Exploring the spatial and spatiotemporal patterns of severe food insecurity across Africa (2015–2021)

Abstract

Food insecurity is a rapidly increasing global challenge. It has multiple adverse effects on public health and nutrition outcomes. The level and patterns of severe food insecurity vary by region. This study, therefore, aims to investigate the spatial and spatio-temporal patterns of severe food insecurity across the African continent. Data used in this study include the annual prevalence of severe food insecurity from 2015 to 2021, obtained from the FAO. Spatial analytical techniques such as Global Moran’s I, Anselin’s Local Moran I, and Getis-Ord Gi* statistic were used to determine the extent of spatial clustering of severe food insecurity and detect severe food insecurity hotspot (high-risk) areas over time. Kulldorff’s space–time scan statistic was also used to detect temporal and space–time clusters of severe food insecurity. A Poisson model was utilized for this purpose. The results revealed that severe food insecurity varies unevenly across the continent over time and there was a significant clustering of severe food insecurity from 2015 to 2021 at a 5% significance level. Accordingly, Democratic Republic of the Congo and Central African Republic (2015–2021), Uganda (2015 and 2017–2020), Zambia (2019–2020), Angola (2019–2021) and Sierra Leone (2017) were identified as hotspot (high-risk) areas for severe food insecurity at 5% level of significance. The space–time cluster analysis identified six significant clustered areas. The most likely significant space–time cluster was located in Somalia, East Africa (LLR = 6,081,314.44, RR = 2.41, P-value < 0.000), which occurred between 2015 and 2017. The largest secondary significant space–time cluster was located in the continent’s central regions between 2015 and 2017 (LLR = 44,393,763, RR = 2.26, P < 0.000). The study concludes that intervention efforts should consider the spatial heterogeneity of severe food insecurity over time to prevent and control this issue.

Full text

Exploring the spatial and
spatiotemporal patterns of severe
food insecurity across Africa (2015–
2021)
Mesn M. Ayalew1, Zelalem G. Dessie1,2, Aweke A. Mitiku1,3 & Temesgen Zewotir2
Food insecurity is a rapidly increasing global challenge. It has multiple adverse eects on public health
and nutrition outcomes. The level and patterns of severe food insecurity vary by region. This study,
therefore, aims to investigate the spatial and spatio-temporal patterns of severe food insecurity across
the African continent. Data used in this study include the annual prevalence of severe food insecurity
from 2015 to 2021, obtained from the FAO. Spatial analytical techniques such as Global Moran’s I,
Anselin’s Local Moran I, and Getis-Ord Gi* statistic were used to determine the extent of spatial
clustering of severe food insecurity and detect severe food insecurity hotspot (high-risk) areas over
time. Kulldor’s space–time scan statistic was also used to detect temporal and space–time clusters of
severe food insecurity. A Poisson model was utilized for this purpose. The results revealed that severe
food insecurity varies unevenly across the continent over time and there was a signicant clustering of
severe food insecurity from 2015 to 2021 at a 5% signicance level. Accordingly, Democratic Republic
of the Congo and Central African Republic (2015–2021), Uganda (2015 and 2017–2020), Zambia
(2019–2020), Angola (2019–2021) and Sierra Leone (2017) were identied as hotspot (high-risk) areas
for severe food insecurity at 5% level of signicance. The space–time cluster analysis identied six
signicant clustered areas. The most likely signicant space–time cluster was located in Somalia, East
Africa (LLR = 6,081,314.44, RR = 2.41, P-value < 0.000), which occurred between 2015 and 2017. The
largest secondary signicant space–time cluster was located in the continent’s central regions between
2015 and 2017 (LLR = 44,393,763, RR = 2.26, P < 0.000). The study concludes that intervention eorts
should consider the spatial heterogeneity of severe food insecurity over time to prevent and control
this issue.
Keywords Severe food insecurity, Spatial pattern, Spatiotemporal pattern, Space–time cluster, Africa
Food insecurity is a rapidly increasing global challenge. It describes the circumstances in which individuals
cannot consistently access a healthy diet needed for a healthy lifestyle11. is may be the result of scarce food or
not having the means to buy food. Food insecurity can vary in intensity. A person without eating food for a day
or longer is considered to be experiencing severe food insecurity11. is individual may have largely experienced
hunger. Conversely, a person is said to be experiencing moderate food insecurity if they have limited access to
nutritious food in terms of both quality and quantity. is doesn’t necessarily mean they are starving but are
unsure about their ability to obtain food consistently due to a lack of money or other resources. Food insecurity
can increase the risk of malnutrition, stunting, micronutrient deciencies, or obesity in adults.
In 2020, the global hunger rate was 20.2% in Africa, 9.1% in Asia, 8.6% in Latin America and the Caribbean,
5.8% in Oceania and less than 2.5% in North America and Europe33. is gure indicates the existence of
signicant regional disparities and the heaviest burden is found in Africa. Food insecurity in Africa is a critical
and persistent challenge that aects millions of people across the continent. According to13 report, Africa has
been unable to end hunger or ensure regular access to a healthy diet for its citizens, which is one of the Sustainable
Development Goals (SDGs).
e levels and trends of food insecurity also vary signicantly among the subregions of Africa. In 2020, the
rate of severe food insecurity was highest in the central region of the continent (35.8%), followed by Western
1Department of Statistics, Bahir Dar University, P.O.Box 79, Bahir Dar, Ethiopia. 2School of Mathematics, Statistics
and Computer Science, University of KwaZulu-Natal, Durban, South Africa. 3Global Change Institute, University of
the Witwatersrand, Johannesburg, South Africa. email: m2mulu@gmail.com
OPEN
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Africa (28.8%), Eastern Africa(28.7%), Southern Africa(22.5%) and Northern Africa (9.5%) , as reported by13.
For moderate food insecurity, however, the rate is highest in , the rate is higher in Western Africa (39.5%)
and Eastern Africa (36.6%), followed by Central Africa (34.2%), Southern Africa (27.0%), and Northern Africa
(20.7%). In 2020, 346.4 million Africans (25% of the population) suered severe food insecurity, with Central,
Eastern and Western Africa being the most aected regions, whereas 452 million experienced moderate food
insecurity. Despite a decline in moderate or severe food insecurity from 2000 to 2014, it has risen since 2015. In
2020, 281.6 million Africans faced hunger, an increase of 46.3 million from 2019 and 89.1 million from 201413.
ese regional disparities in food insecurity were conrmed by research conducted by 2, 7, 14, 21, 29, 31, 44, 50-52 across
various regions.
Several factors contribute to food insecurity, including incidence of malaria39–41,climate change7,8,36,37, food
price ination, extreme rains and drought24, low economic growth30,36, COVID-19 pandemic10, social and
political instability and conicts1,3,4,6,7,9, GDP per capita, livestock availability, education and land holding20,28,
irrigation16,25,26, arable land31, food production, imports, infrastructure and access to improved water sources17,
rapid population growth15,27,36, economic incentives in agriculture38and coecient of variation of habitual
caloric consumption distribution and/or unequal dietary energy distribution14.
Geographical patterns and disparities in food insecurity have been explored at local or household levels
in various countries, including Ethiopia35, Democratic Republic of the Congo21, South Africa32, Democratic
Republic of the Congo, Somalia and Sudan2, Kenya22 as well as at the country level within Sub-Saharan Africa
14, 29, 31. However, there has been less focus on these patterns across Africa at the country level. FAO relies on
aggregate country-level data to provide a standardized and comparable analysis, oering a broader view of food
insecurity dynamics. It utilizes food insecurity monitoring tools such as the Famine Early Warning Systems
Network (FEWS NET) and the Global Information and Early Warning System (GIEWS). e reports mainly use
descriptive statistics, primarily percentages, to track food insecurity trends across countries over time. Although
the percentages oer valuable insights into the magnitude of food insecurity, they lack information on statistical
signicance and oen lack spatial and temporal depth. Although useful, these reports only capture food insecurity
at a particular moment or location. ey fail to show how food insecurity simultaneously evolves across space
and time. To address this gap, we applied an evidence-based approach to identify statistically signicant areas
of food insecurity using advanced spatial tools such as Global Moran’s I, Anselin’s Local Moran’s I, Getis-Ord
Gi*, and Kulldor’s space–time scan statistic. ese tools enabled us to conduct a detailed analysis of spatial and
temporal clusters, revealing where and when food insecurity intensies or improves. e study also determined
whether severe food insecurity clusters are geographically or is randomly distributed, detecting specic clusters
and outliers relative to neighboring areas. ese spatio-temporal insights are crucial for shaping food security
policies, prioritizing interventions, identifying hotspots and coldspots and directing aid to the most aected
regions where both time and resources are oen limited. erefore, this study aims to investigate the spatial and
spatio-temporal patterns of severe food insecurity across Africa using aggregated country-level data.
Material and Methods
Study Area
is study was carried out across the African continent. e African continent is the second largest in the
world, covering an area of over 30 million square kilometers. It is located mainly in the Eastern and Northern
Hemispheres. Its borders are the Red Sea (northeast), the Indian Ocean (southeast), the Atlantic Ocean (west),
and the Mediterranean Sea (north). It comprises 54 recognized countries, each with its unique culture, history,
and geography.
Study Design and Periods
A retrospective study design was employed to explore the annual spatial, temporal and spatiotemporal patterns
of the prevalence of severe food insecurity across Africa, using aggregated country-level data from 2015 to 2021.
Data and its Measurement
e analysis utilizes country-level data on the prevalence of severe food insecurity, sourced from the Food and
Agriculture Organization (FAO). Georeferenced point data were obtained from Google Maps, while polygon
shapeles representing the continent were sourced from the Central Statistical Service of Ethiopia.
Prevalence is calculated by dividing the number of people who are severely food insecure by the total
population of the country. FAO collects this data through direct interviews about individuals’ experiences with
limited access. Food insecurity is a multidimensional issue and a single indicator is insucient to measure it
adequately. Hence, FAO uses the Food Insecurity Experience Scale (FIES) to measure it12. is tool is intended
to assess personality, aptitude/intelligence, social psychology, and health-related conditions. e questionnaire
consists of eight yes/no questions about individual or household food-related behaviors and experiences with
access to enough food. e responses were analyzed collectively to produce food insecurity estimates.
Model
e study used Global Moran’s I, Anselin’s Local Moran’s I, Getis-Ord Gi* and Kulldor’s space–time scan
statistic to evaluate spatial and space–time clustering and to detect hotspot (high-risk) areas.
Moran’s I statistic
Global Moran’s I statistic was utilized to assess the spatial autocorrelation, which measures the degree of similarity
or dissimilarity of values between neighbouring spatial units23. A positive Moran’s I indicate clustering of similar
values, while a negative value suggests clustering of dissimilar values. Moran’s I ranges from -1 to 1: a signicant
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positive value shows positive spatial autocorrelation (similar values cluster), a signicant negative value indicates
spatial dispersion (dissimilar values cluster), and values near zero suggest a random spatial distribution (no
spatial autocorrelation). e measure I is calculated by comparing each observed area i to its neighbouring areas
using the weights, from the proximity matrix for all j = 1,…, K. Moran’s I is calculated as:
I
=K
∑
i

=jwij
∑
i
∑
jwij
(
Yi−Y
)(
Yj−Y
)
∑i(
Yi
−
Y
)
2 (1)
where K is the total number of countries,
wij
is the spatial weight linking country i and j,
Yi
and
Yj
are the severe
food insecurity values for the country i and j, respectively, and
Y
is the mean of the severe food insecurity values.
Getis-Ord Gi* Statistic
e Getis-Ord Gi* statistic, a Local Indicator of Spatial Association (LISA), was used to detect local hotspots
(high-risk areas) and coldspots (low-risk areas) of severe food insecurity. A large Gi* value (positive Z score)
indicates hotspots, while a small Gi* value (negative Z score) indicates coldspots. Z-scores near zero suggest no
signicant clustering. e Gi* is computed as:
G
i∗=
∑K
j=1 wij yj−Y
∑K
j=1 wij
S
√
K
∑
K
j=1 wij 2−(
∑
K
j=1 wij )
2
K
−1
(2)
Y
=
∑
K
j=1 Yj
K,S =
√∑
K
j=1 yj2
K
−(Y)
2
where
wij
is the spatial weight connecting country i and j,
Yi
and
Yj
are the values of severe food insecurity for
country i and j respectively, K is the total number of countries considered for this study.
Kulldor’s Space–time Scan Statistic
is statistical method was used to jointly detect areas of events and time periods where the incidence rates
are signicantly higher than expected by comparing observed events in each cluster to the expected number
under a uniform distribution. Maps were generated using a scanning window to show potential clusters and
their statistical tests such as the likelihood ratio test, P-value, etc. e analysis was performed using SaTScan
soware designed by18. e hypothesis tested whether the relative risk inside the scanning window was the same
as outside, with H0 assuming equal risk and H1 suggesting a higher risk inside the window compared to outside.
Under the assumption of the Poisson model, the likelihood(LL) function for a given window is proportional to:
LL
≈
(
n
E[n]
)n(
N−n
N
−
E[n]
)N−n
I
()
(3)
where N is the total number of severe food insecure people, n is the observed number of severe food insecure
people inside the window, E[n] is the expected number of severe food insecure people within the window under
H0, N-E[n] is the expected number of severe food insecure people outside the window and I() is an indicator
function. When we scan only high-rate clusters, I() = 1 when the window has more severe food-insecure people
than expected under H0 and 0 otherwise. When we scan low-rate clusters, the situation is opposite. When we
scan both high and low-rate clusters simultaneously, I() = 1 for all windows.
Software
e study utilized R version 4.4.1 and SaTScan version 10.2.5 soware programs for data analysis and spatial
clustering assessments. ese tools enabled robust statistical and spatial analysis, facilitating comprehensive
examination and visualization of the data.
Results
Spatial Patterns of Severe Food Insecurity
e overall prevalence trend of severe food insecurity in Africa from 2015 to 2021 is shown in Fig.1. e study’s
ndings showed that severe food insecurity in the African continent increased over the study years. As indicated
in the Fig.1, the data shows a consistent increase in severe food insecurity rates. In 2021, the highest rate was
recorded, as illustrated in Fig.1. e rising incidence of severe food insecurity underscores the urgent need for
eective policy interventions and humanitarian responses to address the factors driving this crisis.
Figure 2 illustrates a visual representation of the spatial distribution of the prevalence of severe food
insecurity across the African continent from 2015 to 2021. e map suggests that the incidence of severe food
insecurity varied substantially across the countries within the continent. During the study periods, South Sudan,
Somalia, the Central African Republic, the Democratic Republic of the Congo, Republic of the Congo, Malawi,
Guinea, Mozambique, and Liberia notably experienced higher rates of severe food insecurity among African
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countries. In addition to these countries, Rwanda experienced a high prevalence rate of severe food insecurity,
particularly between 2018 and 2021. Similarly, Zimbabwe exhibited a high prevalence rate from 2015 to 2017
(Fig.2). e overall distribution suggests that while some regions faced persistent and chronic food insecurity,
others experienced uctuations in severity over time, underscoring the complexity and dynamic nature of the
food security landscape across Africa during this period (Fig.2).
Global Spatial Autocorrelation of Severe Food Insecurity
e degree of spatial clustering of the incidence of severe food insecurity in Africa during the study period is
displayed in Table 1. e analysis utilized the Global Moran’s I statistic, which measures spatial autocorrelation.
Across the study years, the Global Moran’s I statistic for the prevalence of severe food insecurity was positive
(ranging from 0.22 to 0.285) and statistically signicant (P-value < 0.05). is suggests that spatial clustering
existed within the data. e degree of clustering was higher in 2020 (Global Moran’s I = 0.2849 & P-value = 0.0017).
ese ndings’ implications are signicant; they suggest that severe food insecurity is not randomly distributed
across the continent but instead exhibits a pattern of concentration in certain areas.
LISA Cluster & Outlier Analysis of Severe Food Insecurity
In Fig. 3, we constructed thematic maps showing the rate of severe food insecurity across the continent.
e global Moran’s statistic, as discussed in the previous section, provides a single number representing the
spatial autocorrelation of the dataset. To gain deeper insights, we employed LISA (Local Indicators of Spatial
Association), which computes each country’s local Moran’s I statistic. e local Moran’s I measure how similar
the values of the given observation are to those of its neighboring observations. is means the local Moran’s I
compares each observation to its local neighborhood. is helps us to identify spatial clusters of values at the
local level. When local Moran’s I is positive, it indicates that the unit is surrounded by units with similar values.
Figure3 illustrates the ve distinct types of spatial associations and outliers produced by the LISA analysis
for the prevalence of severe food insecurity. e colors red (high-high) and green (low-low) represent the
spatial clustering of the high (hotspot) and low (coldspot) values of severe food insecurity rates of the respective
countries, respectively. ese countries are surrounded by others with similar values, exhibiting a statistically
signicant positive spatial autocorrelation. For instance, the Central African Republic and the Democratic
Republic of the Congo experienced high rates of severe food insecurity from 2015 to 2021, while Guinea faced
similar challenges from 2016 to 2021 and these countries were surrounded by neighboring countries with similar
high prevalence rates. Conversely, certain countries, such as Madagascar (from 2015 to 2021) and Uganda (in
2020 and 2021), experienced low prevalence rates of severe food insecurity and were surrounded by neighboring
nations with similarly low rates.
In Fig.3, the regions colored orange (high-low) and yellow (low–high)represent countries that are spatial
outliers within the dataset. ese countries are surrounded by neighboring nations that exhibit dissimilar
values. is means that while these outlier countries face either high or low rates of severe food insecurity,
their immediate surroundings reect the opposite trend. Hence, these regions exhibit signicant negative spatial
autocorrelation. For instance, Zambia (2015–2021), Uganda (2015–2019) and Angola (2018–2021) experienced
high rates of severe food insecurity and were surrounded by countries with lower rates. On the other hand,
countries such as Algeria and Burkina Faso (2015–2021 illustrate the opposite scenario. ese countries had a
Fig. 1. Annual distribution of prevalence of severe food insecurity in Africa.
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low rate of severe food insecurity and were surrounded by countries that faced high levels of food insecurity.
Furthermore, the areas labeled in gray color in Fig.3 indicate regions where the prevalence of severe food
insecurity did not show signicant spatial autocorrelation. is lack of signicant spatial relationship suggests
that the distribution of food insecurity in these areas is more random, without distinct clustering patterns,
thereby requiring dierent analytical approaches for eective intervention and resource allocation.
Fig. 2. Observed spatial pattern of severe food insecurity incidence over time, 2015–2021.
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Detection of High-risk Areas (Hotspots) for Severe Food Insecurity
Hotspot and coldspot analysis for each country in a dataset is mapped in Fig.4 to describe the spatial cluster of
severe food insecurity in the continent using the Getis-Ord Gi* statistic. e computed z-scores and p-values
allow us to identify the signicant hotspots and coldspots among the countries. Even though a country has a high
value, it might not be a statistically signicant hotspot. A country with a high value of severe food insecurity is
not necessarily a statistically signicant hotspot unless it is surrounded by neighboring countries with similarly
high values. Likewise, for a country to be classied as a coldspot, it must show low rates of severe food insecurity,
along with neighboring countries sharing similarly low rates. e larger signicant positive z-scores signal more
intense clustering of high values (hotspots), while smaller signicant negative z-scores indicate more intense
clustering of low values (coldspots). Z-scores close to zero suggest a lack of spatial clustering. In Fig.4, areas
marked in red color represent hotspots (risk area), while those in orange indicate coldspots for the corresponding
year. e areas highlighted in gray reveal those regions where the incidence of severe food insecurity doesn’t
show any statistically signicant spatial autocorrelation. For instance, the Democratic Republic of the Congo and
the Central African Republic (2015–2021), Uganda (2015 and 2017–2020), Zambia (2019–2020), Angola (2019–
2021) and Sierra Leone (2017) were identied as hotspot areas for severe food insecurity, with signicance levels
of 5%.
Temporal Cluster Analysis of Severe Food Insecurity
Figure5 illustrates the levels and trends of severe food insecurity across various countries on the African
continent. As shown in Fig.5, the rate of severe food insecurity varied among the countries on the continent
during the study periods. Most countries experienced an upward trend in severe food insecurity from 2015 to
2021. is observed rise in severe food insecurity aligns with the continent’s overall prevalence increase depicted
in Fig.1, which provides a broader context for the situation on the continent. e trends indicated in both
gures underscore the growing challenges many countries face in securing adequate food resources for their
populations.
e results of our temporal scanning are illustrated in Fig.6, which provides a comprehensive overview
of the trends in severe food insecurity over time. e temporal cluster analysis, conducted with a two-year
time aggregation length, revealed that severe food insecurity had a high risk from 2015 to 2017. e highest
aggregated levels of severe food insecurity was recorded between 2015 and 2017 in Somalia (LLR = 6,081,314.44,
RR = 2.41, P < 0.000). During this period, 20,800,000 severe food insecurity cases were reported in this area, and
the risk of severe food insecurity was (RR = 2.41) very high (Table 2).
Spatiotemporal Cluster Analysis of Severe Sood Insecurity
Table 2 and Fig.7 illustrate the results of Kulldor’s retrospective space–time scan statistic. A Poisson model
was used for this purpose. is statistical approach helps pinpoint where and when the incidence of severe food
insecurity was most concentrated across Africa at the country level from 2015 to 2021.
Table 2 details the statistical metrics associated with each cluster, including the relative risk (RR), likelihood
ratio (LLR) and the timeframes during which these clusters were detected. Identifying signicant clusters
underscores the need for urgent and sustained humanitarian eorts to alleviate severe food insecurity in the
high-risk areas identied in the analysis.
Figure7 highlights both the most likely and secondary statistically signicant spatio-temporal clusters of
severe food insecurity rates in the study region. A circle detects the circular window scan of the studied region
and the signicant clusters. is indicates that where the rate of severe food insecurity was much higher than
expected during specic time periods.  is space–time cluster analysis identied six major areas of severe
food insecurity among the African countries analyzed. ese secondary statistically signicant clusters were
predominantly located in the western, central, eastern and southeastern parts of the continent at dierent
periods, with the largest cluster being located in the central regions of the continent (LLR = 44,393,763,
RR = 2.26, P < 0.000)( Table 2). Seven countries are clustered in this region, with a high-risk period from 2015 to
2017. Notably, the most likely signicant spatio-temporal cluster was detected in Somalia, located in East Africa,
(LLR = 6,081,314.44, RR = 2.41, P < 0.000), which occurred between 2015 and 2017. During this period, the total
number of severely food insecure people in this area was 20,800,000. e risk of severe food insecurity was 2.41
times (RR = 2.41) higher than elsewhere (Table 2).
Year Moran Test Statistic P-value Remark
2015 0.2200 < 0.009 Clustered
2016 0.2258 < 0.009 Clustered
2017 0.2254 < 0.009 Clustered
2018 0.2459 < 0.005 Clustered
2019 0.2661 < 0.003 Clustered
2020 0.2849 < 0.002 Clustered
2021 0.2637 < 0.003 Clustered
Tab le 1. Summary of global spatial autocorrelation results.
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Discussion
is study aims to investigate the spatial and spatio-temporal variation of severe food insecurity across African
countries based on FAO datasets from 2015 to 2021. e focus is to examine how the distribution and intensity
of severe food insecurity vary across dierent regions and over time, oering a comprehensive understanding
of both geographic and temporal trends. By identifying patterns in the incidence and persistence of severe
food insecurity, this study provides crucial insights that are necessary for eective monitoring, prevention, and
intervention eorts.
Fig. 3. LISA cluster mapping of prevalence of severe food insecurity.
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Severe food insecurity levels and trends vary signicantly across African countries over time. is study
found that the prevalence of severe food insecurity in Africa was 17.8%, 18.8%, 19.5%, 19.8%, 20.7%, 22.2%
and 23.4% from 2015 to 2021 respectively. is nding aligns with the studies done by13,36, highlighting similar
trends in food insecurity across the continent. is increase may be due to climate change, social vulnerability
and incidence of malaria. For instance, a study by42 examined the spatiotemporal vulnerability to climate change
in Madhya Pradesh, India, using a Climate Vulnerability Index that combines a Climate Index and a Composite
Social Vulnerability Index. e Climate Index represents exposure to climate change, while the Composite Social
Fig. 4. Hotspot and coldspot detection of severe food insecurity.
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Fig. 6. Temporal Cluster Analysis of Severe Food Insecurity, 2015–2021.
Fig. 5. Trends of Severe Food Insecurity, 2015–2021.
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Vulnerability Index consists of the Socioeconomic Vulnerability Index and the Infrastructural Vulnerability
Index. e study found a signicant decrease in composite social vulnerability over the decades while climate
exposure has signicantly increased. Additionally, the ndings of 46-47 indicate that communities aected by
malaria were associated with chronic food insecurity.
To evaluate the geographic disparities and spatiotemporal patterns of severe food insecurity throughout
African between 2015 and 2021, Global Moran’s I, local Moran’s I , Getis-Ord Gi* and Kulldor ’s space–time
scan statistic were employed.ese methodologies provide a comprehensive framework for assessing how the
incidence of severe food insecurity is distributed across dierent regions and how it changes over time. e
ndings indicate that the prevalence of severe food insecurity varies substantially across countries within the
continent, underscoring the need for region-specic analyses.is result is consistent with the ndings from
other studies of5, 7, 13, 29, 34. For instance, the study conducted by7showed that Western Europe, North America,
Oceania and parts of East Asia are among the most food-secure regions, while Sub-Saharan Africa, South Asia,
West Asia and parts of Southeast Asia face the greatest challenges in food security. e analysis reveals that Europe
is the most food-secure while Sub-Saharan Africa represents the least food-secure region. is trend is further
reinforced by our ndings, which indicate that countries in Sub-Saharan Africa exhibit the most severe levels
of food insecurity. Specically, South Sudan, the Central African Republic, the Republic of the Congo, Malawi,
Fig. 7. Space–time cluster detection of severe food insecurity.
S.No Clusters Dete cted Time Frame Population No. of Cases Expected C ases Observed/Expected RR LLR P-value
1Central African Republic, Cameroon,
Congo, Chad, DRC, Equatorial
Guinea, South Sudan 2015 to 2017 150,683,373 184,108,540 86,374,801.76 2.13 2.26 44,393,763.97 < 0.000
2 Mozambique, Malawi, Zimbabwe 2015 to 2017 62,895,291 76,700,000 36,170,425.55 2.12 2.17 17,583,608.32 < 0.000
3Somalia 2015 to 2017 15,418,290 20,800,000 8,692,013.73 2.39 2.41 6,081,314.44 < 0.000
4 Sierra Leone, Guinea, Liberia 2015 to 2017 25,328,582 29,100,000 14,652,689.36 1.99 2.00 5,576,198.12 < 0.000
5All 2020 to 2021 603,854,260 555,543,747.34 1.09 1.13 2,968,266.73 < 0.000
6Libya 2020 to 2021 6,470,110 2,800,000 2,706,017.89 1.03 1.03 1615.88 < 0.000
Tab le 2. Spatiotemporal cluster analysis of severe food insecurity.
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Guinea, Somalia, Mozambique, the Democratic Republic of the Congo, and Liberia recorded the highest rates
of severe food insecurity among African countries during the study period. Furthermore, data from Rwanda
indicate a high level of severity during the years 2015 and from 2019 to 2021 while Zimbabwe experienced
severe food insecurity from 2015 to 2017. Similarly, the studies by48, 49 indicate that Eastern Africa has a higher
prevalence of severe malnutrition than other regions. Likewise, the research by50, 51 highlights substantial spatial
heterogeneity in child malnutrition, suggesting that its distribution is not random. e ndings of52 further
conrm the presence of spatial autocorrelation in malnutrition, particularly in the prevalence of underweight
children under ve in Ethiopia, Kenya, and Nigeria. Local geographically weighted analyses also show that armed
conict, temperature and rainfall are positively correlated with underweight prevalence in Somali (Ethiopia),
Mandera and Turkana (Kenya), and Borno and Yobe (Nigeria). ese ndings align with our own research,
indicating that severe food insecurity’s spatial distribution is not random across Africa.
In addition to statistical analysis, we constructed thematic maps to visually represent the hotspot (high-risk)
and coldspot areas, thereby illustrating the spatial clusters of severe food insecurity across the African continent
during the study year. e ndings revealed that the Democratic Republic of the Congo and the Central African
Republic (2015–2021), Uganda (2015 and 2017–2020), Zambia (2019–2020), Angola (2019–2021) and Sierra
Leone (2017) were identied as hotspot (high-risk) areas for severe food insecurity, with signicance levels
of 5%. is indicates that these countries consistently faced elevated levels of food insecurity during the study
period, which necessitates urgent attention and intervention. ese results are consistent with the ndings of
previous studies conducted by7, 13, 19, which also identied these regions as facing signicant challenges related
to food security.
Geographic disparities in severe food insecurity are attributed to climate change and social vulnerabilities, as
explored by various scholars. For instance44, provides a framework for assessing the impacts of climate risk on
national-level food security through a vulnerability index. e analysis results found evidence that climate risk
exacerbates food insecurity, especially in sub-Saharan Africa and South Asia. is nding is consistent with the
current research, as most food-insecure countries are in sub-Saharan Africa. Similarly, a study by43 assesses the
vulnerability of West African countries to climate change, identifying ten countries (Niger, Mauritania, Mali,
Burkina Faso, Liberia, Senegal, Guinea Bissau, Guinea, Benin, and Sierra Leone) exhibiting vulnerability levels
above 50%. Among these, Niger, Mali, and Mauritania showed the highest levels of vulnerability while Ghana,
Cape Verde, and Gambia showed the lowest.
Furthermore, a study by45 examines the spatiotemporal patterns of social vulnerability in Alexandria, Egypt,
inuenced by factors such as poverty, inequality, unemployment, housing issues, and limited access to basic
services like safe drinking water and sanitation. e study nds that social vulnerability is unevenly distributed
across Alexandria, with most areas classied as medium vulnerability, some as high (hotspots), and others
as low (coldspots). erefore, the uneven distribution of climate change and social vulnerability signicantly
contributes to varying levels of food insecurity across Africa.
e temporal and space–time cluster analysis provides a valuable framework for comparing the observed
and expected number of severely food-insecure people within dened regions over time. is analysis is crucial
for understanding how food insecurity dynamics change and assessing the eectiveness of interventions to
alleviate this persistent issue. e results revealed that the number of severely food-insecure people observed
was higher than expected gures. e number of severely food-insecure people observed was higher than
expected within the dened scanning window over time. Moreover, the level of severity of food insecurity varied
among the countries on the continent during the study period. e majority of the countries experienced an
increased prevalence rate of severe food insecurity between 2015 and 2021.ese ndings are consistent with
the continent’s overall trend of increment of food insecurity from 2015 to 2021 as reported by13,36. e temporal
cluster analysis with a two-year aggregation length revealed that severe food insecurity was at a high risk from
2015 to 2017. During this period, the highest level of food insecurity was recorded in Somalia. is might be due
to the country’s political instability and ination.
Finally, Kulldor’s retrospective space–time scan statistic was used to detect spatio-temporal clusters of the
incidence of severe food insecurity across the African continent at the country level from 2015 to 2021. is
statistical approach is particularly eective for identifying geographic patterns over time, allowing for a nuanced
understanding of how food insecurity uctuates across dierent regions. e results revealed that the incidence of
severe food insecurity exhibited signicant space–time heterogeneity. e space–time cluster analysis identied
six major clustered areas among the African countries, highlighting regions where severe food insecurity is
particularly concentrated.Signicant high-risk clustered areas were found in the continent’s western, central,
eastern and southeastern regions at dierent periods. ese ndings are consistent with the previous study done
by7, which also emphasized the regional disparities in food security. Among the identied clusters, the largest
secondary statistically signicant spatio-temporal cluster was located in the central region of the continent, with
seven countries clustered in this region. is clustering suggests a need for targeted intervention strategies that
address the specic challenges faced by these nations. Furthermore, the most likely signicant spatio-temporal
cluster was found in Somalia, East Africa, occurring between 2015 and 2017. e identication of Somalia
as a focal point for spatio-temporal clustering underscores the urgent need for comprehensive humanitarian
assistance and sustainable development initiatives to improve food security in this country.
Conclusions
is study investigated the spatial and spatio-temporal patterns of severe food insecurity across the African
continent from 2015 to 2021. e ndings revealed the existence of clear spatial and spatio-temporal clusters
of severe food insecurity across Africa. e existence of severe food insecurity clusters in space and over time
indicates the presence of substantial variability in food access. It has implications that should be addressed
for better population diets and nutrition. Specically, the Democratic Republic of the Congo and the Central
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African Republic (2015–2021), Uganda (2015 and 2017–2020), Zambia (2019–2020), Angola (2019–2021) and
Sierra Leone (2017) were identied as hotspot areas for severe food insecurity. Given the clear identication
of these high-risk areas, we recommend governments, international organizations, and stakeholders prioritize
resources and interventions in these hotspots to mitigate the impact of severe food insecurity. Prevention and
control programs should be designed to address the root causes of food insecurity in these regions. Furthermore,
continuous monitoring and evaluation of food security patterns across the continent will ensure that interventions
remain relevant and eective in addressing this critical issue.
Policy Implications
Governments, international organizations, and other stakeholders can develop eective strategies to combat
severe food insecurity in Africa by implementing the following recommendations:
• Prioritize resources and interventions in identied hotspot (high-risk) areas such as the Democratic Republic
of the Congo, Central African Republic, Uganda, Zambia, Angola, and Somalia. ese regions require urgent
and sustained assistance to address severe food insecurity.
• Implement continuous monitoring systems that use spatial and spatio-temporal analysis to detect emerging
patterns of food insecurity. is will allow for timely responses to new or worsening crises.
• Encourage collaboration among countries in hotspot regions to address transboundary challenges contribut-
ing to severe food insecurity, such as conict, migration, and resource management.
• Strengthen the role of regional organizations in coordinating food security eorts and providing technical
and nancial support.
• Invest in agricultural research and innovation to develop new technologies and practices that enhance pro-
ductivity and reduce vulnerability to food insecurity.
• Expand social protection programs such as food assistance, cash transfers, and school feeding programs in
hotspot areas to provide immediate relief to vulnerable populations.
• Strengthen the capacity of local governments and organizations to manage food security programs eectively.
is includes training in data collection, analysis, and program implementation.
• Increase public awareness and education on nutrition and food security to empower communities to proac-
tively manage their food resources.
• Promote integrated approaches that link food security with health, education, and economic development
policies to create comprehensive and sustainable solutions.
Data Availability
e data is available upon request by contacting the corresponding author at m2mulu@gmail.com.
Received: 28 June 2024; Accepted: 4 November 2024
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Author Contributions
MMA conceptualized,designed, analyzed and wrote the manuscript. ZGD, AAM and TTZ supervised and
revised the manuscript. All authors approved the submitted manuscript.
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Funding
Not applicable.
Declarations
Competing of Interest
e authors declare no any competing of interests.
Ethical Approval
Ethical approval for this study was obtained from the Graduate Studies Ethics Committee at Bahir Dar
University. All procedures performed in studies were in accordance with the ethical standards of the
institutional research committee.
Consent for Publication
Not applicable.
Additional information
Correspondence and requests for materials should be addressed to M.M.A.
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