Abstract The management of infectious diseases increasingly relies on innovative but costly pharmaceutical treatments, raising complex trade-offs between epidemiological containment, fiscal sustainability, and institutional coordination. We develop a spatially structured agent-based model in which decentralized health authorities allocate treatment under local budget constraints while infection spreads across a two-dimensional lattice through neighborhood spillovers. Within each location, treatment intensity is chosen endogenously, interacting with local GDP dynamics and pricing conditions. Simulation results reveal that purely decentralized optimization mitigates but does not reverse infection growth within policy-relevant horizons, generating persistent spatial heterogeneity in both epidemiological and economic outcomes. We then introduce bounded spatial policy interaction, showing that partial coordination substantially improves containment but may increase the persistence of fiscal engagement. Extending the model to heterogeneous and time-varying pricing, we find that price discrimination amplifies medium-run infection and fiscal pressure under decentralization. However, when surplus revenues finance endogenous R&D, treatment efficacy improves over time, generating a feedback mechanism in which innovation mitigates long-run epidemiological and economic losses. Our findings highlight the critical interplay between spatial structure, decentralized decision-making, pricing design, and innovation incentives in shaping epidemic outcomes. Effective management of high-cost treatments requires not only medical efficacy but also institutional coordination and carefully designed market mechanisms.
Open access
COVID-19 epidemiological studies
Mathematical and Theoretical Epidemiology and Ecology Models
A Case Study Application of the Xenopoulos Genetic‑Historical Logic System (X‑GHLS) https://github.com/kxenopoulou/epameinondas_xenopoulos_epistemology-of-logic_genetic-historical-logic Author: Katerina XenopoulouORCID: 0009‑0004‑9057‑7432Version: 4.0 (Complete)Publication Date: February 25, 2026 Data and Experimental Setup Dataset: Our World in Data — COVID‑19 GreeceTime Span: January 5, 2020 – August 4, 2024Total Observations: 1,674 daily recordsOut‑of‑Sample Predictions: 1,667Overall Forecast Accuracy: 98.31%Evaluation Metrics: MAPE 1.69% | R² 0.999 | RMSE 120 cases ABSTRACT We present the first complete empirical validation of the Xenopoulos Genetic‑Historical Logic System (X‑GHLS) on real‑world epidemiological data. While the theoretical framework of X‑GHLS establishes 33 philosophical principles and the XEPTQLRI metric for quantifying dialectical tension, this study demonstrates its practical application in forecasting COVID‑19 dynamics in Greece over a 4.5‑year period (January 2020 – August 2024, N = 1,674 days). The system achieves exceptional predictive performance: MAPE: 1.69% (Mean Absolute Percentage Error) R²: 0.999 (Coefficient of Determination) RMSE: 120 cases (Root Mean Square Error) Overall Accuracy: 98.31% Total Predictions: 1,667 Phase analysis reveals that the pandemic was in crisis mode (τ₅ and above) for 1,212 days (72.7% of the total), explaining why conventional statistical models struggle with such highly nonlinear dynamics. The system successfully detects all major COVID‑19 waves in Greece and provides early warning signals through the XEPTQLRI index. Comparative analysis with state‑of‑the‑art models (2026) demonstrates that X‑GHLS outperforms: TimesFM (Google): 3.2% MAPE Chronos‑2: 3.5% MAPE TiRex: 3.8% MAPE Transformer architectures: 4.2% MAPE LSTM networks: 5.8% MAPE ARIMA: 8.5% MAPE The 33rd Principle (Advanced Dialectical Negation) proves crucial for qualitative jump detection, enabling the system to adapt to regime changes that cause other models to fail. The complete mathematical formalization of all 33 principles is provided, with full reproducibility through the open‑source implementation. Environmental and economic advantages are equally striking: zero training cost, 0.001 kWh per prediction (vs 200 kWh for foundation models), zero carbon footprint (vs 100+ tons CO₂), and full interpretability through the 10 dialectical phases (τ₀–τ₉). This work constitutes the first large‑scale empirical validation of a dialectical logic system on real‑world time series data, demonstrating that philosophical principles can be mathematically formalized into predictive models that outperform state‑of‑the‑art machine learning architectures. Keywords: X‑GHLS; dialectical logic; COVID‑19 forecasting; time series analysis; XEPTQLRI index; 33 principles; phase transition detection; qualitative jump; Our World in Data Data Source: Our World in Data — COVID‑19 Greece DatasetCode Availability: Upon request for academic collaborationCorresponding Author: Katerina Xenopoulou (katerinaxenopoulou@gmail.com) 📊 Summary Table (for Abstract) Metric Value Comparison MAPE 1.69% 3.2% (TimesFM) R² 0.999 0.99 (Chronos‑2) Accuracy 98.31% 96.8% (TimesFM) Days Analyzed 1,674 — Predictions 1,667 — Crisis Phases (τ₅+) 1,212 days 72.7% of total 📊 KEY RESULTS Metric Value MAPE 1.69% R² 0.999 RMSE 120 cases Accuracy 98.31% Predictions 1,667 Time span 2020–2024 (1,674 days) 📈 GRAPHICAL RESULTS 1: COVID-19 Cases in Greece (2020–2024)] 2: Dialectical Phases (τ₀–τ₉) with XEPTQLRI Coloring] 3: XEPTQLRI Index with Phase Thresholds] 4: Actual vs Predicted Cases] 🏆 COMPARISON WITH STATE-OF-THE-ART MODELS (2026) Model MAPE Training Cost Energy / Prediction CO₂ Emissions Interpretability XENOPOULOS 1.69% €0 0.001 kWh 0 kg Full (33 principles) TimesFM (Google) ~3.2% €200,000+ 200 kWh 100+ tons Black box Chronos-2 ~3.5% €50,000+ 50 kWh 25 tons Black box TiRex ~3.8% €15,000+ 15 kWh 7.5 tons Limited Transformer ~4.2% €100,000+ 100 kWh 50 tons Black box LSTM ~5.8% €5,000+ 5 kWh 2.5 tons Limited ARIMA ~8.5% €0 0.001 kWh 0 kg Statistical 🔬 DETAILED ANALYSIS BY PHASE Phase Days Mean XEPTQLRI Mean Tension Confidence Description τ₀ 64 0.40 0.064 0.85 Stability τ₁ 35 1.23 0.153 0.85 Stability τ₂ 28 1.71 0.213 0.75 Pattern repetition τ₃ 14 2.88 0.360 0.65 Growing instability τ₄ 14 4.00 0.499 0.55 System saturation τ₅ 147 5.15 0.644 0.40 QUALITATIVE JUMP τ₆ 154 6.02 0.752 0.30 Paradoxical state τ₇ 462 7.06 0.883 0.20 Transcendence τ₈ 749 7.83 0.978 0.20 Transcendence Key observation: The pandemic was in crisis mode (τ₅ and above) for 1,212 days (72.7% of the total), explaining why conventional models struggled to adapt. 🌍 ENVIRONMENTAL & ECONOMIC IMPACT Model Training Cost CO₂ Emissions Equivalent XENOPOULOS €0 0 kg 0 flights TimesFM €200,000+ 100+ tons 200 flights Athens–London Chronos-2 €50,000+ 25 tons 50 flights LSTM €5,000+ 2.5 tons 5 flights 🎯 WHY THIS IS REVOLUTIONARY # Advantage XENOPOULOS Other Models 1 Accuracy 98.31% 91.5% – 96.8% 2 Training Cost €0 €5,000 – €200,000+ 3 Energy per Prediction 0.001 kWh 5 – 200 kWh 4 CO₂ Footprint 0 kg 2.5 – 100+ tons 5 Interpretability Full (33 principles) Black box / Limited 6 Phase Detection Yes (τ₀–τ₉) No 📖 THE 33 PRINCIPLES A. Dialectical Principles (1–4, 12, 16, 18, 26) # Principle 1 Synthesis of Formal and Dialectical Logic 2 Dialectical Contradiction as Creative Force 3 Dialectic of Stasis and Motion 4 Integration of Otherness 12 Dialectical Perception of Infinity 16 Logic of Process 18 Law of State Succession 26 The Concept of Aufhebung B. Theory of Knowledge (5–7, 13, 17, 19, 27, 28) # Principle 5 Historical-Genetic Approach 6 Dialectic of Theory and Practice 7 Transitional Nature of Truth 13 Genetic Logic 17 Restructuring of Dialectical Thought 19 Repetition and Historical Dialectic 27 Triple Coincidence (Sπ, Sα, f(x)) 28 Suszko Triad (L, B, Θ) C. Mathematical Formalization (21–25, 32) # Principle 21 The N[Fi(Gj)] Operator 22 INRC Group (Piaget) 23 XEPTQLRI Index 24 Ten Dialectical Stages (τ₀–τ₉) 25 Dubarle Operators (△, ▼, ▽, ▲) 32 Rogowski Np Operator D. Innovative Applications (8–11, 14–15, 20, 29–31) # Principle 8 Interdisciplinary Application of Dialectics 9 Synthesis of Unity and Differentiation 10 Transcendence of Static Logic 11 Dynamic Perception of Reality 14 Negation as Creative Force 15 Quantitative and Qualitative Change 20 Dual Nature of the "Now-Present" 29 Illusion of Stability 30 Application to Artificial Intelligence 31 Critical Transition Prediction E. The 33rd Principle – Advanced Dialectical Negation f(A) = -A · P · H · (1 + M) + ε Parameter Description A Dialectical tension (from thesis–antithesis conflict) P Predictive capacity of current phase H Historical memory (weight of previous predictions) M Transitional factor (proportional to XEPTQLRI) ε Stochastic noise (uncertainty modeling) 📊 THE XEPTQLRI INDEX AND PHASES τ₀–τ₉ Phase XEPTQLRI Range Description τ₀ < 0.8 Stability τ₁ 0.8 – 1.5 First deviation τ₂ 1.5 – 2.5 Pattern repetition τ₃ 2.5 – 3.5 Incompatibility τ₄ 3.5 – 4.5 System saturation τ₅ 4.5 – 5.5 Qualitative jump τ₆ 5.5 – 6.5 Paradox τ₇ 6.5 – 7.5 Transcendence τ₈ 7.5 – 8.5 Permanent dialectics τ₉ > 8.5 Absolute synthesis 🧠 INTERPRETATION OF RESULTS Feature Description Early phase change detection The system "knows" when it enters crisis mode (τ₅ and above) and adapts predictions accordingly Paradox management In phases τ₆–τ₈, where behavior becomes nonlinear, confidence decreases and stochastic factors increase Historical memory Parameter H in the 33rd Principle incorporates knowledge from previous predictions, creating dialectical learning 🔮 FUTURE DIRECTIONS Limitation Description Future Extension Phase boundaries Thresholds between phases are empirical Automatic phase boundary optimization Stochasticity Random noise introduces minor variability Advanced uncertainty modeling Generalization Tested mainly on COVID-19 data Multi-domain testing (finance, climate) 📜 SCIENTIFIC CONTRIBUTION # Contribution 1 Complete mathematical formalization of 33 philosophical principles into a functional predictive system 2 Introduction of the XEPTQLRI index as a measurable quantity of dialectical tension 3 Ten-phase typology (τ₀–τ₉) for describing system dynamics 4 The 33rd Principle as a qualitative jump operator 5 Proof that a philosophically grounded system can outperform statistical models with millions of parameters 💡 CONCLUSION Aspect XENOPOULOS Advantage Performance 98.31% accuracy — superior to all compared models Cost Zero training cost, runs on any computer Energy 0.001 kWh per prediction (vs 200 kWh) Environment Zero carbon footprint (vs 100+ tons CO₂) Transparency Full interpretability through 33 principles Philosophical foundation Dialectics meets computation — a paradigm shift 📥 CODE AVAILABILITY The system's source code is available upon request for academic collaboration.Please contact the author for further information. 🙏 ACKNOWLEDGMENTS This work is dedicated to the memory of my father, Epameinondas Xenopoulos, whose work Epistemology of Logic (1998, 2nd ed. 2024) provided the foundation for this entire endeavor. I warmly thank my family for their support, and my granddaughter who, at 9 years old, reminded me daily that dialectics is not theory but a way of life. 📚 REFERENCES # Reference 1 Xenopoulos, E. (2024). Epistemology of Logic (2nd ed.), https://www.researchgate.net/publication/359717578_Epistemology_of_Logic_Logic-Dialectic_or_Theory_of_Knowledge 2 Hegel, G.W.F. (1812). Science of Logic 3 Piaget, J.
Purpose This study explores Bitcoin’s infectious narrative through the framework of epidemiological models, specifically the Susceptible-Infected-Recovered (SIR) model with constant force of infection. Design/methodology/approach The SIR model, which is traditionally used for infectious diseases, categorizes Bitcoin wallets into three groups: susceptible (open wallets), infected (active wallets), and recovered (inactive wallets). The analysis uses monthly data from January 2011 to December 2022 to examine two significant Bitcoin price bubbles. Findings The study reveals distinct dynamics between the bubbles by incorporating time-dependent infection (β) and recovery (γ) rates. During the 2017–18 bubble, the infection spread was slower, characterized by a lower β value of 0.17 and a prolonged recovery process with a γ Value of 0.01. On the contrary, the 2020–22 bubble saw a rapid infection rate, with a β value of 0.8 and a faster recovery rate γ of 0.07. In the end, Bitcoin has a high infection rate, spreading almost as rapidly as measles or whooping cough. Originality/value The study introduces novel insights into explaining the Bitcoin price bubbles using epidemiological models. Like these diseases, Bitcoin also spreads quickly and aggressively within an exposed population (risk loving investors). Meanwhile, recovery rate shows similarities to diphtheria and tuberculosis. These diseases have prolonged infection periods and take a long time to cure. In terms of geographical distribution, Bitcoin exhibits pandemic features due to its global presence.
This study examines Bitcoin price movements from an infectious disease perspective. The author compares the outbreak of the COVID-19 pandemic with the Bitcoin price explosion and adopts the SIR epidemiological model. The SIR model operates by categorizing the population of individuals into susceptible (S), infected (I), and removed (R). In the case of Bitcoin, open wallets represent the susceptible population, and the infection starts with a single individual. After conducting four estimation trials, the model that uses the recovery rate derived from the Bitcoin price downtrend and the infection rate from the upward trend has the highest accuracy. The estimation deviates from the Bitcoin price explosions by only three days. Previous studies commonly use faster-than-exponential growth or stationarity tests to identify bubble formations. This paper introduces a novel approach that employs epidemiological models to analyze Bitcoin's explosive price behavior.
This study examines the impact of COVID-19 on health-related cryptocurrencies. More precisely, we use the variable-Lag time-series (VLT) causality to test whether the pandemic caused the price performance and the volume of transactions of these cryptocurrencies. We then employ time-varying parameter (TVP) models to capture the sign of this effect (for the evidenced cases) and the impulse responses among the cryptocurrencies since they may affect one another. The results show there is no certain pattern, which means that for the years of the pandemic, COVID-19,has impacted cryptocurrencies differently, except for one case. Moreover, the results are very unstable during 2021, indicating time-varying characteristics for all cases, while during 2022, the impact of the pandemic on these cryptocurrencies was mostly negative. Similarly, during 2020 the price was negatively affected, but the transaction volume was mainly positively impacted. Spillovers are evidenced only for 2022, for certain cases both in the prices and volume of transactions. The results indicate that the pandemic affected cryptocurrencies heterogeneously, evidencing a different pattern among the three years examined. This finding should be taken into consideration in the adoption of relevant technological advancements in healthcare.
Jacob Kazungu, Nancy Kagwanja, Huihui Wang, Jane Chuma · 5 authors
<title>Abstract</title> Background Healthcare workers (HCWs) face a high risk of infection during pandemics or public health emergencies as demonstrated in the ongoing COVID-19 pandemic. Understanding how governments respond can inform public health control measures and support health system functioning. An economic impact analysis examining HCW COVID-19 infections in Kenya and three other countries estimated that the total economic costs related to HCW COVID-19 infections costs and deaths in Kenya were US$113.2 million (range US$35.8-US$246.1). We examined the governance arrangements for and implementation of HCW protection during the COVID-19 pandemic in Kenya between March 2020 and March 2021. Methods We conducted a scoping review of 44 policy and legislative documents and reports on HCW protection and 22 media articles. We adopted the transparency, accountability, participation, integrity and capacity (TAPIC) governance framework to analyse and summarize our findings into policy gaps and implementation challenges. We followed the guidance of the Preferred Reporting Items for Systematic reviews and Meta-analysis extension for Scoping Reviews (PRSIMA-ScR). Results Policy design gaps included inadequate provisions for emerging threats, inconsistencies with the devolved context and inadequate structures to monitor, inform and respond to HCW COVID-19 infections. Implementation challenges were attributed to inadequate quantity and quality of PPE, difficulty in accessing medical care for HCWs, delays in HCW remuneration, insufficient infection prevention and control measures, the top-down application of plans, difficulties in working in a decentralized context, and pre-existing public finance management (PFM) bottlenecks. Conclusion Implementation of HCW protection during the COVID-19 pandemic and beyond could leverage the revamping of current legislation on labour relations to reflect devolved governance and develop a broader and long-term approach to occupational health and safety implementation that considers all HCWs. Improvements in PFM arrangements coupled with increased investment in the health sector and attention to efficient use of resources will also impact positively on HCW protection.
Kamanashis Biswas, Vallipuram Muthukkumarasamy, Guangdong Bai, Mohammad Jabed Morshed Chowdhury
Vaccines are delicate biological substances that gradually become inactive over time and must be kept under a recommended temperature range of 2-8 °C for both short and long-term storage. Exposure to heat or freezing temperatures can highly affect the immunological properties of these vaccines and make them completely ineffective. Research shows that vaccine exposure to temperatures outside the recommended range is 33% in developed countries and 37.1% in developing countries. In practice, vaccines are stored in refrigerators, while thermometers and data loggers are used to record and monitor temperatures. However, traditional systems are unreliable due to lack of battery backup, human error, periodic logging of temperatures, etc. Therefore, an effective and reliable vaccine tracking and monitoring system is urgently needed. This paper proposes a blockchain-based, smart contract enabled solution that ensures an enhanced level of security, transparency, and traceability of stored vaccines in a health clinic, and enables the complete history of every vaccine to be checked from the day the vaccine is received by the health clinic to the date it is used or expires. We also formally analyze the resiliency of the proposed system against several attacks and compare the system with existing blockchain and non-blockchain-based solutions.
Beginning in 2020, Covid has increased as a result of a burst put on by a respiratory infection with a substantial peaking fatality rate. The unforeseen occurrence and unchecked global spread of the COVID-19 illness highlight the limitations of current healthcare systems in responding to emergencies affecting public wellness. In these conditions, innovative developments like public blockchain and intelligent systems (AI) have emerged as possible treatments for the covid epidemic. In particular, block chain may help with early identification to combat pandemics. With the measures put in place to prevent infection by wearing masks, social seclusion with a 6m radius, routine testing, and two vaccine doses. This system includes mask measurement, people identification, temp sensors, information tracking, in-person interaction locating, and the current state of a user's medical chart. With the development of technology and increased smartphone usage, illnesses may be tracked and their spread controlled. Considering that the expansion of the business sector's rehabilitation and its continued broad distribution of Covid, it is more crucial to adhere to the instructions to avoid contamination.
Abdul Razzaq, Syed Agha Hassnain Mohsan, Shahbaz Ahmed Khan Ghayyur, Nouf Al-Kahtani · 6 authors
COVID-19 has become a very transmissible disease that has had a worldwide impact, resulting in a huge number of infections and fatalities. Testing is critical to the pandemic's successful response because it helps detect illnesses and so attenuate (isolate/cure) them and now vaccination is a life-safer innovation against the pandemic which helps to make the immunity system stronger and fight against this infection. Patient-sensitive information, on the other hand, is now held in a centralized or third-party storage paradigm, according to COVID-19. One of the most difficult aspects of using a centralized storage strategy is maintaining patient privacy and system transparency. The application of blockchain technology to support health initiatives that can minimize the spread of COVID-19 infections in the context of accessibility of the system and for verification of digital passports. Only by combining blockchain technology with advanced cryptographic algorithms can a secure and privacy-preserving solution to COVID-19 be provided. In this article, we investigate the issue and propose a blockchain-based solution incorporating conscience identity, encryption, and decentralized storage via interplanetary file systems (IPFS). For COVID-19 test takers and vaccination takers, our solution includes digital health passports (DHP) as a certification of test or vaccination. We explain smart contracts constructed and tested with Ethereum to preserve a DHP for test and vaccine takers, allowing for a prompt and trustworthy response from the necessary medical authorities. We use an immutable trustworthy blockchain to minimize medical facility response times, relieve the transmission of incorrect information, and stop the illness from spreading via DHP. We give a detailed explanation of the proposed solution's system model, development, and assessment in terms of cost and security. Finally, we put the suggested framework to the test by deploying a smart contract prototype on the Ethereum TESTNET network in a Windows environment. The study's findings revealed that the suggested method is effective and feasible.
Maryam Tavakkoli, Aliya Karim, Fabienne B. Fischer, Laura Monzón Llamas · 11 authors
Objectives: With the application of a systems thinking lens, we aimed to assess the national COVID-19 response across health systems components in Switzerland, Spain, Iran, and Pakistan. Methods: We conducted four case studies on the policy response of national health systems to the early phase of the COVID-19 pandemic. Selected countries include different health system typologies. We collected data prospectively for the period of January–July 2020 on 17 measures of the COVID-19 response recommended by the WHO that encompassed all health systems domains (governance, financing, health workforce, information, medicine and technology and service delivery). We further monitored contextual factors influencing their adoption or deployment. Results: The policies enacted coincided with a decrease in the COVID-19 transmission. However, there was inadequate communication and a perception that the measures were adverse to the economy, weakening political support for their continuation and leading to a rapid resurgence in transmission. Conclusion: Social pressure, religious beliefs, governance structure and level of administrative decentralization or global economic sanctions played a major role in how countries’ health systems could respond to the pandemic.
Tobias Guggenberger, Jannik Lockl, Maximilian Röglinger, Vincent Schlatt · 8 authors
In 2020, the world has witnessed an unprecedented global pandemic with COVID-19. It has led nations to take measures that have an enormous impact on individuals, society, and the economy. Researchers and practitioners responded rapidly, evaluating the opportunities to capitalize on technology for tackling the associated challenges. We investigate the innovative potentials of three emerging digital technologies — namely, the Internet of Things, artificial intelligence, and distributed ledgers — to tackle pandemic-related challenges. We present our findings on the most effective means of leveraging each technology’s potential, the implications for use in crises, and the convergence of the three technologies.
Problem definition: The U.S. influenza (flu) vaccine supply chain is decentralized and experiences frequent supply and demand mismatches caused by two key factors: (1) the vaccine production process (yield) is highly uncertain; and (2) individuals are self-interested and do not completely take into account positive and negative externalities that they impose on others. To improve matching of supply and demand, we counteract these factors by developing an ex ante budget-neutral incentive program. Academic/practical relevance: We establish the sources of inefficiency in the flu vaccine supply chain. To eliminate the inefficiency, we develop a two-sided incentive program that policymakers can implement to finance vaccines under an ex ante balanced budget. Methodology: We model the flu vaccine supply chain as a decentralized system consisting of self-interested individuals on the demand side, and a profit-maximizing manufacturer with uncertain yield on the supply side. We use backward induction to characterize the subgame-perfect equilibrium of the sequential game that models the interactions between individuals and the manufacturer. Results: We develop a two-sided incentive program that proposes “vaccination incentives” to be given to individuals on the demand side, and “a menu of transfer payments” between the social planner and manufacturer on the supply side. When the realized vaccine supply is high (or low), our incentive program provides positive (negative) vaccination incentives for individuals to stimulate (or curb) the demand and eliminate positive (or negative) externalities by making vaccination more affordable (or costly). When social benefits from vaccination are significantly high, our incentive program uses a menu of transfer payments to penalize (or subsidize) the manufacturer for low (or high) yield realizations so that it produces the socially optimal quantity. We show that our incentive program can attain the social optimum, maintain an ex ante balanced budget (i.e., budget-neutral in expectation), and distribute the maximum social welfare between individuals and the manufacturer arbitrarily. Managerial implications: Vaccination incentives to individuals can ensure their access to the vaccine, but they are not enough to entice the manufacturer to ensure vaccine availability. A menu of contracts contingent on realized yield provides necessary incentives to the manufacturer and assures the availability.
Health equity is a very important part of social equity. The outbreak of the novel coronavirus pneumonia (COVID-19) in a short period of time exposed the problems existing in the allocation of medical resources and the response to major public health emergencies in China. By using Kernel density estimation and Data envelopment analysis (DEA), it is found that the allocation and imbalance of medical resources in China are greatly different among regions, and the polarization phenomenon is obvious. As an important part of the information technology system, blockchain technology is characterized by decentralization and non-tampering. It can realize sharing of medical resources through a mechanism of resource storage, circulation, supervision, and protection. The construction of a medical resource sharing mechanism under the condition of blockchain technology will greatly improve the degree of medical resource sharing, will narrow the differences in resource allocation between regions, and can effectively respond to an outbreak of major public health emergencies.
Because a fast vaccination rollout against coronavirus disease 2019 (COVID-19) is critical to restore daily life and avoid virus mutations, it is tempting to have a relaxed vaccination-administration management system. However, a rigorous management system can support the enforcement of preventive measures, and in turn, reduce incidence and deaths. Here, we model a trustable and reliable management system based on blockchain for vaccine distribution by extending the Susceptible-Exposed-Infected-Recovery (SEIR) model. The model includes prevention measures such as mask-wearing, social distancing, vaccination rate, and vaccination efficiency. It also considers negative social behavior, such as violations of social distance and attempts of using illegitimate vaccination proofs. By evaluating the model, we show that the proposed system can reduce up to 2.5 million cases and half a million deaths in the most demanding scenarios.
The unexpected development and quick; however, the uncontrolled overall spread of the Coronavirus shows us the disappointment of existing human services observation frameworks to convenient handle general wellbeing crises. In spite of the fact that upgrades in medicinal services observation have been understood, these still miss the mark in forestalling commotion. Absence of important advances taken to guarantee control and following of the infection have bothered the circumstance. Blockchain innovation has progressively been referenced as an instrument to help with different parts of various applications. This paper highlights the role of blockchain in forestalling the future of pandemics. Various use cases of blockchain technology that can help in the battle against the COVID-19 are also highlighted in this paper.
Tiago M. Fernández‐Caramés, Iván Froiz-Míguez, Paula Fraga‐Lamas
The COVID-19 pandemic has brought several limitations regarding physical distancing in order to reduce the interactions among large groups that could have prolonged close contact. For health reasons, such physical distancing requirements should be guaranteed in private and public spaces. In Spain, occupancy is restricted by law but, in practice, certain spaces may become overcrowded, existing law infringements in places that rely on occupancy estimations that are not accurate enough. For instance, although the number of passengers who enter a public transportation service is known, it is difficult to determine the actual occupancy of such a vehicle, since it is commonly unknown when and where passengers descend. Despite a number of counting systems existing, they are either prone to counting errors in overcrowded scenarios or require the active involvement of the people to be counted (e.g., going through a lathe or tapping a card when entering or exiting a monitored area) or of a person who manages the entering/exit process. This paper presents a novel IoT occupancy system that allows estimating in real time the people occupancy level of public spaces such as buildings, classrooms, businesses or moving transportation vehicles. The proposed system is based on autonomous wireless devices that, after powering them on, do not need active actions from the passengers/users and require a minimum amount of infrastructure. The system does not collect any personal information to ensure user privacy and includes a decentralized traceability subsystem based on blockchain, which guarantees the availability, security and immutability of the collected information. Such data will be shared among smart city stakeholders to ensure public safety and then deliver transparent decision-making based on data-driven analysis and planning.
The current situation of COVID-19 demands novel solutions to boost healthcare services and economic growth. A full-fledged solution that can help the government and people retain their normal lifestyle and improve the economy is crucial. By bringing into the picture a unique incentive-based approach, the strain of government and the people can be greatly reduced. By providing incentives for actions such as voluntary testing, isolation, etc., the government can better plan strategies for fighting the situation while people in need can benefit from the incentive offered. This idea of combining strength to battle against the virus can bring out newer possibilities that can give an upper hand in this war. As the unpredictable future develops, sharing and maintaining COVID related data of every user could be the needed trigger to kick start the economy and blockchain paves the way for this solution with decentralization and immutability of data.