Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art

SN Computer Science (2020) 1:197 https://doi.org/10.1007/s42979-020-00209-9 SURVEY ARTICLE Forecasting Models for Coronavirus Disease (COVID‑19): A Survey of the State‑of‑the‑Art Gitanjali R. Shinde1 · Asmita B. Kalamkar1 · Parikshit N. Mahalle1,2 · Nilanjan Dey3 · Jyotismita Chaki4 · Aboul Ella Hassanien5 Received: 1 April 2020 / Accepted: 28 May 2020 / Published online: 11 June 2020 © Springer Nature Singapore Pte Ltd 2020 Abstract COVID-19 is a pandemic that has affected over 170 countries around the world. The number of infected and deceased patients has been increasing at an alarming rate in almost all the affected nations. Forecasting techniques can be inculcated thereby assisting in designing better strategies and in taking productive decisions. These techniques assess the situations of the past thereby enabling better predictions about the situation to occur in the future. These predictions might help to prepare against possible threats and consequences. Forecasting techniques play a very important role in yielding accurate predictions. This study categorizes forecasting techniques into two types, namely, stochastic theory mathematical models and data science/ machine learning techniques. Data collected from various platforms also play a vital role in forecasting. In this study, two categories of datasets have been discussed, i.e., big data accessed from World Health Organization/National databases and data from a social media communication. Forecasting of a pandemic can be done based on various parameters such as the impact of environmental factors, incubation period, the impact of quarantine, age, gender and many more. These techniques and parameters used for forecasting are extensively studied in this work. However, forecasting techniques come with their own set of challenges (technical and generic). This study discusses these challenges and also provides a set of recommendations for the people who are currently fighting the global COVID-19 pandemic. Keywords COVID-19 · Forecasting models · Machine learning method · Prediction · Big data · Epidemic · Pandemic Introduction The world has been facing threats in the form of pandemics periodically over the centuries. The aftermath of these pandemics have always had a huge impact on the world and have also turned the tables over. COVID-19, the current devastating pandemic is also running its course currently in the world. Not only economies are crashing but the overall strengths and morals of the heavily impacted nations are being compromised. In order to do accurate predictions understanding of natural progression of disease is very important. A disease generally progresses because of the exposure to the infection. Because of this exposure to infection hosts are formed. Hosts 1 Department of Computer Engineering, Smt. Kashibai Navale College of Engineering, Pune, Maharashtra, India Asmita B. Kalamkar 2 Department of Communication, Media and Information Technologies, Aalborg University, Copenhagen, Denmark Parikshit N. Mahalle 3 Department of Information Technology, Techno International New Town, Kolkata, India Nilanjan Dey 4 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India Jyotismita Chaki 5 Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt * Gitanjali R. Shinde Aboul Ella Hassanien SN Computer Science Vol.:(0123456789) 197 Page 2 of 15 refer to the group of people who are more susceptible to get affected. When an infected host comes in contact with more people then disease starts to spread. Figure 1 depicts the host formation and progression [1]. The diseases like COVID-19, SARC, PLAGUE, etc., are acquired diseases. It means diseases spread through pathogenic agents (virus or bacteria or any microorganism). A traditional model for the cause of the infectious disease is defined. It is called as an Epidemiologic Triad. It is depicted in Fig. 2. The four important factors involved in the epidemiologic triad are environmental factors, carrier agent, infected hosts and the pathogens. The agent is usually the carrier of the infection. The infection is transmitted to the host when an agent comes in contact with the host under a certain environment. A pathogen is also known as a vector. A vector is an organism that transmits the infection via virus or bacteria from one host to another [2]. Pandemics are often referred to as outbreaks because of their spread pattern. The type of the outbreak determines the mortality rate of the disease. Over the last few years, it has been seen that because of the change in lifestyle, increased global travel and urbanization, infectious diseases quickly escalate into a pandemic. To prevent these epidemics, strong policies need to be administered. Otherwise, the situation can take a drastic turn rapidly. Since the beginning, mankind has faced epidemics and pandemics. The first epidemic faced by mankind was in the early 1300’s called black death. It was one of the worst pandemics Fig. 1  Host formation and progression Fig. 2  Epidemiologic Triad SN Computer Science SN Computer Science (2020) 1:197 seen by humankind. This epidemic took millions of lives. It has been observed that this disease targeted most of the elderly people and people who are exposed to psychological stressors [3, 4]. The next pandemic faced by people was in the early 1500’s called smallpox where 50% of the mortality rate was observed [5]. After which mankind had to face one of the deadliest pandemics called the fifth cholera pandemic which took more 1.5 million lives [6]. Following this, in 1918 one of the devastating Spanish flu influenza pandemics was observed. This pandemic took 20–110 million lives. In 1957, the Asian flu influenza pandemic was occurred which took nearly 0.7–1.5 million lives [6, 7]. In 1981, the world witnessed a new pandemic: HIV/AIDS. It was observed that more than 70 million patients were infected with the virus. According to WHO, Global health observatory data 36.7 million deaths occurred due to this pandemic [8, 9]. After the HIV/AIDS pandemic, the world witnessed a new wave of different pandemics starting with SARS in 2003. This pandemic affected 4 continents and 37 countries across the globe [10, 11]. In 2009 swine flu pandemic took place in which about 151,700–575,500 deaths were reported [12, 13]. SARS pandemic was followed by the MERS pandemic in 2012. It affected 22 countries across the globe [14]. Two pandemics then followed the MERS. First was the Ebola pandemic in 2013 followed by the zika pandemic in 2015. Both the pandemics reported deaths in thousands [15, 16]. Currently, the whole world is witnessing the COVID-19 pandemic. More than 100 plus countries till date are majorly affected by COVID-19. This count is increasing as each passing day. Throughout the history of these epidemics, one thing was observed, that is, with the progress in time, the (...truncated)