Spatio-temporal analysis of environmental and climatic factors impacts on malaria morbidity in Ondo State, Nigeria

Abstract

This study examined the spatio-temporal dynamics of malaria epidemiological patterns considering environmental(vegetation, water bodies, slope, elevation) and climatic factors (rainfall, temperature and relative humidity) in Ondo State, Nigeria, from 2013 to 2017 using ArcGIS 10.4 and QGIS software. The factors influencing malaria were studied using a multi-criteria analysis (Analytical Hierarchical Process-AHP). The trend analysis revealed an increase in cases over time, indicating a significant increase in the occurrence of malaria in all study areas. The most important climatic variable impacting malaria transmission in the study was temperature. Nevertheless, other environmental and climatic factors causing transmission include vegetation, water bodies, slopes, elevation, rainfall, and relative humidity. With the exception of Okitipupa, the study identified high-risk locations (vulnerable areas/hot spots) in almost all of the local government areas, while Ondo East, Akure South, Akoko South West, and Owo are the most vulnerable areas. The findings reveal that the malaria incidence is high in the developed LGAs having more towns where temperature is higher due to several anthropogenesis activities, high population and increased land-use. Thus, in-depth epidemiological studies on malaria should be undertaken in Ondo State and other regions of Nigeria considering environmental factors impacting malaria incidence as this will enable one to ascertain the major factors influencing the disease, thereby taking adequate measures to curb the increase in incidence.

1. Introduction

Malaria (a parasitic disease) is one of the most common and lethal infections, affecting millions of people in many underdeveloped nations. This disease is endemic throughout tropical and subtropical Africa, and it has been a significant public health issue. According to Ref. [1], the Sub-Saharan Africa region bears the heaviest burden, with the Democratic Republic of the Congo and Nigeria accounting for more than 35% of global malaria deaths.

Malaria is a vector borne disease caused by the plasmodium parasite which is transmitted through the bite of an infected anopheles mosquito, the vector [2] from one person to another. The only five species of malaria parasite (Plasmodium) that can transmit malaria to humans are P.falciparum (the most deadly), P.vivax, P.ovale, P.malariae and P.knowlesi. Only Plasmodium (P) malariae can cause malaria in higher primates while the other parasites cannot cause malaria in animals, except in humans [3].

Given that good health is not only a basic human necessity, but also a fundamental human right and a prerequisite for economic progress, adequate concern should be given to human health regularly and this must not be undermined or neglected. Malaria is holoendemic in Nigeria, with a steady transmission rate throughout the year which comprises a distinctive rainy and dry season [4]. The rate of malaria infection across space depends on dynamic processes involving complex climatic, environmental, physical, and social variables operating differently in space [5]. The epidemiology of malaria varies geographically depending on the local malaria transmission intensity or endemicity class. Climatic factors are among the major factors responsible for the prevalence of malaria and since climate change is inevitable, total elimination of malaria may be very difficult though the morbidity and mortality can be alleviated [6]. opined that the levels of malaria risk and transmission intensity exhibit significant spatial and temporal variability related to variations in amount of rainfall, temperature, altitude, topography, and human settlement pattern. The risk of malarial morbidity and mortality varies spatially and temporally [7].

Changes in temperature, rainfall, and relative humidity due to climate change are expected to influence malaria directly by modifying the behavior and geographical distribution of malaria vectors and by changing the length of the life cycle of the parasite. Climate change is also expected to affect malaria indirectly by changing ecological relationships that are important to the organisms (vector, parasite, and host) involved in malaria transmission [8]. Rainfall increases the opportunity for egg-laying by increasing the number of potential breeding sites for anopheles mosquitoes to lay eggs, which can reach adulthood within nine to twelve days that are necessary for the mosquito life cycle.

Rainfall is one of the climatic variables that aid in the multiplication of mosquito breeding places and increasing humidity, which improves mosquito survival rates. The rainy season is a fertile period for the breeding sites, which are numerous. These species (P.falciparum) have the highest population density during the rainy season and these account for the high incidence of malaria at this period of the year [9].

Land use and land cover (LULC) have been shown to promote the breeding of mosquito and increase in malaria prevalence [[10], [11], [12], [13], [14], [15], [16]] and such LULC include industries that use water for production, congested settlements without proper drainage system, agricultural activities (e.g. irrigation. Understanding spatial and temporal distributions of a disease is often accomplished by applying statistical methods to surveillance data and generating a map that describes the variations in risk [17]. Remote sensing and GIS technology is highly useful in this process for faster and reliable result, for instance Ref. [28], used time series analysis to forecast malaria incidence based on monthly case reports and environmental factors in Karuzi Burudi, from 1997 to 2003” [19]. applied GIS to assess malaria risk.

Remote sensing (RS) and GIS applications are indispensable modus operandi for the study of malaria epidemiology as this is very vital in understanding spatial and temporal distributions of diseases in different locations by analyzing the influencing factors, generating incidence and risk maps which are essential for malaria prevention and control. RS is an imperative tool that can be used to generate information about an object without a direct contact with it while GIS is a computer based system involving software, hardware and human intellect for capturing, analyzing, integrating, manipulating, managing, storing and displaying georeferenced or geospatial data. Few studies have been done with the application of remote sensing and GIS techniques to examine the spatial pattern of malaria epidemiology in relation to risk environmental and climatic factors in Ondo state. It is against this background that this study attempts to analyze the impacts of environmental and climatic factors on malaria morbidity in Ondo State. Due to a lack of relevant data at the time of this study, five-year (2013–2017) data were used, indicating some of the limitations of this work. This study was undertaken primarily to determine the spatio-temporal patterns of malaria morbidity in the study area examine the relationship between malaria morbidity and environmental/climatic factors; and analyze the trend of malaria morbidity from 2013 to 2017 in the study.

2. Methodology

2.1. Study area

The study area is Ondo State and is situated in the South-western humid forest of Nigeria between latitudes 5°45′N and 8°15′N, and longitudes 4°45′E and 6° 00′E (Fig. 1).

Fig. 1.

The study area.

Ondo State is a humid sub-tropical (Lowland Tropical Rain Forest type) with two distinct seasons: rainy/wet season (April–October) and dry seasons (November–March). The climate has slight variations from year to year. Annual rainfall varies from 1,150 mm in the northern to 2,550 mm in the south with mean annual rainfall exceeding 2000 mm. The mean monthly relative humidity is less than 70%.

2.2. Data acquisition

Data obtained for this study were monthly malaria incidence report of eight (8) local governments’ areas (LGA) in Ondo State from 2013 to 2017 while the administrative shape files of the study area was obtained from the Centre for Space Research and Application (CESRA), Nigeria. Landsat OLI/TIRS and Shuttle Radial Thematic Map (SRTM) images were acquired from the United States Geological Surveys' archives (USGS). The Malaria data from the Ministry of health were collected in Microsoft excel format which could be used for downstream analysis. Thereafter, the relevant data were extracted and were saved as CSV (comma delimited) in the format which ArcGIS software can analyze. The data were analyzed using the ArcGIS, ENVI 5.1 and QGIS software packages.

To determine the spatiotemporal pattern of malaria incidence, the number of malaria cases per year was divided by the total population of the same year, then multiplied by a multiple of 1000 due to the population of the study area. The study years were projected using the 2006 Nigerian National Population Census (NPC) data, which was used in the computation of the incidence rate per LGA, and the incidence maps were created using ArcGIS software (version 10.4).

To determine the topographic variable, the Shuttle Radial Thematic Map (SRTM) image covering Ondo State was used to clip out the sampled local government areas (LGAs) and then classified into three classes for better interpretation of the slope and elevation in relation to malaria incidence. The slope was derived from the SRTM image using ArcGIS 4.10 software. The slope raster layer was classified into three classes, where gentler slopes are the area highly susceptibility to malaria, moderate slope, area quietly susceptible malaria while steeper slopes are areas with lesser malaria hazard. Mathematically, slope is the rise over the run, in which rise equals change in elevation and run equals horizontal distance.

$$Slope=\frac{Rise\left(ft\right)}{Run\left(ft\right)}$$ (1)

$$\mathrm{E}\mathrm{l}\mathrm{e}\mathrm{v}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}=\frac{Slope}{HorizontalDistance\left(m\right)}$$ (2)

A supervised classification algorithm was used to classify 2017 Landsat OLI/TIRS satellite image and four classes were identified: vegetation, built-up, bare land, and water bodies (Table 1). These variables and malaria case were analyzed to show the relationship between malaria incidences.

Table 1.

Image Classification Scheme (Adapted from Anderson et al., 1976).

S/No	Land use/Land cover	Characteristics
1.	Built-up Area	This consists of concrete and impervious surface majorly,-commercial, Industrial, Institutional and residential buildings -large open transportation facilities and local roads
2.	Vegetation	Consists of Vegetation (forest) cover, cultivated land grassland
3.	Bare land	This mainly consists of concrete surfaces and sand fields
4.	Water body	It consist of pen water bodies such as, streams, lakes and rivers

2.3. NDVI

The Normalized Difference Vegetation Index (NDVI) of the study area was determined using the band 4, the near infrared (NIR) band and band 5, the red band of 2017 OLI/TIRS Landsat image of the study area. The NDVI was calculated using its formula:

$$NDVI=\frac{NIR-R}{NIR+R}$$ (3)

where NDVI is the Normalized Difference Vegetation Index, NIR-Near Infra-Red; R is red. The NDVI map was created and classified in ArcGIS 10.4 using the NDVI threshold values (−1 to +1). Green surface has NDVI value of 0 and 1 while water/cloud is usually less than zero.

Multi-Criteria Decision Method (MCDM) was used to carry out susceptibility analysis. Distance to water bodies, rivers, Normalized Difference Vegetation Index (NDVI), slope, and elevation were all taken into account. The criteria were initially ordered according to their degrees of susceptibility (Table 2), and then weights were allocated to the parameters based on their importance. The vulnerability layer was constructed by superimposing the parameters used to create the susceptibility map.

Table 2.

Weight of environmental factors influencing malaria prevalence.

S/No	Factor	Weight	Class	Ranking	Susceptibility
1.	Distance to water body	35	0–500	5	Very High
			500–1000	4	High
			1000–2000	3	Low
			2000 and above	2	Very Low
2.	Slope	10	0–5	3	Very High
			5–8.5	2	High
			8.5 and above	1	Moderate
	Elevation	10	-1–133.65	5	Very High
			133.65–259.89	4	High
			259.89–1072	3	Moderate
4.	NDVI	15	0.038–1	1	Low
			0.1–0.2	2	High
			0.2–0.4	3	Very high

The weighing of malaria influencing factors (the determinants) is necessary in determining the levels of susceptibility or vulnerability of human to malaria of different parts of the study area. The nature of an environment has influence on malaria incidence or prevalence. Table 2 shows the weight of environmental factors influencing malaria prevalence.

2.4. Statistical analysis

Multiple linear regression analysis was performed to determine the relationship between malaria incidence with temperature, rainfall and relative humidity. Here, the monthly average rainfall, temperature and relative humidity of the study area for the period of five years (2013–2017) were regressed against the total monthly malaria incidence rate. Statistical Package for Social Sciences (SPSS) performed collinearity diagnostics on the variables to check for the assumption of multi-collinearity, normality and linearity. SPSS performed the regression coefficient² analysis and the result was generated in tables showing the Variance Inflation Factor (VIF), tolerance and significance values.

The limitations associated with this study include insufficient data on malaria prevalence and climatic factors which limited this study to only five years (2013–2017), exclusion of some relevant details on the malaria data, non-availability of clear satellite imagery covering the entire LGAs in Ondo State and this led to the elimination of some LGAs.

3. Results and discussion

3.1. Trends of malaria incidence

The general trend of malaria cases from 2013 to 2017 is revealed in Fig. 2 and this indicates that malaria incidences increases yearly. With the exception of Akure South, there was a rapid and significant increase in malaria occurrence between 2016 and 2017. The highest number of malaria incidence (cases) was found in Ondo East, which could be attributed to the rural area's dense vegetation, lack of access to proper medical facilities, or unsanitary environment. There are usually fewer chances of contact with vectors in developed urban areas because of the absence of the vector's habitat, however, many cases of urban malaria occur due to both the adaptation of vectors to urban settings and the presence of infected individuals from rural areas [20]. Except in 2014, malaria incidence decreases in Akure South year after year between 2013 and 2017. The decrease in Akure South between 2015 and 2017 could be ascribed to awareness on the impact in the previous year (2014), access to good health facilities, residents' attempts to keep their surroundings clean (hygienic environment), or proper public education on the disease's impact.

Fig. 2.

Trend analysis of malaria incidence between 2013 and 2017.

3.2. Impacts of environmental variables on malaria incidence

This study determined the influence of temperature, rainfall and relative humidity on malaria using multiple linear regression analysis. Collinearity diagnostics test was carried out on the meteorological variables in order to check for the assumption of multi-collinearity. The tolerance value is shown in Table 3 for each of the independent variables which is above 0.1 and this is also supported by the Variance Inflation Factor (VIF) which is less than 10 for all the variables and so the assumption of multi-collinearity is not violated. Likewise, the normality and linearity assumptions are not violated. Therefore, to determine the independents variables in the regression model with the most influencing power on the dependent variable (incidence rate), Table 3 shows that in standardized coefficients the Beta value (−0.605) for average maximum temperature is the highest (ignoring the negative signs in front), this means average maximum temperature makes the strongest unique contribution to explaining the dependent variable while the variance explained by all other variables in the model are controlled for. This connotes that as temperature increases, the incidence rate decreases and vice versa. The Beta value for relative humidity has the least contribution to the model. Only average maximum temperature is making a statistically significant contribution to the prediction of incidence rate per 1000 as the significance is less than 0.05. In three decimal places the significance value for temperature is 0.000 while that of rainfall is 0.088 and relative humidity is 0.351.

Table 3.

Regression Coefficients.².

Model	Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Correlations			Collinearity Statistics
	B	Std. Error	Beta			Zero	Partial	Part	Tolerance	VIF
1 (Constant)	607.320	142.186		4.271	0.000
AVE.MAX TEMPERATURE	−16.678	4.235	−.605	−3.938	0.000	−.480	−.549	−.545	.810	1.234
AMOUNT OF RAINFALL	−.220	.125	−.254	−1.1754	−.104	−.104	−.281	−.243	.913	1.095
RELATIVE HUMIDITY	−.351	.372	−.141	−.944	.041	.041	−.155	−.131	.858	1.165

a. Dependent Variable: INCIDENCE RATE PER 1000.

4. Spatial relationship between climatic factors and malaria incidence

Spatial relationship between relative humidity, temperature, and rainfall are shown in Fig. 3a, b and 3c. In Fig. 3a, the highest spatial pattern of relative humidity (73.77–80.22) coincided with the high malaria cases (51.57–72.32 cases per 1000 persons) in Owo and Ondo LGAs. But the highest malaria incidence ranging from 72.33 to 99.60 per 1000 persons was found in Ondo East with high relative humidity. However, the lowest (63.51–64.44) relative humidity was in Odigbo and Okitipupa with the lowest 21.75–23.15) malaria cases; also Idanre and Akoko South West recorded lowest relative humidity with low malaria incidence (23.16–51.56 cases per 1000 persons). This is an indication that relative humidity also has a strong influence on malaria morbidity. Humid conditions favour the ecological reproduction and transmission of the malaria parasite [21].

Fig. 3.

Spatial relationship between relative humidity (a), temperature (b) and rainfall (c).

As shown in Fig. 3b, the lowest temperatures ranging from 29.15⁰C-29.54 °C were recorded in most of the LGAs except in Akure where the temperature was very high ranging between 30.34 °C - 41.70 °C. However, small parts of Idanre had moderate and high temperature but its largest portion recorded low temperature. The finding shows that the highest (72.33–99.60) malaria incidence occurred in Ondo East where the temperature was high during the study period. This is because mosquito does thrive under high temperature. Hence, malaria transmission decreases as temperature increases. Also, Ondo East LGA is a rural area where there are less activities compared to urban area like Akure in Ondo state. Malaria incident was moderate in Akure, the state capital where the highest temperature was recorded and this could be associated with high population and several anthropogenetic activities in the area (urban area) which could make the area very hot.

Fig. 3b shows the spatial distribution of rainfall and malaria incidence between 2013 and 2017. Odigbo and Okitipupa had the highest rainfall (167.41–179.08 mm) with the lowest malaria incidence (21.75–23.15 per 1000 people) over the study period. This implies that within the study period rainfall may not significantly influence malaria incidence in Odigbo and Okitipupa and the low incidence might be associated with control measures undertaken in the areas. It is worth noting that rainfall is not the only major factor responsible for high malaria incidence in a particular locality, as proven in this study. This is similar to a study conducted by Ref. [22].

Human settlement pattern, water-bodies, elevation, slope and temperature are also malaria determinants. Fig. 3c also show that Ondo East had the highest malaria incidence rate (51.57–72.32 cases per 1000 persons) with high rainfall (152.23–167.40 mm). Also, Ondo West and Akoko South West recorded high rainfall with high malaria incidence (51.57–72.32 cases per 1000 persons) while Akoko South West and Owo had the least rainfall (118.58–131.75 mm) but experienced high malaria incidence and this could be due to other factors which might have influence the malaria cases as Akoko South West and Owo are urban areas with large settlement and water-bodies.

Malaria disease may be prevalent or transmitted even in the absence of considerable rainfall or heat, although extreme rainfall and heat enhance the risk of high morbidity [[23], [24], [25], [26]]. Numerous studies have suggested that weather-related factors are the primary causes of malaria; however, the results of this study demonstrate that other environmental factors, including elevation, slope, vegetation, water bodies (ponds, drainage, stagnant water), and rivers, also play a significant role in malaria incidence and transmission.

4.1. Relationship between elevation, slope and malaria incidence

Fig. 4 reveals the relationship between elevation and malaria case in the study area and from this finding it can be deduced that majority of the locations are prone to malaria infection as the elevation values of the area are lower than 1,500 m which is the maximum [2] limit for elevation (or altitude) that could influence high malaria incidence. This implies that areas with elevation less than or equal to 1,500 m are highly susceptible to malaria infection and such could be classified as areas with high malaria risk. Nevertheless [2], stated that in favorable climatic conditions, the disease can occur at altitudes up to almost 3000 m but most often the there is a high risk at altitudes less 1,500 m. It is very important to note that the lower the elevation value of an area, the higher the risk index and the higher the value, the lower the risk index.

Fig. 4.

Spatial pattern of elevation and malaria incidence.

The finding in Fig. 5 shows the spatial pattern of slope and malaria incidence in the study area which are categorised into three different classes. The slope's classes include gentle slope (0-5⁰ or 3-5⁰), moderate slope (5°-8.5⁰) and steep slope (8.5⁰). Most parts of the study area is generally characterised with gentle slope (0-5⁰) and Okitipupa as well as Odigbo cover the highest gentle slope areas with the lowest malaria incidence (21.75–23.15) while Ondo East where the highest number of malaria cases was found also has significant area with gentle slope. However, Idanre having the largest steep slope area (≥8.5⁰) recoded moderate incidence (23.16–72.22). This finding shows that gentle slope did have significant influence on malaria incidence in Okitipupa and Odigbo since the areas with gentle slope are expected to have high malaria cases as such regions have capacity to retain water (where surface water movement is stagnant) and this create fertile condition for mosquito's proliferation and transmission. On the other hand, the steep slope areas are locations not vulnerable to malaria incidence as such regions permit water to flow faster thereby restraining the development of stagnant water-bodies.

Fig. 5.

Spatial pattern of slope and malaria incidence.

4.2. Malaria incidence and land use/land 4(LULC)

Land use and land cover change of the environment have both positive and negative influences on malaria incidence [27,28]. asserted that changes in environmental conditions are strongly linked to the distribution, transmission, intensity, and seasonality of malaria cases. Thus, the spatial relationship between land use/land cover and malaria incidence is shown in Fig. 6 and Table 4. The findings in Fig. 6 and Table 4 indicate that among all the LULC factors, vegetation has the highest percentage (80.92%) of area coverage while water-bodies has the lowest (0.0041%). The vegetation has significant influence on malaria incidence in the study area as this fact is justified in Ondo East which had high vegetation with the highest malaria cases (Fig. 6). This finding validate the assertion of Sharma (1991) which stated that disease incidence is very high in the forest and forest fringes as compared to plains or urban areas malaria incidence. And further said that forest cover may double the high rate of malaria in some of the areas recording high malaria cases.

Fig. 6.

Land use/land cover (LULC) and malaria incidence.

Table 4.

Land use/land cover (LULC) analysis of the study area in 2017.

CLASS NAMES	AREA (Sq/Km)	AREA (%)
Vegetation	6249.62	80.92
Built-up	1208.28	15.64
Bare Lands	265.43	3.44
Waterbodies	0.32	0.0041
TOTAL	7723.65	100.0

The green portion of the map represents the vegetation, the red stands for built up while the blue colour represents the water-bodies and the ash stands for bare land in the study area. From this finding, it can be seen that the built-ups including houses are surrounded by vegetation and this may be one of the reasons why Ondo East had the highest incidence rate within the study period. Areas with high vegetation are areas likely having high humid conditions which are favorable to the ecological reproduction and transmission of malaria vector.

4.3. Relationship between malaria incidence and vegetation (Normalized Difference Vegetation Index

(NDVI) esteemed prestigious noble.

The NDVI is a measurement of the photosynthetic activity of plants, and vegetation is one of the main factors that supports the development of mosquitoes. Because it refers to the spatial and temporal dynamics of various vegetation types that are naturally present around the areas where the vector and parasite are found, the NDVI was used in this study as a proxy, among other factors, for suitable conditions for mosquito development [28].

Fig. 7 shows the variation of NDVI within the study area. The NDIV values for the vegetated areas were positive but moderately high ranging from 0.1 to 0.4. These values of NDVI could mainly be associated with the season of the year which the image used was acquired or captured. The NDVI finding in Fig. 7 shows that Ondo East, where most of its area is covered with vegetation, has the highest incidence rate, and this implies that vegetation has a strong influence on malaria incidence in the area. However, Odigbo recorded the lowest incidence rate with high vegetation, and this suggests that vegetation has little influence on malaria incidence in the area. The vegetation ranged from 0.1 to 0.4, which means that the vegetation in the study area is moderately high, while other features (built-up, bare-land, water body) were grouped into values from 0.03 to 0.1. The amount of vegetation might not be very high because the satellite image for NDVI analysis was acquired in the early part of the year (4th January 2018) when there was little or no rain to aid the greenness of the vegetation, which in turn increases the reflectance of the vegetated area. This shows that most of the vegetation within the study area was not photosynthetically active at that time of the year. In a highly vegetated area, the NDVI typically ranges from 0.1 to 0.6, while urban and water values are negative [29]. It is understandable and well known that areas with high NDVI values could have high malaria incidence and vice-versa if there are no other influencing factors.

Fig. 7.

Spatial pattern of vegetation density (NDVI analysis) and malaria incidence of the study area.

5. Conclusion

Malaria is one of the most dangerous and prevalent deadly diseases, claiming millions of people in most developing countries as its proliferation is a global issue. The increase in malaria incidence rate in Ondo state could be attributed to environmental and climatic factors such as deep vegetation, rivers, water bodies, low elevation, gentler slopes, and a high population density in a particular setting. This study has revealed that temperature is the most significant factor influencing malaria incidence among all the factors considered. Similarly, Ondo East, which has the highest rate of vegetation, is where the malaria incidence was most felt within the study period. This has proven that vegetation also has a strong influence on malaria incidence. Using geospatial techniques and statistical analysis as relevant analytical tools in epidemiology to study the relationship between environmental and climatic factors has clearly been unveiled in this study. This is study is very essential to the government and disease control agencies (WHO, National Malaria Elimination Programme-NMEP, The United Nations Children Fund-UNICEF, United State Agency for International Development-USAID, Nigeria Centre for Disease Control-NCDC) as it could serve as a baseline or guide for malaria prevention and control in Ondo state and many regions of the world. Malaria morbidity in Ondo state could increase and have more negative impact on the people if adequate measures are not employed to curb the incidence. The results of this study have unmistakably demonstrated the influences of environmental and climatic factors on the prevalence of malaria in the study area. Through the use of geospatial tools, this research has increased our understanding of the malaria disease. This study proved that places with dense vegetation and elevations of 1,500 m are very prone to malaria sickness, increasing the rate of incidence. It has been found that in order to prevent and control malaria in the state of Ondo, attention must be paid to its causes. Rainfall, water bodies, temperature, slope, and an unclean environment can all have an impact on whether someone gets malaria.

The means to curtail the proliferation of malaria disease and to improve the health of the people include provision of and access to good health facilities, sensitization on malaria issue, as well as distribution of free/subsidised preventive materials (Insecticide-Treated Nets-ITNs, Drugs, Mosquitoes Repellents: Flit, an Insecticide, Coils, Permethrin Treated Clothing etc.). All these measures could prevent and lessen the malaria disease and decease.

Author contribution statement

Dave E. Ekpa: Conceived and designed the experiments; Performed the experiments. Salubi A Eunice: Analyzed and interpreted the data. Johnson Olusola: Analyzed and interpreted the data; Wrote the paper. Akintade Dare: Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement

Data included in article/supp. material/referenced in article.

Declaration of competing interest

The authors declare no competing interests.