ONLINE FORECASTING OF COVID-19 CASES IN NIGERIA USING LIMITED DATA
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,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-06-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340920305771 |
| _version_ | 1818326599707131904 |
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| author | Kabir Abdulmajeed Monsuru Adeleke Labode Popoola |
| author_facet | Kabir Abdulmajeed Monsuru Adeleke Labode Popoola |
| author_sort | Kabir Abdulmajeed |
| collection | DOAJ |
| description | 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 |
| first_indexed | 2024-12-13T12:02:56Z |
| format | Article |
| id | doaj.art-07296f75fbf140e3a1b0ca182e253e62 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-12-13T12:02:56Z |
| publishDate | 2020-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-07296f75fbf140e3a1b0ca182e253e622022-12-21T23:47:03ZengElsevierData in Brief2352-34092020-06-0130105683ONLINE FORECASTING OF COVID-19 CASES IN NIGERIA USING LIMITED DATAKabir Abdulmajeed0Monsuru Adeleke1Labode Popoola2Georgia Institute of Technology, Atlanta, GA, USA; Corresponding author.Osun State University, Osogbo, NigeriaOsun State University, Osogbo, NigeriaThe 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 Nigeriahttp://www.sciencedirect.com/science/article/pii/S2352340920305771Timeseries forecastingAnalytic ModelingEnsemblesSmall DataCoronavirus COVID-19Nigeria NCDC |
| spellingShingle | Kabir Abdulmajeed Monsuru Adeleke Labode Popoola ONLINE FORECASTING OF COVID-19 CASES IN NIGERIA USING LIMITED DATA Data in Brief Timeseries forecasting Analytic Modeling Ensembles Small Data Coronavirus COVID-19 Nigeria NCDC |
| title | ONLINE FORECASTING OF COVID-19 CASES IN NIGERIA USING LIMITED DATA |
| title_full | ONLINE FORECASTING OF COVID-19 CASES IN NIGERIA USING LIMITED DATA |
| title_fullStr | ONLINE FORECASTING OF COVID-19 CASES IN NIGERIA USING LIMITED DATA |
| title_full_unstemmed | ONLINE FORECASTING OF COVID-19 CASES IN NIGERIA USING LIMITED DATA |
| title_short | ONLINE FORECASTING OF COVID-19 CASES IN NIGERIA USING LIMITED DATA |
| title_sort | online forecasting of covid 19 cases in nigeria using limited data |
| topic | Timeseries forecasting Analytic Modeling Ensembles Small Data Coronavirus COVID-19 Nigeria NCDC |
| url | http://www.sciencedirect.com/science/article/pii/S2352340920305771 |
| work_keys_str_mv | AT kabirabdulmajeed onlineforecastingofcovid19casesinnigeriausinglimiteddata AT monsuruadeleke onlineforecastingofcovid19casesinnigeriausinglimiteddata AT labodepopoola onlineforecastingofcovid19casesinnigeriausinglimiteddata |