Causes of recurrence of cholera in African countries

Summary:

Our report focuses on the study of the causes of the recurrence of cholera that is raging. This analysis was done by the method of principal component analysis on 13 African countries and 11 variables which are the health, climatic, environmental, socio-economic and demographic variables which could favor the installation of the disease in our countries. The results of the PCA give us a percentage of variance of the factorial plan of 70.48%. The correlations between the variables show that the recurrence of cholera is favored by high temperatures, rainfall and countries with a relatively very young population. On the other hand, factors such as access to drinking water, sanitation facilities, good hygiene practices accompanied by good implementation of Integrated Water Resources Management contribute to fighting the disease. This indicates that a multisectoral approach is necessary to succeed in slowing down the disease, or even eradicating it.

Keywords: Cholera, Recurrence, Africa, Variable, Individuals, Climatic, Health, Socio-economic, environmental, Principal component analysis

INTRODUCTION

Cholera is a waterborne disease in the family of acute diarrheal diseases caused by the bacterium Vibrio cholerae [1] . Cholera is spread by the fecal-oral route, either directly from person to person or indirectly through contaminated fluids from an environmental reservoir of varying duration, food, and potentially flies and fomites [2] . Indeed, it has been shown that epidemic cholera often occurs near watercourses when weather conditions are favorable for bacterial growth, such that there is an interaction between the aquatic environment and feces [2] . In response, prevention and control efforts have been deployed, including strengthening laboratory capacity, improving epidemiological surveillance systems, and promoting access to safe drinking water and sanitation [3] . However, despite these initiatives, cholera remains endemic in many African countries.

According to the World Health Organization (WHO), Africa is one of the regions most affected by this disease, with peaks in incidence often linked to poor health conditions and humanitarian crises [3] . The first cholera epidemics date back to 1817 in Africa, when the disease spread from the Indian subcontinent through maritime trade routes [4] . Since then, the continent has experienced seven major pandemics, with recurring outbreaks that have strained health systems [5] .

Cholera remains a major health problem in Africa, with recurring epidemics affecting many countries, despite prevention and control efforts implemented by the various organizations working towards its eradication [1] . Although cholera is preventable through simple measures such as improving access to drinking water and basic sanitation infrastructure, epidemics have persisted on the continent for decades [6] .

Despite efforts in vaccination, awareness raising and improved health infrastructure, cholera outbreaks continue to occur at regular intervals, particularly in vulnerable areas [7] , although progress has been made in combating this epidemic, it continues to affect millions of people each year in Africa, particularly in the poorest and most vulnerable regions. This situation leads us to ask the following question: what are the root causes of the recurrence of cholera in Africa and how do these factors interact to maintain its spread?

This research question focuses on identifying the underlying causes of cholera recurrence and seeks to understand the complex interactions between different factors (demographic, environmental, socio-economic, and health) that contribute to the persistence of the disease in Africa.

The bulk of the new cases and deaths have been in Malawi, which is facing its worst cholera outbreak in two decades. Malawi’s neighbors Mozambique and Zambia have also recently reported cases. In East Africa, Ethiopia, Kenya and Somalia are responding to outbreaks amid prolonged and severe drought that has left millions in urgent need of humanitarian assistance. Burundi, Cameroon, the Democratic Republic of the Congo and Nigeria have also reported cases. “We are witnessing a worrying scenario in which conflict and extreme weather events are exacerbating cholera triggers and increasing the toll,” said Dr Matshidiso Moeti, World Health Organization (WHO) regional director for Africa. Western DRC is considered to be affected by cholera outbreaks along the Congo River and its tributaries [8] .

In 2023, 225,857 cases and 3,167 deaths have been reported in 20 African countries facing outbreaks since the beginning of the year. In 2022, nearly 80,000 cases and 1,863 deaths have been recorded in 15 affected countries. If the current rapid upward trend continues, it could surpass the number of cases recorded in 2021, the worst year for cholera in Africa in nearly a decade. The average case fatality rate is currently close to 3%, above the 2.3% reached in 2022, and well above the acceptable level of less than 1% [9] .

Cholera therefore remains a major cause of illness and death in Africa, where it continues to occur in several countries, despite being largely eliminated from developed countries over a century ago. It disproportionately affects populations already strained by conflict, inadequate infrastructure, non-resilient health systems, and poverty [10] .

The causes of cholera persistence in Africa are multiple and interconnected. They include environmental factors, such as water contamination and degraded sanitation infrastructure, as well as socio-economic determinants such as poverty, rapid urbanization and forced displacement [11] . In addition, the fragility of African health systems, characterized by limited access to care and a lack of resources, hampers efforts to prevent and respond to cholera outbreaks.

The main objective of this research is to identify and analyze the root causes of cholera recurrence in Africa, taking into account demographic, health, socio-economic, and environmental factors that contribute to the spread of this disease. Through this study, the aim will be to better understand why cholera persists despite prevention efforts and infrastructure improvements, and also to explore the interactions between these factors in order to propose sustainable solutions to reduce future epidemics.

In order to achieve our main goal, three specific objectives have been defined:

• Analyze environmental conditions and health infrastructure that promote cholera transmission, including access to drinking water and sanitation, as well as the impact of climate change.

• Analyze the socio-economic determinants that influence the vulnerability of populations to cholera, in particular the influence of inequalities in access to health services and precarious living conditions.

• Propose practical and appropriate solutions to limit the recurrence of cholera in Africa, based on the results of the analysis.

This article aims to explore the causes of the recurrence of cholera in Africa, particularly for the year 2023, by highlighting the dynamics that favor its spread and by discussing the measures necessary to mitigate its impact on the most vulnerable populations.

I. Materials and methodologies

I.1 Material

• As part of our work, we mainly used software to help us in the collection, processing, analysis of data and mapping of our study area. These include: - ZOTERO: This is a free application that allows us to manage our bibliography throughout our work.

• KOBOTOOLBOX: Allowed us to generate the questionnaire addressed to our different targets for the collection of data related to cholera.

• R/Rstudio: R is an open source software that is widely used in statistical analysis. It was used to perform our principal component analysis with packages like FactoMinR, FactoShiny, and to write the report with the Rmarkdow component.

• QGIS: Open source geographic information system (GIS) software, QGIS allowed us to map our study area and develop other maps related to our work.

I.2 Methodology

To carry out our work of analysis on the causes of the recurrence of cholera in Africa, we proceeded in stages:

First step : Bibliographic research In order to better orient ourselves and properly approach our theme, we first carried out a literary review in order to understand the important aspects surrounding cholera.

Step Two : Data Collection The data collection for our study was done on the basis of the news on the recurrence of cholera in African countries for the year 2023 according to the WHO. Thus, the choice of individuals was based on the countries having recorded the most cases of cholera and these are among others Burundi, Cameroon, the Democratic Republic of Congo (DRC), Ethiopia, Kenya, Malawi, Mozambique, Nigeria, South Africa, South Sudan, Tanzania, Zambia and Zimbabwe. All these countries have recorded a surge in the number of cholera cases in 2023 according to the WHO report on the number of cholera cases 2023. The choice of variables was made on the basis of the parameters that could influence the recurrence of cholera. We grouped them into five groups and these are among others:

Demographic parameters : We have selected two that we collected from the database of the website ourworldindata.org. These are population density and age distribution (0-15, 15-64 and more). This will allow us to know whether population density positively influences the occurrence of cholera or not and who are most affected by the disease.

Figure 1: Population map from 0 to 15 years old

Figure 2: Population map aged 15 to 64

Figure 3: Population density

• Health parameters: We have selected four, namely the number of cholera cases per country, the rate of access to drinking water, the rate of access to sanitation, the rate of people with access to adequate hygiene services. These variables were obtained from the databases of the sites ourworldindata.org, the Sustainable Development Goals 6 (SDG6) portal (sdg6data.org) and the World Health Organization (WHO) (who-global-cholera-and-awd-dashboard-1-who.hub.arcgis.com). These variables are important because cholera is defined as a waterborne disease and caused mainly by the lack of sanitation, access to drinking water and adequate sanitation infrastructure. They will therefore allow us to see which health variable to focus on the most to help in the fight against the recurrence of cholera.

Figure 4: Situation Rate of access to water

Figure 5: Situation of access rate to sanitation

Figure 6: Situation of hand washing rates and hygiene

Figure 7: Situation of number of cholera cases in 2023

• Climate parameters: These parameters allow us to see if climate factors have an impact on the recurrence of cholera. We have selected two of them, namely annual rainfall and the average annual temperature per country. We made this choice of parameters because the bacteria causing cholera (Vibrio cholerae) lives in water and therefore heavy rains and temperatures could contribute to its development. The data was obtained from the ourworldindata.org database.

Figure 8: Average annual rainfall situation

Figure 9: Average annual temperature situation

• Socio-economic parameters: we have retained the literacy rate by country. This parameter will allow us to see if the intellectual wealth of a country can influence in a positive or negative way. We obtained this data from the site ourworldindata.org.

Figure 10: Literacy rate situation

• Environmental parameters: We chose as a parameter the implementation of integrated water resources management (IWRM) in consideration of the origins of cholera. A good implementation of IWRM should adversely influence the occurrence of cholera in countries. This parameter was obtained from the Sustainable Development Goals 6 (SDG6) portal website (sdg6data.org).

Figure 11: Situation of implementation of IWRM

In addition to the data collected in the various sites, we have developed a questionnaire on how to collect information on certain parameters cited above, accessible at the link: https://ee.kobotoolbox.org/x/axrcDZj5 . This quiz is intented for populations who have already recorded cases of cholera during the year 2023.

Third step; data processing The processing of the collected data was done on the Excel application where we compiled the different individuals (countries) and the different variables chosen for our study. R software was used for our principal component analysis (PCA) through the “FactoMineR” package.

II. Results and discussion

II.1 Results

II.1.1 Presentation of the dataset and mapping of the study area

At the end of the collection, we have thirteen (13) countries, therefore 13 individuals and eleven (11) variables which constitute our data set. The table below summarizes the data and figure 12 represents the mapping of our study area.

Figure 12 Map of study area

Table 1 : Summary of data set for the study

# Loarding data
setwd("C:/Users/HARISSE/Downloads/cours_2iE/S7_GEAAH/RTI/Maladi_hydriqu/Projet_RTI_Groupe_10_GEAAH/Donnee_ACP/")

data = read.csv(file ="data.csv", header = TRUE, sep = ";", quote = "\"",
                dec = ",", row.names = 1)
data[,1:11]

##                                    N_C P_0_15 P_15_64  den GIRE  hand   edu
## Burundi                           1394   45.2   54.85 0.42   47  6.30 75.54
## Cameroon                          6470   42.0   58.05 0.59   40 36.71 78.23
## Democratic Republic of the Congo 52654   46.5   53.46 0.48   32 19.35 80.54
## Ethiopia                         30389   39.3   60.70 0.49   41  8.32 51.77
## Kenya                             8809   37.2   62.78 0.60   59 37.60 82.88
## Malawi                           32530   43.3   57.95 0.51   55 15.31 68.80
## Mozambique                       39101   42.0   56.71 0.46   62 15.00 59.78
## Nigeria                           3457   42.8   57.24 0.55   44 31.08 62.02
## South Africa                      1478   28.3   71.70 0.72   71 44.00 90.00
## South Sudan                       1471   43.0   57.04 0.38   43  5.60 34.52
## Tanzania                          1040   43.1   56.88 0.53   54 28.92 80.20
## Zambia                            4531   42.4   57.55 0.57   58 18.15 87.50
## Zimbabwe                         14148   40.3   59.72 0.55   63 42.46 89.85
##                                    san   wat rain tem
## Burundi                          45.69 62.44 1274  21
## Cameroon                         43.12 69.59 1604  25
## Democratic Republic of the Congo 16.17 35.12 1543  25
## Ethiopia                          9.34 51.51  848  24
## Kenya                            36.53 62.86  630  26
## Malawi                           49.24 71.87 1181  23
## Mozambique                       37.38 63.20 1032  24
## Nigeria                          46.57 79.64 1150  28
## South Africa                     77.63 94.49  495  18
## South Sudan                      16.06 41.19  900  29
## Tanzania                         30.62 60.79 1071  23
## Zambia                           36.30 68.25 1020  23
## Zimbabwe                         34.62 62.29  657  22

library(car)

## Warning: le package 'car' a été compilé avec la version R 4.4.2

## Le chargement a nécessité le package : carData

## Warning: le package 'carData' a été compilé avec la version R 4.4.2

library(carData)
library("clusterSim")

## Warning: le package 'clusterSim' a été compilé avec la version R 4.4.2

## Le chargement a nécessité le package : cluster

## Le chargement a nécessité le package : MASS

library(DataExplorer)

## Warning: le package 'DataExplorer' a été compilé avec la version R 4.4.2

library(FactoInvestigate)
attach(data)
library(corrplot)

## Warning: le package 'corrplot' a été compilé avec la version R 4.4.2

## corrplot 0.95 loaded

library(psych)

## 
## Attachement du package : 'psych'

## L'objet suivant est masqué depuis 'package:car':
## 
##     logit

library(Hmisc)

## Warning: le package 'Hmisc' a été compilé avec la version R 4.4.2

## 
## Attachement du package : 'Hmisc'

## L'objet suivant est masqué depuis 'package:psych':
## 
##     describe

## Les objets suivants sont masqués depuis 'package:base':
## 
##     format.pval, units

library(ggplot2)

## 
## Attachement du package : 'ggplot2'

## Les objets suivants sont masqués depuis 'package:psych':
## 
##     %+%, alpha

library(factoextra)

## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

library(FactoMineR)

II.1.2 ACP result

II.1.2.1 Correlation matrix

mat_cor = cor(data)
col = colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))
corrplot(mat_cor, method="color", col=col(200),  
         type="upper", order="hclust", 
         addCoef.col = "black", # Ajout du coefficient de corrélation
         tl.col="black", tl.srt=90, #Rotation des étiquettes de textes
         , sig.level = 0.1, insig = "blank", 
         # Cacher les coefficients de corrélation sur la diagonale
         diag=FALSE)

Figure 12: Correlation matrix between variables

rcorr(as.matrix(mat_cor[,1:11]))

##           N_C P_0_15 P_15_64   den  GIRE  hand   edu   san   wat  rain   tem
## N_C      1.00   0.74   -0.74 -0.76 -0.73 -0.78 -0.59 -0.81 -0.84  0.69  0.56
## P_0_15   0.74   1.00   -1.00 -0.96 -0.95 -0.91 -0.75 -0.90 -0.93  0.95  0.84
## P_15_64 -0.74  -1.00    1.00  0.96  0.95  0.92  0.75  0.91  0.93 -0.95 -0.84
## den     -0.76  -0.96    0.96  1.00  0.91  0.98  0.88  0.94  0.95 -0.85 -0.87
## GIRE    -0.73  -0.95    0.95  0.91  1.00  0.87  0.79  0.92  0.93 -0.94 -0.90
## hand    -0.78  -0.91    0.92  0.98  0.87  1.00  0.88  0.88  0.90 -0.82 -0.81
## edu     -0.59  -0.75    0.75  0.88  0.79  0.88  1.00  0.84  0.81 -0.62 -0.91
## san     -0.81  -0.90    0.91  0.94  0.92  0.88  0.84  1.00  0.99 -0.79 -0.91
## wat     -0.84  -0.93    0.93  0.95  0.93  0.90  0.81  0.99  1.00 -0.82 -0.88
## rain     0.69   0.95   -0.95 -0.85 -0.94 -0.82 -0.62 -0.79 -0.82  1.00  0.73
## tem      0.56   0.84   -0.84 -0.87 -0.90 -0.81 -0.91 -0.91 -0.88  0.73  1.00
## 
## n= 11 
## 
## 
## P
##         N_C    P_0_15 P_15_64 den    GIRE   hand   edu    san    wat    rain  
## N_C            0.0096 0.0088  0.0061 0.0102 0.0050 0.0545 0.0025 0.0012 0.0186
## P_0_15  0.0096        0.0000  0.0000 0.0000 0.0000 0.0081 0.0001 0.0000 0.0000
## P_15_64 0.0088 0.0000         0.0000 0.0000 0.0000 0.0076 0.0001 0.0000 0.0000
## den     0.0061 0.0000 0.0000         0.0000 0.0000 0.0003 0.0000 0.0000 0.0009
## GIRE    0.0102 0.0000 0.0000  0.0000        0.0005 0.0035 0.0000 0.0000 0.0000
## hand    0.0050 0.0000 0.0000  0.0000 0.0005        0.0003 0.0003 0.0002 0.0021
## edu     0.0545 0.0081 0.0076  0.0003 0.0035 0.0003        0.0014 0.0027 0.0426
## san     0.0025 0.0001 0.0001  0.0000 0.0000 0.0003 0.0014        0.0000 0.0040
## wat     0.0012 0.0000 0.0000  0.0000 0.0000 0.0002 0.0027 0.0000        0.0018
## rain    0.0186 0.0000 0.0000  0.0009 0.0000 0.0021 0.0426 0.0040 0.0018       
## tem     0.0706 0.0013 0.0012  0.0004 0.0002 0.0028 0.0001 0.0001 0.0004 0.0100
##         tem   
## N_C     0.0706
## P_0_15  0.0013
## P_15_64 0.0012
## den     0.0004
## GIRE    0.0002
## hand    0.0028
## edu     0.0001
## san     0.0001
## wat     0.0004
## rain    0.0100
## tem

Figure 13: P-value test

The results of the matrix show a good correlation between the different variables used for our study. This was confirmed with the p-value test which allows us to check if the correlation matrix is significantly different from the identity matrix. The null hypothesis of the test is that the correlation matrix is an identity matrix, which would mean that there are no correlations between the variables. Thus for a p-value less than 5%, we will say that the correlation matrix is not an identity matrix, and that there are significant correlations between the variables which allowed us to conduct the PCA with the data.

II.1.2.2 Inertia and choice of main axes

pca_1 = PCA(X = data, scale.unit = TRUE, ncp = 11, ind.sup = NULL, 
            quanti.sup = NULL, quali.sup = NULL, row.w = NULL, 
            col.w = NULL, graph = FALSE, axes = c(1,2))

fviz_eig(pca_1, addlabels=TRUE, hjust = -0.3) +
  ylim(0, 65)

Figure 14: Decomposition of total inertia

res.PCA<-PCA(data,graph=FALSE)
res.HCPC<-HCPC(res.PCA,nb.clust=3,consol=FALSE,graph=FALSE)
plot.HCPC(res.HCPC,choice='map',draw.tree=FALSE,title='Plan factoriel')

Figure 15 : Classification graph

plot.HCPC(res.HCPC,choice='tree',title='Arbre hiérarchique')

Figure 16 : Classification dendrogram

The first 2 axes of the analysis express 70.48% of the total inertia of the dataset. This means that 70.48% of the information on all of our 11 variables is located in these two axes. Axis 1 is predominant and alone explains 56.82% of the total variability of the data. The choice of axes for our analysis will therefore be made with the first two axes. However, the classification reveals three groups including the presence of an individual who is alone in a group which is South Africa. Consequently, we removed it from our dataset and relaunched the PCA.

II.1.2.3 ACP results without South Africa

Distribution of inertia

setwd("C:/Groupe10GEAAH/Donnee_ACP/")

data_2 = read.csv(file ="data_2.csv", header = TRUE, sep = ";", quote = "\"",
                dec = ",", row.names = 1)
data_2[,1:11]

##              N_C P_0_15 P_15_64  den GIRE  hand   edu   san   wat rain tem
## Burundi     1394   45.2   54.85 0.42   47  6.30 75.54 45.69 62.44 1274  21
## Cameroun    6470   42.0   58.05 0.59   40 36.71 78.23 43.12 69.59 1604  25
## RDC        52654   46.5   53.46 0.48   32 19.35 80.54 16.17 35.12 1543  25
## Ethiopya   30389   39.3   60.70 0.49   41  8.32 51.77  9.34 51.51  848  24
## Kenya       8809   37.2   62.78 0.60   59 37.60 82.88 36.53 62.86  630  26
## Malawi     32530   43.3   57.95 0.51   55 15.31 68.80 49.24 71.87 1181  23
## Mozambique 39101   42.0   56.71 0.46   62 15.00 59.78 37.38 63.20 1032  24
## Nigeria     3457   42.8   57.24 0.55   44 31.08 62.02 46.57 79.64 1150  28
## Soudan_Sud  1471   43.0   57.04 0.38   43  5.60 34.52 16.06 41.19  900  29
## Tanzanie    1040   43.1   56.88 0.53   54 28.92 80.20 30.62 60.79 1071  23
## Zambie      4531   42.4   57.55 0.57   58 18.15 87.50 36.30 68.25 1020  23
## Zimbabwe   14148   40.3   59.72 0.55   63 42.46 89.85 34.62 62.29  657  22

pca_2 = PCA(X = data_2, scale.unit = TRUE, ncp = 15, ind.sup = NULL, 
            quanti.sup = NULL, quali.sup = NULL, row.w = NULL, 
            col.w = NULL, graph = FALSE, axes = c(1,2))
fviz_eig(pca_2, addlabels=TRUE, hjust = -0.3) +
  ylim(0, 65)

Figure 17 : Decomposition of total inertia

The analysis without South Africa gives us a percentage of variance on the factorial plane of 62.06%. This value is higher than the reference value of 54.29% which is a reference inertia at the 0.95 quantile. This quantile of the distribution of inertia percentages was obtained by simulating 3444 random data sets of comparable dimensions on the basis of a normal distribution).

Description of the factorial plan: Dimension 1 and dimension 2

res.PCA<-PCA(data_2,graph=FALSE)
plot.PCA(res.PCA,choix='var',title="Graphe des variables de l'ACP")

plot.PCA(res.PCA,select='contrib  12',habillage='contrib',title="Graphe des individus de l'ACP")

Figure 18 : Graph of individuals

res.PCA<-PCA(data_2,graph=FALSE)
plot.PCA(res.PCA,choix='var',habillage = 'cos2',select='cos2  0',unselect=0,title="Graphe des variables de l'ACP")

plot.PCA(res.PCA,select='contrib  12',habillage='contrib',title="Graphe des individus de l'ACP")

Figure 19 : Cosine-squared graph of variables

Analyzing the above graphs, the interpretation that follows is that: Dimension 1 pits individuals such as Kenya and Zimbabwe against individuals such as the Democratic Republic of Congo (DRC).

The group in which individuals from Kenya and Zimbabwe share high values for the variables water stress rate, population aged 15 to over 64 years and hand washing rate against low values for the variables annual rainfall and population aged 0 to 15 years.

On the other hand, the group to which the DRC individual belongs shares high values for the variable number of cholera cases against low values for the variable rate of access to drinking water.

As for dimension 2, it pits individuals such as Cameroon and Nigeria against individuals such as South Sudan and Ethiopia.

The group of individuals in Cameroon and Nigeria share low values for the variable percentage of population in rural areas.

On the other hand, the group of individuals from South Sudan and Ethiopia share low values for the variables literacy rate and sanitation access rate

-   Description in relation to dimension 1:

fviz_contrib(pca_2, choice = "ind", axes = 1)

Figure 20 : Contribution of individuals to the creation of axis 1

fviz_contrib(pca_2, choice = "var", axes = 1)

Figure 21 : Contribution of variables to the creation of axis 1

Axis 1 has a variance percentage of 38.90% which makes it the dimension containing the most information of our variance. Individuals such as Kenya, DRC, South Sudan and Zimbabwe contribute 89.17% to the creation of this axis.

•  Description in relation to dimension 2:

fviz_contrib(pca_2, choice = "ind", axes = 2)

Figure 22 : Contribution of individuals to the creation of axis 2

fviz_contrib(pca_2, choice = "var", axes = 2)

Figure 23 : Contribution of variables to the creation of axis 2

Axis 2 contains 23.16% of the information on our variance. In terms of contribution to its creation, individuals such as Ethiopia, South Sudan and Cameroon contribute 74.48%.

Correlation between variables

fviz_pca_var(pca_2)

Figure 24 : Correlation circle of variables

Looking at Figure 24, we notice a positive correlation between the number of cholera cases and the variables annual rainfall, population aged 0 to 15 and average annual temperature. This would mean that the more the number of cholera cases increases, the more the values of these parameters increase. Alternatively, the opposite phenomenon is observed with the other variables chosen for our study which, for their part, are negatively correlated with the variable number of cholera cases.

II.1.2.4 Classification of countries by group

res.PCA<-PCA(data_2,graph=FALSE)
res.HCPC<-HCPC(res.PCA,nb.clust=3,consol=FALSE,graph=FALSE)
plot.HCPC(res.HCPC,choice='tree',title='Arbre hiérarchique')

Figure 25 : Classification graph

plot.HCPC(res.HCPC,choice='map',draw.tree=FALSE,title='Plan factoriel')

Figure 26 : Classification dendrogram

plot.HCPC(res.HCPC,choice='3D.map',ind.names=FALSE,centers.plot=FALSE,angle=60,title='Arbre hiérarchique sur le plan factoriel')

Figure 27 : 3D dendrogram

The classification of our individuals reveals the presence of three groups which are characterized by their similarity with respect to certain variables. We have:

•   Group 1:

  It consists of three countries namely the Democratic Republic of Congo (DRC), Ethiopia and South Sudan. These individuals are characterized by low values for the variables sanitation access rate, drinking water access rate and integrated water resources management (IWRM).

Figure 28: Country mapping of group 1

•   Group 2:

  It consists of seven (7) individuals including Cameroon, Nigeria, Zambia, Tanzania, Malawi, Burundi and Mazambique . The similarity between these individuals is that they have high values for the variables rate of access to sanitation and drinking water.

Figure 29: Country mapping of group 2

•   Group 3:

  The third is composed of two countries, Kenya and Zimbabwe, which have high values for the water stress rate, the proportion of the population aged 15 to 64 years and the hand washing rate. On the other hand, they have low values for the amount of annual rainfall and the proportion of the population aged 0 to 15 years.

Figure 30: Country mapping of group 3

II.1.2.6 Linear regression

model1 = lm(formula = N_C ~ edu + san + hand + den + wat + GIRE + P_15_64 + tem + P_0_15 + rain,data= data)
donnees = data.frame(data)
data_vis= data.frame(valeurs_reelles=donnees$N_C,predictions=predict(model1))
ggplot(data_vis,aes(x=valeurs_reelles,y=predictions))+
  geom_point()+
  geom_smooth(method="lm",se=FALSE,color="blue")+
  labs(x="valeurs_reelles",y="predictions")+
  ggtitle(model1)

## `geom_smooth()` using formula = 'y ~ x'

Figure 27: Linear regression representation

Linear regression gives us an R2 of 0.47, which allows us to say that the analysis of cholera recurrence is not suitable for a linear model. This indicates that another model should be chosen to analyze cholera recurrence.

III Discussion

Our study aimed to understand the underlying factors that contribute to cholera recurrence in Africa by focusing on social, environmental and public health aspects.

The results of the PCA give us a percentage of variance on the factorial level of 70.48% which is very significant as axes containing all the information on our 11 variables. The analysis of the correlation matrix and the correlation circle showed that the factors which positively influence the recurrence of cholera are linked to climatic factors such as heavy rainfall, high temperatures.

Moustapha et al having conducted a study on the recurrence of cholera in 2012 arrived at a contradictory result where their analysis showed the absence of association with temperature and rainfall which could favor the occurrence of cholera [12] . On the other hand, Rebaudet et al demonstrated in a study conducted in sub-Saharan Africa in 2012 that fluctuations in the incidence of cholera in continental Africa are also linked to interannual climatic variability. They also demonstrated that in East and West Africa, major cholera epidemics are profoundly influenced by climatic events, the periodic warming of surface waters with its high temperatures and floods [7] .

In demographics, cholera is not negatively influenced by a high population density but is much more recurrent in children aged 0 to 15 years. This implies that countries with a high proportion of children are likely to record cases of cholera if their level of public health access is low. Moussy et al having carried out a study on two decades of diagnosis on cholera came to the conclusion that the increasing incidence of cholera epidemics is mainly located in areas of poor hygiene and overpopulation [13] .

This result is all the more confirmed by the country classifications provided by our ACP. Indeed, the countries of group 1 such as the DRC, Burundi, Malawi, Mozambique, Ethiopia and South Sudan are more sensitive to the recurrence of cholera given their low rate in terms of public health work and high rate in terms of factors favorably influencing the recurrence of cholera. The WHO 2024 cholera situation report on the WHO cholera situation in Africa ranks the DRC first with 81,267 recorded cases, followed by Malawi (59,325 cases), Mozambique (47,227), Ethiopia (38,683), Burundi (1,488) and South Sudan (1,471) over the period from January 1, 2022 to March 31, 2024 [14] . All of these results and similarity with the studies already carried out allow us to have good assurance on the quality of our work regarding the causes of cholera recurrence in Africa.

Although we obtained satisfactory results, we noticed some limitations in our study. Indeed, other factors such as floods, migrations, popular and political conflicts, waste management that can strongly influence the recurrence of cholera could not be taken into account in our analysis given the absence of certain data or their updating in the accessible databases. This could explain the recurrence of cholera in other countries, especially in South Africa, which ranks first in terms of the value of a variable that can slow the progression of cholera.

This is justified by the fact that South Africa is one of the most developed countries in Africa with a GDP of more than 500 billion US dollars (in purchasing power parity, PPP) and a GDP per capita of more than 13,000 dollars in PPP [15] . A study conducted on the cholera upsurge in Nigeria by KO Elimian et al indicates that it is difficult to analyse the factors due to this as there is a lack of evidence on contextual factors and their operational mechanisms in mediating recurrent cholera transmission in Nigeria [16] . In view of all that prevails, it is therefore appropriate to work unilaterally to combat this disease that is raging in our countries. This requires an organized and multi-sectoral intervention in order to properly harmonize all interventions related to the fight against cholera.

Conclusion

The principal component analysis (PCA) that we carried out on the factors contributing to the recurrence of cholera in Africa revealed several significant trends and correlations. Our study revealed that the main variables that influence the recurrence of cholera in African countries include climatic parameters such as high rainfall and high temperatures. A population that is too young also contributes to the recurrence of the disease. Contrary to this, the recurrence of cholera is favored for countries with low rates concerning health parameters (rate of access to water, sanitation and hand washing including hygiene), environmental parameters (Integrated Water Resources Management) and finally socio-economic parameters (literacy rate). These results show the importance of a multisectoral approach that integrates interventions in public health, infrastructure improvement and water resource management in order to combat this disease that is raging in our countries. This involves implementing community awareness programs, improving access to safe drinking water sources, and strengthening sanitation systems. In addition, given the increasing influence of climate change on the recurrence of cholera, it is crucial to integrate environmental considerations into the strategies to be adopted to minimize their impacts on the transmission of the disease.

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