CERMES over the study period (see Table 1). Relying only on
suspected cases would therefore introduce a large number of
misclassified cases. However, our system may suffer from
underreporting from areas where performing a lumbar puncture
and shipping the samples to CERMES may represent logistical
difficulties. Further analyses (not shown here) have documented
that indeed the districts the most remote from CERMES (in Maradi
Table 1. Results of microbiological analyses of cerebrospinal fluid (CSF) samples by epidemiological year.
Year20032004 20052006 200720082009 Total
Collected CSF2073 13241273 3135 115028194027 15801
Meningitis positive CSF in number of cases (% of the collected CSF)
Positive CSF945 (45.6)487(36.8) 370 (29.1)1336 (42.6) 329 (28.6) 1218 (43.2)1872 (46.5) 6557 (41.5)
Bacteria in number of cases (% of the positive results)
N.meningitidis793 (83.9)333 (68.4)166 (44.9)1143 (85.6) 137 (41.6)1060 (87.0)1756 (93.8) 5388 (82.2)
S.pneumoniae103 (10.9) 123 (25.2) 165 (44.6)131 (9.8) 128 (38.9) 116 (9.5) 96 (5.1) 862 (13.1)
H.influenzae 49 (5.2)31 (6.4) 39 (10.5)62 (4.6) 64 (19.5)42 (3.5) 18 (1.0)305 (4.7)
Others0 (0.0) 0 (0.0)0 (0.0) 0 (0.0)0 (0.0) 0 (0.0)2 (0.1) 2 (,0.1)
Serogroups of N. _eningitides in number of cases (% of the meningococci)
A 715 (90.1)280 (84.1) 92 (55.4)539 (47.2)119 (86.9) 993 (93.7) 1705 (97.1) 4443 (82.5)
X 3 (0.4) 12 (3.6) 41 (24.7) 559 (48.9)11 (8.0) 5 (0.5)10 (0.6)641 (11.9)
W13564 (8.1) 31 (9.3) 19 (11.5)24 (2.1)5 (3.6)0 (0.0) 10 (0.6)153 (2.8)
C 0 (0.0) 1 (0.3)0 (0.0) 0 (0.0)0 (0.0) 0 (0.0)0 (0.0) 1 (,0.1)
Y4 (0.5) 2 (0.6)0 (0.0) 1 (0.1) 0 (0.0)0 (0.0)1 (,0.1)8 (0.1)
Undetermined 7 (0.9) 7 (2.1)14 (8.4) 20 (1.7)2 (1.5)62 (5.8)30 (1.7)142 (2.6)
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Figure 1. Cumulative incidence rates of meningitis in Niger from July 2002 to June 2009. A: Incidence of laboratory-confirmed cases of
meningococcal meningitis (MM) at the health centre catchment area level. B: Incidence of suspected cases of meningitis reported to the DSSRE at the
Figure 2. Annual spatial clusters of meningococcal meningitis cases identified in Niger from 2003 to 2009. Each panel shows the
results of both methods, the Anselin’s Local Moran’s I test and the Kulldorff’s spatial scan statistic, for a single epidemiological year.
Spatio-Temporal Clustering of Meningitis in Niger
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and Zinder regions) were sending less CSF specimens than the closer
ones, for a similar number of suspected cases notified to DSSRE.
However, the proportion of negative cases among the received CSF
specimens was fairly similar among the healthcare centres (outside
the capital Niamey). This suggested that the decision to take or not a
CSF sample from a patient based on clinical criteria had no
significant spatial variability. Moreover, our cluster analyses enabled
us to detect the importance of remote regions in the epidemic
dynamics according to the recurrent clusters identified there. Like in
many other settings, the surveillance system may not cover the entire
population of Niger affected by meningitis. However, we can
reasonably assume that most meningitis cases, because of their
severity, end up reaching the healthcare centres, with or without
free healthcare offered to all people suffering from meningitis in
Niger probably reduces social and spatial disparities in care-seeking
behaviours.Thus,for allthereasons above, weareconfidentthat the
surveillancesystemisrepresentative enough andthatunderreporting
did not substantially affect the validity of our results, which are more
likely to reflect the dynamism peculiar to meningitis than the spatial
disparities in the surveillance system efficiency. Incidence estimates
were based on the 2001 censusand constant population growth rates
were applied. We could not take into account possible variations of
population growth rate over time and space, due to the difficulty in
quantifying population migrations.
The second asset of this study was the use of HCCAs as spatial
units for the spatio-temporal analysis of MM. They represent a
more accurate spatial unit of analysis than the district level on
which reactive vaccination strategies and spatio-temporal studies
are usually based [3,6,7,19]. Analysing data at the HCCA level has
greater relevance for understanding the epidemic dynamics, for
making decisions in response to starting epidemics and for
assessing control strategies.
Indeed,thisstudyhasshownthatclustersmostoften included only
a few HCCAs within a district. This finding, previously suggested by
, is important for understanding meningitis epidemics and
should encourage surveillance at the health centre level. Clusters
occurred indifferent HCCAs withinthe samedistricts in consecutive
years, demonstrating strong intra-district heterogeneity and year-to-
year variability of the affected HCCAs. This could result from
outbreaks limited to HCCAs without exceeding the threshold at the
district level: the district is not vaccinated and may be affected by a
large outbreak the following year. Besides, waiting for the threshold
to be reached at the district level to initiate reactive vaccination may
incur unnecessary delays: we showed that a decision based on
threshold estimated at the health centre level might lead to earlier
detection of outbreaks, so more reactive and possibly more cost-
effective vaccination strategies. Thus, adding HCCA-level surveil-
lance to the current district-level surveillance would improve the
timeliness of epidemic detection.
With the introduction of a new meningococcal A conjugate
vaccine (MenAfriVacTM) in the meningitis belt over the next few
years, the use of the health centre catchment areas as spatial units
can also help to monitor more accurately the vaccine supply at a
finer spatial scale, saving doses that could be given inadequately,
and to evaluate its impact and protective efficacy in the population
Figure 3. Frequency of cluster occurrence in Niger from 2003 to 2009. A, B: Frequencies of occurrence of spatial clusters detected by one of
the two methods (Anselin’s Local Moran’s I test or Kulldorff’s spatial scan statistic). C, D: Frequencies of occurrence of spatial clusters detected by
Spatio-Temporal Clustering of Meningitis in Niger
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(herd immunity) at the same level. Although this vaccine brings new
hope to the control of meningitis epidemics, reactive vaccination with
polysaccharide vaccines and research to improve control strategies
will still be needed in the coming years, since it will take several years
to immunize against the A the vulnerable population across the belt
and since other serogroups like W135 may replace meningitis A as
Figure 4. Spatio-temporal clusters of meningococcal meningitis cases identified in Niger from 2003 to 2009. Each panel shows the
results of the Kulldorff’s space-time scan statistic for a single epidemiological year and the incidence rate observed for each HCCA of a spatio-
temporal cluster during the time period associated to that cluster (maximum one week). Spatio-temporal clusters are numbered in chronological
order of occurrence.
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the dominant serogroup . New decision criteria will have to be
found for reactive vaccination. With the additional use of a finer
spatial scale like the HCCAs, an interesting strategy would be real-
time cluster detection, with prospective space-time scan statistic 
or other existing methods .
In the context of a resource-limited country, this study can also
assist public health authorities in their decision-making regarding
resource allocation. The spatial clusters detected in our study were
located in different HCCAs from year to year, but nine of the 42
districts were more recurrently affected by clustering of MM cases.
Thus, these findings provide approaches to better adjust allocation
of resources, including a ready supply of antibiotics and rapid
diagnostic tests [24,25], as well as additional health care personnel.
In order to reduce the reaction time of the vaccination, one may
consider allocating vaccines to these districts’ hospitals prior to the
meningitis season, provided the cold chain can be maintained.
Given cost and organizational constraints, further cost-effective-
ness and feasibility analyses are needed to evaluate this strategy,
before any policy recommendation.
Clusters were more often found in nine districts, including five
bordering Nigeria within a 500 km distance between Doutchi and
Aguie, most likely because of intense mobility of border
populations . However, no consistent annual spatio-temporal
pattern could be found over the study period; hence, no spread in
a systematic geographical direction from a fixed source could be
identified. This is contrary to a study carried out in Mali, which
highlighted a potential south-north spread, with Bamako and
Mopti as probable sources . Instead, our results suggest the
emergence of scattered sources, likely from a pool of carriers when
conditions are favorable to the occurrence of the invasive disease.
Favorable conditions may include climatic conditions occurring
during the dry season (low absolute humidity and dust-laden
Harmattan wind), which would damage the nasopharyngeal
mucous membrane and increase the risk of bloodstream invasion
by a colonizing meningococcus . In this study, we observed
that the latest spatio-temporal clusters during the epidemic season
were often the northernmost ones, which could be correlated with
the northward advance of the Intertropical Front preceding the
arrival of rains from the south, thus raising relative humidity.
However, climatic factors do not entirely explain these spatio-
temporal epidemic patterns. As suggested by Mueller’s hypothet-
ical explanatory model , their role may be limited to the
hyperendemic increase during the dry season, while transition
from a hyperendemic state to highly localized epidemics may be
due to increased transmission, possibly caused by viral respiratory
co-infections. Moreover, in equivalent climatic conditions, an area
in which the proportion of susceptible individuals is higher due to
waning immunity (acquired by infection or vaccination) would be
more prone to outbreaks . Recently, Irving et al  suggested
that population immunity may be a key factor in causing the
unusual epidemiology of meningitis in the Belt. Although density
and distance to primary roads were not individually correlated
with MM incidence at the HCCA level, other socio-demographic
factors (poverty, overcrowded housing, migrations, markets…)
may also have an influence on local transmission of the bacteria
and carriage and contribute to the risk of micro-epidemics of co-
infections . Of note, one spatio-temporal cluster of four adult
cases was detected in February 2009 in Bilma district (see figure 1),
in the oasis town of Dirkou, located on an important south-north
route of trans-Saharan trade and transit migration. Meningococcal
strain variations most likely play a role in the occurrence of
epidemic waves [20,30,31]. In this study, the spatio-temporal
distribution of all N. meningitidis cases was analysed irrespective of
the serogroups. A subsequent analysis will differentiate serogroups
of meningococci as their spatio-temporal patterns may significant-
ly vary [32,33]. Further etiologic studies are needed to explore
causality of the spatio-temporal patterns highlighted in this paper.
Finally, our findings provide an evidence-based approach to
reflect on public health policies and indicate a promising strategy
to improve prevention and control of meningitis in sub-Saharan
Africa. They can serve as an example for other meningitis belt
countries, illustrating what finer scale surveillance and spatial
analyses can offer for prevention and control of meningitis.
Research efforts should now focus on investigating the role of dust,
socio-demographic factors, co-infections and vaccination strategies
on cluster occurrence at the HCCA level, and on developing an
operational decision support tool to respond better to meningitis
outbreaks with the introduction of the new conjugate vaccine.
Anselin’s Local Moran’s I and Kulldorff’s spatial scan
Supporting information on Global Moran’s I,
The authors are indebted to all the doctors and health staff who have sent
CSF specimens and epidemiological forms to CERMES, and also to
DSSRE staff. We thank the CERMES technical staff who analysed the
CSF, Noe ´mie Phulpin for health map creation and database management,
and Oumarou Alto for his contribution to the health map. We also thank
Arnaud Fontanet for helpful comments and suggestions on the manuscript
and Tamara Giles-Vernick for English editing.
Conceived and designed the experiments: JP J-MC J-FJ. Performed the
experiments: JP FG J-MC HBM J-FJ. Analyzed the data: JP FG J-MC
HBM J-FJ. Wrote the paper: JP J-MC J-FJ.
1. Lapeyssonnie L (1963) La me ´ningite ce ´re ´brospinale en Afrique. Bull World
Health Organ 28 Suppl: 1–114.
World Health Organization (1998) Control of epidemic meningococcal disease.
WHO Practical Guidelines. 2nd ed. Geneva: World Health Organization.
World Health Organization (2000) Detecting meningococcal meningitis
epidemics in highly-endemic African countries. Wkly Epidemiol Rec 75:
Sugumaran R, Larson SR, DeGroote JP (2009) Spatio-temporal cluster analysis
of county-based human West Nile virus incidence in the continental United
States. Int J Health Geogr 8.
Moore DA, Carpenter TE (1999) Spatial analytical methods and geographic
information systems: use in health research and epidemiology. Epidemiol Rev
6.Thomson MC, Molesworth AM, Djingarey MH, Yameogo KR, Belanger F,
et al. (2006) Potential of environmental models to predict meningitis epidemics
in Africa. Trop Med Int Health 11: 781–788.
Molesworth AM, Cuevas LE, Connor SJ, Morse AP, Thomson MC (2003)
Environmental risk and meningitis epidemics in Africa. Emerging Infect Dis 9:
Yaka P, Sultan B, Broutin H, Janicot S, Philippon S, et al. (2008) Relationships
between climate and year-to-year variability in meningitis outbreaks: a case
study in Burkina Faso and Niger. Int J Health Geogr 7: 34.
Broutin H, Philippon S, Constantin de Magny G, Courel M-F, Sultan B, et al.
(2007) Comparative study of meningitis dynamics across nine African countries:
a global perspective. Int J Health Geogr 6: 29.
10. Sultan B, Labadi K, Gue ´gan J-F, Janicot S (2005) Climate drives the meningitis
epidemics onset in west Africa. PLoS Med 2: e6.
Spatio-Temporal Clustering of Meningitis in Niger
www.plosntds.org9 March 2012 | Volume 6 | Issue 3 | e1577
11. Jackou-Boulama M, Michel R, Ollivier L, Meynard JB, Nicolas P, et al. (2005) Download full-text
Corre ´lation entre la pluviome ´trie et la me ´ningite a ` me ´ningocoque au Niger.
Med Trop 65: 329–333.
12. Philippon S, Broutin H, Constantin de Magny G, Toure K, Diakite CH, et al.
(2009) Meningococcal meningitis in Mali: a long-term study of persistence and
spread. Int J Infect Dis 13: 103–109.
13. Sidikou F, Djibo S, Taha MK, Alonso JM, Djibo A, et al. (2003) Polymerase
chain reaction assay and bacterial meningitis surveillance in remote areas, Niger.
Emerg Infect Dis 9: 1486–1488.
14. Chanteau S, Sidikou F, Djibo S, Moussa A, Mindadou H, et al. (2006) Scaling
up of PCR-based surveillance of bacterial meningitis in the African meningitis
belt: indisputable benefits of multiplex PCR assay in Niger. Trans R Soc Trop
Med Hyg 100: 677–680.
15. World Health Organization (1999) Laboratory methods for the diagnosis of
meningitis caused by Neisseria meningitidis, Streptococcus pneumoniae, and
Haemophilus influenzae. Geneva: World Health Organization. WHO/CDS/
16. Anselin L (1995) Local indicators of spatial association. Geographical analysis
17. Kulldorff M (1997) A spatial scan statistic. Commun Statist – Theory Meth 26:
18. Kulldorff M, Athas WF, Feurer EJ, Miller BA, Key CR (1998) Evaluating cluster
alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico.
Am J Public Health 88: 1377–1380.
19. Cuevas LE, Savory EC, Hart CA, Thomson MC, Yassin MA (2007) Effect of
reactive vaccination on meningitis epidemics in Southern Ethiopia. J Infect 55:
20. Mueller JE, Gessner BD (2010) A hypothetical explanatory model for
meningococcal meningitis in the African meningitis belt. Int J Infect Dis 14:
21. The Lancet Infectious Diseases (2011) A vaccine against meningitis in Africa.
Lancet Infect Dis 11: 1.
22. Kulldorff M (2001) Prospective time periodic geographical disease surveillance
using a scan statistic. J R Stat Soc Ser A Stat Soc 164: 61–72.
23. Watkins R, Eagleson S, Hall R, Dailey L, Plant A (2006) Approaches to the
evaluation of outbreak detection methods. BMC Public Health 6: 263.
24. Chanteau S, Dartevelle S, Mahamane AE, Djibo S, Boisier P, et al. (2006) New
rapid diagnostic tests for Neisseria meningitidis serogroups A, W135, C, and Y.
PLoS Med 3: e337.
25. Hamidou AA, Djibo S, Mamane AE, Moussa A, Chanteau S (2008)
Serogrouping of non-interpretable Neisseria meningitidis carriage strains, using
rapid diagnostic tests. Trans R Soc Trop Med Hyg 102: 99–100.
26. Maı ¨nassara HB, Molinari N, Dematteı ¨ C, Fabbro-Peray P (2010) The relative
risk of spatial cluster occurrence and spatio-temporal evolution of meningococcal
disease in Niger. Geospat Health 5: 93–101.
27. Greenwood B (1999) Manson Lecture. Meningococcal meningitis in Africa.
Trans R Soc Trop Med Hyg 93: 341–353.
28. Pascual M, Dobson A (2005) Seasonal patterns of infectious diseases. PLoS Med
29. Irving TJ, Blyuss KB, Colijn C, Trotter CL (2011) Modelling meningococcal
meningitis in the African meningitis belt. Epidemiol Infect. pp 1–9.
30. Taha M-K, Deghmane A-E, Antignac A, Zarantonelli ML, Larribe M, et al.
(2002) The duality of virulence and transmissibility in Neisseria meningitidis.
Trends Microbiol 10: 376–382.
31. Leimkugel J, Hodgson A, Forgor AA, Pflu ¨ger V, Dangy J-P, et al. (2007) Clonal
waves of Neisseria colonisation and disease in the African meningitis belt: eight-
year longitudinal study in northern Ghana. PLoS Med 4: e101.
32. Boisier P, Djibo S, Sidikou F, Mindadou H, Kairo KK, et al. (2005)
Epidemiological patterns of meningococcal meningitis in Niger in 2003 and
2004: under the threat of N. meningitidis serogroup W135. Trop Med Int
Health 10: 435–443.
33. Boisier P, Nicolas P, Djibo S, Taha M, Jeanne I, et al. (2007) Meningococcal
Meningitis: Unprecedented Incidence of Serogroup X–Related Cases in 2006 in
Niger. Clin Infect Dis 44: 657–663.
Spatio-Temporal Clustering of Meningitis in Niger
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