A population-based dataset concerning predictors of willingness to get a COVID-19 vaccine in Iran

The global issue of preventing the spread of COVID-19 is challenging. One of the most efficient ways to control the pandemic is to have a full coverage of COVID-19 vaccination. Therefore, this paper collected survey data to understand the intention and willingness of COVID-19 vaccination uptake in Qazvin, Iran. With the use of a paper-and-pencil method and multistage stratified cluster sampling, research personnel approached and interviewed a representative sample of adults in Qazvin (n = 10843) between February 19 and April 9, 2021. The survey asked questions regarding sociodemographic information, fear of COVID-19, perceived COVID-19 infectability, perceived behavioral control over COVID-19 vaccination, subjective norm of COVID-19 vaccination, attitude towards COVID-19 vaccination, and intention to get COVID-19 vaccinated. The data collected from this survey were analyzed using descriptive statistics, which were carried out using the IBM SPSS version 17.0

Fear of COVID-19 and perceived COVID-19 infectability supplement theory of planned behavior to explain Iranians' intention to get COVID-19 vaccinated

One of the most efficient methods to control the high infection rate of the coronavirus disease 2019 (COVID-19) is to have a high coverage of COVID-19 vaccination worldwide. Therefore, it is important to understand individuals’ intention to get COVID-19 vaccinated. The present study applied the Theory of Planned Behavior (TPB) to explain the intention to get COVID-19 vaccinated among a representative sample in Qazvin, Iran. The TPB uses psychological constructs of attitude, subjective norm, and perceived behavioral control to explain an individual’s intention to perform a behavior. Fear and perceived infectability were additionally incorporated into the TPB to explain the intention to get COVID-19 vaccinated. Utilizing multistage stratified cluster sampling, 10,843 participants (4092 males; 37.7%) with a mean age of 35.54 years (SD = 12.00) completed a survey. The survey assessed TPB constructs (including attitude, subjective norm, perceived behavioral control, and intention related to COVID-19 vaccination) together with fear of COVID-19 and perceived COVID-19 infectability. Structural equation modeling (SEM) was performed to examine whether fear of COVID-19, perceived infectability, and the TPB constructs explained individuals’ intention to get COVID-19 vaccinated. The SEM demonstrated satisfactory fit (comparative fit index = 0.970; Tucker-Lewis index = 0.962; root mean square error of approximation = 0.040; standardized root mean square residual = 0.050). Moreover, perceived behavioral control, subjective norm, attitude, and perceived COVID-19 infectability significantly explained individuals’ intention to get COVID-19 vaccinated. Perceived COVID-19 infectability and TPB constructs were all significant mediators in the relationship between fear of COVID-19 and intention to get COVID-19 vaccinated. Incorporating fear of COVID-19 and perceived COVID-19 infectability effectively into the TPB explained Iranians’ intention to get COVID-19 vaccinated. Therefore, Iranians who have a strong belief in Muslim religion may improve their intention to get COVID-19 vaccinated via these constructs

Forward Error Correction for Optical Transponders

Forward error correction is an essential technique required in almost all communication systems to guarantee reliable data transmission close to the theoretical limits. In this chapter, we discuss the state-of-the-art forward error correction (FEC) schemes for fiber-optic communications. Following a historical overview of the evolution of FEC schemes, we first introduce the fundamental theoretical limits of common communication channel models and show how to compute them. These limits provide the reader with guidelines for comparing different FEC codes under various assumptions. We then provide a brief introduction to the general basic concepts of FEC, followed by an in-depth introduction to the main classes of codes for soft decision decoding and hard decision decoding. We include a wide range of performance curves, compare the different schemes, and give the reader guidelines on which FEC scheme to use. We also introduce the main techniques to combine coding and higher-order modulation (coded modulation), including constellation shaping. Finally, we include a guide on how to evaluate the performance of FEC in transmission experiments. We conclude the chapter with an overview of the properties of some state-of-the-art FEC schemes used in optical communications and an outlook