Recursive prediction model: a preliminary application to lassa fever outbreak in Nigeria
Lassa fever (LF) is endemic in West Africa and Nigeria in particular. Since 1969 when the disease was discovered, a seasonal outbreak is often reported in Nigeria. Many researchers have reported inconsistent or varying numbers of suspected, confirmed and death cases since 2012 to date. To enhance th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2023
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| Online Access: | http://journalarticle.ukm.my/22903/ http://journalarticle.ukm.my/22903/1/SML%2016.pdf |
| _version_ | 1848815711593431040 |
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| author | Okwonu, Friday Zinzendoff Nor Aishah Ahad, Hashibah Hamid, Sharipov, Olimjon Shukurovich |
| author_facet | Okwonu, Friday Zinzendoff Nor Aishah Ahad, Hashibah Hamid, Sharipov, Olimjon Shukurovich |
| author_sort | Okwonu, Friday Zinzendoff |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Lassa fever (LF) is endemic in West Africa and Nigeria in particular. Since 1969 when the disease was discovered, a seasonal outbreak is often reported in Nigeria. Many researchers have reported inconsistent or varying numbers of suspected, confirmed and death cases since 2012 to date. To enhance this reportage, and due to the high mortality rate associated with LF, it is pertinent to design a suitable and robust model that could predict or estimate the number of LF cases based on the onset data. To achieve these, we proposed a recursive prediction (RP) model that could do predictions with the onset data. The Pearson correlation coefficient (R), and R2 are applied to determine the performance analysis of the model. The RP model predicted 96.7% confirmed cases and 89.6% death cases for the first three months of 2022 based on the onset data. The model was also applied to predict COVID-19 death cases during the six weeks of the outbreak in India. The result showed a comparable prediction with the regression output for the COVID-19 death cases. This study demonstrated that the proposed model could be applied to perform prediction for any disease of unknown etiology during the onset of the disease outbreak without any treatment similar to the COVID-19 outbreak. The performance analysis of the RP showed that the model is useful to predict the increasing trend of an outbreak of a disease with unknown etiology without prior treatment experience and vaccines. |
| first_indexed | 2025-11-15T00:54:19Z |
| format | Article |
| id | oai:generic.eprints.org:22903 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:54:19Z |
| publishDate | 2023 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:229032024-01-18T08:42:18Z http://journalarticle.ukm.my/22903/ Recursive prediction model: a preliminary application to lassa fever outbreak in Nigeria Okwonu, Friday Zinzendoff Nor Aishah Ahad, Hashibah Hamid, Sharipov, Olimjon Shukurovich Lassa fever (LF) is endemic in West Africa and Nigeria in particular. Since 1969 when the disease was discovered, a seasonal outbreak is often reported in Nigeria. Many researchers have reported inconsistent or varying numbers of suspected, confirmed and death cases since 2012 to date. To enhance this reportage, and due to the high mortality rate associated with LF, it is pertinent to design a suitable and robust model that could predict or estimate the number of LF cases based on the onset data. To achieve these, we proposed a recursive prediction (RP) model that could do predictions with the onset data. The Pearson correlation coefficient (R), and R2 are applied to determine the performance analysis of the model. The RP model predicted 96.7% confirmed cases and 89.6% death cases for the first three months of 2022 based on the onset data. The model was also applied to predict COVID-19 death cases during the six weeks of the outbreak in India. The result showed a comparable prediction with the regression output for the COVID-19 death cases. This study demonstrated that the proposed model could be applied to perform prediction for any disease of unknown etiology during the onset of the disease outbreak without any treatment similar to the COVID-19 outbreak. The performance analysis of the RP showed that the model is useful to predict the increasing trend of an outbreak of a disease with unknown etiology without prior treatment experience and vaccines. Penerbit Universiti Kebangsaan Malaysia 2023 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/22903/1/SML%2016.pdf Okwonu, Friday Zinzendoff and Nor Aishah Ahad, and Hashibah Hamid, and Sharipov, Olimjon Shukurovich (2023) Recursive prediction model: a preliminary application to lassa fever outbreak in Nigeria. Sains Malaysiana, 52 (8). pp. 2395-2406. ISSN 0126-6039 https://www.ukm.my/jsm/english_journals/vol52num8_2023/contentsVol52num8_2023.html |
| spellingShingle | Okwonu, Friday Zinzendoff Nor Aishah Ahad, Hashibah Hamid, Sharipov, Olimjon Shukurovich Recursive prediction model: a preliminary application to lassa fever outbreak in Nigeria |
| title | Recursive prediction model: a preliminary application to lassa fever outbreak in Nigeria |
| title_full | Recursive prediction model: a preliminary application to lassa fever outbreak in Nigeria |
| title_fullStr | Recursive prediction model: a preliminary application to lassa fever outbreak in Nigeria |
| title_full_unstemmed | Recursive prediction model: a preliminary application to lassa fever outbreak in Nigeria |
| title_short | Recursive prediction model: a preliminary application to lassa fever outbreak in Nigeria |
| title_sort | recursive prediction model: a preliminary application to lassa fever outbreak in nigeria |
| url | http://journalarticle.ukm.my/22903/ http://journalarticle.ukm.my/22903/ http://journalarticle.ukm.my/22903/1/SML%2016.pdf |