Babasola O Olugasa1, Eugene A Odigie, Mike Lawani, Johnson F Ojo. 1. Department of Veterinary Public Health and Preventive Medicine, Centre for Control and Prevention of Zoonoses, Faculty of Veterinary Medicine, Ibadan, Oyo State, Nigeria.
Abstract
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.
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.