TY - JOUR
T1 - Time series modeling of Monkeypox incidence in Central Africa’s endemic regions
AU - Chukwu, Chidozie Williams
AU - Obaido, George
AU - Mienye, Ibomoiye Domor
AU - Aruleba, Kehinde
AU - Esenogho, Ebenezer
AU - Modisane, Cameron
PY - 2025/12
Y1 - 2025/12
N2 - Monkeypox is a re-emerging zoonotic viral disease endemic to the Democratic Republic of Congo (DRC) and other Central African countries, with recurrent outbreaks posing persistent public health threats. Accurate short-term forecasts of Mpox incidence are essential for guiding surveillance, preparedness, and timely interventions. In this study, we analyzed daily confirmed Mpox cases data from May 1, 2022 to May 31, 2025, using autoregressive integrated moving average and Prophet models, complemented by wavelet analysis. Model performance varied by country, reflecting differences in reporting the quality and outbreak intensity. Prophet generally outperformed ARIMA in settings with smoother incidence trajectories, while ARIMA was more effective in capturing abrupt local fluctuations. Wavelet analysis further revealed country-specific temporal–frequency patterns, highlighting differences in epidemic periodicity across the region. These findings underscore the utility of combining statistical and decompositional approaches for Mpox forecasting in resource-limited settings. Strengthening surveillance systems, improving data quality, and adopting flexible, country-specific forecasting frameworks will be critical for developing effective early-warning systems and guiding evidence-based interventions in Central Africa.
AB - Monkeypox is a re-emerging zoonotic viral disease endemic to the Democratic Republic of Congo (DRC) and other Central African countries, with recurrent outbreaks posing persistent public health threats. Accurate short-term forecasts of Mpox incidence are essential for guiding surveillance, preparedness, and timely interventions. In this study, we analyzed daily confirmed Mpox cases data from May 1, 2022 to May 31, 2025, using autoregressive integrated moving average and Prophet models, complemented by wavelet analysis. Model performance varied by country, reflecting differences in reporting the quality and outbreak intensity. Prophet generally outperformed ARIMA in settings with smoother incidence trajectories, while ARIMA was more effective in capturing abrupt local fluctuations. Wavelet analysis further revealed country-specific temporal–frequency patterns, highlighting differences in epidemic periodicity across the region. These findings underscore the utility of combining statistical and decompositional approaches for Mpox forecasting in resource-limited settings. Strengthening surveillance systems, improving data quality, and adopting flexible, country-specific forecasting frameworks will be critical for developing effective early-warning systems and guiding evidence-based interventions in Central Africa.
UR - https://doi.org/10.1016/j.mlwa.2025.100778
U2 - 10.1016/j.mlwa.2025.100778
DO - 10.1016/j.mlwa.2025.100778
M3 - Article
VL - 22
JO - Machine Learning with Applications
JF - Machine Learning with Applications
ER -
