On March 11 th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14 th day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases.

/pdf/time-series-forecasting-of-covid-19-transmission-in-canada-1jhrh59xyb.pdf

Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks.

Empirical methods in pedestrian, crowd and evacuation dynamics: Part I. Experimental methods and emerging topics

Hazardous chemical leakage accidents and emergency evacuation response from 2009 to 2018 in China: A review

Deep Neural Network-based Optimization Framework for Safety Evacuation Route during Toxic Gas Leak Incidents

Effective evacuation policies in emergency situations are important to save lives. To develop such policies, simulation models based on cellular automata have been used for crowd evacuation dynamics. In most previous studies of crowd evacuations, an evacuee is represented by a 1 × 1 square. However, a rectangle (1 × 2) representation is more suitable for such models than the square representation because of evacuees’ shoulder width. The rectangle representation gives two new features to evacuees’ behaviors: moving sideways and turning. We study the effects of these behaviors on crowd evacuation dynamics. Hence, we constructed a cellular automaton model where evacuees whose shoulder widths are 1 × 2 try to escape from a room in an emergency situation. The simulation results showed that turning behavior can make the evacuation time shorter and there is an optimal turning rate for the crowd evacuation. Our findings contribute to the effective control of evacuees in emergency situations.

Cellular automaton model with turning behavior in crowd evacuation

With the increasing of risk potential in crowded places, evacuation management becomes practically important to ensure the safety of crowds. The studies of crowd evacuation in normal or emergency situations have become a hot topic. Due to the distinct advantages of high efficiency, strong scalability and simple implementation, cellular automata models (CA) have become one of the most widely-used models for evacuation. However, the practical requirements of evacuation propose some important challenges for CA models, for example, to accurately characterize both position and velocity of individuals, to depict environments and accidents, and to describe human behaviors. In the last 20 years, there are many studies aiming at resolving the above challenges. Starting from the challenges mentioned above, this paper tries to give a review of CA models, specially used for crowd evacuation. Firstly, we give an overview of CA models for evacuation, and put forward research paradigm, modeling framework and classification of CA models. The models used for evacuation are classified into three kinds of categories, i.e. lattice gas model, floor field model, and other field-based models. The last category includes potential field model, electrostatic-induced potential field model, cost potential field model, etc. Then, three main challenges of CA models for evacuation are presented, and the improvements for each type of challenge are summarized. Typical simulation scenarios and research issues are further proposed. Finally, the advantages and disadvantages of CA models are illustrated from the aspects of implementation, performance, scalability, accuracy and applicability.

A review of cellular automata models for crowd evacuation

Relationship between behavior aggressiveness and pedestrian dynamics using behavior-based cellular automata model

For serious accidents such as toxic/flammable release, fire and explosion in chemicals-concentrated areas, one primary issue is to evacuate on-site workers from potential affected zones. In this paper, based on corresponding reports, photos and videos, some accidents involving emergency evacuation are firstly investigated to present important and interesting problems to be considered in practice. These problems are summarized into three kinds of aspects including the poor efficiency of emergency plans, the low accuracy and high time-consuming calculation for evacuation route planning, and the neglection of evacuee's behavior and decision-making. Two main factors influencing the behavior and decision-making of evacuees are concluded as potential risk and impact from hazard sources, and the visibility at different locations. Then, the literature with respect to the emergency evacuation under toxic gas leakage, fire and visibility-limited conditions are reviewed. The key problems are related to ignoring the time-consuming requirement in emergency activities and domino effects of accidents, and the oversimplification of corresponding models. At last, from the famous cellular automata (CA) for emergency evacuation and atmospheric dispersion, a new general research framework for evacuation in chemicals-concentrated areas is proposed. It relies strongly on the quantitative assessment of risk, the establishment of individual visibility field, the use of CA coupled with artificial neural networks, and the optimal route planning. This paper could be useful for realistic problems on emergency evacuation in chemicals-concentrated areas.

Review on the emergency evacuation in chemicals-concentrated areas

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A review of cellular automata models for crowd evacuation

Relationship between behavior aggressiveness and pedestrian dynamics using behavior-based cellular automata model

Review on the emergency evacuation in chemicals-concentrated areas