Article ; Online: ONLINE FORECASTING OF COVID-19 CASES IN NIGERIA USING LIMITED DATA.
2020 Volume 30, Page(s) 105683
Abstract: ... Nonetheless, we propose an online forecasting mechanism that streams data from the Nigeria Center ... data infrastructures, modeling and forecasting COVID-19 becomes an extremely difficult endeavor ... for Disease Control to update the parameters of an ensemble model which in turn provides updated COVID-19 forecasts ...
| Abstract | The novel Coronavirus disease (COVID-19) was first identified in Wuhan, China in December 2019 but later spread to other parts of the world. The disease as at the point of writing this paper has been declared a pandemic by the World Health Organization (WHO). The application of mathematical models, artificial intelligence, big data, and similar methodologies are potential tools to predict the extent of the spread and effectiveness of containment strategies to stem the transmission of this disease. In societies with constrained data infrastructures, modeling and forecasting COVID-19 becomes an extremely difficult endeavor. Nonetheless, we propose an online forecasting mechanism that streams data from the Nigeria Center for Disease Control to update the parameters of an ensemble model which in turn provides updated COVID-19 forecasts every 24 hours. The ensemble combines an Auto-Regressive Integrated Moving Average model (ARIMA), Prophet - an additive regression model developed by Facebook, and a Holt-Winters Exponential Smoothing model combined with Generalized Autoregressive Conditional Heteroscedasticity (GARCH). The outcomes of these efforts are expected to provide academic thrust in guiding the policymakers in the deployment of containment strategies and/or assessment of containment interventions in stemming the spread of the disease in Nigeria. |
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| Keywords | covid19 |
| Language | English |
| Publishing date | 2020-05-08 |
| Publishing country | Netherlands |
| Document type | Journal Article |
| ZDB-ID | 2786545-9 |
| ISSN | 2352-3409 ; 2352-3409 |
| ISSN (online) | 2352-3409 |
| ISSN | 2352-3409 |
| DOI | 10.1016/j.dib.2020.105683 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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