# A Comparative Evaluation of Multi-Criteria Decision Analysis Techniques for Selecting the Appropriate Vector Management Strategy in the Northern Zone of Plateau State, Nigeria

Nanlok Henry Nimlang, Gilles Dusserre, Sandrine Bayle, Kivanc Ertugay, Stefan Janaqi

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Nanlok Henry Nimlang, Gilles Dusserre, Sandrine Bayle, Kivanc Ertugay, Stefan Janaqi. A Comparative Evaluation of Multi-Criteria Decision Analysis Techniques for Selecting the Appropriate Vector Management Strategy in the Northern Zone of Plateau State, Nigeria. American Journal of Multidisciplinary Research and Innovation, 2024, 3 (3), pp.79-84. 10.54536/ajmri.v3i3.2717. hal-04597822

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# A Comparative Evaluation of Multi-Criteria Decision Analysis Techniques for Selecting the Appropriate Vector Management Strategy in the Northern Zone of Plateau State, Nigeria

Nanlok Henry Nimlang, Gilles Dusserre, Sandrine Bayle, Kivanc Ertugay, Stefan Janaqi

► To cite this version:

Nanlok Henry Nimlang, Gilles Dusserre, Sandrine Bayle, Kivanc Ertugay, Stefan Janaqi. A Comparative Evaluation of Multi-Criteria Decision Analysis Techniques for Selecting the Appropriate Vector Management Strategy in the Northern Zone of Plateau State, Nigeria. American Journal of Multidisciplinary Research and Innovation, 2024, 3 (3), pp.79-84. 10.54536/ajmri.v3i3.2717. hal-04597822

HAL Id: hal-04597822

https://imt-mines-ales.hal.science/hal-04597822v1

Submitted on 3 Jun 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
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Distributed under a Creative Commons Attribution 4.0 International License
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# A Comparative Evaluation of Multi-Criteria Decision Analysis Techniques for Selecting the Appropriate Vector Management Strategy in the Northern Zone of Plateau State, Nigeria

Nanlok Henry Nmimlang<fnref n="1" />*, Gilles Dusserre<fnref n="1" />, Sandrine Bayle<fnref n="1" />, Kivanc Ertugay<fnref n="1" />, Stefan Janaqi<fnref n="1" />

## Article Information

Received: April 24, 2024

Accepted: May 27, 2024

Published: June 01, 2024

## Keywords

*Malaria, Malaria Risk,*
*Correlation, Vector Management,*
*Malaria Transmission*

## ABSTRACT

Accurate assessment of the complexity of malaria transmission dynamics requires the selection of appropriate Multi-Criteria Decision Analysis (MCDA) techniques that enable thorough analysis of risk factor parameters and structuring of decision problems into well-defined outcomes. A comparative study and analysis of multi-criteria decision-making methods such as TOPSIS, PROMETHEE II, MAUT and COPRAS was carried out using the reference rankings of MULTIMOORA and MOOSRA to evaluate the consistency of the results of these techniques and to rank the risk factors according to the vulnerability of mosquito breeding habitats and the risk of malaria transmission in the study area. The results indicate that TOPSIS ranked the alternative similarly to the reference ranking, while PROMETHEE II, MAUT, and COPRAS were ranked equally according to the weighted Spearman correlation coefficient and the weighted sum rank similarity coefficient analysis. The degree of consistency with which the risk factors in the study area were analyzed through this comparative study shows that the TOPSIS technique is suitable for its application in malaria epidemiology to determine the appropriate intervention measure for vector management in the northern zone of up to certain Plateau State.

## INTRODUCTION

Malaria, along with other diseases such as anaemia, remains a major public problem in developing countries, with socioeconomic consequences for children (Quadri et al., 2022; Youssefi et al., 2022). In most rural areas with limited healthcare facilities, traditional medication with herbs such as Parkia biglobosa is also often used as primary care, in addition to hospital visits for diagnosis and treatment (Ibrahim et al., 2023). In malaria epidemiology, Multi-Criteria Decision Analysis (MCDA) is a valuable tool employed in evaluating different malaria risk factors and guiding the selection of suitable vector management strategies. The selection of MCDA techniques is critical for assessing the intricacy of malaria transmission dynamics by thoroughly analysing the parameters of the risk factor into simple, and structured decision-making problems that guarantee accurate outcomes (Kassaw et al., 2018; Zhao et al., 2020). Various MCDA approaches have been applied to various fields of applications based on how well they transform data into expert results (Diaby et al., 2013; Guarini et al., 2018). The choice of these techniques in solving any decision problem are influenced by a number of factors such as the complexity of algorithms, criteria weighting methods, representation of preference evaluation criteria, handling of uncertain data, and data aggregation type (Bączkiewicz et al., 2021; Taherdoost & Madanchian, 2023). Since the evaluation of different MCDA techniques in solving decision problems often yield disparate transformations that produce disparate results, decision-makers are frequently faced with the challenge of selecting the best MCDA technique for analyzing these decision problems (Salabun

et al., 2020; Yildirim et al., 2021). Thus, when comparing these techniques, it is imperative to ensure that the results are consistent and logical, as the results often impact real-life decisions (Wieckowski & Szyjewski, 2022). Additionally, it is crucial to analyse the consistency of the results obtained from the comparison of MCDA techniques since decision-makers frequently depend on them to choose the best course of action for reducing the prevalence of malaria. Most often, the main objective of using MCDA techniques is to choose the best option from a set of alternatives by establishing a preference ranking among different decision options based on their performance against a variety of criteria (Steele et al., 2009).

Based on database searches, our study found that the AHP is the most often utilized MCDA method in the field of malaria epidemiology. Thus, there is a need to evaluate other MCDA techniques to ascertain their applicability in malaria epidemiology, particularly in mapping and modelling malaria transmission risk. A Web of Science database search conducted with the keywords "Malaria AND Multi-Criteria Decision Analysis OR Multi-Criteria Decision Method AND Analytic Hierarchy Process" indicated 2,806 studies that have been carried out in various fields related to malaria. However, only 1,151 studies utilizing other MCDA techniques were carried out after AHP was removed from the query. This indicates that AHP is the method most frequently employed to assess data related to malaria that influences decision-making processes in this domain. Therefore, it is essential to compare other MCDA techniques on the basis of consistency in order to determine which other

<footnote n="1">¹ Laboratory for Science of Risk (LSR), IMT Mines Ales, France</footnote>

<footnote>* Corresponding author's e-mail: nanlock.nimlang@mines-ales.fr</footnote>
<figure><img src="image_2.png" /></figure>

# A Comparative Evaluation of Multi-Criteria Decision Analysis Techniques for Selecting the Appropriate Vector Management Strategy in the Northern Zone of Plateau State, Nigeria

Nanlok Henry Nmimlang<fnref n="1" />*, Gilles Dusserre<fnref n="1" />, Sandrine Bayle<fnref n="1" />, Kivanc Ertugay<fnref n="1" />, Stefan Janaqi<fnref n="1" />

## Article Information

Received: April 24, 2024

Accepted: May 27, 2024

Published: June 01, 2024

## Keywords

Malaria, Malaria Risk, Correlation, Vector Management, Malaria Transmission

## ABSTRACT

Accurate assessment of the complexity of malaria transmission dynamics requires the selection of appropriate Multi-Criteria Decision Analysis (MCDA) techniques that enable thorough analysis of risk factor parameters and structuring of decision problems into well-defined outcomes. A comparative study and analysis of multi-criteria decision-making methods such as TOPSIS, PROMETHEE II, MAUT and COPRAS was carried out using the reference rankings of MULTIMOORA and MOOSRA to evaluate the consistency of the results of these techniques and to rank the risk factors according to the vulnerability of mosquito breeding habitats and the risk of malaria transmission in the study area. The results indicate that TOPSIS ranked the alternative similarly to the reference ranking, while PROMETHEE II, MAUT, and COPRAS were ranked equally according to the weighted Spearman correlation coefficient and the weighted sum rank similarity coefficient analysis. The degree of consistency with which the risk factors in the study area were analyzed through this comparative study shows that the TOPSIS technique is suitable for its application in malaria epidemiology to determine the appropriate intervention measure for vector management in the northern zone of up to certain Plateau State.

## INTRODUCTION

Malaria, along with other diseases such as anaemia, remains a major public problem in developing countries, with socioeconomic consequences for children (Quadri et al., 2022; Youssefi et al., 2022). In most rural areas with limited healthcare facilities, traditional medication with herbs such as Parkia biglobosa is also often used as primary care, in addition to hospital visits for diagnosis and treatment (Ibrahim et al., 2023). In malaria epidemiology, Multi-Criteria Decision Analysis (MCDA) is a valuable tool employed in evaluating different malaria risk factors and guiding the selection of suitable vector management strategies. The selection of MCDA techniques is critical for assessing the intricacy of malaria transmission dynamics by thoroughly analysing the parameters of the risk factor into simple, and structured decision-making problems that guarantee accurate outcomes (Kassaw et al., 2018; Zhao et al., 2020). Various MCDA approaches have been applied to various fields of applications based on how well they transform data into expert results (Diaby et al., 2013; Guarini et al., 2018). The choice of these techniques in solving any decision problem are influenced by a number of factors such as the complexity of algorithms, criteria weighting methods, representation of preference evaluation criteria, handling of uncertain data, and data aggregation type (Bączkiewicz et al., 2021; Taherdoost & Madanchian, 2023). Since the evaluation of different MCDA techniques in solving decision problems often yield disparate transformations that produce disparate results, decision-makers are frequently faced with the challenge of selecting the best MCDA technique for analyzing these decision problems (Salabun

et al., 2020; Yildirim et al., 2021). Thus, when comparing these techniques, it is imperative to ensure that the results are consistent and logical, as the results often impact real-life decisions (Wieckowski & Szyjewski, 2022). Additionally, it is crucial to analyse the consistency of the results obtained from the comparison of MCDA techniques since decision-makers frequently depend on them to choose the best course of action for reducing the prevalence of malaria. Most often, the main objective of using MCDA techniques is to choose the best option from a set of alternatives by establishing a preference ranking among different decision options based on their performance against a variety of criteria (Steele et al., 2009).

Based on database searches, our study found that the AHP is the most often utilized MCDA method in the field of malaria epidemiology. Thus, there is a need to evaluate other MCDA techniques to ascertain their applicability in malaria epidemiology, particularly in mapping and modelling malaria transmission risk. A Web of Science database search conducted with the keywords "Malaria AND Multi-Criteria Decision Analysis OR Multi-Criteria Decision Method AND Analytic Hierarchy Process" indicated 2,806 studies that have been carried out in various fields related to malaria. However, only 1,151 studies utilizing other MCDA techniques were carried out after AHP was removed from the query. This indicates that AHP is the method most frequently employed to assess data related to malaria that influences decision-making processes in this domain. Therefore, it is essential to compare other MCDA techniques on the basis of consistency in order to determine which other

<footnote n="1">¹ Laboratory for Science of Risk (LSR), IMT Mines Ales, France</footnote>

<footnote>* Corresponding author's e-mail: nanlock.nimlang@mines-ales.fr</footnote>
approaches are appropriate for assessing malaria risk factors and comprehend their applicability to vector management practices.

To the best of our knowledge, no study has conducted a comparative evaluation of MCDA techniques using different risk factors to assess malaria transmission risk in the study area. In endemic areas such as the northern zone of Plateau State, comparative analysis of MCDA techniques is essential to influence the selection of MCDA technique for analysis and assessment of various malaria risk factors and consequently improve the quality of decision making in managing the burden of the disease.

This study aims to compare various MCDA techniques in terms of their consistency, leveraging on the most prominent malaria risk factors in the Northern Zone of Plateau State. Thus, the study will guide and enhance the effectiveness of vector management practice in the region. Preference Ranking Organization Method for Enrichment Evaluation II (PROMETHEE II) (Goswami, 2020), Multi-Attribute Utility Theory (MAUT) (Taufik et al., 2021), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) (Chakraborty, 2022), and Complex Proportional Assessment (COPRAS) (Lu et al., 2021) were used to evaluate the most prominent malaria risk factors identified in the Northern Zone of plateau State.

Previous research has employed a comparative analysis of various MCDA techniques for practical applications, such as the evaluation of laptops (Wieckowski & Szyjewski, 2022), choosing the optimal technique for identifying areas susceptible to erosion (Patel et al., 2023), sustainable

transport (Broniewicz & Ogrodnik, 2021), choosing cameras (Bączkiewicz et al., 2021), real estate and land management practices (Guarini et al., 2018), etc.

The article is structured as follows: Section 2 evaluates the study area, different malaria risk factors, expert consensus on selecting risk factors, assessment of various MCDA techniques, ranking references, and analysis of correlation coefficients. Section 3 examines the assessment of various MCDA techniques, while Section 4 discusses the results of the analyzed data and draws a conclusion.

# MATERIALS AND METHODS

## Study Area

The Northern Zone of Plateau State, Nigeria consists of six Local Government Areas (L.G.As), namely Bassa, Barkin Ladi, Jos East, Jos North, Jos South, and Riyom. The region is spatially located at latitude 9°20' to 10°20'N and longitude 8°30' to 9°50'E. Bauchi State borders it to the northeast, Kaduna State to the northwest and Mangu and Bokkos L.G.As to the south. Climatic conditions are divided into two distinct seasons: the rainy season, which is typically influenced by the West African monsoon and lasts from May to October, and the harmattan (dry) season, which originates in the Sahara and often lasts from November to April. The annual average rainfall and temperature are 1200 mm and 24 °C, respectively (Binbol et al., 2020). The altitude includes the few regions in Nigeria that are above 1000 m. The most distinctive hydrological feature of the study area is its watershed, which drains rapidly into existing water bodies (static and flowing) during rainfall (Emma Martin & Burgess Neil, 2022).

<figure><img src="image_3.png" /><figcaption>**Figure 1:** Map of study area showing its hydrological control</figcaption></figure>
approaches are appropriate for assessing malaria risk factors and comprehend their applicability to vector management practices.

To the best of our knowledge, no study has conducted a comparative evaluation of MCDA techniques using different risk factors to assess malaria transmission risk in the study area. In endemic areas such as the northern zone of Plateau State, comparative analysis of MCDA techniques is essential to influence the selection of MCDA technique for analysis and assessment of various malaria risk factors and consequently improve the quality of decision making in managing the burden of the disease.

This study aims to compare various MCDA techniques in terms of their consistency, leveraging on the most prominent malaria risk factors in the Northern Zone of Plateau State. Thus, the study will guide and enhance the effectiveness of vector management practice in the region. Preference Ranking Organization Method for Enrichment Evaluation II (PROMETHEE II) (Goswami, 2020), Multi-Attribute Utility Theory (MAUT) (Taufik et al., 2021), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) (Chakraborty, 2022), and Complex Proportional Assessment (COPRAS) (Lu et al., 2021) were used to evaluate the most prominent malaria risk factors identified in the Northern Zone of plateau State.

Previous research has employed a comparative analysis of various MCDA techniques for practical applications, such as the evaluation of laptops (Wieckowski & Szyjewski, 2022), choosing the optimal technique for identifying areas susceptible to erosion (Patel et al., 2023), sustainable

transport (Broniewicz & Ogrodnik, 2021), choosing cameras (Bączkiewicz et al., 2021), real estate and land management practices (Guarini et al., 2018), etc.

The article is structured as follows: Section 2 evaluates the study area, different malaria risk factors, expert consensus on selecting risk factors, assessment of various MCDA techniques, ranking references, and analysis of correlation coefficients. Section 3 examines the assessment of various MCDA techniques, while Section 4 discusses the results of the analyzed data and draws a conclusion.

# MATERIALS AND METHODS

## Study Area

The Northern Zone of Plateau State, Nigeria consists of six Local Government Areas (L.G.As), namely Bassa, Barkin Ladi, Jos East, Jos North, Jos South, and Riyom. The region is spatially located at latitude 9°20' to 10°20'N and longitude 8°30' to 9°50'E. Bauchi State borders it to the northeast, Kaduna State to the northwest and Mangu and Bokkos L.G.As to the south. Climatic conditions are divided into two distinct seasons: the rainy season, which is typically influenced by the West African monsoon and lasts from May to October, and the harmattan (dry) season, which originates in the Sahara and often lasts from November to April. The annual average rainfall and temperature are 1200 mm and 24 °C, respectively (Binbol et al., 2020). The altitude includes the few regions in Nigeria that are above 1000 m. The most distinctive hydrological feature of the study area is its watershed, which drains rapidly into existing water bodies (static and flowing) during rainfall (Emma Martin & Burgess Neil, 2022).

<figure><img src="image_3.png" /><figcaption>**Figure 1:** Map of study area showing its hydrological control</figcaption></figure>
## Data Collection

Malaria risk factors ranging from environmental (Vegetation Health Index, waterbody distribution, and landuse, landcover classes), climatic (ambient temperature and rainfall), and socioeconomic (population density) were identified as some of the most prevalent risk factors influencing malaria transmission in the study area through experts' consensus and literature review.

Rainfall and temperature data was collected from the Jos meteorological weather station, while the vegetation health indices and landuse/landcover data were extracted from Landsat 8 OLI/TIR image (https://earthexplorer.usgs.gov/). The distribution of water bodies (static and flowing) was extracted from 0.5 metres-resolution Google Earth image using QGIS extensions. Whereas the population density data was collected from the National Population Commission.

## Data Analysis

The interpolation tool was used to analyze the ambient temperature and rainfall. Analysis of the vegetation health index was done using the Landsat 8 image. Similarly, the Landsat 8 image was classified into 6 classes with the maximum likelihood supervised classification technique based on the propensity of the different classes to transmit malaria: bare surfaces, rock outcrops, settlements, cultivated areas, forested areas, and water bodies. Lastly, the spatial distribution analysis of population density was also carried out. The risk factor's parameter used in this study was further reclassified into six classes based on the vulnerability of mosquitoes breeding habitats and malaria transmission risk.

Five experts with at least five years of experience in malaria-related fields evaluated and compared the relative importance of the risk factors using a 9-degree pairwise comparison matrix. Furthermore, the weights of these factors were calculated based on the factors' vulnerability to malaria risk.

Furthermore, the identified malaria risk factors were assessed using TOPSIS, MAUT, PROMETHEE II and COPRAS techniques and then ranked based on their propensity for malaria transmission in the study area. Multi-Objective Optimisation by Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), and Multi-Objective Optimisation on the basis of Simple Ratio Analysis (MOOSRA) were used as reference ranking. Using the reference rankings of MULTIMOORA and MOOSRA, a comparative analysis of TOPSIS, PROMETHEE II, MAUT, and COPRAS was conducted to determine the reliability of the consistency results of these techniques in ranking the risk factors.

Lastly, the correlation between TOPSIS, PROMETHEE II, MAUT, and COPRAS techniques was ascertained through weighted Spearman correlation coefficient and weighted sum (WS) rank similarity coefficient analysis.

## RESULTS

The analysis of the weight of risk factors based on the propensity of the factors to malaria transmission, performed by pairwise comparison matrix calculations in Table 1, is presented in the following order: Rainfall (C4) in millilitres > LULC(C5), scaled 1-6 > Population (C3) > Vegetation health index (C2) > Water bodies (C1) in kilometres > Temperature (C6) in degrees Celsius.

<table><caption>Table 1: Weights of malaria risk factors and the classification of criteria into cost and profits</caption><thead><tr><th></th><th>C<sub>1</sub></th><th>C<sub>2</sub></th><th>C<sub>3</sub></th><th>C<sub>4</sub></th><th>C<sub>5</sub></th><th>C<sub>6</sub></th></tr></thead><tbody><tr><th>Weight</th><td>0.047</td><td>0.079</td><td>0.125</td><td>0.445</td><td>0.271</td><td>0.033</td></tr><tr><th>Type</th><td>Cost</td><td>Profit</td><td>Profit</td><td>Profit</td><td>Cost</td><td>Profit</td></tr></tbody></table>

The calculation of MCDA techniques used in the alternatives ranking analysis and preference ranking of MULTIMOORA and MOOSRA is shown in Table 2. The alternative ranking order showed that PROMOTHEE, MAUT and COPRAS were ranked equally, while TOPSIS ranked the alternatives similarly to the reference ranking. On the other hand, the reference ranking of MULTIMOORA and MOOSRA showed an equivalent placement of the

alternatives with TOPSIS. Furthermore, the correlation analysis of MCDA techniques shows that PROMETHEE, MAUT and COPRAS in Figure 1 and Table 3 had a negative correlation and relationship with the reference ranking of MULTIMOORA and MOOSRA in terms of assessing the malaria risk factors used in this study. While TOPSIS showed a positive correlation and relationship with the reference ranking when evaluating the different risk factors.

<table><caption>Table 2: TTOPSIS, PROMETHEE, MAUT, COPRAS, MULTIMOORA and MOOSRA ranking for malaria risk factor selection</caption><thead><tr><th>Ai</th><th>TOPSIS</th><th>PROMETHEE</th><th>MAUT</th><th>COPRAS</th><th>MULTIMOORA</th><th>MOOSRA</th></tr></thead><tbody><tr><th>C<sub>1</sub></th><td>1</td><td>6</td><td>6</td><td>6</td><td>1</td><td>1</td></tr><tr><th>C<sub>2</sub></th><td>2</td><td>5</td><td>5</td><td>5</td><td>2</td><td>2</td></tr><tr><th>C<sub>3</sub></th><td>3</td><td>4</td><td>4</td><td>4</td><td>3</td><td>3</td></tr><tr><th>C<sub>4</sub></th><td>4</td><td>3</td><td>3</td><td>3</td><td>4</td><td>4</td></tr><tr><th>C<sub>5</sub></th><td>5</td><td>2</td><td>2</td><td>2</td><td>5</td><td>5</td></tr><tr><th>C<sub>6</sub></th><td>6</td><td>1</td><td>1</td><td>1</td><td>6</td><td>6</td></tr></tbody></table>
## Data Collection

Malaria risk factors ranging from environmental (Vegetation Health Index, waterbody distribution, and landuse, landcover classes), climatic (ambient temperature and rainfall), and socioeconomic (population density) were identified as some of the most prevalent risk factors influencing malaria transmission in the study area through experts' consensus and literature review.

Rainfall and temperature data was collected from the Jos meteorological weather station, while the vegetation health indices and landuse/landcover data were extracted from Landsat 8 OLI/TIR image (https://earthexplorer.usgs.gov/). The distribution of water bodies (static and flowing) was extracted from 0.5 metres-resolution Google Earth image using QGIS extensions. Whereas the population density data was collected from the National Population Commission.

## Data Analysis

The interpolation tool was used to analyze the ambient temperature and rainfall. Analysis of the vegetation health index was done using the Landsat 8 image. Similarly, the Landsat 8 image was classified into 6 classes with the maximum likelihood supervised classification technique based on the propensity of the different classes to transmit malaria: bare surfaces, rock outcrops, settlements, cultivated areas, forested areas, and water bodies. Lastly, the spatial distribution analysis of population density was also carried out. The risk factor's parameter used in this study was further reclassified into six classes based on the vulnerability of mosquitoes breeding habitats and malaria transmission risk.

Five experts with at least five years of experience in malaria-related fields evaluated and compared the relative importance of the risk factors using a 9-degree pairwise comparison matrix. Furthermore, the weights of these factors were calculated based on the factors' vulnerability to malaria risk.

Furthermore, the identified malaria risk factors were assessed using TOPSIS, MAUT, PROMETHEE II and COPRAS techniques and then ranked based on their propensity for malaria transmission in the study area. Multi-Objective Optimisation by Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), and Multi-Objective Optimisation on the basis of Simple Ratio Analysis (MOOSRA) were used as reference ranking. Using the reference rankings of MULTIMOORA and MOOSRA, a comparative analysis of TOPSIS, PROMETHEE II, MAUT, and COPRAS was conducted to determine the reliability of the consistency results of these techniques in ranking the risk factors.

Lastly, the correlation between TOPSIS, PROMETHEE II, MAUT, and COPRAS techniques was ascertained through weighted Spearman correlation coefficient and weighted sum (WS) rank similarity coefficient analysis.

## RESULTS

The analysis of the weight of risk factors based on the propensity of the factors to malaria transmission, performed by pairwise comparison matrix calculations in Table 1, is presented in the following order: Rainfall (C4) in millilitres > LULC(C5), scaled 1-6 > Population (C3) > Vegetation health index (C2) > Water bodies (C1) in kilometres > Temperature (C6) in degrees Celsius.

<table><caption>Table 1: Weights of malaria risk factors and the classification of criteria into cost and profits</caption><thead><tr><th></th><th>C<sub>1</sub></th><th>C<sub>2</sub></th><th>C<sub>3</sub></th><th>C<sub>4</sub></th><th>C<sub>5</sub></th><th>C<sub>6</sub></th></tr></thead><tbody><tr><th>Weight</th><td>0.047</td><td>0.079</td><td>0.125</td><td>0.445</td><td>0.271</td><td>0.033</td></tr><tr><th>Type</th><td>Cost</td><td>Profit</td><td>Profit</td><td>Profit</td><td>Cost</td><td>Profit</td></tr></tbody></table>

The calculation of MCDA techniques used in the alternatives ranking analysis and preference ranking of MULTIMOORA and MOOSRA is shown in Table 2. The alternative ranking order showed that PROMOTHEE, MAUT and COPRAS were ranked equally, while TOPSIS ranked the alternatives similarly to the reference ranking. On the other hand, the reference ranking of MULTIMOORA and MOOSRA showed an equivalent placement of the

alternatives with TOPSIS. Furthermore, the correlation analysis of MCDA techniques shows that PROMETHEE, MAUT and COPRAS in Figure 1 and Table 3 had a negative correlation and relationship with the reference ranking of MULTIMOORA and MOOSRA in terms of assessing the malaria risk factors used in this study. While TOPSIS showed a positive correlation and relationship with the reference ranking when evaluating the different risk factors.

<table><caption>Table 2: TTOPSIS, PROMETHEE, MAUT, COPRAS, MULTIMOORA and MOOSRA ranking for malaria risk factor selection</caption><thead><tr><th>Ai</th><th>TOPSIS</th><th>PROMETHEE</th><th>MAUT</th><th>COPRAS</th><th>MULTIMOORA</th><th>MOOSRA</th></tr></thead><tbody><tr><th>C<sub>1</sub></th><td>1</td><td>6</td><td>6</td><td>6</td><td>1</td><td>1</td></tr><tr><th>C<sub>2</sub></th><td>2</td><td>5</td><td>5</td><td>5</td><td>2</td><td>2</td></tr><tr><th>C<sub>3</sub></th><td>3</td><td>4</td><td>4</td><td>4</td><td>3</td><td>3</td></tr><tr><th>C<sub>4</sub></th><td>4</td><td>3</td><td>3</td><td>3</td><td>4</td><td>4</td></tr><tr><th>C<sub>5</sub></th><td>5</td><td>2</td><td>2</td><td>2</td><td>5</td><td>5</td></tr><tr><th>C<sub>6</sub></th><td>6</td><td>1</td><td>1</td><td>1</td><td>6</td><td>6</td></tr></tbody></table>
The analysis of the weighted Spearman correlation coefficient and the weighted sum similarity coefficient (WS) presented in Figure 1 shows that PROMETHEE, MAUT and COPRAS were equally correlated with a value of 1.0, indicating that they are equal in terms of evaluation of the alternatives are equal to the various risk factors, while TOPSIS on the other hand showed the

negative correlations and relationships in assessing the risk factors of the above MCDA techniques.

However, TOPSIS showed a positive correlation, relationship, and equal ranking in evaluating the alternatives as the reference ranking techniques of MULTIMOORA and MOOSRA, as shown in Table 3.

<figure><img src="image_4.png" /><figcaption>Figure 2: Correlation analysis for TOPSIS, PROMETHEE, MAUT and COPRAS MCDA techniques ranking</figcaption></figure>

<table><caption>**Table 3:** Correlation analysis of TOPSIS, PROMETHEE, MAUT, and COPRAS with preference ranking of MULTIMOORA and MOOSRA techniques</caption><thead><tr><th></th><th>TOPSIS</th><th>PROMETHEE</th><th>MAUT</th><th>COPRAS</th></tr></thead><tbody><tr><th>RW</th><td>1</td><td>-0.907</td><td>-0.907</td><td>-0.907</td></tr><tr><th>WS</th><td>1</td><td>-1</td><td>-1</td><td>-1</td></tr></tbody></table>

## DISCUSSION

The criteria weight analysis of malaria risk factors used in this study suggests that rainfall is the key factor contributing to malaria transmission risk, accounting for approximately 44%. Furthermore, because the risk factor ranking analysis used the same weights for all MCDA techniques, only TOPSIS showed similarity to the preference ranking. This means that TOPSIS ensured a high level of consistency in determining the impact of risk factors affecting malaria transmission risk in the study area. Because the various risk factors used in this study were evaluated in such a logical and consistent manner, this decision-making method is helpful in prioritizing the various risk factors and determining the disease hotspot for malaria transmission. In addition, the evaluation of the method based on the ranking of risk factors makes it clear which important intervention measures need to be implemented. Analysis of criteria weights by TOPSIS indicates that priority is given to vector management interventions that address vector distribution around bodies of water, particularly during the rainy season. This is in addition to maintaining good sanitary conditions around settlements and adhering to protective measures against the vectors, such as: E.g., constant use of insecticide-treated nets (ITNs), indoor residual spraying (IRS), etc.

In addition to AHP, which has been demonstrated to be extensively utilized in the field of malaria epidemiology for the assessment of various risk factors impacting the disease's transmission as well as the identification of appropriate vector management strategies. The comparison of TOPSIS, PROMETHEE, MAUT and COPRAS shows that TOPSIS had similarities and correlations with the preference ranking of MULTIMOORA and MOOSRA, suggesting that it is a useful tool for assessing malaria risk and identifying appropriate vector interventions. Also, this indicates that TOPSIS showed consistency in comparison to the other MCDA techniques used in determining the relative importance and ranking of the malaria risk factors, based on the analysis of the malaria risk factors used in this study. The degree of consistency with which the risk factors in the study area were analyzed through this comparative comparison demonstrates that the TOPSIS technique is suitable for its application in malaria epidemiology to determine the suitability of the appropriate vector management intervention measure for application in the Northern Zone of to be determined Plateau State. The negative correlation and relationship shown by PROMETHEE, MAUT and COPRAS with the preference ranking of MULTIMOORA and MOOSRA suggests that there are differences in the evaluation of
The analysis of the weighted Spearman correlation coefficient and the weighted sum similarity coefficient (WS) presented in Figure 1 shows that PROMETHEE, MAUT and COPRAS were equally correlated with a value of 1.0, indicating that they are equal in terms of evaluation of the alternatives are equal to the various risk factors, while TOPSIS on the other hand showed the

negative correlations and relationships in assessing the risk factors of the above MCDA techniques.

However, TOPSIS showed a positive correlation, relationship, and equal ranking in evaluating the alternatives as the reference ranking techniques of MULTIMOORA and MOOSRA, as shown in Table 3.

<figure><img src="image_4.png" /><figcaption>Figure 2: Correlation analysis for TOPSIS, PROMETHEE, MAUT and COPRAS MCDA techniques ranking</figcaption></figure>

<table><caption>Table 3: Correlation analysis of TOPSIS, PROMETHEE, MAUT, and COPRAS with preference ranking of MULTIMOORA and MOOSRA techniques</caption><thead><tr><th></th><th>TOPSIS</th><th>PROMETHEE</th><th>MAUT</th><th>COPRAS</th></tr></thead><tbody><tr><th>RW</th><td>1</td><td>-0.907</td><td>-0.907</td><td>-0.907</td></tr><tr><th>WS</th><td>1</td><td>-1</td><td>-1</td><td>-1</td></tr></tbody></table>

## DISCUSSION

The criteria weight analysis of malaria risk factors used in this study suggests that rainfall is the key factor contributing to malaria transmission risk, accounting for approximately 44%. Furthermore, because the risk factor ranking analysis used the same weights for all MCDA techniques, only TOPSIS showed similarity to the preference ranking. This means that TOPSIS ensured a high level of consistency in determining the impact of risk factors affecting malaria transmission risk in the study area. Because the various risk factors used in this study were evaluated in such a logical and consistent manner, this decision-making method is helpful in prioritizing the various risk factors and determining the disease hotspot for malaria transmission. In addition, the evaluation of the method based on the ranking of risk factors makes it clear which important intervention measures need to be implemented. Analysis of criteria weights by TOPSIS indicates that priority is given to vector management interventions that address vector distribution around bodies of water, particularly during the rainy season. This is in addition to maintaining good sanitary conditions around settlements and adhering to protective measures against the vectors, such as: E.g., constant use of insecticide-treated nets (ITNs), indoor residual spraying (IRS), etc.

In addition to AHP, which has been demonstrated to be extensively utilized in the field of malaria epidemiology for the assessment of various risk factors impacting the disease's transmission as well as the identification of appropriate vector management strategies. The comparison of TOPSIS, PROMETHEE, MAUT and COPRAS shows that TOPSIS had similarities and correlations with the preference ranking of MULTIMOORA and MOOSRA, suggesting that it is a useful tool for assessing malaria risk and identifying appropriate vector interventions. Also, this indicates that TOPSIS showed consistency in comparison to the other MCDA techniques used in determining the relative importance and ranking of the malaria risk factors, based on the analysis of the malaria risk factors used in this study. The degree of consistency with which the risk factors in the study area were analyzed through this comparative comparison demonstrates that the TOPSIS technique is suitable for its application in malaria epidemiology to determine the suitability of the appropriate vector management intervention measure for application in the Northern Zone of to be determined Plateau State. The negative correlation and relationship shown by PROMETHEE, MAUT and COPRAS with the preference ranking of MULTIMOORA and MOOSRA suggests that there are differences in the evaluation of
the criterion weights or the ranking of the alternatives of the malaria risk factors. However, the positive correlation and relationship between TOPSIS and the preference ranking of MULTIMOORA and MOOSRA implies that there is concordance in the assessment of the weights and risk factors. This relationship is crucial for selecting appropriate MCDA techniques in malaria risk analysis and consequently improves decision-making processes in managing the malaria burden in the study area.

Although leveraging the intuition of experts in the field of malaria is crucial for decision-makers in combating the scourge of the disease, the evaluation of MCDA techniques with the reference ranking of MULTIMOORA and MOOSRA in this study has some limitations in terms of being subjected to degree of bias or subjectivity since it relies mainly on some assumptions in determining the relative importance and ranking of the criterion weights of the various risk factors. In addition, the numerical values of the different risk factors used to determine the weights and to calculate the rankings established by the different MCDA techniques were considered chronologically, ranging from the smallest to the highest value indicating the maximum potential of the attributes. The lack of randomness attributed to the data set used to determine risk factor weights is also a limitation as it affects the arrangement of the rankings produced using these methods. The lack of randomness attributed to the data set used to determine risk factor weights is also a limitation as it affects the arrangement of the rankings produced using these methods.

## CONCLUSIONS

MCDA has proven to be a useful tool in the field of malaria epidemiology, helping to select the most appropriate vector management strategies by assessing various malaria risk factors. Based on the vulnerability of mosquito breeding habitats and the risk of malaria transmission, this study assessed the main malaria risk factors in the northern region of Plateau State, ranging environmental, climatic, and socioeconomic factors. A comparative comparison and analysis of TOPSIS, PROMETHEE II, MAUT, and COPRAS was conducted using the reference rankings of MULTIMOORA and MOOSRA to establish the reliability and consistency of the results obtained from the ranking of risk factors.

The evaluation of the different risk factors within the study area showed that TOPSIS ranked the alternatives similarly to the reference ranking, whereas PROMOTHEE, MAUT, and COPRAS were ranked equally in the evaluation of the alternative ranking. The TOPSIS technique assessed the various risk factors used in this study in such a logical and consistent manner that this decision-making method is helpful in prioritizing the various risk factors and determining the disease hotspot for malaria transmission.

## REFERENCES

Bączkiewicz, A., Wątróbski, J., Kizielewicz, B., & Sałabun, W. (2021). Towards Objectification of Multi-Criteria

Assessments: A Comparative Study on MCDA Methods. *Proceedings of the 16th Conference on Computer Science and Intelligence Systems, FedCSIS 2021*, 417–425. https://doi.org/10.15439/2021F61

Binbol, N. L., Abdullahi, M. L., & Yusuf, Y. A. (2020). A temporal analysis of rainfall characteristics on the jos plateau, nigeria. https://www.researchgate.net/publication/348779648

Broniewicz, E., & Ogrodnik, K. (2021). A comparative evaluation of multi-criteria analysis methods for sustainable transport. *Energies*, 14(16). https://doi.org/10.3390/en14165100

Chakraborty, S. (2022). TOPSIS and Modified TOPSIS: A comparative analysis. *Decision Analytics Journal*, 2, 100021. https://doi.org/10.1016/j.dajour.2021.100021

Diaby, V., Campbell, K., & Goeree, R. (2013). Multi-criteria decision analysis (MCDA) in health care: a bibliometric analysis. *Operations research for health care*, 2(1-2), 20-24. https://doi.org/10.1016/j.orhc.2013.03.001

Emma Martin, & Burgess Neil. (2022, December 31). Jos Plateau Forest-Grassland _ One Earth. One Earth. https://www.oneearth.org/ecoregions/jos-plateau-forest-grassland/

Goswami, S. S. (2020). Outranking Methods: Promethee i and Promethee II. *Foundations of Management*, 12(1), 93–110. https://doi.org/10.2478/fman-2020-0008

Guarini, M. R., Battisti, F., & Chiovitti, A. (2018). A methodology for the selection of multi-criteria decision analysis methods in real estate and land management processes. *Sustainability (Switzerland)*, 10(2). https://doi.org/10.3390/su10020507

Ibrahim, D. F., Hassan, A. S., Sani, A., Zakariyya, A., Shema, F. B., Zubair, R. U., & Riruwai, G. Y. (2023). Effect of Aqueous Stem Bark Extract of Parkia Biglobosa on the Histological Morphology of Liver of Adult Wistar Rats. *American journal of medical science and innovation*, 2(1), 13–20. https://doi.org/10.54536/ajmsi.v2i1.1130

Kassaw, M., Agelo, W. A., Zewdie, A., & Ameneshewa, W. (2018). Identifying Malaria Epidemic Prone Area Hotspot Map by Using Geospatial Technologies and Spatial Multi Criteria Evaluation Techniques: The Case of Majang Zone, Gambella Region, Eth... Identifying Malaria Epidemic Prone Area Hotspot Map by Using Geospatial Technologies and Spatial Multi Criteria Evaluation Techniques: The Case of Majang Zone, Gambella Region, Ethiopia. *IOSR Journal Of Humanities And Social Science (IOSR-JHSS)*.

Lu, J., Zhang, S., Wu, J., & Wei, Y. (2021). Copras method for multiple attribute group decision making under picture fuzzy environment and their application to green supplier selection. *Technological and Economic Development of Economy*, 27(2), 369–385. https://doi.org/10.3846/tede.2021.14211

Patel, A., Ramana Rao, K. V., Rajwade, Y. A., Saxena, C. K., Singh, K., & Srivastava, A. (2023). Comparative Analysis of MCDA Techniques for Identifying
the criterion weights or the ranking of the alternatives of the malaria risk factors. However, the positive correlation and relationship between TOPSIS and the preference ranking of MULTIMOORA and MOOSRA implies that there is concordance in the assessment of the weights and risk factors. This relationship is crucial for selecting appropriate MCDA techniques in malaria risk analysis and consequently improves decision-making processes in managing the malaria burden in the study area.

Although leveraging the intuition of experts in the field of malaria is crucial for decision-makers in combating the scourge of the disease, the evaluation of MCDA techniques with the reference ranking of MULTIMOORA and MOOSRA in this study has some limitations in terms of being subjected to degree of bias or subjectivity since it relies mainly on some assumptions in determining the relative importance and ranking of the criterion weights of the various risk factors. In addition, the numerical values of the different risk factors used to determine the weights and to calculate the rankings established by the different MCDA techniques were considered chronologically, ranging from the smallest to the highest value indicating the maximum potential of the attributes. The lack of randomness attributed to the data set used to determine risk factor weights is also a limitation as it affects the arrangement of the rankings produced using these methods. The lack of randomness attributed to the data set used to determine risk factor weights is also a limitation as it affects the arrangement of the rankings produced using these methods.

## CONCLUSIONS

MCDA has proven to be a useful tool in the field of malaria epidemiology, helping to select the most appropriate vector management strategies by assessing various malaria risk factors. Based on the vulnerability of mosquito breeding habitats and the risk of malaria transmission, this study assessed the main malaria risk factors in the northern region of Plateau State, ranging environmental, climatic, and socioeconomic factors. A comparative comparison and analysis of TOPSIS, PROMETHEE II, MAUT, and COPRAS was conducted using the reference rankings of MULTIMOORA and MOOSRA to establish the reliability and consistency of the results obtained from the ranking of risk factors.

The evaluation of the different risk factors within the study area showed that TOPSIS ranked the alternatives similarly to the reference ranking, whereas PROMOTHEE, MAUT, and COPRAS were ranked equally in the evaluation of the alternative ranking. The TOPSIS technique assessed the various risk factors used in this study in such a logical and consistent manner that this decision-making method is helpful in prioritizing the various risk factors and determining the disease hotspot for malaria transmission.

## REFERENCES

* Bączkiewicz, A., Wątróbski, J., Kizielewicz, B., & Sałabun, W. (2021). Towards Objectification of Multi-Criteria

Assessments: A Comparative Study on MCDA Methods. *Proceedings of the 16th Conference on Computer Science and Intelligence Systems, FedCSIS 2021*, 417–425. https://doi.org/10.15439/2021F61

* Binbol, N. L., Abdullahi, M. L., & Yusuf, Y. A. (2020). A temporal analysis of rainfall characteristics on the jos plateau, nigeria. https://www.researchgate.net/publication/348779648

* Broniewicz, E., & Ogrodnik, K. (2021). A comparative evaluation of multi-criteria analysis methods for sustainable transport. *Energies*, 14(16). https://doi.org/10.3390/en14165100

* Chakraborty, S. (2022). TOPSIS and Modified TOPSIS: A comparative analysis. *Decision Analytics Journal*, 2, 100021. https://doi.org/10.1016/j.dajour.2021.100021

* Diaby, V., Campbell, K., & Goeree, R. (2013). Multi-criteria decision analysis (MCDA) in health care: a bibliometric analysis. *Operations research for health care*, 2(1-2), 20-24. https://doi.org/10.1016/j.orhc.2013.03.001

* Emma Martin, & Burgess Neil. (2022, December 31). Jos Plateau Forest-Grassland _ One Earth. One Earth. https://www.oneearth.org/ecoregions/jos-plateau-forest-grassland/

* Goswami, S. S. (2020). Outranking Methods: Promethee i and Promethee II. *Foundations of Management*, 12(1), 93–110. https://doi.org/10.2478/fman-2020-0008

* Guarini, M. R., Battisti, F., & Chiovitti, A. (2018). A methodology for the selection of multi-criteria decision analysis methods in real estate and land management processes. *Sustainability (Switzerland)*, 10(2). https://doi.org/10.3390/su10020507

* Ibrahim, D. F., Hassan, A. S., Sani, A., Zakariyya, A., Shema, F. B., Zubair, R. U., & Riruwai, G. Y. (2023). Effect of Aqueous Stem Bark Extract of Parkia Biglobosa on the Histological Morphology of Liver of Adult Wistar Rats. *American journal of medical science and innovation*, 2(1), 13–20. https://doi.org/10.54536/ajmsi.v2i1.1130

* Kassaw, M., Agelo, W. A., Zewdie, A., & Ameneshewa, W. (2018). Identifying Malaria Epidemic Prone Area Hotspot Map by Using Geospatial Technologies and Spatial Multi Criteria Evaluation Techniques: The Case of Majang Zone, Gambella Region, Eth... Identifying Malaria Epidemic Prone Area Hotspot Map by Using Geospatial Technologies and Spatial Multi Criteria Evaluation Techniques: The Case of Majang Zone, Gambella Region, Ethiopia. *IOSR Journal Of Humanities And Social Science (IOSR-JHSS)*.

* Lu, J., Zhang, S., Wu, J., & Wei, Y. (2021). Copras method for multiple attribute group decision making under picture fuzzy environment and their application to green supplier selection. *Technological and Economic Development of Economy*, 27(2), 369–385. https://doi.org/10.3846/tede.2021.14211

* Patel, A., Ramana Rao, K. V., Rajwade, Y. A., Saxena, C. K., Singh, K., & Srivastava, A. (2023). Comparative Analysis of MCDA Techniques for Identifying
Erosion-Prone Areas in the Burhanpur Watershed in Central India for the Purposes of Sustainable Watershed Management. *Water (Switzerland)*, 15(22). https://doi.org/10.3390/w15223891

Quadri, J. A., Onabanjo, O. O., Oladunjoye, Z. M., & Olusanya, A. G. (2022). Establishing the Nexus Between Iron Status and Markers of Immune Functions Among School Age Children in Ogun State, Nigeria. *American Journal of Multidisciplinary Research and Innovation*, 1(4), 51–66. https://doi.org/10.54536/ajmri.v1i4.559

Sałabun, W., Watróbski, J., & Shekhovtsov, A. (2020). Are MCDA methods benchmarkable? A comparative study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II methods. *Symmetry*, 12(9). https://doi.org/10.3390/SYM12091549

Steele, K., Carmel, Y., Cross, J., & Wilcox, C. (2009). Uses and misuses of multicriteria decision analysis (MCDA) in environmental decision making. *Risk Analysis: An International Journal*, 29(1), 26-33. https://doi.org/10.1111/j.1539-6924.2008.01130.x

Taherdoost, H., & Madanchian, M. (2023). Multi-Criteria Decision Making (MCDM) Methods and Concepts. *Encyclopedia*. 3(1), 77-87. https://doi.org/10.3390/encyclopedia3010006

Taufik, I., Alam, C. N., Mustofa, Z., Rusdiana, A., & Uriawan, W. (2021). Implementation of Multi-Attribute Utility Theory (MAUT) method for

selecting diplomats. *IOP Conference Series: Materials Science and Engineering*, 1098(3), 032055. https://doi.org/10.1088/1757-899x/1098/3/032055

Wieckowski, J., & Szyjewski, Z. (2022). Practical Study of Selected Multi-Criteria Methods Comparison. *Procedia Computer Science*, 207, 4565-4573. https://doi.org/10.1016/j.procs.2022.09.520

Yildirim, F. S., Sayan, M., Sanlidag, T., Uzun, B., Ozsahin, D. U., & Ozsahin, I. (2021). Comparative Evaluation of the Treatment of COVID-19 with Multicriteria Decision-Making Techniques. *Journal of Healthcare Engineering*, 2021. https://doi.org/10.1155/2021/8864522

Youssefi, F., Javad Valadan Zoej, M., Ali Hanafi-Bojd, A., Borahani Dariane, A., Khaki, M., & Safdarinezhad, A. (2022). Predicting the location of larval habitats of Anopheles mosquitoes using remote sensing and soil type data. *International Journal of Applied Earth Observation and Geoinformation*, 108. https://doi.org/10.1016/j.jag.2022.102746

Zhao, X., Thanapongtharm, W., Lawawirojwong, S., Wei, C., Tang, Y., Zhou, Y., Sun, X., Cui, L., Sattabongkot, J., & Kaewkungwal, J. (2020). Malaria risk map using spatial multi-criteria decision analysis along yunnan border during the pre-elimination period. *American Journal of Tropical Medicine and Hygiene*, 103(2), 793-809. https://doi.org/10.4269/ajtmh.19-0854
Erosion-Prone Areas in the Burhanpur Watershed in Central India for the Purposes of Sustainable Watershed Management. *Water (Switzerland)*, 15(22). https://doi.org/10.3390/w15223891

Quadri, J. A., Onabanjo, O. O., Oladunjoye, Z. M., & Olusanya, A. G. (2022). Establishing the Nexus Between Iron Status and Markers of Immune Functions Among School Age Children in Ogun State, Nigeria. *American Journal of Multidisciplinary Research and Innovation*, 1(4), 51–66. https://doi.org/10.54536/ajmri.v1i4.559

Sałabun, W., Watróbski, J., & Shekhovtsov, A. (2020). Are MCDA methods benchmarkable? A comparative study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II methods. *Symmetry*, 12(9). https://doi.org/10.3390/SYM12091549

Steele, K., Carmel, Y., Cross, J., & Wilcox, C. (2009). Uses and misuses of multicriteria decision analysis (MCDA) in environmental decision making. *Risk Analysis: An International Journal*, 29(1), 26-33. https://doi.org/10.1111/j.1539-6924.2008.01130.x

Taherdoost, H., & Madanchian, M. (2023). Multi-Criteria Decision Making (MCDM) Methods and Concepts. *Encyclopedia*. 3(1), 77-87. https://doi.org/10.3390/encyclopedia3010006

Taufik, I., Alam, C. N., Mustofa, Z., Rusdiana, A., & Uriawan, W. (2021). Implementation of Multi-Attribute Utility Theory (MAUT) method for

selecting diplomats. *IOP Conference Series: Materials Science and Engineering*, 1098(3), 032055. https://doi.org/10.1088/1757-899x/1098/3/032055

Wieckowski, J., & Szyjewski, Z. (2022). Practical Study of Selected Multi-Criteria Methods Comparison. *Procedia Computer Science*, 207, 4565-4573. https://doi.org/10.1016/j.procs.2022.09.520

Yildirim, F. S., Sayan, M., Sanlidag, T., Uzun, B., Ozsahin, D. U., & Ozsahin, I. (2021). Comparative Evaluation of the Treatment of COVID-19 with Multicriteria Decision-Making Techniques. *Journal of Healthcare Engineering*, 2021. https://doi.org/10.1155/2021/8864522

Youssefi, F., Javad Valadan Zoej, M., Ali Hanafi-Bojd, A., Borahani Dariane, A., Khaki, M., & Safdarinezhad, A. (2022). Predicting the location of larval habitats of Anopheles mosquitoes using remote sensing and soil type data. *International Journal of Applied Earth Observation and Geoinformation*, 108. https://doi.org/10.1016/j.jag.2022.102746

Zhao, X., Thanapongtharm, W., Lawawirojwong, S., Wei, C., Tang, Y., Zhou, Y., Sun, X., Cui, L., Sattabongkot, J., & Kaewkungwal, J. (2020). Malaria risk map using spatial multi-criteria decision analysis along yunnan border during the pre-elimination period. *American Journal of Tropical Medicine and Hygiene*, 103(2), 793-809. https://doi.org/10.4269/ajtmh.19-0854