Development of a time-trend model for analyzing and predicting case-pattern of Lassa fever epidemics in Liberia, 2013-2017

Babasola O Olugasa; Eugene A Odigie; Mike Lawani; Johnson F Ojo

doi:10.4103/1596-3519.149892

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ORIGINAL ARTICLE

Year : 2015 | Volume : 14 | Issue : 2 | Page : 89-96

Development of a time-trend model for analyzing and predicting case-pattern of Lassa fever epidemics in Liberia, 2013-2017

Babasola O Olugasa¹, Eugene A Odigie¹, Mike Lawani², Johnson F Ojo³
¹ Department of Veterinary Public Health and Preventive Medicine, Centre for Control and Prevention of Zoonoses, Faculty of Veterinary Medicine, Ibadan, Oyo State, Nigeria
² Department of Epidemiology and Medical Statistics, Faculty of Public Health, College of Medicine, Ibadan, Oyo State, Nigeria
³ Department of Statistics, Faculty of Science, University of Ibadan, Ibadan, Oyo State, Nigeria

Correspondence Address:
Babasola O Olugasa
Centre for Control and Prevention of Zoonoses, Department of Veterinary Public Health and Preventive Medicine, 101 Faculty of Veterinary Medicine, University of Ibadan, Ibadan, Oyo State
Nigeria

Source of Support: Support for improving postgraduate programmes for surveillance of human-animal diseases in West Africa was provided by John D. and Catherine T. MacArthur Foundation (Grant No. 97944) to the University of Ibadan, Nigeria. Ethics approval for the study was granted by the Liberian Institute for Biomedical Research (EC/LIBR/014/039). Facilitation of in-country study was provided by Cuttington University, Suakoko Liberia, Conflict of Interest: None

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DOI: 10.4103/1596-3519.149892

Objective: The objective was to develop a case-pattern model for Lassa fever (LF) among humans and derive predictors of time-trend point distribution of LF cases in Liberia in view of the prevailing under-reporting and public health challenge posed by the disease in the country. Materials and Methods: A retrospective 5 years data of LF distribution countrywide among humans were used to train a time-trend model of the disease in Liberia. A time-trend quadratic model was selected due to its goodness-of-fit (R² = 0.89, and P < 0.05) and best performance compared to linear and exponential models. Parameter predictors were run on least square method to predict LF cases for a prospective 5 years period, covering 2013-2017. Results: The two-stage predictive model of LF case-pattern between 2013 and 2017 was characterized by a prospective decline within the South-coast County of Grand Bassa over the forecast period and an upward case-trend within the Northern County of Nimba. Case specific exponential increase was predicted for the first 2 years (2013-2014) with a geometric increase over the next 3 years (2015-2017) in Nimba County. Conclusion: This paper describes a translational application of the space-time distribution pattern of LF epidemics, 2008-2012 reported in Liberia, on which a predictive model was developed. We proposed a computationally feasible two-stage space-time permutation approach to estimate the time-trend parameters and conduct predictive inference on LF in Liberia.

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