The consequences of infectious diseases can range from mild illness to severe morbidity and mortality. Outbreaks of infectious diseases can have significant social and economic impacts, such as the disruption of daily life and the strain on healthcare systems and resources. Some of the factors that contribute to the spread of infectious diseases include inadequate hygiene practices, limited access to clean water and sanitation facilities, overcrowding, global travel and trade, and the emergence of antibiotic-resistant strains of bacteria. The most recent outbreak of COVID-19 has resulted in a global crisis [1]. In several countries, educational institutions, social activities centers, sports complexes, airports, logistics, etc., have been affected. Notably, these disruptions would not only be a narrow problem for the affected communities, but they would also have protracted consequences, such as increased recession. According to recent research, [2], the pandemic situation costs 2.5% to 3% economic instability of the global GDP every month. The economic consequences of the current recession are dispersed unequally. More significantly, based on the crises experienced in the past, it is observed that younger and lower-salaried workers have been the most financially impacted [2]. Forecasting futuristic events and multi-event identification [3] can assist in motivating common people to take into account productive steps to slow down the propagation of the epidemic.

Several studies on mathematical, computational, and statistical aspects of monitoring the situation of infectious diseases have recently been published [4], [5]. To forecast the spread of an epidemic, this model employs a set of time-dependent differential equations. The empirical dataset from COVID-19 is a time-series prediction and a sequence of observations, like meta-predictors and artificial neural networks-based methods, are all inherent to statistics [6], [7]. One of the main benefits of ANN-based approaches over machine learning techniques is that they may feed raw data and instantly find the appropriate characteristics [8]. Based on a variety of variables like accuracy, speed, performance, latency, and computational cost, ANN provides efficient results [9], [10]. In epidemic monitoring, data validation and authenticity are critical for public findings and conclusions based on documented or published statistical information. Therefore, the development of tracking applications became important in assisting the prevention of virus propagation as well as maintaining data authenticity and consistency.

Addressing the problem of infectious disease requires a multifaceted approach that includes preventive measures such as vaccination, proper hygiene practices, and access to clean water and sanitation facilities. Early detection and rapid response are also crucial in controlling the spread of infectious diseases, along with effective communication and coordination between healthcare providers, public health authorities, and individuals in affected communities. Blockchain technology can potentially be used to combat the spread of infectious diseases in several ways. Here are some possible use cases:

Contact Tracing: Blockchain can be used to create a secure and transparent system for contact tracing. A decentralized Blockchain network could be used to track the movement of infected individuals, along with the people they have been in contact with. This could help public health officials to identify potential clusters of infections and quickly contain the spread of the disease.

Vaccine Distribution: Blockchain can be used to create a tamper-proof system for tracking the distribution of vaccines. This could help ensure that vaccines are distributed fairly and that they reach the people who need them the most. It could also help prevent fraud and ensure that vaccines are not diverted to the black market.

Supply Chain Management: Blockchain can be used to create a transparent and secure system for tracking medical supplies and equipment. This could help prevent shortages and ensure that resources are distributed where they are needed most.

Research and Development: Blockchain can be used to create a secure and decentralized platform for sharing research data and collaborating on the development of new treatments and vaccines. This could help accelerate the pace of scientific discovery and improve the effectiveness of global efforts to combat infectious diseases.

This research presents a framework based on Blockchain technology to track and trace information on dashboards. We observed that our framework successfully updates hourly statistical information and keeps the track record of the information registrar to avoid data manipulation by un-authorized users. It is important to note that while Blockchain technology has the potential to address some of the challenges associated with infectious diseases, it is not a silver bullet solution. It should be used in conjunction with other measures such as vaccination campaigns, public health education, and robust healthcare systems. In light of the foregoing, we propose a novel approach to find the infected nodes in social complex networks to prevent the spread of infectious diseases.

RNA Viroids evolve to their surroundings by developing new variants on a regular basis, mostly by producing a set of nucleotide alterations [11]. Due to the limited sequencing capabilities of their RNA-dependent RNA polymerase-encoding (RdRP) genes, the reproduction of Ribonucleic Acid (RNA) viruses mutation might be the result. The genetic variants caused by viral RdRP may aid an evolving disease's adaptation to new hosts. RNA virus mutation rates, on the other hand, have been shown to vary in previous studies. Nonetheless, during the epidemic, SARS-CoV-2 spread to several communities worldwide. The most recent mutant (B.1.1.529-Omicron) is a rapidly progressing mutation that poses a danger to COVID-19 immunization efficacy [12]. Scientists are working feverishly to keep track of the unique coronavirus strain that causes COVID-19. Many of the alterations in this mutant have already been found in other variants, including Delta. Currently, maintaining focus on its evolution is the major objective. B.1.1.529-Omicron was detected in Botswana for the first time, on November 11, 2021, [13] and was detected in a traveler from South Africa who was visiting Hong Kong [14]. Omicron comprises the groups BA.1, BA.2, BA.3, BA.4, BA.5, and their descendants. It moreover incorporates recirculating, recombining BA.1/BA.2 forms like XE. W.H.O (World Health Organization) recommends that these descendent generations should be tracked by public health authorities as unique generations and that comparative analyses of their viral traits should be conducted. Researchers are still trying to figure out how the variant prevents inherent antibodies from being evoked by vaccinations, as well as if it causes more or less serious illnesses than other versions. B.1.1.529-Omicron was labeled as a variation of concern by W.H.O on 26 November 2021 [15], with the recommendation of W.H.O's Technical Advisory Committee on SARS-CoV-2 Virus Development experts. The W.H.O. has added Omicron to its list of variants of concern, which already have Aplpha, Beta, Gamma, and Delta. The novel SARS-CoV-2 evokes memories of COVID-19's early days when movement from Wuhan, China was forbidden. According to recent studies [16], in contrast to resilience after one dosage, the Chinese Sinopharm, AstraZeneca, and Pfizer coronavirus vaccines are highly effective against the variation after two dosages. Viruses constantly change, and most of the changes are rather minimal. Some, however, can render the disease more aggressive or deadly, so these alterations are more prevalent. Some of the changes may also be damaging to the infection itself.

Innovative and developing solutions like machine learning, deep learning [17], and Blockchain potentially assist in addressing the issues in the current situation. Blockchain, particularly, has the ability to impact a range of sectors, such as finance, procurement, and medical organizations. Blockchain is a distributed system having remarkable properties including resistant data architecture, traceability, and cryptography security measures embedded inside [18]. Because of its fundamental encrypted architecture, which is utilized to authorize participants in the network, the Blockchain's distributed system is tamper-proof. Furthermore, being able to change operations uploaded to the Blockchain network takes a lot of effort since once an operation is authenticated and certified, it is attached to prior transactions with distinct hashing. Therefore, changing one operation would modify the hash, informing everyone, and rendering it very hard to delete or amend the information. Additionally, all participants in the network have an approach to the data recorded on the Blockchain, increasing transparency across users.

Blockchains have a number of possible applications that could aid in the fight against future outbreaks [14]. It is being used to streamline vaccination and medicine clinical trials, boost people's awareness, manage funds and fundraising efforts publicly, and serve as a dependable statistics tracking system. We focus on the information tracing utilization scenario in this article since Blockchains allow for data collection and transmission to be kept safe and reliable. COVID-19 information can be acquired from a variety of reliable sources, including the National Health Commission of the People's Republic of China [19], World Health Organization (W.H.O) [20], and the Centers for Disease Control (CDC) [21]. Therefore, developing state-of-the-art platforms that impose security constraints and data confidentiality are important. Distributed monitoring systems are essential for obtaining public data and statistics through reputable sources for presentation on distributed ledgers and interfaces. In specific perspectives, such as information processing, performance measurement, accessibility, etc, Table 1 emphasizes the difference between the Blockchain framework and a typical single integrated system.

Our major objective is, to use decentralized applications (smart contracts) and oracles to check statistics and thus reduce the spread of falsified data. This specific scenario is significant since there has been a recent uptick in claims of disinformation on numerous social media sites. It's also crucial to follow the message's originator in order to determine individuals who are propagating conspiracies, misinformation, provocative statements, and false information. The following are the primary achievements of this paper:

We used an Artificial Neural Network model to analyze the volume of infected cases/deaths and forecast the fluctuation in circumstances in terms of increase/decrease in cases.

We presented a Blockchain-based framework to;

Improve patient involvement by giving them access and control.

Facilitate collaborative practice professionalism.

Reconcile medications and ensure patients' safety.

Automating the procedure of detecting and identifying patients.

We are presenting a methodology and algorithms that designate the core concepts of the recommended Blockchain-based monitoring system, provide an in-depth schematic comprehensive analysis of participant interconnections in the Blockchain-based tracking system and are backed up with evidence of different situations of overall network implementations.

The proposed model is related to the study of Blockchain complex networks and, more specifically, to the design of a novel mechanism for evaluating and identifying infected nodes (infected individuals) in complex networks.

Because of its decentralized characteristics, Blockchain eliminates the necessity of outsiders, which can significantly decrease the likelihood of unauthorized modification and fake news stories, as well as boost the dependability of knowledge for the general public and medical professionals. Fake information adds to the turmoil and generates financial and psychological harm. As a result, publishing facts and findings on a Blockchain network protects them from being tampered with and renders them accountable, enabling to eliminate bogus information and data. Because information supplied through such a system is dependable, precise, tamper-free, and visible, Blockchain makes a good coronavirus monitoring solution. As a result, policymakers will be better informed about the situation of the epidemic, allowing for improved management, such as anticipating the pandemic, restricting potential locations, and monitoring the infection's transmission. From an ever-growing set of public information, Acoer [22], has produced a HashLog dashboard that lets users to see the degree of epidemic propagation and patterns over time. Furthermore, the Acoer Coronavirus HashLog uses data from W.H.O [20], C.D.C [21], and social media websites to create visual analytical frameworks connected with clinical information [22].

Intruders are utilizing coronavirus mapping to transmit malicious soft wares. Such intruders pose as interactive visualizations that follow the virus's propagation. Visitors are duped towards providing sensitive data such as personal credentials, passwords, and credit card information, etc. The attackers subsequently exploit these personal data on the darknet or use it to financially abuse people. Many scammers employ fake coronavirus tracking applications to force individuals to pay ransom payments to avoid the leak of their important private data. Moreover, the audience is constantly bombarded with disinformation and false information garbage. The issues and challenges of central data platforms can be solved using Blockchain.