Open AccessAdaptive cluster sampling with model based approach for estimating total number of hidden COVID-19 carriers in Nigeria
Author(s) -
O. M. Olayiwola,
Anthony Idowu Ajayi,
Oluwafemi Clement Onifade,
O. A. Wale-Orojo,
Bright Ajibade
Publication year - 2020
Publication title -
statistical journal of the iaos
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.286
H-Index - 16
eISSN - 1875-9254
pISSN - 1874-7655
DOI - 10.3233/sji-200718
Subject(s) - covid-19 , population , cluster (spacecraft) , contact tracing , pandemic , computer science , statistics , inference , bayesian probability , geography , infectious disease (medical specialty) , mathematics , medicine , environmental health , artificial intelligence , disease , pathology , programming language
Infectious diseases can inflict immense losses and suffering on the human population. As at 23rd of June, 2020 COVID-19 pandemic has caused 20,919 cases, 25 deaths and 7,109 had recovered in Nigeria. Nigeria Centre for Disease Control (NCDC) is tracing COVID 19 carriers for designing effective control measures and to prevent the spread. Authors have modeled COVID-19 cases, but there is a dearth of information on estimating the total number of hidden COVID-19 carriers in the population. Adaptive cluster sample was used for exploring populations of hidden COVID-19 carriers. The data on daily cases of COVID-19 were extracted from NCDC website. Nigeria population was partitioned into 37 regions (states and FCT). We considered a model based approached in Bayesian framework to make inference about the number of COVID-19 carriers in Nigeria. The fitted model showed that all COVID-19 carriers will only be captured at once if contact tracing is combined with methodology designed in this work.