Use of oral polio vaccine and the incidence of COVID-19 in the world.

BACKGROUND: Several live attenuated vaccines were shown to provide temporary protection against a variety of infectious diseases through stimulation of the host innate immune system. OBJECTIVE: To test the hypothesis that countries using oral polio vaccine (OPV) have a lower cumulative number of cases diagnosed with COVID-19 per 100,000 population (CP100K) compared with those using only inactivated polio vaccine (IPV). METHODS: In an ecological study, the CP100K was compared between countries using OPV vs IPV. We used a random-effect meta-analysis technique to estimate the pooled mean for CP100K. We also used negative binomial regression with CP100K as the dependent variable and the human development index (HDI) and the type of vaccine used as independent variables. RESULTS: The pooled estimated mean CP100K was 4970 (95% CI 4030 to 5900) cases per 100,000 population for countries using IPV, significantly (p<0.001) higher than that for countries using OPV-1580 (1190 to 1960). Countries with higher HDI prefer to use IPV; those with lower HDI commonly use OPV. Both HDI and the type of vaccine were independent predictors of CP100K. Use of OPV compared to IPV could independently decrease the CP100K by an average of 30% at the mean HDI of 0.72. CONCLUSIONS: Countries using OPV have a lower incidence of COVID-19 compared to those using IPV. This might suggest that OPV may either prevent SARS-CoV-2 infection at individual level or slow down the transmission at the community level.

Identifying Important Attributes for Prognostic Prediction in Traumatic Brain Injury Patients. A Hybrid Method of Decision Tree and Neural Network.

BACKGROUND: Generally, traumatic brain injury (TBI) patients do not have a stable condition, particularly after the first week of TBI. Hence, indicating the attributes in prognosis through a prediction model is of utmost importance since it helps caregivers with treatment-decision options, or prepares the relatives for the most-likely outcome. OBJECTIVES: This study attempted to determine and order the attributes in prognostic prediction in TBI patients, based on early clinical findings. A hybrid method was employed, which combines a decision tree (DT) and an artificial neural network (ANN) in order to improve the modeling process. METHODS: The DT approach was applied as the initial analysis of the network architecture to increase accuracy in prediction. Afterwards, the ANN structure was mapped from the initial DT based on a part of the data. Subsequently, the designed network was trained and validated by the remaining data. 5-fold cross-validation method was applied to train the network. The area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy rate were utilized as performance measures. The important attributes were then determined from the trained network using two methods: change of mean squared error (MSE), and sensitivity analysis (SA). RESULTS: The hybrid method offered better results compared to the DT method. The accuracy rate of 86.3 % vs. 82.2 %, sensitivity value of 55.1 % vs. 47.6 %, specificity value of 93.6 % vs. 91.1 %, and the area under the ROC curve of 0.705 vs. 0.695 were achieved for the hybrid method and DT, respectively. However, the attributes' order by DT method was more consistent with the clinical literature. CONCLUSIONS: The combination of different modeling methods can enhance their performance. However, it may create some complexities in computations and interpretations. The outcome of the present study could deliver some useful hints in prognostic prediction on the basis of early clinical findings for TBI patients.