Forecasting TB Incidence in India Using the Multilayer Perceptron Neural Network
Abstract
India has a population of about 1.2 billion and is one of the Asian
nations with a high TB disease burden. Modeling TB incidence is very important
in order to assess the impact of TB control measures in the country. In this
research article, the ANN approach was applied to analyze TB incidence in
India. The employed annual 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 TB incidence in India. The results of
the study indicate that TB incidence will remain high although a slight
decrease is expected from 198 cases/100 000/year in 2019 to 198 cases/100
000/year in 2023. Therefore, the Indian government is encouraged to intensify
TB surveillance and control programs despite the fact that it is currently
battling COVID-19. If the government becomes complacent in the Control of TB,
the country is likely to see a sharp increase in new TB cases hence increase in
TB incidence over the period 2021-2023.
Country : Zimbabwe
1 Dr. Smartson. P. NYONI2 Thabani NYONI
ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
Department of Economics, University of Zimbabwe, Harare, Zimbabwe
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