It is paramount to assess the risk of biases in may arise from diagnostic test accuracy (DTA) study as it will affect the accuracy and validity of the tests. These biases can be found in published researches and here we look at COVID-19 DTA studies. The evaluation of bias risk in diagnostic research is mainly performed using QUADAS-2. The aim of this review was to determine potential selection and information biases in diagnostic test accuracy studies and strategies to minimize risk of biases. Literature review related to diagnostic test accuracy study is identified through an online search of databases namely PubMed, ScienceDirect, Research Gate, Google Scholar, and official government websites range. Six potential biases in four QUADAS-2 domains are identified in COVID-19 diagnostic test accuracy study which are 1) spectrum bias in patient selection; 2) interpretation bias in index test; 3) differential misclassification bias and nondifferential misclassification bias in reference standard; and 4) partial verification bias and differential verification bias in patient flow. The identified biases exert effects on accuracy of COVID-19 diagnostic tests. Six strategies are recommended to reduce these biases, hence, improving the accuracy of COVID-19 diagnostic tests. The best diagnostic test can give benefits to the population in the mass screening program during COVID-19.

Introduction: Dog bite is a global public health concern because it is the main risk factor for human rabies and it particularly affects children. In Nigeria, most dog bites are from stray dogs and their vaccination status remains unknown. Thus, this study aimed to investigate the prevalence and associated factors of dog bite among public primary school children in Madawaki district Gusau, Nigeria. Methods: A cross-sectional study was conducted using an unequal stratified sampling technique among 999 children aged 11 to 13-year-old at seven public primary schools from September 2017 to January 2020. A self-reported history of dog bites, socio-demographic characteristics, knowledge on interaction with dogs, knowledge on the implication of dog bites, risk behaviours of children and dog ownership were collected using a validated questionnaire. Results: The prevalence of dog bites was 54.5%. The predictors of dog bite among the children were being a male (Adjusted Odds Ratio; AOR = 2.252; Confidence Interval; CI = 1.719, 2.949), dog ownership by respondent’s family (AOR = 1.456; Cl = 1.089, 1.946), dog ownership by neighbours of the respondents (AOR = 1.752; Cl =1.334, 2.303), total score for knowledge on interaction with dogs (AOR = 0.884; Cl = 0.858, 0.911), and total score for risk behaviours of children (AOR = 1.020; Cl =1.004, 1.035). Conclusion: Poor knowledge on interaction with dogs, children’s risk behaviours around dogs, dog ownership and being male were the factors associated with a high prevalence of dog bite among primary school children from this study. These factors should be considered in order to develop an effective awareness campaign in schools to reduce the prevalence of dog bites among children.