Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models

Busari, S.I.; Samson, T.K.

doi:10.1016/j.sciaf.2022.e01404

Cited by 10 publications

(8 citation statements)

References 19 publications

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“…The empirical results prove our models validity, giving value to our approachs superiority. The fact that already some research studies have proven that the regression models if framed over a robust mathematical model, can provide insights into the pandemics evolution (Busari and Samson, 2022), gives even more value to our work, justifying our approach.…”

Section: Discussionmentioning

confidence: 80%

“…Much importantly, there is also a comparison of techniques regarding the modeling and the forecasting of the spread of the pandemic, by investigating and predicting the new cases, for the case study of Nigeria, employing various models, i.e. regression techniques, autoregressive integrated moving average models and machine learning approaches Busari and Samson, 2022). The overall results provide evidence of the models superiority, some of them being also certain regression models.…”

Section: Literature Reviewmentioning

confidence: 99%

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Modeling the COVID-19: A case study based on oil futures

Alghalith,

Floros,

Daglis

et al. 2023

Preprint

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The COVID-19 pandemic necessitated the production of mathematical models that were able to explain and thoroughly study various aspects and features of the pandemic. In this work, we provide a well-stated mathematical model to explain and simulate the evolution of the COVID-19 pandemic. To test our models performance and validity , we utilize actual surveillance data from the pandemic, capturing the results of this empirical investigation. According to the results, our model is valid, since all estimates are statistically significant, and the coefficients explain the evolution of the pandemic. We also explore the presence of potential non-linear dependence between future oil prices and the number of confirmed COVID-19 cases simulated by our model.

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Section: Discussionmentioning

confidence: 80%

Section: Literature Reviewmentioning

confidence: 99%

Modeling the COVID-19: A case study based on oil futures

Alghalith,

Floros,

Daglis

et al. 2023

Preprint

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“…Our literature review uncovered evidence on the relative performance of these models in many countries including Nigeria, Bangladesh, India, South Korea, Poland, Italy, Belgium, Germany, the United Kingdom, and the United States [ 8 – 16 ]. Our review revealed three important insights about the relative performance of alternative modelling approaches.…”

Section: Narrative Reviewmentioning

confidence: 99%

The importance of investing in data, models, experiments, team science, and public trust to help policymakers prepare for the next pandemic

Grieve,

Yang,

Abbott

et al. 2023

PLOS Glob Public Health

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The COVID-19 pandemic has brought about valuable insights regarding models, data, and experiments. In this narrative review, we summarised the existing literature on these three themes, exploring the challenges of providing forecasts, the requirement for real-time linkage of health-related datasets, and the role of ‘experimentation’ in evaluating interventions. This literature review encourages us to broaden our perspective for the future, acknowledging the significance of investing in models, data, and experimentation, but also to invest in areas that are conceptually more abstract: the value of ‘team science’, the need for public trust in science, and in establishing processes for using science in policy. Policy-makers rely on model forecasts early in a pandemic when there is little data, and it is vital to communicate the assumptions, limitations, and uncertainties (theme 1). Linked routine data can provide critical information, for example, in establishing risk factors for adverse outcomes but are often not available quickly enough to make a real-time impact. The interoperability of data resources internationally is required to facilitate sharing across jurisdictions (theme 2). Randomised controlled trials (RCTs) provided timely evidence on the efficacy and safety of vaccinations and pharmaceuticals but were largely conducted in higher income countries, restricting generalisability to low- and middle-income countries (LMIC). Trials for non-pharmaceutical interventions (NPIs) were almost non-existent which was a missed opportunity (theme 3). Building on these themes from the narrative review, we underscore the importance of three other areas that need investment for effective evidence-driven policy-making. The COVID-19 response relied on strong multidisciplinary research infrastructures, but funders and academic institutions need to do more to incentivise team science (4). To enhance public trust in the use of scientific evidence for policy, researchers and policy-makers must work together to clearly communicate uncertainties in current evidence and any need to change policy as evidence evolves (5). Timely policy decisions require an established two-way process between scientists and policy makers to make the best use of evidence (6). For effective preparedness against future pandemics, it is essential to establish models, data, and experiments as fundamental pillars, complemented by efforts in planning and investment towards team science, public trust, and evidence-based policy-making across international communities. The paper concludes with a ‘call to actions’ for both policy-makers and researchers.

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“…Beberapa penelitian telah melakukan peramalan menggunakan berbagai teknik atau metode Autoregressive Integrated Moving Average (ARIMA) pada berbagai bidang. Pada bidang kesehatan, metode ini digunakan untuk meramalkan jumlah kasus Covid19 di Nigeria dengan menggunakan historis data kasus dari bulan Februari 2020 sampai dengan November 2021 [4]. Metode ini juga digunakan pada bidang energi untuk mendapatkan model peramalan permintaan bensin berdasarkan historis data konsumsi bensin yang tersedia sampai dengan tahun 2030 [5].…”

Section: Pendahuluanunclassified

Penerapan Model ARIMA Dalam Memprediksi Penjualan Produk Minuman Teh Botol Sosro Ukuran 350 mL

2022

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This study aims to provide suggestions for improvements in overcoming stock shortages of soft drink products using a forecasting method. The results of such forecasting will be compared with the forecasting methods used by the company at this time. The Autoregressive Integrated Moving Average (ARIMA) method was used in this study to improve the accuracy of demand forecasting in soft drink products (TBE 350 mL K12 Aseptic). This study used product sales data for the period January 2016 to January 2022. Based on the results of calculation and data processing, it is known that the best model is ARIMA (2,1,0) with a MAPE value of 35,966%. Meanwhile, the method used by the company has a MAPE value of 36.569%. It Shows that the ARIMA method (2,1,0) has better forecasting accuracy compared to the company's forecasting method with a MAPE difference of 0.604%. The validation results were obtained forecasting in January 2022 with ARIMA (2,1,0) of 22,569 cartons, while the company's method was 21,194 cartons. This shows that the ARIMA method (2,1,0) is more accurate in forecasting because it has a forecast value in the January 2022 period close to the actual demand value, which is 23,193 cartons. The ARIMA model equation (2,1,0) for forecasting soft drink products in the following month is Zt = 0,494Zt-1 + 0,210Zt-2 + 0,297Zt-3

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Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models

Cited by 10 publications

References 19 publications

Modeling the COVID-19: A case study based on oil futures

Modeling the COVID-19: A case study based on oil futures

The importance of investing in data, models, experiments, team science, and public trust to help policymakers prepare for the next pandemic

Penerapan Model ARIMA Dalam Memprediksi Penjualan Produk Minuman Teh Botol Sosro Ukuran 350 mL

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