Sains Malaysiana 52(8)(2023): 2395-2406

http://doi.org/10.17576/jsm-2023-5208-16

Recursive Prediction Model: A Preliminary Application to Lassa Fever Outbreak in Nigeria

(Model Ramalan Rekursif: Aplikasi Awal untuk Wabak Demam Lassa di Nigeria)

FRIDAY ZINZENDOFF OKWONU¹, NOR AISHAH AHAD^2,3,*, HASHIBAH HAMID³ & OLIMJON SHUKUROVICH SHARIPOV⁴

 ¹Department of Mathematics, Faculty of Science, Delta State University, P.M.B.1, Abraka, Nigeria

²Institute of Strategic Industrial Decision Modelling, School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia

³School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia

⁴School of Mathematics, Department of Statistics, National University of Uzbekistan

Diserahkan: 10 Disember 2022/Diterima: 24 Julai 2023

Abstract

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.

Keywords: Case fatality ratio; Lassa fever; prediction; recursive

Abstrak

Demam Lassa (LF) ialah endemik khusus di Afrika Barat dan Nigeria. Sejak 1969 apabila penyakit itu ditemui, wabak bermusim sering dilaporkan di Nigeria. Ramai penyelidik telah melaporkan bilangan kes yang disyaki, disahkan dan kematian yang tidak tekal atau berberbeza-beza sejak 2012 hingga kini. Untuk menambahbaik pelaporan ini dan disebabkan oleh kadar kematian yang tinggi yang dikaitkan dengan LF, adalah penting untuk memperkenalkan model yang sesuai dan teguh yang boleh meramalkan atau menganggarkan bilangan kes LF berdasarkan data permulaan. Untuk mencapai tujuan ini, kami mencadangkan model ramalan rekursif (RP) yang boleh melakukan ramalan dengan data permulaan. Pekali korelasi Pearson (R) dan R² digunakan untuk menentukan analisis prestasi model. Model RP meramalkan 96.7% kes disahkan dan 89.6% kes kematian untuk tiga bulan pertama 2022 berdasarkan data permulaan. Model itu juga digunakan untuk meramalkan kes kematian COVID-19 selama enam minggu wabak berlaku di India. Hasil menunjukkan ramalan yang setanding dengan modelregresi untuk kes kematian COVID-19. Kajian juga mendapati bahawa model yang dicadangkan boleh digunakan untuk melakukan ramalan bagi mana-mana penyakit yang etiologinya tidak diketahui semasa permulaan wabak penyakit tanpa sebarang rawatan, seperti wabak COVID-19. Analisis prestasi RP mendedahkan bahawa model ini berguna untuk meramalkan peningkatan trend wabak penyakit dengan etiologi yang tidak diketahui tanpa pengalaman rawatan dan vaksin.

Kata kunci: Demam Lassa; nisbah kematian kes; ramalan; rekursif

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*Pengarang untuk surat-menyurat; email: aishah@uum.edu.my