An atmospheric pressure ion funnel with a slit entrance for enhancing signal and resolution in high resolution differential ion mobility mass spectrometry.

Differential ion mobility (DMS) is a versatile ion separation method that is often integrated with mass spectrometry (MS). In DMS, extremely high electric fields are used such that ion mobility depends non-linearly on electric field and thus, ion separations can be more orthogonal to MS than lower field ion mobility-based methods. DMS can have sufficiently high resolution to be used for enantiomer analysis of small molecules and to separate protein ions with peak widths comparable to those obtained for peptides. However, the performance of high resolution DMS-MS can be limited owing to the substantial loss of ions (>10-fold) that can occur upon their transfer from atmospheric pressure (where DMS separation typically occurs) to vacuum through a narrow conductance limited inlet (e.g. capillary) to the MS. Here, results from simulated ion trajectory simulations suggest that in high resolution DMS most ions can be lost by 'crashing' onto the narrow capillary inlet after exiting the DMS separation channel. To enhance DMS sensitivity and resolving power, an integrated DMS-MS interface concept is reported that consists of a slit electrode and a 12-electrode atmospheric pressure ion funnel (APIF). By using an APIF with slit entrance, the simulated ion transmission efficiencies increase by up to 257% for singly charged ions ([DMMP + H]+, [tryptophan + H]+, and [(2-dodecanone)2 + H]+) and by 209% for [ubiquitin + 12H]12+, without compromising resolving power. The use of APIF improves the ion focussing from the DMS exit to the MS capillary to improve sensitivity, and the slit ensures that ion dispersion in the analytically relevant direction perpendicular to the DMS electrodes is restricted to enhance resolution. By narrowing the slit of the DMS-Slit-APIF interface, the DMS resolving power can be increased further by at least 20%. Overall, these results indicate that the integrated DMS-Slit-APIF interface is promising for improving the sensitivity and resolution for many different types of DMS-MS experiments.

Modeling the epidemic control measures in overcoming COVID-19 outbreaks: A fractional-order derivative approach.

Novel coronavirus named SARS-CoV-2 is one of the global threads and uncertain challenges worldwide faced at present. It has stroke rapidly around the globe due to viral transmissibility, new variants (strains), and human unconsciousness. Lack of adequate and reliable vaccination and proper treatment, control measures such as self-protection, physical distancing, lockdown, quarantine, and isolation policy plays an essential role in controlling and reducing the pandemic. Decisions on enforcing various control measures should be determined based on a theoretical framework and real-data evidence. We deliberate a general mathematical control measures epidemic model consisting of lockdown, self-protection, physical distancing, quarantine, and isolation compartments. Then, we investigate the proposed model through Caputo fractional order derivative. Fixed point theory has been used to analyze the Caputo fractional-order derivative model's existence and uniqueness solutions, whereas the Adams-Bashforth-Moulton numerical scheme was applied for numerical simulation. Driven by extensive theoretical analysis and numerical simulation, this work further illuminates the substantial impact of various control measures.

Analysis of individual strategies for artificial and natural immunity with imperfectness and durability of protection.

As protection against infectious disease, immunity is conferred by one of two main defense mechanisms, namely (i) resistance generated by previous infection (known as natural immunity) or (ii) by being vaccinated (known as artificial immunity). To analyze, a modified SVIRS epidemic model is established that integrates the effects of the durability of protection and imperfectness in the framework of the human decision-making process as a vaccination game. It is supposed that immunized people become susceptible again when their immunity expires, which depends on the duration of immunity. The current theory for most voluntary vaccination games assumes that seasonal diseases such as influenza are controlled by a temporal vaccine, the immunity of which lasts for only one season. Also, a novel perspective is established involving an individual's immune system combined with self-interest to take the vaccine and natural immunity obtained from infection by coupling a disease-spreading model with an evolutionary game approach over a long period. Numerical simulations show that the longer attenuation helps significantly to control the spread of disease. Also discovered is the entire mechanism of active and passive immunities, in the sense of how they coexist with natural and artificial immunity. Thus, the prospect of finding the optimal strategy for eradicating a disease could help in the design of effective vaccination campaigns and policies.

Ambient Pressure Ion Funnel: Concepts, Simulations, and Analytical Performance.

In mass spectrometry (MS), a major loss of ions can readily occur during their transfer from atmospheric pressure to a lower pressure, which limits performance. Here, we report an ion funnel that can be used to effectively focus ions at ambient pressure (∼777 Torr) to significantly enhance performance in electrospray ionization (ESI) MS. For seven singly charged test ions (m/z 124-1131), the ambient pressure ion funnel (APIF) is demonstrated to improve ion abundances, sensitivity, and detection limits by up to factors of ∼17, ∼16, and ∼3, respectively, compared to the operation of conventional ESI-MS. Simulated ion trajectories were used to rationalize the enhanced performance of the APIF, which is attributed primarily to using a relatively high RF field amplitude to radially confine ions, a high DC field, and a wide exit ring electrode. The effective focusing of ions at ambient pressures should be beneficial in the future for improving the performance of (i) additional methods that ionize molecules at atmospheric pressure, (ii) ambient pressure ion mobility-based instruments, and (iii) high flow rate liquid chromatography mass spectrometry platforms.

Cost-efficiency analysis of voluntary vaccination against n-serovar diseases using antibody-dependent enhancement: A game approach.

Records of epidemics acknowledge immunological multi-serotype illnesses as an important aspect of the occurrence and control of contagious diseases. These patterns occur due to antibody-dependent-enhancement (ADE) among serotype diseases, which leads to infection of secondary infectious classes. One example of this is dengue hemorrhagic fever and dengue shock syndrome, which comprises the following four serotypes: DEN-1, DEN-2, DEN-3, and DEN-4. The evolutionary vaccination game approach is able to shed light on this long-standing issue in a bid to evaluate the success of various control programs. Although immunization is regarded as one of the most accepted approaches for minimizing the risk of infection, cost and efficiency are important factors that must also be considered. To analyze the n-serovar aspect alongside ADE consequence in voluntary vaccination, this study establishes a new mathematical epidemiological model that is dovetailed with evolutionary game theory, an approach through which we explored two vaccine programs: primary and secondary. Our findings illuminate that the 'cost-efficiency' effect for vaccination decision exhibits an impact on controlling n-serovar infectious diseases and should be designed in such a manner as to avoid adverse effects. Furthermore, our numerical result justifies the fact that adopting ADE significantly boosted emerging disease incidence, it also suggest that the joint vaccine policy works even better when the complex cyclical epidemic outbreak takes place among multi serotypes interactions. Research also exposes that the primary vaccine is a better controlling tool than the secondary; however, introducing a highly-efficiency secondary vaccine against secondary infection plays a key role to control the disease prevalence.

The role of advanced and late provisions in a co-evolutionary epidemic game model for assessing the social triple-dilemma aspect.

In the context of voluntary vaccination, we consider two additional provisions as well as pre-emptive vaccination for a unified model over epidemiology and evolutionary game theory to assess the extent to which advanced and late provisions restrict the spread of disease. To circumvent infection, people can be vaccinated pre-emptively before the epidemic season, but the imperfectness of vaccination or an unwillingness to be vaccinated may cause people instead to either be late-vaccinated or use self-protection. Here, self-protection corresponds to actions such as wearing a mask, washing hands, or using a mosquito net and is defined as the third strategy after pre-emptive vaccination (the first strategy) and late-vaccination (the second strategy). Our model can reproduce multiple social dilemma situations resulting from what is known as the vaccination dilemma (originating from preemptive vaccination), which works on a global time scale (i.e., repeated seasons approaching social equilibrium), and also from two other dilemmas due to late provisions, which work on a local time scale (i.e., every time step in a single season). To reproduce how an individual can acquire information for adaptation from neighbors or the society for a suitable provision, we introduce several strategy-updating rules for both global and local time scales and this behavioral feedback has a significant effect to reducing a transmissible disease. We also establish the social efficiency deficit (SED) indicator for a triple-dilemma game to quantify the existence of a social dilemma. Relying fully on a theoretical framework, our model provides a new perspective for evaluations: (i) how much more advantageous and effective pre-emptive vaccination is in eradicating a communicable disease compared with late provisions such as late vaccination and self-protection, and (ii) the extent of the social dilemma resulting from each of the three provisions, given the new idea of SED. The main effect of the triple-dilemma is that expensive provision displays no SED (no dilemma) until the efficiency or effectiveness of provisions reaches a certain level.

Separation of Isobaric Mono- and Dimethylated RGG-Repeat Peptides by Differential Ion Mobility-Mass Spectrometry.

Methylation of arginine residues in proteins, an enzyme-mediated post-translational modification (PTM), is important for mRNA processing and transport and for the regulation of many protein-protein interactions. However, proteolytic peptides resulting from alternative sites of post-translational methylation have identical masses and cannot be readily separated by standard liquid chromatography-mass spectrometry. Unlike acetylation or phosphorylation, methylation of arginine does not strongly affect the charge states of peptide ions, multiple instances of methylation can occur on a single amino acid residue, and the relative mass of the modification is <1% that of the typical proteolytic peptide. High field asymmetric waveform ion mobility spectrometry (FAIMS) is an orthogonal separation method to liquid chromatography that can rapidly separate gaseous ions prior to detection by mass spectrometry. Here, we report that FAIMS can be used to separate arginine-methylated peptides that differ by the position of a single methyl group for both mono- and dimethylated variants. Although the resolution of separation for these arginine-methylated peptides improved with increasing amounts of helium in the FAIMS carrier gas as expected, we found that the site of methylation can strongly affect the dependence of the electric field used for ion transmission on the extent of helium in the carrier gas. Thus, certain isobaric peptides can be cotransmitted at high helium concentrations whereas lower concentrations can be used for successful separations of such peptide mixtures. The capability to rapidly resolve isobaric arginine-methylated peptides should be useful in the future for the detailed analysis of protein arginine methylation in biological samples.

Exceptional Multiple Foreign-Body Ingestion by a Patient with Schizophrenia.

Swallowing foreign body in adult is uncommon. This mostly occurs accidentally or in psychologically unsound patient. A 32-years-old male patient with abdominal pain admitted in surgery department of Mymensingh Medical College Hospital, Mymensingh, Bangladesh with a history of swallowing various objects. After endoscopic confirmation and psychological evaluation he underwent laparotomy and 29 different objects were removed from his stomach by Gastrotomy. He was psychiatrically evaluated after recovery from operation and was found to be suffering from Schizophrenia with cannabis use. The aim of reporting this case can raise awareness at the patients complains should be taken seriously to prevent morbidity and even mortality.

Development and comparative investigation of Ag-sensitive layer based SAW and QCM sensors for mercury sensing applications.

Piezoelectric acoustic wave devices integrated with noble metal surfaces provide exciting prospects for the direct measurement of toxic gas species such as mercury (Hg) in the atmosphere. Even though gold (Au) based acoustic wave sensors have been utilized extensively for detecting Hg, the potential of using other metal surfaces such as silver (Ag) is yet to be thoroughly studied. Here, we developed Ag sensitive layer-based surface acoustic wave (SAW) and quartz crystal microbalance (QCM) sensors and focused on their comparative analysis for Hg sensing applications with parameters such as the sensor sensitivity, selectivity, adsorption/desorption isotherm and Hg diffusion into the surface thoroughly studied. The SAW sensor was fabricated with nickel (Ni) interdigitated transducer (IDT) electrodes and a Ag thin film on the delay line of the device. In the case of the QCM sensor, the electrodes were constructed of Ag thin film and simultaneously employed as a sensitive layer. Mercury sensing experiments were conducted for a range of concentrations between 24-365 ppbv without/with the presence of some common industrial interfering gas species (i.e. ammonia, acetaldehyde, ethyl mercaptan, dimethyl disulphide, methyl ethyl ketone and humidity) at various operating temperatures in the range of 35-95 °C. The SAW sensor was found to possess up to 70 times higher response magnitudes than its QCM counterpart at 35 °C while up to 30 and 23 times higher response magnitudes were observed for the SAW sensor at elevated temperatures of 75 and 95 °C, respectively. Furthermore, the SAW sensor showed good selectivity (>89%) toward Hg(0) vapor in the presence of all the interferents tested at an operating temperature of 75 °C while the QCM sensor exhibited significant cross-sensitivity when ethyl mercaptan was introduced along with Hg(0) vapor. Overall, it is indicative that Ag-based acoustic wave sensors do have great potential for Hg sensing applications, given that right operating conditions are applied.

A Nanoengineered Conductometric Device for Accurate Analysis of Elemental Mercury Vapor.

We developed a novel conductometric device with nanostructured gold (Au) sensitive layer which showed high-performance for elemental mercury (Hg(0)) vapor detection under simulated conditions that resemble harsh industrial environments. That is, the Hg(0) vapor sensing performance of the developed sensor was investigated under different operating temperatures (30-130 °C) and working conditions (i.e., humid) as well as in the presence of various interfering gas species, including ammonia (NH3), hydrogen sulfide (H2S), nitric oxide (NO), carbon mono-oxide (CO), carbon dioxide (CO2), sulfur dioxide (SO2), hydrogen (H2), methane (CH4), and volatile organic compounds (VOCs) such as ethylmercaptan (EM), acetaldehyde (MeCHO) and methyl ethyl ketone (MEK) among others. The results indicate that the introduction of Au nanostructures (referred to as nanospikes) on the sensor's surface enhanced the sensitivity toward Hg(0) vapor by up-to 450%. The newly developed sensor exhibited a limit of detection (LoD) (∼35 µg/m(3)), repeatability (∼94%), desorption efficiency (100%) and selectivity (∼93%) when exposed to different concentrations of Hg(0) vapor (0.5 to 9.1 mg/m(3)) and interfering gas species at a chosen operating temperature of 105 °C. Furthermore, the sensor was also found to show 91% average selectivity when exposed toward harsher industrial gases such as NO, CO, CO2, and SO2 along with same concentrations of Hg(0) vapor in similar operating conditions. In fact, this is the first time a conductometric sensor is shown to have high selectivity toward Hg(0) vapor even in the presence of H2S. Overall results indicate that the developed sensor has immense potential to be used as accurate online Hg(0) vapor monitoring technology within industrial processes.

Mercury Sorption and Desorption on Gold: A Comparative Analysis of Surface Acoustic Wave and Quartz Crystal Microbalance-Based Sensors.

Microelectromechanical sensors based on surface acoustic wave (SAW) and quartz crystal microbalance (QCM) transducers possess substantial potential as online elemental mercury (Hg(0)) vapor detectors in industrial stack effluents. In this study, a comparison of SAW- and QCM-based sensors is performed for the detection of low concentrations of Hg(0) vapor (ranging from 24 to 365 ppbv). Experimental measurements and finite element method (FEM) simulations allow the comparison of these sensors with regard to their sensitivity, sorption and desorption characteristics, and response time following Hg(0) vapor exposure at various operating temperatures ranging from 35 to 75 °C. Both of the sensors were fabricated on quartz substrates (ST and AT cut quartz for SAW and QCM devices, respectively) and employed thin gold (Au) layers as the electrodes. The SAW-based sensor exhibited up to ∼111 and ∼39 times higher response magnitudes than did the QCM-based sensor at 35 and 55 °C, respectively, when exposed to Hg(0) vapor concentrations ranging from 24 to 365 ppbv. The Hg(0) sorption and desorption calibration curves of both sensors were found to fit well with the Langmuir extension isotherm at different operating temperatures. Furthermore, the Hg(0) sorption and desorption rate demonstrated by the SAW-based sensor was found to decrease as the operating temperature increased, while the opposite trend was observed for the QCM-based sensor. However, the SAW-based sensor reached the maximum Hg(0) sorption rate faster than the QCM-based sensor regardless of operating temperature, whereas both sensors showed similar response times (t90) at various temperatures. Additionally, the sorption rate data was utilized in this study in order to obtain a faster response time from the sensor upon exposure to Hg(0) vapor. Furthermore, comparative analysis of the developed sensors' selectivity showed that the SAW-based sensor had a higher overall selectivity (90%) than did the QCM counterpart (84%) while Hg(0) vapor was measured in the presence of ammonia (NH3), humidity, and a number of volatile organic compounds at the chosen operating temperature of 55 °C.

Selective detection of elemental mercury vapor using a surface acoustic wave (SAW) sensor.

The detection of elemental mercury (Hg(0)) within industrial processes is extremely important as it is the first major step in ensuring the efficient operation of implemented mercury removal technologies. In this study, a 131 MHz surface acoustic wave (SAW) delay line sensor with gold electrodes was tested towards Hg(0) vapor (24 to 365 ppbv) with/without the presence of ammonia (NH3) and humidity (H2O), as well as volatile organic compounds (VOCs) such as acetaldehyde (MeCHO), ethylmercaptan (EM), dimethyl disulfide (DMDS) and methyl ethyl ketone (MEK), which are all common interfering gas species that co-exist in many industrial applications requiring mercury monitoring. The developed sensor exhibited a detection limit of 0.7 ppbv and 4.85 ppbv at 35 and 55 °C, respectively. Furthermore, a repeatability of 97% and selectivity of 92% in the presence of contaminant gases was exhibited by the sensor at the chosen operating temperature of 55 °C. The response magnitude of the developed SAW sensor towards different concentrations of Hg(0) vapor fitted well with the Langmuir extension isotherm (otherwise known as loading ratio correlation (LRC)) which is in agreement with our basic finite element method (FEM) work where an LRC isotherm was observed for a simplified model of the SAW sensor responding to different Hg contents deposited on the Au based electrodes. Overall, the results indicate that the developed SAW sensor can be a potential solution for online selective detection of low concentrations of Hg(0) vapor found in industrial stack effluents.

Assessing the instantaneous social dilemma on social distancing attitudes and vaccine behavior in disease control.

In the face of infectious disease outbreaks, the collective behavior of a society can has a profound impact on the course of the epidemic. This study investigates the instantaneous social dilemma presented by individuals' attitudes toward vaccine behavior and its influence on social distancing as a critical component in disease control strategies. The research employs a multifaceted approach, combining modeling techniques and simulation to comprehensively assess the dynamics between social distancing attitudes and vaccine uptake during disease outbreaks. With respect to modeling, we introduce a new vaccination game (VG) where, unlike conventional VG models, a 2-player and 2-strategy payoff structure is aptly embedded in the individual behavior dynamics. Individuals' willingness to adhere to social distancing measures, such as mask-wearing and physical distancing, is strongly associated with their inclination to receive vaccines. The study reveals that a positive attitude towards social distancing tends to align with a higher likelihood of vaccine acceptance, ultimately contributing to more effective disease control. As the COVID-19 pandemic has demonstrated, swift and coordinated public health measures are essential to curbing the spread of infectious diseases. This study underscores the urgency of addressing the instantaneous social dilemma posed by individuals' attitudes. By understanding the intricate relationship between these factors, policymakers, and healthcare professionals can develop tailored strategies to promote both social distancing compliance and vaccine acceptance, thereby enhancing our ability to control and mitigate the impact of disease outbreaks in the future.

Enhancing vaccination strategies for epidemic control through effective lockdown measures.

Evolutionary epidemiology models have substantially impacted the study of various infections and prevention methods in the biology field. These models are called Susceptible, Lockdown, Vaccinated, Infected, and Recovered (SLVIR) epidemic dynamics. We explore how human behavior, particularly in the context of disease transmission, is influenced by two intervention strategies: vaccination and lockdown, both of which are grounded in the principles of evolutionary game theory (EGT). This comprehensive study using evolutionary game theory delves into the dynamics of epidemics, explicitly focusing on the transition rate from susceptibility to immunity and susceptibility to lockdown measures. Our research involves a thorough analysis of the structural aspects of the SLVIR epidemic model, which delineates disease-free equilibria to ensure stability in the system. Our investigation supports the notion that implementing lockdown measures effectively reduces the required level of vaccinations to curtail the prevalence of new infections. Furthermore, it highlights that combining both strategies is particularly potent when an epidemic spreads rapidly. In regions where the disease spreads comparatively more, our research demonstrates that lockdown measures are more effective in reducing the spread of the disease than relying solely on vaccines. Through significant numerical simulations, our research illustrates that integrating lockdown measures and efficient vaccination strategies can indirectly lower the risk of infection within the population, provided they are both dependable and affordable. The outcomes reveal a nuanced and beneficial scenario where we examine the interplay between the evolution of vaccination strategies and lockdown measures, assessing their coexistence through indicators of average social payoff.

Behavioral game of quarantine during the monkeypox epidemic: Analysis of deterministic and fractional order approach.

This work concerns the epidemiology of infectious diseases like monkeypox (mpox) in humans and animals. Our models examine transmission scenarios, including transmission dynamics between humans, animals, and both. We approach this using evolutionary game theory, specifically the intervention game-theoretical (IGT) framework, to study how human behavior can mitigate disease transmission without perfect vaccines and treatments. To do this, we use non-pharmaceutical intervention, namely the quarantine policy, which demonstrates the delayed effect of the epidemic. Additionally, we contemplate quarantine-based behavioral intervention policies in deterministic and fractional-order models to show behavioral impact in the context of the memory effect. Firstly, we extensively analyzed the model's positivity and boundness of the solution, reproduction number, disease-free and endemic equilibrium, possible stability, existence, concavity, and Ulam-Hyers stability for the fractional order. Subsequently, we proceeded to present a numerical analysis that effectively illustrates the repercussions of varying quarantine-related factors, information probability, and protection probability. We aimed to comprehensively examine the effects of non-pharmaceutical interventions on disease control, which we conveyed through line graphs and 2D heat maps. Our findings underscored the significant influence of strict quarantine measures and the protection of both humans and animals in mitigating disease outbreaks. These measures not only significantly curtailed the spread of the disease but also delayed the occurrence of the epidemic's peak. Conversely, when quarantine maintenance policies were implemented at lower rates and protection levels diminished, we observed contrasting outcomes that exacerbated the situation. Eventually, our analysis revealed the emergence of animal reservoirs in cases involving disease transmission between humans and animals.

Understanding the relationship between stay-at-home measures and vaccine shortages: a conventional, heterogeneous, and fractional dynamic approach.

In light of the global prevalence of a highly contagious respiratory disease, this study presents a novel approach to address the pressing and unanticipated issues by introducing a modified vaccination and lockdown-centered epidemic model. The rapid spread of the disease is attributed to viral transmissibility, the emergence of new strains (variants), lack of immunization, and human unawareness. This study aims to provide policymakers with crucial insights for making informed decisions regarding lockdown strategies, vaccine availability, and other control measures. The research adopts three types of models: deterministic, heterogeneous, and fractional-order dynamics, on both theoretical and numerical approaches. The heterogeneous network considers varying connectivity and interaction patterns among individuals, while the ABC fractional-order derivatives analyze the impact of integer-order control in different semi-groups. An extensive theoretical analysis is conducted to validate the proposed model. A comprehensive numerical investigation encompasses deterministic, stochastic, and ABC fractional-order derivatives, considering the combined effects of an effective vaccination program and non-pharmaceutical interventions, such as lockdowns and shutdowns. The findings of this research are expected to be valuable for policymakers in different countries, helping them implement dynamic strategies to control and eradicate the epidemic effectively.

Analyzing evolutionary game theory in epidemic management: A study on social distancing and mask-wearing strategies.

When combating a respiratory disease outbreak, the effectiveness of protective measures hinges on spontaneous shifts in human behavior driven by risk perception and careful cost-benefit analysis. In this study, a novel concept has been introduced, integrating social distancing and mask-wearing strategies into a unified framework that combines evolutionary game theory with an extended classical epidemic model. To yield deeper insights into human decision-making during COVID-19, we integrate both the prevalent dilemma faced at the epidemic's onset regarding mask-wearing and social distancing practices, along with a comprehensive cost-benefit analysis. We explore the often-overlooked aspect of effective mask adoption among undetected infectious individuals to evaluate the significance of source control. Both undetected and detected infectious individuals can significantly reduce the risk of infection for non-masked individuals by wearing effective facemasks. When the economical burden of mask usage becomes unsustainable in the community, promoting affordable and safe social distancing becomes vital in slowing the epidemic's progress, allowing crucial time for public health preparedness. In contrast, as the indirect expenses associated with safe social distancing escalate, affordable and effective facemask usage could be a feasible option. In our analysis, it was observed that during periods of heightened infection risk, there is a noticeable surge in public interest and dedication to complying with social distancing measures. However, its impact diminishes beyond a certain disease transmission threshold, as this strategy cannot completely eliminate the disease burden in the community. Maximum public compliance with social distancing and mask-wearing strategies can be achieved when they are affordable for the community. While implementing both strategies together could ultimately reduce the epidemic's effective reproduction number ([Formula: see text]) to below one, countries still have the flexibility to prioritize either of them, easing strictness on the other based on their socio-economic conditions.

Impact of human cooperation on vaccination behaviors.

This paper studies a dynamic vaccination game model embedded with vaccine cost-effectiveness and dyadic game during an epidemic, assuming the appearance of cooperation among individuals from an evolutionary perspective. The infection dynamics of the individuals' states follow a modified S/VIS (susceptible/vaccinated-infected-susceptible) dynamics. Initially, we assume that the individuals are unsure about their infection status. Thus, they make decisions regarding their options based on their neighbors' perceptions, the prevalence of the disease, and the characteristics of the available vaccines. We then consider the strategy updating process IBRA (individuals-based risk assessment) concerning an individual's committing vaccination based on a neighbor's decision. In the perspective of social dilemma, it presents the idea of social efficiency deficit to find the gap between social optimum and Nash equilibrium point based on dilemma strength by considering vaccine decision. The cost and cooperative behavior depend on disease severity, neighbor's attitude, and vaccine properties to obtain a reduced-order optimal solution to control infectious diseases. Vaccine factors (efficiency, cost, and benefit) are crucial in changing human vaccine decisions and cooperative behavior. It turns out that, even in the prisoner's dilemma case, where all defection attitude occurs, vaccine uptake (cooperation) increases. Finally, extensive numerical studies were presented that illustrate interesting phenomena and investigate the ultimate extent of the epidemic, vaccination coverage, average social benefits, and the social efficiency deficit concerning optimal strategies and the dynamic vaccine attitudes of individuals. PACS numbers. Theory and modeling; computer simulation, 87.15. Aa; Dynamics of evolution, 87.23. Kg.

Behavioral vaccination policies and game-environment feedback in epidemic dynamics.

Many policymakers have adopted voluntary vaccination policies to alleviate the consequences of contagious diseases. Such policies have several well-established feathers, i.e. they are seasonal, depending on an individual's decision, adaptive, and control epidemic activity. Here, we study ideas from behavioral epidemiology embedded with a vaccination game and pairwise two-player two-strategy game to represent the environmental feedback in an SVIR model by using a composite information index including disease incidence, vaccine factors and cooperative behavior on a global time scale (repeated season). In its turn, the information index's game dynamics to participate in the vaccine program (cooperation) is supposed to reflect the feedback-evolving dynamics of competitive cognitions and the environment. The assuming model is described by two different evolutionary game systems connected by an unknown external public opinion environment feedback. The embedded model is described by an inherited system showing a behavioral aspect, i.e. pairwise game indicates an individual's cooperative behavior, and a vaccine game refers to vaccine-cost influence. This is a novel attempt to stabilize the two different decision processes to pool them into a single index. Extensive simulations suggest a rich spectrum of achievable results, including epidemic control, human behavior, social dilemma, and policy suggestions.

Coupled simultaneous analysis of vaccine and self-awareness strategies on evolutionary dilemma aspect with various immunity.

On evolutionary game theory (EGT), two intervention policies: vaccination and self-awareness, are considered to account for how human attitude impacts disease spreading. Although these interventions can impose, their implementation may depend on the various immunity systems such as shield immunity, innate immunity, waning immunity, natural immunity, and artificial immunity. This framework provides an epidemic SEIRVA (susceptible-exposed-infected-removed-vaccinated-aware) model and two EGT dynamics to analyze the interplay between the immunity system and social learning interventions. The prospect of exploring the individual's strategy and social dilemma for removing a disease could assist design an effective vaccine program and self-awareness policy. Also, we evaluated the indicator of social efficiency deficit (SED) for a social dual-dilemma to measure the presence of a dilemma situation. Extensive theoretical analysis displays that stability includes the reproduction number, conditions for positivity and uniqueness, and the strength number analyzed in the equilibria, including fundamental properties validated by numerical simulation of the discretization method that appraises a variety of graphs at adjusting parameters. We present extensive numerical studies investigating the affect of controlling parameters, individual vulnerability, optimal policies, and individual costs. It turns out that, even with the affordable vaccine, individuals may have very different behaviors; self-awareness strategy plays a vital role in controlling diseases.