Contact Tracing Incentive for COVID-19 and Other Pandemic Diseases From a Crowdsourcing Perspective

Estimation of Total Cost Required in Controlling COVID-19 Outbreaks by Financial Incentives

In this article, we present a Monte Carlo simulation (MCS) to estimate the total cost required to control the spread of the COVID-19 pandemic by financial incentives. One of the greatest difficulties in controlling the spread of the COVID-19 pandemic is that most infected people are not identified and can transmit the virus to other people. Therefore, there is an urgent need to rapidly identify and isolate the infected people to avoid the further spread of COVID-19. To achieve this, we can consider providing a financial incentive for the people who voluntarily take the COVID-19 test and test positive. To prevent the abuse of the financial incentive policy, several conditions should be satisfied to receive the incentive. For example, an incentive is offered only if the recipients know who infected them. Based on the data obtained from epidemiological investigations, we calculated an estimated total cost of financial incentives for the policy by generating various possible infection routes using the estimated parameters and MCS. These results would help public health policymakers implement the proposed method to prevent the spread of the COVID-19 pandemic. In addition, the incentive policy can support various preparations such as hospital bed preparation, vaccine development, and so forth.

Minimizing Viral Transmission in COVID-19 Like Pandemics: Technologies, Challenges, and Opportunities

Coronavirus (COVID-19) pandemic has incurred huge loss to human lives throughout the world. Scientists, researchers, and doctors are trying their best to develop and distribute the COVID-19 vaccine throughout the world at the earliest. In current circumstances, different tracking systems are utilized to control or stop the spread of the virus till the whole population of the world gets vaccinated. To track and trace patients in COVID-19 like pandemics, various tracking systems based on different technologies are discussed and compared in this paper. These technologies include, cellular, cyber, satellite-based radio navigation and low range wireless technologies. The main aim of this paper is to conduct a comprehensive survey that can overview all such tracking systems, which are used in minimizing the spread of COVID-19 like pandemics. This paper also highlights the shortcoming of each tracking systems and suggests new mechanisms to overcome such limitations. In addition, the authors propose some futuristic approaches to track patients in prospective pandemics, based on artificial intelligence and big data analysis. Potential research directions, challenges, and the introduction of next-generation tracking systems for minimizing the spread of prospective pandemics, are also discussed at the end.

A comprehensive review on variants of SARS-CoVs-2: Challenges, solutions and open issues

SARS-CoV-2 is an infected disease caused by one of the variants of Coronavirus which emerged in December 2019. It is declared a pandemic by WHO in March 2020. COVID-19 outbreak has put the world on a halt and is a major threat to the public health system. It has shattered the world with its effects on different areas as the pandemic hit the world in a number of waves with different variants and mutations. Each variant and mutation have different transmission and infection rates in the human population. More than 609 million people have tested positive and more than 6.5 million people have died due to this disease as per 14th September 2022. Despite of numerous efforts, precautions and vaccination the infection has grown rapidly in the world. In this paper, we aim to give a holistic overview of COVID-19 its variants, game theory perspective, effects on the different social and economic areas, diagnostic advancements, treatment methods. A taxonomy is made for the proper insight of the work demonstrated in the paper. Finally, we discuss the open issues associated with COVID-19 in different fields and futuristic research trends in the area. The main aim of the paper is to provide comprehensive literature that covers all the areas and provide an expert understanding of the COVID-19 techniques and potentially be further utilized to combat the outbreak of COVID-19.

Sensing and making sense of tourism flows and urban data to foster sustainability awareness: a real-world experience

Tourism is one of the world's largest industries fundamentally arising from mobility as a form of capital. In destination islands that have a delicate ecosystem to maintain, this source of income can become problematic in terms of sustainability. A difficulty in making people aware of this issue is also represented by the fact that such sustainability-related issues (and their causes) are often not "visible" to citizens. To foster awareness about the relationship between sustainability and tourism in well-known destinations, we design a platform that engages users at two levels of participation: i. at the IoT and sensors level, in order to let them becoming providers of big data, deploying and enlarging the pervasive infrastructure; ii. at the (big) data visualization level, with the aim of engaging them in making sense of large volumes of data related to sustainability. This paper presents the design and implementation of a real-world experience where a low-cost collaborative platform made it possible to sense and visualize tourist flows and urban data into a rich interactive map-based visualization, open to the local communities. We deployed our case study in the Madeira archipelago, engaging locals and visitors of the island in two exploratory studies focused on measuring the impact of providing users with meaningful representations of tourism flows and related unperceivable aspects that affect the environmental sustainability. Analysing the findings of the two studies, we discuss the potentiality of using such a system to make sense of big data, fostering awareness about sustainability issues, and we point to future open challenges about citizens' participation in sensing and making sense of big data.