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Spatial Distribution and Modeling of Soil Transmitted Helminthes Infection in Nigeria

Background and Objective: Soil transmitted helminthes (STH) infection affects more than two billion people worldwide with Nigeria having the highest burden in Sub-Saharan Africa. This study examined the spatial and potential distribution of STHs in Nigeria. Materials and Methods: Secondary data were assessed from 98 journals and the prevalence of Ascaris lumbricoides, Trichuris trichiura and hookworms were extracted from them. The spatial distribution of the parasites was performed using DIVA-GIS software. The climatic variables (temperature and precipitation) and elevation variable (altitude) were used in the modeling of the parasites using Maximum Entropy (MaxEnt) modeling tool. Results: The average prevalence of A. lumbricoides, Hookworms and T. trichiura in Nigeria is 25.17%, 16.86% and 9.74% respectively. Delta State (62.08%) had the highest infection of A. lumbricoides followed by Oyo (55.50%) and Kano (44.40%) while 14 states had prevalence of below 20.0%. Akwa Ibom and Kano States had the highest average prevalence of 55.80% for hookworm infections. Delta, Oyo and Benue States had prevalence of 38.08%, 35.80%, and 35.40% respectively while 22 states had prevalence of hookworm below 20.0%. T. trichiura had the least average prevalence among the three STHs. Akwa Ibom State had the highest prevalence for this parasite with 40.40% followed by Lagos, Delta and Kwara States with prevalence values of 24.85%, 24.05% and 23.37% respectively. Other states in the Federation had prevalence values of less than 12% with Federal Capital Territory (FCT), Abuja and Borno States having the least prevalence of 0.80 and 0.90% respectively. The potential distribution that reveals than southern Nigeria has been at high risk of infections. Precipitations of the wettest month, altitude, precipitation of the warmest quarter are the major environmental variables that affect the distribution of STH. Conclusion: Nigeria is characterized by varying prevalence of STHs. All states in Nigeria are endemic for STHs. Southern Nigeria was observed to have higher prevalence of STHs due How to cite this paper: Yaro, C.A., Kogi, E. and Luka, S.A. (2018) Spatial Distribution and Modeling of Soil Transmitted Helminthes Infection in Nigeria. Advances in Infectious Diseases, 8, 82-107. https://doi.org/10.4236/aid.2018.82010 Received: July 24, 2017 Accepted: June 22, 2018 Published: June 25, 2018 Copyright © 2018 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access


Introduction
Soil-transmitted helminths (STHs) (Ascaris lumbricoides, hookworm and Trichuris trichiura) are responsible for more than 40% of the worldwide morbidity from all tropical infections [1]. STH is ranked first among the seven neglected tropical diseases (NTDs) most prevalent in Nigeria as having the highest burden on the population [2]. Social-ecological systems govern the transmission of STH and thriving in areas with poor sanitation and hygiene [3]. Hotez et al. [4] and Oluwole et al. [5] reported that Nigeria has the highest infected people with STH in sub-Saharan Africa with children of age 5 -14 years at high risk of infection and morbidity. Despite the recognition of the importance of STH infections coupled with the control strategies, the infections from these parasites still remain the major cause of malnutrition, iron-deficiency and anaemia in Nigeria [2] and children co-infected with these parasites have been shown to have hampered cognitive and physical development that leads to inefficiency in learning and school achievements [6] [7]. The Federal Ministry of Health, Nigeria in 2012 highlighted the need to scale-up interventions across NTDs programmes in their five-year master plan due to the impact of these parasites.
The use of geospatial health resource data and improved software analysis to produce low-cost digital health maps and transmission models for tropical diseases was encouraged by Malone [8] as these models helped policy makers and researchers in the health sector to concentrate in areas of high risks thereby helping to reduce operational cost.
Predictive modelling of infection geographic distributions by using environmental conditions of sites of known occurrence constitutes an important technique in analytical biology and its applications in epidemiology are enormous [9]. Predictions of infection risk areas where prevalence data are lacking can also be supplied by spatial statistical models [10].
Maximum entropy (MaxEnt) modeling is a general-purpose method for making predictions of inferences from incomplete information [11]. Maximum entropy modeling is useful in epidemiology as it allows the use of presence-only datasets [11] making it of merit for research and development in parasitology which usually has presence-only datasets [12]. The uses of modeling techniques such as MaxEnt that require only presence data are therefore extremely valuable [13]. MaxEnt modeling technique also allows the use of both continuous and categorical data that enables interaction between variables [14] producing a bet- Although, spatial and potential distribution of STHs was carried out by Oluwole et al. [5] using survey data obtained by the Federal Ministry of Health, Nigeria in the year 2011, the need to updated and use other data obtained from different parts of the country to assess the distribution of these parasites would help to improve existing distribution maps. This study will help to identify areas of high suitability for soil transmitted helminth infections as well as rank environmental variables according to their relative importance in the determination of infections.
The purpose of study was to assess the spatial and potential distribution of Ascaris lumbricoides, Trichuris trichiura and hookworms under present climate change by using DIVA-GIS (an open access Geographic Information System for mapping and visualization) and MaxEnt ecological niche modeling tools.

Ethics Statement
Data used for this study are secondary data. Therefore, no specific ethical approval was required.

Environmental Data
The climatic variables such as temperature and precipitation influenced global and meso scales and topographic variables such as altitude and aspect that likely affects species distributions at meso and topo-scales while land-cover variables like percent canopy cover influence distributions at the micro-scale [11]. Hence, the use of the climatic and topographic variables in the prediction of distributions of soil transmitted parasites was used in Nigeria.
In this study, a total of 19 bioclimatic variables of present climate  for Mission were also downloaded from http://www.worldclim.org/. These bioclimatic variables can be summarized into 3 main variables: altitude, temperature and precipitation. Temperature and precipitation affect the rate of embryonation of STH parasites. As altitude increases, temperature also decreases, so highlands were colder and lowlands were warmer, which in turn affects the development rate of these parasites. files. These files were converted from text files to shape file files using DIVA-GIS and were geo-referenced on the map of Nigeria. The prevalence of these parasites were categorized <5, ≥5 to <10, ≥10 to <20, ≥20 to <40 and ≥40.

Ecological Niche Modeling
The potential distribution of soil transmitted helminthes parasites were modelled using MaxEnt software version 3.3.3k download from http://www.cs.princeton.edu/~schapire/maxent/. MaxEnt uses environmental data at occurrence and background locations to predict the distribution of a species across a landscape [11] [15]. In this study, MaxEnt was used for mapping potential geographic distribution of soil transmitted helminthes in Nigeria. This modeling tool was selected based on the reasons of Sarma et al. [16]: it is present only modeling algorithm (i.e. absence data are not required), the performance has been relatively better than other modeling methods and the model has been hardly influenced by small sample sizes and hence prediction will be relatively robust. It has been shown to be among the top performing modeling tools by Elith et al. [17].
Probability of presence of each of the STH was estimated by MaxEnt using the mean prevalence of each of the STH obtained for all the states in Nigeria which serve as the presence records to generate background points that was used in finding the maximum entropy distribution [16]. Regularization of the prevalence was performed to control over-fitting. This modeling tool uses five different features to perform its statistics; linear, quadratic, product, threshold and hinge features to produce a geographical distribution of species within a define area.
MaxEnt produces a logistic output format used in the production of a continuous map that provides visualization of graded prevalence with an estimated  [16].
The 19 environmental variables and the elevation data obtained were used for the ecological niche modeling. The level of significance of contribution of the altitude and 19 bioclimatic variables was used to calculate jackknife (a method of assessing the variability of data by repeating calculation on the sets of data obtained by removing one value from the complete set) and area under the receiver operating characteristics curve (AUC) was used to evaluate the model performance. The AUC values vary from 0.5 to 1.0; an AUC value of 0.5 showed that model predictions were not better than random, values < 0.5 were worse than random, 0.5 -0.7 signifies poor performance, 0.7 -0.9 signifies reasonable/moderate performance and >0.9 indicates high model performance [18].
Model validation was performed according to Sarma et al. [16] as follows; using the "sub-sampling" procedure in MaxEnt. About 75% of the parasites prevalence data were used for model calibration and the remaining 25% for model validation. Ten replicates were run and average AUC values for training and test datasets were calculated. Maximum iterations were set at 5000. Percent contribution (PC), permutation importance (PI) and jackknife procedures in MaxEnt were used to investigate the relative importance of different bioclimatic predictors. Sensitivity and specificity of infections were also measured. Sensitivity, which was also named the true positive rate, can measure the ability to correctly identify areas infected. Its value equals the rate of true positives and the sum value of true positives and false negatives. Specificity, which is also named the true negative rate, can measure the ability to correctly identify areas uninfected.
Its value equals the rate of true negatives and the sum value of false positives and true negatives.
Data were analyzed using one-way analysis of variance (ANOVA), followed by Turkey's test. All statistical analyses were performed using the Statistical

Spatial Distribution of STH Infections in Nigeria
The average prevalence of A. lumbricoides, Hookworms and T. trichiura in Nigeria were 25.17%, 16.86% and 9.74% respectively. While A. lumbricoides has the highest average prevalence (

Predicted Risk of A. lumbricoides Infections
The predicted high risk areas of A. lumbricoides infections were South-East and South-West region of Nigeria with probability of ≥0. 75 Figure 1).

Model Performance and Influencing Factors
The average percent contribution (PC) and permutation importance (PI) of the 20 variables used in the modeling of STH in this study were also assessed. In this study, precipitation of the wettest month had the highest PC and PI of 20.4 and  Table 2).
The receiver operating characteristics (ROC) curve obtained as an average of the 10 replications runs is shown in Figure 2. Specificity was calculated. The  shows higher sensitivity and specificity for the presence of these parasites.
The relative importance of each variable to the prevalence of A. lumbricoides was assessed with the jackknife test in Figure 3 which gave a total training gain     For hookworms, a total training gain of 1.86 and an AUC value of 0.948 (red bars) was observed ( Figure 4). Also, precipitation of the wettest month had the highest training gain of 1.26 and an AUC value of 0.889 when used alone. This variable is therefore considered the most informative in predicting the prevalence of hookworms.
The variable assessment for T. trichiura was observed to be different from the other parasites. A total training gain of 1.96 and an AUC value of 0.95 were observed with minimum temperature of the coldest month having the highest impact on the prevalence of this parasite when used alone with a training gain of 1.3 followed by precipitation of the wettest month and altitude with training gain of 1.1 both. For the AUC values, altitude was highest with 0.906 followed by precipitation of the wettest month and minimum temperature of the coldest month with AUC values of 0.892 and 0.882 respectively ( Figure 5).
The modeling result of STH in Nigeria revealed that most of the areas at high risk of infections were the states in the South-East (Anambra, Enugu, Imo, Abia and Ebonyi), South-South (Edo, Rivers, Delta, Cross-River, Akwa-Ibom and Bayelsa) and some states in the South-West (Lagos, Ogun and Ondo) and North Central (Benue and Kogi) and few states in the extreme northern part of the country such as Kebbi and Niger and parts of Taraba and Adamawa States. These areas are observed to have high precipitation throughout the year and were mostly in locations of low altitude. Kebbi, Niger and Taraba States fell within the high risk region, despite their low precipitation and high altitude when compared to the southern region.

Predicted Risk of Hookworms Infections
The five states in South-East Nigeria (Enugu, Imo, Owerri, Ebonyi and Anambra), Edo and Delta States in South-South Nigeria and Lagos and Ogun States in South-West Nigeria are in the high risk areas of hookworm infections with probability of ≥0. 75. Also, all the states in South-South, South-East, four states in the South-West (Lagos, Ogun, Ondo and part of Osun) and part of some states in North-Central (Kogi, Benue, Nasarawa, Niger and FCT) were within areas with risk of probability of ≥0.50. Some states in Northern Nigeria fall within the low risk areas. Part of Kebbi, Sokoto, Plateau, Taraba, Jigawa, Yobe and Borno States with probability of 0.00 to ≤0.10 ( Figure 6).

Discussion
Most regions in Nigeria were characterized by varying prevalence of STHs. This study revealed that all states in Nigeria were endemic for STHs with some regions at higher risk than others. The high prevalence of STHs in Southern Nigeria observed in this study was due to the favorable environmental conditions such as high level of precipitation and the low altitude of these regions [115].  [116] stated that other factors such as socio-economic status, unhygienic practices, ignorance and poverty are among the factors contributing to the high prevalence of STHs in this region.
Ugbomoiko et al. [117] reported that the use of leaves and paper to clean up af- From the spatial distribution of STHs, most communities in Nigeria were characterized with prevalence below 20%. Similar observation was reported by Oluwole et al. [79] who stated that the overall low infection of 20% could be due to the periodic deworming of school-aged children. Although, the risk prediction shows that increase in prevalence of these parasites was expected in areas presently having prevalence of below 20%. The results obtained using 20 environmental variables revealed that precipitation of the wettest month; altitude and minimum temperature of the coldest month were the three major environmental variables that determine the distribution of these parasites in Nigeria.
Similar observation was noted in the studies of Otto [118], Spindler [119] and Oluwole et al. [79], they stated that high humidity promotes quick embryonation of A. lumbricoides eggs. Studies by Chammartin et al. [120] and Lai et al. [121] in Bolivia and China respectively also reports the influence of temperature on the distribution of STHs. Oluwole et al. [79] states that the low predicted risk in Northern Nigeria could be due to the extreme heat and short wet season. High In this present study, A. lumbricoides was observed to have the widest spatial and potential distribution in Nigeria with many areas at risk compared to hookworms and T. trichiura. This was similar to what was observed by Pullan et al. [122] in Kenya, Chammartin et al. [120] in Bolivia, Lai et al. [121] in China.

Conclusion
Nigeria is endemic for the A. lumbricoides, hookworms and Trichuris trichiura with an average prevalence of 25.17%, 16.86% and 9.74% respectively. Precipitation of the wettest month, altitude, precipitation of the warmest quarter, temperature seasonality and annual precipitation are the five environmental variables that majorly affect the distribution of soil transmitted helminth infections in Nigeria. Southern Nigeria has high prevalence of soil transmitted helminth infections than Northern Nigeria.

Recommendations
The model generated will help to guide individuals in the high risk areas to take preventive and curative measures. The finding also provides us with the scientific understanding of the effect of precipitation, temperature and altitude on the distribution of STHs. Based on this knowledge, planning and control of STHs in Nigeria will yield greater result with the limited funding and human resources available. Advances in Infectious Diseases termine the distribution of these parasites. This study will help researchers, policy makers in the health sector and other relevant authorities to intensify control strategies in areas of high risk of infections. It reveals that increasing altitude leads to decreased prevalence and increasing temperature and precipitation lead to increased prevalence.