Influence of COVID-19 vaccination on the dynamics of new infected cases in the world.

The initial COVID-19 vaccinations were created and distributed to the general population in 2020 thanks to emergency authorization and conditional approval. Consequently, numerous countries followed the process that is currently a global campaign. Taking into account the fact that people are being vaccinated, there are concerns about the effectiveness of that medical solution. Actually, this study is the first one focusing on how the number of vaccinated people might influence the spread of the pandemic in the world. From the Global Change Data Lab "Our World in Data", we were able to get data sets about the number of new cases and vaccinated people. This study is a longitudinal one from 14/12/2020 to 21/03/2021. In addition, we computed Generalized log-Linear Model on count time series (Negative Binomial distribution due to over dispersion in data) and implemented validation tests to confirm the robustness of our results. The findings revealed that when the number of vaccinated people increases by one new vaccination on a given day, the number of new cases decreases significantly two days after by one. The influence is not notable on the same day of vaccination. Authorities should increase the vaccination campaign to control well the pandemic. That solution has effectively started to reduce the spread of COVID-19 in the world.

Overview and cross-validation of COVID-19 forecasting univariate models

Researchers have been working with different models to forecast COVID-19 cases. Many of their estimates are not accurate. This study aims to propose the best model to forecast COVID-19 cumulative cases using a machine learning technic. It is a work that focused on time series univariate models because there are too many debates about the quality of the pandemic data. To increase the likelihood of the findings, we avoided many variables modeling and proposed a robust process to forecast COVID-19 cumulative cases. It will help international institutions to take optimal decisions about the world economy and response to the pandemic. Consequently, we used the data titled ”Coronavirus Pandemic (COVID-19)” from ”Our World in Data” about cases from 22 January 2020 to 30 November 2020. We computed Error Trend Season (ETS), Exponential smoothing with multiplicative error-trend, and ARIMA on the training data sets. In addition, we calculated the Mean Absolute Percentage Error (MAPE) per model. Among those models, we notice that ETS (with additive error-trend and no season) has the smallest MAPE statistics compared to the others. The findings revealed that with the ETS model we need at least 100 days to have good forecasts with a MAPE threshold of 1%

Cross-Validation Comparison of COVID-19 Forecast Models.

Many papers have proposed forecasting models and some are accurate and others are not. Due to the debatable quality of collected data about COVID-19, this study aims to compare univariate time series models with cross-validation and different forecast periods to propose the best one. We used the data titled "Coronavirus Pandemic (COVID-19)" from "'Our World in Data" about cases for the period of 31 December 2019 to 21 November 2020. The Mean Absolute Percentage Error (MAPE) is computed per model to make the choice of the best fit. Among the univariate models, Error Trend Season (ETS), Exponential smoothing with multiplicative error-trend, and ARIMA; we got that the best one is ETS with additive error-trend and no season. The findings revealed that with the ETS model, we need at least 100 days to have good forecasts with a MAPE threshold of 5%.

The influence of passenger air traffic on the spread of COVID-19 in the world.

Countries in the world are suffering from COVID-19 and would like to control it. Thus, some authorities voted for new policies and even stopped passenger air traffic. Those decisions were not uniform, and this study focuses on how passenger air traffic might influence the spread of COVID-19 in the world. We used data sets of cases from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University and air transport (passengers carried) from the World Bank. Besides, we computed Poisson, QuasiPoisson, Negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models with cross-validation to make sure that our findings are robust. Actually, when passenger air traffic increases by one unit, the number of cases increases by one new infection.