Background The COVID-19 pandemic conditions are still prevalent in Iran and other countries and the monitoring system is gradually discovering new cases every day. Therefore, it is a cause for concern around the world, and forecasting the number of future patients and death cases, although not entirely accurate, helps the governments and health-policy makers to make the necessary decisions and impose restrictions to reduce prevalence. Methods In this study, we aimed to find the best model for forecasting the number of confirmed and death cases in Iran. For this purpose, we applied nine models including NNETAR, ARIMA, Hybrid, Holt-Winter, BSTS, TBATS, Prophet, MLP, and ELM network models. The quality of forecasting models is evaluated by three performance metrics, RMSE, MAE, and MAPE. The best model is selected by the lowest value of performance metrics. Then, the number of confirmed and the death cases forecasted for the 30 next days. The used data in this study is the absolute number of confirmed, death cases from February 20 to August 15, 2020. Results Our findings suggested that based on existing data in Iran, the suitable model with the lowest performance metrics for confirmed cases data obtained MLP network and the Holt-Winter model is the suitable model for forecasting death cases in the future. These models forecasted on September 14, 2020, we will have 2,484 new confirmed and 114 new death cases of COVID-19. Conclusion According to the results of this study and the existing data, we concluded that the MLP and Holt-Winter models had the lowest error in forecasting in comparison to other methods. Some models had fitted poorly in the test phase and this is because many other factors that are either not available or have been ignored in this study and can affect the accuracy of forecast results. Based on the trend of data and forecast results, the number of confirmed cases and death cases are almost constant and decreasing, respectively. However, due to disease progression and ignoring the recommendations and protocols of the Ministry of health, there is a possibility of re-emerging this disease more seriously in Iran and this requires more preventive care.

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Background: Acinetobacter is one of the most important pathogens that causes high prevalence of infection in clinical centers. Natural products are major sources of new anti-infective agents and considered as an alternative medicine for the treatment of infectious diseases. Objective: This study aims to evaluate the antibacterial activity of aqueous and methanol extract of Artemisia annua on the clinically isolated Acinetobacter and the combination of the plant extract with amikacin and imipenem. Methods: The aqueous and methanol extract of the aerial parts of this plant was prepared by maceration. Antibacterial activity was determined by the Broth microdilution method. The presence of adeI and adeB genes in Acinetobacter isolates was investigated. The combination effects of antibiotics were performed using the checkerboard method. The effect of plant extract on bacterial efflux pumps was also investigated. Results: MIC values of the methanol extract of A. annua were from 4.46 mg/ml to 8.92 mg/ml. The effects of the interaction of the plant extract with the amikacin and imipenem represent a significant synergistic influence. Findings indicated that the plant extract was able to inhibit the efflux pump remarkably. Conclusion: The combination of the plant extract with amikacin and imipenem can be used in the treatment of infections caused by antibiotic-resistant Acinetobacter and probably other infectious diseases.

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