Prediction of Art Coverage in Togo Using Artificial Neural Networks
Abstract
In this research
article, the ANN approach was applied to analyze ART coverage in Togo. The
employed data covers the period 2000-2018 and the out-of-sample period ranges
over the period 2019-2023. The residuals and forecast evaluation criteria
(Error, MSE and MAE) of the applied model indicate that the model is stable in
forecasting ART coverage in Togo. The results of the study indicate that ART
coverage will be around 62 % over the period 2019-2023.The government is
encouraged to create more demand for HIV testing & ART services, strengthen
TB/HIV collaboration and improve ART access for key populations.
Country : Zimbabwe
1 Dr. Smartson. P. NYONI2 Thabani NYONI
ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
Department of Economics, University of Zimbabwe, Harare, Zimbabwe
Bishop, C.M. (1995). Neural
networks for pattern recognition. Oxford: Oxford University Press.
Fojnica, A., Osmanoviae &
Badnjeviae A (2016). Dynamic model of tuberculosis-multiple strain prediction
based on artificial neural network. In proceedings of the 2016 5th
Mediterranean conference on embedded computing pp290-293.
Gambhir S., Malik SK., & Kumar
Y (2018). The diagnosis of dengue disease: An evaluation of three machine
learning approaches. International Journal of Healthcare Information Systems
and Informatics 2018; 13:1–19.
Guo P., Liu T., Zhang Q., Wang L.,
Xiao J & Zhang Q (2017). Developing a dengue forecast model using machine
learning: A case study in China. PLoS Neglected Tropical Diseases.
Kaushik AC & Sahi. S (2018).
Artificial neural network-based model for orphan GPCRs.Neural.Comput.Appl.
29,985-992.
Laureano-Rosario AE., Duncvan AP.,
Mendez-Lazaro PA., Garcia-Rejon JE., Gomez-Carro S., & Farfan-Ale J (2018).
Application of artificial neural networks for dengue fever outbreak predictions
in the northwest coast of Yucatan, Mexico and San Juan, Puerto Rico. Tropical
Medicine and Infectious Disease 2018;3:5.
Naizhuo Zhao., Katia Charland.,
Mabel Carabali., Elaine O., Nsoesie., Mathieu MaheuGiroux., Erin Rees., Mengru
Yuan., Cesar Garcia Balaguera., Gloria Jaramillo Ramirez., & Kate Zinszer
(2020). Machine learning and dengue forecasting: Comparing random forests and
artificial neural networks for predicting dengue burden at national and
sub-national scales in Colombia. PLOS Neglected Tropical Diseases.
Scavuzzo JM., Trucco F., Espinosa
M., Tauro C B., Abril M., & Scavuzzo CM (2018). Modeling dengue vector
population using remotely sensed data and machine learning. Acta Tropica
185:167–175. https://doi.org/10.1016/j.actatropica.2018.05.003 PMID:
29777650.
Smartson. P. Nyoni, Thabani Nyoni,
Tatenda. A. Chihoho (2020) PREDICTION OF DAILY NEW COVID-19 CASES IN GHANA
USING ARTIFICIAL NEURAL NETWORKS IJARIIE Vol-6 Issue-6 2395-4396.
Smartson. P. Nyoni., Thabani
Nyoni., Tatenda. A. Chihoho (2020)
PREDICTION OF DAILY NEW COVID-19 CASES IN EGYPT USING ARTIFICIAL NEURAL
NETWORKS IJARIIE- Vol-6 Issue-6 2395-4396.
Vapnik V (1998) “Statistical
Learning Theory”, New York: Wiley.
Weng SF., Reps J., Kai J.,
Garibaldi JM &Qureshi N (2017).Can machine learning improve cardiovascular
risk prediction using routine clinical data? Plos One.
Yan C Q., Wang R B., Liu C H.,
Jiang Y (2019). Application of ARIMA model in predicting the incidence of
tuberculosis in China from 2018-2019.Zhonghua 40(6):633-637.
Zhang G P, “Time series forecasting
using a hybrid ARIMA and neural network model”, Neurocomputing 50: 159–175.
