Predictors of Lassa fever diagnosis in suspected cases reporting to health facilities in Nigeria

Ochu, Chinwe Lucia; Ntoimo, Lorretta; Onoh, Ikenna; Okonofua, Friday; Meremikwu, Martin; Mba, Sandra; Iniobong, Akanimo; Nwafor, Obinna; Dalhat, Mahmood; Ohonsi, Cornelius; Arinze, Chinedu; Esu, Ekpereonne; Igumbor, Ehimario; Dan-Nwafor, Chioma; Ilori, Elsie; Adetifa, Ifedayo

doi:10.1038/s41598-023-33187-y

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…This difference could be due to variations in occupation, exposure to rodents, or differences in immune responses. Studies have shown that males, especially farmers and hunters, are more likely to be exposed to rodents [22,23]. Genetic, hormonal, and behavioral factors may also contribute to varying susceptibility between males and females [24].…”

Section: Predictors Of the Likelihood Of Con Rmed Lf Cases Among Ward...mentioning

confidence: 99%

Ecological Correlates and Predictors of Lassa Fever Incidence in Ondo State, Nigeria 2017 - 2021: An Emerging Urban Trend

Cadmus,

Taiwo,

Akinseye

et al. 2023

Preprint

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Lassa fever (LF) is a highly prevalent disease in many West African countries, including Nigeria. Efforts to combat the disease have primarily focused on rural areas where interactions between rodents and humans are common. However, recent studies indicate a shift in the occurrence of LF from rural to urban areas. We analysed secondary data of reported LF outbreaks from 2017 to 2021 in Ondo State, Nigeria, to identify the distribution pattern, ecological variations, and other determinants of disease spread from the ward level. Data utilised include LF incidence, ecological variables involving elevation, population, nighttime light intensity, vegetation, temperature, market presence, road length, and building area coverage. ArcGIS Pro 3.0 software was employed for spatial analysis. Results revealed a spatio-temporal clustering of LF incidents between 2017 and 2021, with an increasing trend followed by a decline in 2021. All wards in Owo Local Government Area were identified as hotspots for LF. The ecological variables exhibited significant correlations with the number of LF cases in the wards, except for maximum temperature. Notably, these variables varied significantly between wards with confirmed LF and those without. Therefore, it is important to prioritise strategies for mitigating LF outbreaks in urban areas of Nigeria and other West African countries.

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Section: Predictors Of the Likelihood Of Con Rmed Lf Cases Among Ward...mentioning

confidence: 99%

Ecological Correlates and Predictors of Lassa Fever Incidence in Ondo State, Nigeria 2017 - 2021: An Emerging Urban Trend

Cadmus,

Taiwo,

Akinseye

et al. 2023

Preprint

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The Lassa fever cases and mortality in Nigeria: Quantile Regression versus Machine Learning Models

Samson,

Aromolaran,

Akingbade

2024

J Public Health Afr

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Introduction. Lassa fever (LF) is caused by the Lassa fever virus (LFV). It is endemic in West Africa, of which 25% of the infections are ascribed to Nigeria. This disease affects mostly the productive age and hence a proper understanding of the dynamics of this disease will help in formulating policies that would help in curbing the spread of LF. Objectives. The objective of this study is to compare the performance of quantile regression models with that of Machine Learning models. Methods. Data between between 7th January 2018 2018 and 17th December, 2022 on suspected cases, confirmed cases and deaths resulting from LF were retrieved from the Nigeria Centre for Disease Control (NCDC). The data obtained were fitted to quantile regression models (QRM) at 25%, 50% and 75% as well as to Machine learning models. The response variable being confirmed cases and mortality due to Lassa fever in Nigeria while the independent variables were total confirmed cases, the week, month and year. Results. Result showed that the highest monthly mean confirmed cases (56) and mortality (9) from LF were reported in February. The first quarter of the year reported the highest cases of both confirmed cases and deaths in Nigeria. Result also revealed that for the confirmed cases, quantile regression at 50% outperformed the best of the MLM, Gaussian-matern5/2 GPR (RMSE= 10.3393 versus 11.615), while for mortality, the medium Gaussian SVM (RMSE =1.6441 versus 1.8352) outperformed QRM. Conclusion. Quantile regression model at 50% better captured the dynamics of the confirmed cases of LF in Nigeria while the medium Gaussian SVM better captured the mortality of LF in Nigeria. Among the features selected, confirmed cases was found to be the most important feature that drive its mortality with the implication that as the confirmed cases of Lassa fever increases, is a significant increase in its mortality. This therefore necessitates a need for a better intervention measures that will help curb Lassa fever mortality as a result of the increase in the confirmed cases. There is also a need for promotion of good community hygiene which could include; discouraging rodents from entering homes and putting food in rodent proof containers to avoid contamination to help hart the spread of Lassa fever in Nigeria.

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Predictors of Lassa fever diagnosis in suspected cases reporting to health facilities in Nigeria

Cited by 2 publications

References 23 publications

Ecological Correlates and Predictors of Lassa Fever Incidence in Ondo State, Nigeria 2017 - 2021: An Emerging Urban Trend

Ecological Correlates and Predictors of Lassa Fever Incidence in Ondo State, Nigeria 2017 - 2021: An Emerging Urban Trend

The Lassa fever cases and mortality in Nigeria: Quantile Regression versus Machine Learning Models

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