Child stunting prevalence determination at sector level in Rwanda using small area estimation

Exploring higher education students’ satisfaction for quality improvement: A case study of the African Centre of Excellence in Data Science

Byombi, K. V. and Ruranga, C. (2019). The effect of trade credit on financial performance of manufacturing Small and Medium Enterprises (SMEs) in Rwanda. EPRN Rwanda research paper series, Vol. 2: 80-92, April 2019

Byombi, K. V. and Ruranga, C. (2020). The effect of short-term bank loan financing on financial performance of manufacturing Small and Medium Enterprises (SMEs) in Rwanda. EPRN Rwanda research paper series, Vol. 3: 21-41, June 2020.

Nzabanita, J., Ngaruye, I., Ruranga, C., Masabo, E., Mutezimana, E.,Niyobuhungiro, J., Uwimana, A., Nizeyimana, J. P., Uwonkunda, J. and Ndanguza, D. (2022). Effects of High School Leavers’ Characteristics on the Academic Performance in Higher Education: Empirical Evidence from Rwanda. Journal of Somali Studies, 9 (3):73-93, December 2022,

Masabo, E., Nzabanita, J., Ngaruye, Ruranga, C., Nizeyimana. J. P., Uwonkunda, J. and Ndanguza, D. (2023). Early detection of students at risk of poor performance in Rwanda higher education using machine learning techniques. International Journal of Information Technology, 1-10, June 2023. https://doi.org/10.1007/s41870-023-01334-3

P.E. McSharry (2017). Parsimonious risk assessment and the role of transparent diverse models. In “Risk Modeling for Hazards and Disasters”. Editor: G. Michel, Elsevier, London, UK. ISBN: 9780128040713.

J. W. Taylor and P. E. McSharry (2017). Univariate methods for short-term load forecasting. In Advances in electric power and energy: power systems engineering, Editor M. El-Hawary, IEEE Press/Wiley.

C. Njuguna & P.E. McSharry (2017). Predicting socioeconomic status using big data.In Big data and human development: improved processes or a deeper digital divide?. Editors: M. Graham, S. Ojanpera, E. Lopez. Wiley, London, UK.

Habamwabo D, McSharry P. Healthcare monitoring based on digital transactions at pharmacies:
Malaria in Kigali. Paper presented at: Proceedings - 2016 IEEE International Conference onBioinformatics and Biomedicine, BIBM 20162017

L.H. Kaack, J. Apt, M.G. Morgan, P.E. McSharry (2016). The applicability of empirical prediction intervals to energy forecasting. Energy: Expectations and Uncertainty, 39th IAEE International Conference, Jun 19-22, 2016.

Nishimwe, A., Ruranga, C., Musanabaganwa, C., Mugeni, R., Semakula, M., Nzabanita, J., Kabano, I., Uwimana, A., Utumatwishima. J. N., Kabakambira, J. D., Uwineza, A., Halvorsen, L., Descamps, F., Houghtaling, J., Burke, B., Bahati, O., Bizimana, C., Jansen, S., Twizere, C., Nkurikiyeyezu, K., Birungi, F., Nsanzimana, S. and Twagirumukiza, M. (2022). Leveraging artificial intelligence and data science techniques in harmonizing, sharing, accessing and analyzing SARS-COV-2/COVID-19 data in Rwanda (LAISDAR Project): study design and rationale. BMC Medical Informatics and Decision Making 22, 214 (2022)

Ngabo, D., Dong, W., Ibeke, E., Iwendi, C. and Masabo, E., 2021. Tackling pandemics in smart cities using machine learning architecture. Mathematical biosciences and engineering, 18(6)

H. Oliver, P.E. McSharry (2016). A design proto pattern for continuously evaluated forecasting in IBM InfoSphere Streams. Journal of Software: Practice and Experience. DOI:10.1002/spe.2316.

R. Löwe, H. Madsen, P.E. McSharry (2016). Objective Classification of rainfall for online operation of urban water systems based on Gaussian-inspired techniques. Water 8(3): 87

C. Njuguna, P.E. McSharry (2016). Constructing spatiotemporal poverty indices from big data. Journal of Business Research 70: 318-327

S. Brown, P.E. McSharry (2016). Improving accuracy and usability of growth charts: Case study of Rwanda. British Medical Journal Online 6: e009046.

Innocent Ngaruye, Joseph Nzabanita, Dietrich von Rosen & Martin Singull. (2017). Small area estimation under a multivariate linear model for repeated measures data. Communications in Statistics-Theory and Methods. http://dx.doi.org/10.1080/03610926.2016.1248784

L.H. Kaack, J. Apt, M.G. Morgan, P.E. McSharry (2017). Empirical prediction intervals improve energy forecasting. Proceedings of the National Academy of Science 114 (33): 8752–8757. DOI: 10.1073/pnas.1619938114

Kasahun Takele, Temesgen Zewotir, Denis Ndanguza(2020).Quantile Regression for Identifying the Determinants of Child Malnutrition. African Journal of Applied Statistics: Afr. J. Appl. Stat. 7(1): 755-779 (January 2020). DOI: 10.16929/ajas/2020.755.241

Kasahun Takele, Temesgen Zewotir, Denis Ndanguza(2020).Examining the spatial variations of co-morbidity among young children in Ethiopia. BMC pediatrics:https://link.springer.com/article/10.1186/s12887-020-02198-3

J. Batamuliza and D. Hanyurwimfura, "A secure and efficient anonymous certificateless signcryption for Key Distribution Scheme for Smart Grid," 2020 21st International Arab Conference on Information Technology (ACIT)

Takele, K., Zewotir, T., & Ndanguza, D. (2020). Examining the spatial variations of co-morbidity among young children in Ethiopia. BMC pediatrics, 20(1), 1-10

Takele, K., Zewotir, T., & NDANGUZA, D. (2020). Quantile Regression for Identifying the Determinants of Child Malnutrition. African Journal of Applied Statistics, 7(1), 755-779

Takele, K., Zewotir, T., & Ndanguza, D. (2020). Spatial Joint Modelling of Children Under-Five Malnutrition in Ethiopia. J Hum Ecol, 70(1-3), 132-142

Ruranga, C.; Mugenzi, M. and Mutabazi, A. (2018). Spatial price transmission analysis in beans and rice markets of Rwanda. Rwanda Handbook of Economic and Social Policy,Chapter 13, Vol. 1, Pages 397- 419, ISSN 1403-0462, 
ISBN 978-91-86345-78-5, Jönköping International Business School, January 2018

Takele, K., Zewotir, T., & Ndanguza, D. (2019). Understanding correlates of child stunting in Ethiopia using generalized linear mixed models. BMC Public Health, 19(1), 1-8

Takele, K., Zewotir, T., & Ndanguza, D. (2019). Risk factors of morbidity among children under age five in Ethiopia. BMC public health, 19(1), 1-9

Musabanganji, E., Ruranga, C., Nzabanita, J., Nkikabahizi, F., Ndizeye, I., and Lebailly, P. (2019). Productivity and Competitiveness of Rwandan Agriculture: A Case Study of the Maize sector. AGROFOR International Journal, 4(1): 32-40

Musabanganji, E., Ruranga, C., and Maniriho, A. (2019). Migration decisions among rural households in Rwanda: what does the push-and-pull model reveal? AGROFOR International Journal, 4(3): 68-75

Batamuliza, J., & Hanyurwimfura, D. (2021). Identity based encryption with equality test. Information Security Journal: A Global Perspective, 30(2), 111-124

Batamuliza, J., & Hanyurwimfura, D. (2021). Identity based encryption with equality test. Information Security Journal: A Global Perspective, 30(2), 111-124

Muremyi, R., Dominique, H., Niragire, F., Ignace, K., & Abayisenga, S. (2021). Analysis of the effect of health insurance on health care utilization in Rwanda: a secondary data analysis of Rwanda integrated living condition survey 2016-2017 (EICV 5). PAMJ-One Health, 4(10)

Ruranga, C.andHacker, S. (2020). The Determinants of Households Having Savings Accounts in Rwanda.Rwanda Journal of Social Sciences, Humanities and Business (ISSN 2708-759X (Print); ISSN 2708-760

Maniriho, A., Musabanganji, E., Nkikabahizi, F., Ruranga, C.,and Lebailly, P. (2021). Analysis of the Determinants of Household Expenditures in Rwanda. UKH Journal of Social Sciences 5(1): 8-17, June 2021.DOI: https://doi.org/10.25079/ukhjss.v5n1y2021.pp8-17

Mutai, C.K., McSharry, P.E., Ngaruye, I. et al. Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa. BMC Med Res Methodol 21, 159 (2021). https://doi.org/10.1186/s12874-021-01346-2

Nzasingizimana,Tharcisse; Ocaya,Bruno; Musabanganji, Edouard; and Ndengo, Marcel; “Determinants of Deposit and Lending Interest Rates in Rwanda: Econometric Approach”, IJSR, Volume 8 Issue 8, August 2019.

Ndagijimana, J., Nzasingizimana, T., & Heshmati, A. (2018, September 13). An Analysis of the Determinants of Youth Employment in Rwanda. UKH Journal of Social Sciences, 2(2), 1-10.

Dukunde A, Ntaganda JM, Kasozi J, Nzabanita J. Prediction of prevalence of type 2 diabetes in Rwanda using the metropolis-hasting sampling. Afri Health Sci. 2021;21(2). 702-709. https://dx.doi.org/10.4314/ahs.v21i2.28

C. Mutai Reviews in Antiviral Therapy & Infectious Diseases 10, 2020

Semakula M, Niragire F, Faes C (2020) Bayesian spatio-temporal modeling of malaria risk in Rwanda. PLOS ONE 15(9): e0238504. https://doi.org/10.1371/journal.pone.0238504

Semakula M, Niragire F, Umutoni A, et alThe secondary transmission pattern of COVID-19 based on contact tracing in RwandaBMJ Global Health 2021;6:e004885.

Nsanzimana S, Semakula M, Ndahindwa V, Remera E, Sebuhoro D, Uwizihiwe JP, Ford N, Tanner M, Kanters S, Mills EJ, Bucher HC. Retention in care and virological failure among adult HIV+ patients on second-line ART in Rwanda: a national representative study. BMC Infect Dis. 2019 Apr 5;19(1):312. doi: 10.1186/s12879-019-3934-2. PMID: 30953449; PMCID: PMC6451213

Mutesa L, Ndishimye P, Butera Y, Souopgui J, Uwineza A, Rutayisire R, Ndoricimpaye EL, Musoni E, Rujeni N, Nyatanyi T, Ntagwabira E, Semakula M, Musanabaganwa C, Nyamwasa D, Ndashimye M, Ujeneza E, Mwikarago IE, Muvunyi CM, Mazarati JB, Nsanzimana S, Turok N, Ndifon W. A pooled testing strategy for identifying SARS-CoV-2 at low prevalence. Nature. 2021 Jan;589(7841):276-280. doi: 10.1038/s41586-020-2885-5. Epub 2020 Oct 21. Erratum in: Nature. 2021 Jul;595(7865):E3. PMID: 33086375.

Habimana, D., Haughton, J., Nkurunziza, J., & Haughton, D. M.-A. (2021). Measuring the impact of unconditional cash transfers on consumption and poverty in Rwanda. World Development Perspectives, 23, 100341.

Habimana, D., (2021). The effects of Public Works Program on Poverty and Financial Inclusion: Evidence from Rural Rwanda (Abstract). Conference proceeding of the 63rd ISI World Statistics Congress 2021 (forthcoming).

Habimana, D., (2021). Impact of microcredit program on poverty and employment in Rwanda (Abstract). Book of Abstracts of the International Institute of Social and Economic Sciences, 2021, Prague, Czech Republic.

Roger Muremyi et al. Prediction of out-of-pocket health expenditures in Rwanda using machine learning techniques. Pan African Medical Journal. 2020;37:357. [doi: 10.11604/pamj.2020.37.357.27287]

Yego, N. K., Kasozi, J., & Nkurunziza, J. (2021). A Comparative Analysis of Machine Learning Models for the Prediction of Insurance Uptake in Kenya. Data, 6(11), 116. MDPI AG. Retrieved from http://dx.doi.org/10.3390/data6110116

Mbonyinshuti, F.; Nkurunziza, J.; Niyobuhungiro, J.; Kayitare, E. The Prediction of Essential Medicines Demand: A Machine Learning Approach Using Consumption Data in Rwanda. Processes 2022, 10, 26. https://doi.org/10.3390/pr10010026

Abdou, M., Musabanganji, E. & Musahara, H. (2021). Tourism Demand Modelling and Forecasting: A Review of Literature. African Journal of Hospitality, Tourism and Leisure, 10(4):1370-1393. DOI: https://doi.org/10.46222/ajhtl.19770720-168

Nimonka Bayale, Fousseini Traoré, Souleymane Diarra & Faustin Maniraguha(2022): Endogenous threshold effects and transmission channels of foreign aid on economic growth: evidence from WAEMU zone countries, Transnational Corporations Review, DOI:10.1080/19186444.2022.2028541

Mugenzi, P., Rusuhuzwa, T. K., Uwimana, A., & Rwanda, R. (2021). Finding the Network Structure of Rwandan Interbank Market. International Journal of Financial Research, 12(3), 435-445.

Chilyabanyama ON, Chilengi R, Laban NM, Chirwa M, Simunyandi M, Hatyoka LM, et al. (2021) Comparing growth velocity of HIV exposed and non-exposed infants: An observational study of infants enrolled in a randomized control trial in Zambia. PLoS ONE 16(8): e0256443. https://doi.org/10.1371/journal.pone.0256443

Chilyabanyama ON, Chilengi R, Ngaruye I, Iqbal NT, Bosomprah S. Statistical Models for Estimating Linear Growth Velocity: A Systematic Review. Int J Nutr Pharmacol Neurol Dis 2021;11:262-6

Bett, Nicholas, Juma Kasozi, and Daniel Ruturwa. 2022. Temporal Clustering of the Causes of Death for Mortality Modelling. Risks 10: 99. https://doi.org/10.3390/risks10050099
 

Abdou, M., Musabanganji, E. & Musahara, H. (2022). Factors Affecting the Visitation to National Parks Using Machine Learning Techniques: The Case of National Parks in Rwanda. African Journal of Hospitality, Tourism and Leisure, 11(2):457-474. DOI: https://doi.org/10.46222/ajhtl.19770720.236

Ngango, J., Nkurunziza, F., Rumanzi, M. S., & Cyamweshi, R. A. (2022). Determinants of sustainable agricultural intensification adoption and impacts on household productivity and consumption in Rwanda. Journal of Agriculture and Rural Development in the Tropics and Subtropics, 123 (1), 39–50.

Abdou, M., Musabanganji, E. & Musahara, H. (2022). Determinants of Tourism Demand Using Machine Learning Techniques. African Journal of Hospitality, Tourism and Leisure, 11(2), 770-780. DOI:https://doi.org/10.46222/ajhtl.19770720.256
 

Murorunkwere, B. F., Tuyishimire, O., Haughton, D., & Nzabanita, J. (2022). Fraud Detection Using Neural Networks: A Case Study of Income Tax. Future Internet, 14(6), 168. https://doi.org/10.3390/fi14060168

Ndagijimana S, Kabano I and JM Ntaganda, Analysis of risk factors that influence stunting among Rwandan children under the age of five, Afr. J. Food Agric. Nutr. Dev. 2022; 22(5): 20480-20497

Jules Ngango, Fabrice Nkurunziza & Joseph Ndagijimana | Goodness Aye (Reviewing editor:) (2022) Assessing rural farmers’ willingness to pay for crop insurance scheme: Evidence from Rwanda, Cogent Economics & Finance, 10:1, DOI: 10.1080/23322039.2022.2104780
 

Maurice, M., Damien, H. (2022). Modeling Urban Human Mobility and Predicting Planning Transportation Facilities Using K-Means Clustering Algorithm. In: Kahraman, C., Tolga, A.C., Cevik Onar, S., Cebi, S., Oztaysi, B., Sari, I.U. (eds) Intelligent and Fuzzy Systems. INFUS 2022. Lecture Notes in Networks and Systems, vol 505. Springer, Cham. https://doi.org/10.1007/978-3-031-09176-6_38

D. K. Uwizera, C. Ruranga and P. McSharry, "Deep Learning Inter-city Road Conditions in East Africa Focusing on Rwanda for Infrastructure Prioritization using Satellite Imagery and Mobile Data," in SAIEE Africa Research Journal, vol. 114, no. 1, pp. 14-24, March 2023, doi: 10.23919/SAIEE.2023.9962789.

D. K. Uwizera, C. Ruranga and P. McSharry, "Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa," in SAIEE Africa Research Journal, vol. 113, no. 4, pp. 138-151, Dec. 2022, doi: 10.23919/SAIEE.2022.9945864.

D. Uwizera and P. McSharry, "Forecasting and monitoring maize production using satellite imagery in Rwanda," 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), 2017, pp. 51-56, doi: 10.1109/TIAR.2017.8273685.

Habimana D. & Haughton J. (2022). Does Rwanda’s flagship microcredit programme boost agriculture and incomes? African Journal of Agricultural and Resource Economics Volume 17, Number 3 (2022), pp 239–254

S. Ndagijimana, I. H. Kabano, E. Masabo, and J. M. Ntaganda, “Prediction of Stunting Among Under-5 Children in Rwanda Using Machine Learning Techniques,” J Prev Med Public Health 2023; 56(1): 41-49.

J. Mwitirehe, C. W. Kipruto and C. Ruranga, "Towards an Effective Monitoring, Evaluation and Learning (MEL) System: Challenges and Solutions in a Data Science Perspective," 2022 12th International Conference on Advanced Computer Information Technologies (ACIT), Ruzomberok, Slovakia, 2022, pp. 130-135, doi: 10.1109/ACIT54803.2022.9912749.

Bett, N.; Kasozi, J.; Ruturwa, D. Dependency Modeling Approach of Cause-Related Mortality and Longevity Risks: HIV/AIDS. Risks 2023, 11, 38. https://doi.org/10.3390/risks11020038

Faustin Maniraguha & Etienne Ndemezo (2022): Exports, finance, and economic growth: does financial development boost the impact of exports on economic growth in Sub-Saharan Africa?, Macroeconomics and Finance in Emerging Market Economies, DOI:10.1080/17520843.2022.2160077

Chilyabanyama ON, Chilengi R, Laban NM, Chirwa M, Simunyandi M, Hatyoka LM, et al. (2021) Comparing growth velocity of HIV exposed and non-exposed infants: An observational study of infants enrolled in a randomized control trial in Zambia. PLoS ONE 16(8): e0256443. https://doi.org/10.1371/journal.pone.0256443

Murorunkwere BF, Ihirwe JF, Kayijuka I, Nzabanita J, Haughton D. Comparison of Tree-Based Machine Learning Algorithms to Predict Reporting Behavior of Electronic Billing Machines. Information. 2023; 14(3):140. https://doi.org/10.3390/info14030140

Muremyi, R., Haughton, D., Niragire, F., Kabano, I. (2023). Determining Factors Influencing Out-of-Pocket Health Care Expenditures in Low- and Middle-Income Countries: A Systematic Review. In: , et al. Sustainable Education and Development – Sustainable Industrialization and Innovation. ARCA 2022. Springer, Cham. https://doi.org/10.1007/978-3-031-25998-2_32

Maniragaba VN, Atuhaire LK, Rutayisire PC. Undernutrition among the children below five years of age in Uganda: a spatial analysis approach. BMC Public Health. 2023 Feb 24;23(1):390. doi: 10.1186/s12889-023-15214-9. PMID: 36829169; PMCID: PMC9960483.

Semakula M, Niragire F, Faes C. Spatio-Temporal Bayesian Models for Malaria Risk Using Survey and Health Facility Routine Data in Rwanda. Int J Environ Res Public Health. 2023 Feb 28;20(5):4283. doi: 10.3390/ijerph20054283. PMID: 36901291; PMCID: PMC10001932.

Belle Fille Murorunkwere, Dominique Haughton, Joseph Nzabanita, Francis Kipkogei & Ignace Kabano (2023) Predicting tax fraud using supervised machine learning approach, African Journal of Science, Technology, Innovation and Development, DOI: 10.1080/20421338.2023.2187930

Kemboi Yego, N.; Kasozi, J.; Nkurunzinza, J. Optimizing Pension Participation in Kenya through Predictive Modeling: A Comparative Analysis of Tree-Based Machine Learning Algorithms and Logistic Regression Classifier. Risks 2023, 11, 77. https://doi.org/10.3390/risks11040077

Tuyishime E, Kayitesi C, Musengimana G, Malamba S, Moges H, Kankindi I, Escudero H, Habimana Kabano I, Oluoch T, Remera E, Chukwu A
Population Size Estimation of Men Who Have Sex With Men in Rwanda: Three-Source Capture-Recapture Method
JMIR Public Health Surveill 2023;9:e43114
URL: https://publichealth.jmir.org/2023/1/e43114
DOI: 10.2196/43114

Semakula M, Niragire F, Nsanzimana S, Remera E, Faes C. Spatio-temporal dynamic of the COVID-19 epidemic and the impact of imported cases in Rwanda. BMC Public Health. 2023 May 23;23(1):930. doi: 10.1186/s12889-023-15888-1. PMID: 37221533; PMCID: PMC10203687.

François Mbonyinshuti et al. Application of random forest model to predict the demand of essential medicines for noncommunicable diseases management in public health facilities. Pan African Medical Journal. 2022;42:89. [doi: 10.11604/pamj.2022.42.89.33833]

Mbonyinshuti F, Takarinda KC, Ade S, Manzi M, Iradukunda PG, Kabatende J, Habiyaremye T, Kayumba PC. Evaluating the availability of essential drugs for hypertension, diabetes and asthma in rural Rwanda, 2018. Public Health Action. 2021 Mar 21;11(1):5-11. doi: 10.5588/pha.20.0033. PMID: 33777715; PMCID: PMC7987247.

Habimana D. & Haughton J. (2023). Do Public Works Programs Have an Impact? Evidence from Rwanda. Journal of African Development [Forthcoming]

Fabrice Nkurunziza, Richard Kabanda & Patrick McSharry (2023) Determinants of shock-coping mechanisms adoption and rural household consumption in Rwanda: A two-stage analysis considering both idiosyncratic and covariate shocks, Cogent Economics & Finance, 11:2, DOI: 10.1080/23322039.2023.2238462

Mutai, C.K., McSharry, P.E., Ngaruye, I. et al. Use of unsupervised machine learning to characterise HIV predictors in sub-Saharan Africa. BMC Infect Dis 23, 482 (2023). https://doi.org/10.1186/s12879-023-08467-7

Mutai, C. K., McSharry, P. E., Ngaruye, I., & Musabanganji, E. (2023). TEMPORAL TRENDS OF HIV PREVALENCE IN SUB-SAHARAN AFRICA. JP Journal of Biostatistics, 23(3), 263–279.

Dukunde, A., Ntaganda, J. M., Kasozi, J., & Nzabanita, J. (2023). Prediction of the Prevalence of Hypertension and Associated Risk Factors in Rwanda Using Gibbs Sampling Method. Diseases, 11(2), 87.