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.

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%

Introduction: Patients with Systemic Lupus Erythematosus (SLE) are seen late in specialized medical consultation in Benin. The objective of this work was to assess general practitioners' knowledge in Cotonou about SLE. Materials and Methods: This work was a cross-sectional study that was led in the city of Cotonou from July 1 to September 30, 2017. In the study population, we have general practitioners who practice in the city. Data collection was set in response to a self-questionnaire. Result: The survey involved 209 general practitioners. The average age was 27.5 years with a minimum of 22 and a maximum of 34. Most of them practiced in private clinics. Besides, 17 doctors (8.1%) reported that they never heard of lupus. Among the 192 remaining, only one had an average knowledge of lupus, while the others had insufficient knowledge. Conclusion: From this study, we got that SLE is still little known by the general practitioner. Increasing the knowledge of general practitioners' knowledge of lupus is compulsory to improve the screening rate.

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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%.