The 2019 novel-coronavirus has affected 181 countries with approximately 1197405 confirmed cases (by 5th April). Understanding the transmission dynamics of the infection in each country which got affected on a daily basis and evaluating the effectiveness of control policies are critical for our further actions. To date, the statistics of COVID-19 reported cases show that more than 80% of infected are mild cases of disease, around 14% of infected have severe complications, and about 5% are categorized as critical disease victims. Today's report (5th April 2020; daily updates in the prepared website) shows that the confirmed cases of COVID-19 in the United States, Spain, Italy, and Germany are 308850, 126168, 124632, and 96092, respectively. Calculating the total case fatality rate (CFR) of Italy (4th April 2020), about 13.3% of confirmed cases have passed away. Compared with South Korea's rate of 1.8% (seven times lower than Italy) and China's 4% (69% lower than Italy), the CFR of Italy is too high. Some effective policies that yielded significant changes in the trend of cases were the lockdown policy in China, Italy, and Spain (the effect observed after some days), the shutdown of all nonessential companies in Hubei (the effect observed after 5 days), combined policy in South Korea, and reducing working hours in Iran.

The 2019-Novel-Coronavirus (COVID-19) has affected 181 countries and out of about 1197405 confirmed cases (By April 5). Understanding the transmission dynamics of the infection in each country which affected on a daily basis and evaluating the effectiveness of control policies is critical for our further actions. To date, the statistics of COVID-19 reported cases show more than 80 percent of infected had a mild case of disease, while around 14 percent of infected experienced a severe one and about 5 percent are categorized as critical disease victims. Today's report (2020-04-05; daily updates in the prepared website) shows the confirmed cases of COVID-19 in the US, Spain, Italy, and Germany are 308850, 126168, 124632, and 96092; respectively. Calculating the total Case Fatality Rate (CFR) of Italy (2020-04-04), about 13.3% of confirmed cases passed away. Compared to South Korea's rate of 1.8% (7 times lower than Italy) and China's 4% (69% lower than Italy), the CFR of Italy is too high. There are some effective policies that yield significant changes in the trend of cases. The lockdown policy in China, Italy, and Spain (the effect observed after some days), Shutdown of all non-essential companies in Hubei (the effect observed after 5 days), combined policy in South Korea, and reducing working hours in Iran.

We study the linear Lindenbaum algebra of Basic Propositional Calculus, called linear basic algebra. (© 2003 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

<div>Artificial Neural networks are one of the most widely applied approaches for classification problems. However, developing an errorless artificial neural network is in practice impossible, due to the statistical nature of such networks. The employment of artificial neural networks in critical applications has rendered any such emerging errors, in these systems, incredibly more significant. Nevertheless, the real consequences of such errors have not been addressed, especially due to lacking verification approaches. This study aims to develop a verification method that eliminates errors through the integration of multiple artificial neural networks. In order to do this, first of all, a special property has been defined, by the authors, to extract the knowledge of these artificial neural networks. </div><div>Furthermore, a multi-agent system has been designed, itself comprised of multiple artificial neural networks, in order to check whether the aforementioned special property has been satisfied, or not. Also, in order to help examine the reasoning concerning the aggregation of the distributed knowledge, itself gained through the combined effort of separate artificial neural networks and acquired external information sources, a dynamic epistemic logic-based method has been proposed.</div><div>Finally, we believe aggregated knowledge may lead to self-awareness for the system. As a result, our model shall be capable of verifying specific inputs, if the cumulative knowledge of the entire system proves its correctness. </div><div>In conclusion, and formulated for multi-agent systems, a knowledge-sharing algorithm (Abbr. MASKS) has been developed. Which after being applied on the MNIST dataset successfully reduced the error rate to roughly one-eighth of previous runs on individual artificial neural network in the same model. </div>