Performance Evaluation of ARIMA and FB-Prophet Forecasting Methods in the Context of Endemic Diseases: A Case Study of Gedaref State in Sudan


  • Hussein Ali Hussein Department of Information Technology, University of Gedaref, Gedaref, Sudan
  • Mukhtar M. E. Mahmoud Kassala University image/svg+xml
  • Haroun A. Eisa Department of Computer Science, Alsharg Ahlia College, Kassala, Sudan



smart city, artificial intelligence, pneumonia and malaria diseases, endemic diseases, Gedaref state


Today, artificial intelligence is a key tool for turning a city into a smart city, and advances in information and communication technology (ICT) have led to the development of smart cities with many different parts. Smart Health is one of these components and is used to improve healthcare by providing services such as disease forecasting, early diagnosis, and others. There are various machine learning algorithms available now that can help with S-Health services, but which is better for disease forecasting? Gedaref State, for example, has some of Sudan's heaviest rains, and malaria and pneumonia are widespread throughout the year. Predicting future trends for these diseases has been a major focus for researchers in order for Gedaref's administration and the state's ministry of health to design effective ways to prevent and control the development of these diseases, as well as to prepare an adequate stock of medicine. As a result, it is necessary to establish a trustworthy and accurate forecasting model to aid Gedaref's government in developing economic and medical strategies for dealing with these diseases, as well as taking action on medical resource allocation. This study uses a time series dataset collected from the state's ministry of health to estimate malaria and pneumonia as common diseases in Gedaref state, Sudan, five months later. To comprehend the overall number of cases of diseases, two forecasting methodologies, namely the ARIMA and Prophet models, are applied to the disease's dataset. The performance of the ARIMA and FB-Prophet forecasting systems in predicting malaria and pneumonia diseases in Gedaref State is compared in this study. The data was collected from the state's ministry of health between January 2017 and December 2021. The results reveal that the ARIMA technique outperforms the FB-Prophet forecasting method in both malaria (RMSE: 182.8, MAE: 141.6, MAPE: 0.0057, and MASE: 0.0537) and pneumonia (RMSE: 1400.3, MAE: 1001.4, MAPE: 0.0513, and MASE: 0.9136).


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Author Biographies

Hussein Ali Hussein, Department of Information Technology, University of Gedaref, Gedaref, Sudan



Mukhtar M. E. Mahmoud, Kassala University





Haroun A. Eisa, Department of Computer Science, Alsharg Ahlia College, Kassala, Sudan




S. Myeong and K. Shahzad, “Integrating Data-Based Strategies and Advanced Technologies with Efficient Air Pollution Management in Smart Cities,” Sustainability, vol. 13, no. 13, p. 7168, Jun. 2021, doi: DOI:

S. R., S. R. S., H. R., A. S., and R. K. C., “Artificial Intelligence in Smart cities and Healthcare,” EAI Endorsed Transactions on Smart Cities, vol. 6, no. 3, p. e5, Sep. 2022, doi: 10.4108/eetsc.v6i3.2275. DOI:

M. A. A. Osman, J. O. Onono, L. A. Olaka, M. M. Elhag, and E. M. Abdel-Rahman, “Climate Variability and Change Affect Crops Yield under Rainfed Conditions: A Case Study in Gedaref State, Sudan,” Agronomy, vol. 11, no. 9, p. 1680, Aug. 2021, doi: DOI:

L. C. S. PINHEIRO, L. M. FEITOSA, F. F. D. SILVEIRA, and N. BOECHAT, “Current Antimalarial Therapies and Advances in the Development of Semi-Synthetic Artemisinin Derivatives,” Anais da Academia Brasileira de Ciências, vol. 90, no. 1 suppl 2, pp. 1251–1271, 2018, doi: DOI:

Z. Rustam, R. P. Yuda, H. Alatas, and C. Aroef, “Pulmonary rontgen classification to detect pneumonia disease using convolutional neural networks,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 18, no. 3, p. 1522, Jun. 2020, doi: DOI:

S. Makridakis, R. J. Hyndman, and F. Petropoulos, “Forecasting in social settings: The state of the art,” International Journal of Forecasting, vol. 36, no. 1, pp. 15–28, Jan. 2020, doi: DOI:

W. D. Lesmono, S. Mardiyati, D. Lestari, and A. H. A. Zili, “Forecasting tuberculosis morbidity rate in Indonesia using autoregressive integrated moving average (ARIMA) method,” Journal of Physics: Conference Series, vol. 1725, p. 012031, Jan. 2021, doi: DOI:

P. Kumar, R. Sharma, and S. K. Singh, “Predictive Analysis of Real-Time Strategy using Face book’s Prophet Model on Covid-19 Dataset of India,” Journal of Pharmaceutical Research International, pp. 305–312, Nov. 2021, doi: DOI:

ERSÖZ, N. Ş., GÜNER, P., AKBAŞ, A., & BAKİR-GUNGOR, B. (2022). Comparative performance analysis of Arima, Prophet, and holt-winters forecasting methods on European COVID-19 data. International Journal of 3D Printing Technologies and Digital Industry. DOI:

Ye, Z. (2019). Air pollutants prediction in Shenzhen based on Arima and Prophet Method. E3S Web of Conferences, 136, 05001. DOI:

Kumar Jha, B., & Pande, S. (2021). Time Series forecasting model for supermarket sales using FB-prophet. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). DOI:

Sirisha, U. M., Belavagi, M. C., & Attigeri, G. (2022). Profit prediction using Arima, Sarima and LSTM models in time series forecasting: A Comparison. IEEE Access, 10, 124715–124727. DOI:

Ning, Y., Kazemi, H., & Tahmasebi, P. (2022). A comparative machine learning study for time series oil production forecasting: Arima, LSTM, and prophet. Computers & Geosciences, 164, 105126. DOI:

Deniz A., Kiziloz, H.E., Sevinc, E., and Dokeroglu, T., “Predicting the severity of COVID-19 patients using a multi-threaded evolutionary feature selection algorithm,” Expert Syst., Vol. 39, Issue. 5, 12949, 2022. DOI:

Alabdulrazzaq, H., Alenezi, M. N., Rawajfih, Y., Alghannam, B. A., Al-Hassan, A. A., Al-Anzi, F. S., “On the accuracy of ARIMA based prediction of COVID-19 spread” Results in Pyhsics, Vol. 27, 2021. DOI:

Taylor, S. J., Letham, B., “Forecasting at Scale,” Am. Stat., Vol. 72, Issue. 1, Pages 37–45, 2018. DOI:

Mahanty, M., Swathi, K. Teja, K. S., Bhattacharyya, D., “A Prophet Model to Forecast Spread of Covid-19 Pandemic,” Journal of Xidian University, Vol 14, Issue 7, Pages 949-962, 2020. DOI:




How to Cite

H. Ali Hussein, M. M. E. Mahmoud, and H. A. Eisa, “Performance Evaluation of ARIMA and FB-Prophet Forecasting Methods in the Context of Endemic Diseases: A Case Study of Gedaref State in Sudan”, EAI Endorsed Trans Smart Cities, vol. 7, no. 2, p. e1, Mar. 2023.