Model analysis and data validation of structured prevention and control interruptions of emerging infectious diseases.

The design of optimized non-pharmaceutical interventions (NPIs) is critical to the effective control of emergent outbreaks of infectious diseases such as SARS, A/H1N1 and COVID-19 and to ensure that numbers of hospitalized cases do not exceed the carrying capacity of medical resources. To address this issue, we formulated a classic SIR model to include a close contact tracing strategy and structured prevention and control interruptions (SPCIs). The impact of the timing of SPCIs on the maximum number of non-isolated infected individuals and on the duration of an infectious disease outside quarantined areas (i.e. implementing a dynamic zero-case policy) were analyzed numerically and theoretically. These analyses revealed that to minimize the maximum number of non-isolated infected individuals, the optimal time to initiate SPCIs is when they can control the peak value of a second rebound of the epidemic to be equal to the first peak value. More individuals may be infected at the peak of the second wave with a stronger intervention during SPCIs. The longer the duration of the intervention and the stronger the contact tracing intensity during SPCIs, the more effective they are in shortening the duration of an infectious disease outside quarantined areas. The dynamic evolution of the number of isolated and non-isolated individuals, including two peaks and long tail patterns, have been confirmed by various real data sets of multiple-wave COVID-19 epidemics in China. Our results provide important theoretical support for the adjustment of NPI strategies in relation to a given carrying capacity of medical resources.

Proliferation in malaria parasites: How resource limitation can prevent evolution of greater virulence.

For parasites, robust proliferation within hosts is crucial for establishing the infection and creating opportunities for onward transmission. While faster proliferation enhances transmission rates, it is often assumed to curtail transmission duration by killing the host (virulence), a trade-off constraining parasite evolution. Yet in many diseases, including malaria, the preponderance of infections with mild or absent symptoms suggests that host mortality is not a sufficient constraint, raising the question of what restrains evolution toward faster proliferation. In malaria infections, the maximum rate of proliferation is determined by the burst size, the number of daughter parasites produced per infected red blood cell. Larger burst sizes should expand the pool of infected red blood cells that can be used to produce the specialized transmission forms needed to infect mosquitoes. We use a within-host model parameterized for rodent malaria parasites (Plasmodium chabaudi) to project the transmission consequences of burst size, focusing on initial acute infection where resource limitation and risk of host mortality are greatest. We find that resource limitation restricts evolution toward higher burst sizes below the level predicted by host mortality alone. Our results suggest resource limitation could represent a more general constraint than virulence-transmission trade-offs, preventing evolution towards faster proliferation.

Size dependent antipredator responses in a fish-shrimp mutualism.

For prey, taking refuge from predators has obvious fitness benefits but may also be costly by impinging on time and effort available for feeding or attracting mates. The antipredator responses of refuge-seeking animals are therefore predicted to vary strategically depending on how threatening they perceive the risk. To test this, we studied the impacts of a simulated predatory threat on the antipredator responses of wild sandy prawn-gobies (Ctenogobiops feroculus) that co-inhabit burrows with Alpheus shrimp (family Alpheidae) in a mutualistic relationship. We exposed goby-shrimp pairs, repeatedly on three separate occasions, to an approaching threat and measured the antipredator behaviours of both partners. We found that re-emerging from the burrow took longer in large compared to small fish. Moreover, quicker re-emergence by small-but not medium or large-sized gobies-was associated with an earlier flight from the approaching threat (i.e. when the threat was still further away). Finally, the goby and shrimp sharing a burrow were matched in body size and their risk-taking behaviour was highly dependent on one another. The findings contribute to our understanding of how an individual's phenotype and perception of danger relates to its risk-taking strategy, and how mutualistic partners can have similar risk sensitivities.

Game of strokes: Optimal & conversion strategy algorithms with simulations & application.

Strategic decision-making for sequential move games requires rationality and continuity of rationality to guarantee maximum payoffs at all nodes/stages/levels. Rationality and continuity of rationality in a player's behaviour are not often observed and/or maintained thus, leading to less optimal outcomes. More so, the belief in an opponent's rationality, on the other hand, co-determines the level of effort a player employs while making strategic decisions. Given irrationality and discontinuity of rationality in a sequential move game with mover advantages, there are strategic steps (algorithms) to convert and/or maintain the mover advantages of an irrational player. In this paper, the conversion strategy algorithms, as well as the optimal strategy algorithms, are developed using the Beta Limit Sum (BLS) strategy model and the game of strokes. The simulation exercises confirm that the BLS strategy model is an optimal solution for the finite sequential game of strokes. One of the key applications of these strategies is that of resource economics like environmental resources (clean water, air & land). These are public goods, as such, the optimal strategy entails that the community cooperates (as one entity) and takes the same actions or strategy to maintain a healthy and clean state of the communal environmental resources.

Refresh Rate-Based Caching and Prefetching Strategies for Internet of Things Middleware.

One of the research directions in Internet of Things (IoT) is the field of Context Management Platforms (CMPs) which is a specific type of IoT middleware. CMPs provide horizontal connectivity between vertically oriented IoT silos resulting in a noticeable difference in how IoT data streams are processed. As these context data exchanges can be monetised, there is a need to model and predict the context metrics and operational costs of this exchange to provide relevant and timely context in a large-scale IoT ecosystem. In this paper, we argue that caching all transient context information to satisfy this necessity requires large amounts of computational and network resources, resulting in tremendous operational costs. Using Service Level Agreements (SLAs) between the context providers, CMP, and context consumers, where the level of service imperfection is quantified and linked to the associated costs, we show that it is possible to find efficient caching and prefetching strategies to minimize the context management cost. So, this paper proposes a novel method to find the optimal rate of IoT data prefetching and caching. We show the main context caching strategies and the proposed mathematical models, then discuss how a correctly chosen proactive caching strategy and configurations can help to maximise the profit of CMP operation when multiple SLAs are defined. Our model is accurate up to 0.0016 in Root Mean Square Percentage Error against our simulation results when estimating the profits to the system. We also show our model is valid using the t-test value tending to 0 for all the experimental scenarios.

A new method to identifying optimal adjustment strategy when the car cockpit is uncomfortable: optimal state distance method.

With the rapid development of the automobile industry, the comfort of the cockpit has become the standard for judging the quality of the car. People have also put forward higher requirements for cockpit comfort. In the process of driving, the cockpit environment will constantly change, and the comfort will also change. When the comprehensive comfort level of the cockpit decreases and the occupants feel uncomfortable, the cockpit comfort should be adjusted. In this article, a cockpit comfort evaluation model is established to realize the evaluation of cockpit comfort. In addition, we elaborate the theory of optimal state distance, where the numerical magnitude of the optimal state distance is used to reflect the extent to which an indicator deviates from its optimal state. Also, a cockpit optimal adjustment strategy identification model is established based on the theory, which can obtain the optimal adjustment strategy in a certain cockpit operating environment, facilitate the timely adjustment of the corresponding actuator, and realize the dynamic monitoring and adjustment of cockpit comfort. This project provides a reference direction for cockpit comfort adjustment, which is of great significance for future research and development of automotive cockpit comfort.

Mathematical analysis of a two-tiered microbial food-web model for the anaerobic digestion process.

In this research paper, we presented a four-dimensional mathematical system modeling the anaerobic mineralization of phenol in a two-step microbial food-web. The inflowing concentrations of the hydrogen and the phenol are considered in our model. We considered the case of general class of nonlinear growth kinetics, instead of Monod kinetics. Due to some conservative relations, the proposed model was reduced to a two-dimensional system. The stability of the steady states was carried out. Based on the species growth rates and the three main operating parameters of the model, represented by the dilution rate and input concentrations of the phenol and the hydrogen, we showed that the system can have up to four steady states. We gave the necessary and sufficient conditions ensuring the existence and the stability for each feasible equilibrium state. We showed that in specific cases, the positive steady state exists and is stable. We gave numerical simulations validating the obtained results.

Variants of the Stochastic Sir Models and Vaccination Strategies.

Several options of the stochastic SIR epidemics model with limited treatment are proposed. For these methods, the efficiency of different vaccination strategies is demonstrated, and a method for obtaining the optimal vaccination strategy minimizing the cost functional is proposed.

Reducing manufacturing carbon emissions: Optimal low carbon production strategies respect to product structures and batches.

In the production process of industrial products, different product structures, batches, and the selection of different production methods directly affect the resource utilization, distribution, consumption, and carbon emission generation in the production process. In this study, a strategy to select low carbon production methods for product structure and batch is proposed to advance resource management and carbon emission reduction in manufacturing production processes. Specifically, taking a typical casting industry as an example, we analyze the two factors of product structures and batches on the resource consumption and environmental impact of the production process to establish a production process carbon emission model; using forty casting products as the research objects, the clustering algorithm and multiple linear regression analysis method are used to establish the influence relationship between product structure, batch, and production carbon emissions. Based on the characteristics of product structure and batch, a strategy is proposed for selecting a low carbon production method. The study shows that in sand casting production, the 3D printing method is more low carbon for small volumes, reducing 56.057 % of carbon emissions. However, traditional technology is more low carbon for large volumes, which can reduce at least 6.778 % of carbon emissions. In addition, as the number of casting batches increases, the advantage of low carbon in traditional casting technology will rise. The results of this study may provide a new way to help the manufacturing industry develop and optimize the environmental impact of the product manufacturing process.

The common interests of health protection and the economy: evidence from scenario calculations of COVID-19 containment policies.

We develop a novel approach integrating epidemiological and economic models that allows data-based simulations during a pandemic. We examine the economically optimal opening strategy that can be reconciled with the containment of a pandemic. The empirical evidence is based on data from Germany during the SARS-CoV-2 pandemic. Our empirical findings reject the view that there is necessarily a conflict between health protection and economic interests and suggest a non-linear U-shape relationship: it is in the interest of public health and the economy to balance non-pharmaceutical interventions in a manner that further reduces the incidence of infections. Our simulations suggest that a prudent strategy that leads to a reproduction number of around 0.75 is economically optimal. Too restrictive policies cause massive economic costs. Conversely, policies that are too loose lead to higher death tolls and higher economic costs in the long run. We suggest this finding as a guide for policy-makers in balancing interests of public health and the economy during a pandemic.

Altered neural correlates of optimal decision-making in individuals with depressive status.

Making optimal decisions by computing risk and benefit is necessary for humans. However, whether individuals with depressive status could utilize the optimal strategy to guide decision and its neural correlates remain unclear. The current study explored these issues by combining a decision task and high temporal-resolution electroencephalogram (EEG). The decision task involved an eight-box trial in which participants successively decided whether to open a box containing a potential reward or punishment, deciding to stop guaranteed they would retain the rewards already accumulated. Theoretically, the optimal strategy in the task was to stop at the fourth box, which had the largest expected value. We found that individuals with depressive status stopped fewer trials at the fourth box, relative to healthy controls, indicating their impaired optimal strategy during decision-making. Moreover, compared to healthy controls, individuals with depressive status showed weaker P2 amplitude and weaker beta-band oscillation at the frontocentral scalp when deciding whether to open the fourth box. Additionally, for healthy controls but not for individuals with depressive status, the P2 amplitude fully mediated the relationship between participants' degree of expected benefit (as reflected by the recreational risk-taking scale) and the frequency of trials stopped at the fourth box. Overall, this study revealed that the P2 amplitude and beta-band oscillation might explain the altered optimal decision-making in individuals with depressive status.

Optimal strategy of in-game items with conspicuous consumption: whether to provide the grinding version?

In the virtual world, whether or not to spend money on in-game items distinguishes paying players from non-paying players. Due to the existence of conspicuous psychology, paying players will greatly increase their conspicuous utility after purchasing an item in addition to the utility of the item itself. In this case, whether providing different versions of items can bring greater revenue to the game company is a question worth investigating. In this study, two analytical models considering conspicuous intensity are developed to compare the optimal pricing strategy of the game company providing the single-version item or dual-version items. The single-version item can only be purchased, while a relatively low-quality version that can be obtained by grinding is provided in the dual-version strategy. Grinding means using time in games to get items instead of spending in games. The results suggest that it is more profitable for companies to offer dual-version items when conspicuous intensity is strong. Game companies can also adjust the time needed to acquire the grinding version item and the quality gap between the two versions to achieve greater revenue. The research contributes to providing a theoretical basis and decision support for game companies to decide whether to provide different versions of in-game items.

Optimal Vaccination Strategy in the Stochastic Epidemic Limited-Treatment Model.

The author analyzes the stochastic SIR epidemic model with vaccination and limited treatment. A method for obtaining the optimal vaccination strategy that minimizes the cost functional is proposed.

Strategical district cooling system operation in hub airport terminals, a research focusing on COVID-19 pandemic impact.

Part load ratio is often observed in real operations of airport terminal cooling system. This phenomenon is more obvious during the COVID-19 pandemic, as sudden flight restrictions impacting cooling demand are widely adopted in hub airport terminals. This research aims to propose optimal strategies of multi-chiller in airport terminals based on cooling load characteristics modeling, to tackle the aforementioned issues. Numerical experiments based on a real-world Chinese airport terminal are conducted to validate the proposed method. The results show that an average cooling load drop of 30% is observed from scenario of normal flight before COVID-19 to scenario of COVID-19 Period flight, and the average cooling load drop reaches to 44% from scenario of busy flight before COVID-19 to scenario of COVID-19 Period flight. The results also reflect that cooling load presents synchronous trend with passenger flow, but presents asynchronous trend with outdoor temperature. The influence of outdoor temperature on cooling demand delays due to building envelops. It indicates that simple superimposition according to passenger flow change for chiller operation number is reliable, efficient and effective, but is not suitable for outdoor temperature change. The findings are helpful to develop optimal strategies for further real-time control of multi-chiller.

Cost-effectiveness analysis of including contrast-enhanced ultrasound in management of pancreatic cystic neoplasms.

PURPOSE: Pancreatic cystic neoplasms (PCN) management consists of non-invasive imaging studies (CT, MRI), with a high resource burden. We aimed to determine the cost-effectiveness of including contrast-enhanced ultrasound (CEUS) in the management of PCN without risk features. MATERIALS AND METHODS: By using a decision-tree model in a hypothetical cohort of patients, we compared management strategy including CEUS with the latest Fukuoka consensus, European and Italian guidelines. Our strategy for BD-IPMN/MCN < 1 cm includes 1 CEUS annually. For those between 1 and 2 cm, it includes CEUS 4 times/year during the first year, then 3 times/year for 4 years and then annually. For those between 2 and 3 cm, it comprises MRI twice/year during the first one, then alternating 2 CEUS and 1 MRI yearly. RESULTS: CEUS surveillance is the dominant strategy in all scenarios. CEUS surveillance average cost is 1,984.72 €, mean QALY 11.79 and mean ICER 181.99 €. If willingness to pay is 30,000 €, 45% of patients undergone CEUS surveillance of BDIPMN/MCN < 1 cm would be within budget. CONCLUSION: Guidelines strategies are very effective, but costs are relatively high from a policy perspective. CEUS surveillance may be a cost-effective strategy yielding a nearly high QALYs, an acceptable ICER, and a lower cost.

Estimating optimal dynamic treatment strategies under resource constraints using dynamic marginal structural models.

Methods for estimating optimal treatment strategies typically assume unlimited access to resources. However, when a health system has resource constraints, such as limited funds, access to medication, or monitoring capabilities, medical decisions must account for competition between individuals in resource usage. The problem of incorporating resource constraints into optimal treatment strategies has been solved for point exposures (1), that is, treatment strategies entailing a decision at just one time point. However, attempts to directly generalize the point exposure solution to dynamic time-varying treatment strategies run into complications. We sidestep these complications by targeting the optimal strategy within a clinically defined subclass. Our approach is to employ dynamic marginal structural models to estimate (counterfactual) resource usage under the class of candidate treatment strategies and solve a constrained optimization problem to choose the optimal strategy for which expected resource usage is within acceptable limits. We apply this method to determine the optimal dynamic monitoring strategy for people living with HIV when resource limits on monitoring exist using observational data from the HIV-CAUSAL Collaboration.

Optimal Allocation of the Limited COVID-19 Vaccine Supply in South Korea.

Initial supply of the coronavirus disease (COVID-19) vaccine may be limited, necessitating its effective use. Herein, an age-structured model of COVID-19 spread in South Korea is parameterized to understand the epidemiological characteristics of COVID-19. The model determines optimal vaccine allocation for minimizing infections, deaths, and years of life lost while accounting for population factors, such as country-specific age distribution and contact structure, and various levels of vaccine efficacy. A transmission-blocking vaccine should be prioritized in adults aged 20-49 years and those older than 50 years to minimize the cumulative incidence and mortality, respectively. A strategy to minimize years of life lost involves the vaccination of adults aged 40-69 years, reflecting the relatively high case-fatality rates and years of life lost in this age group. An incidence-minimizing vaccination strategy is highly sensitive to vaccine efficacy, and vaccines with lower efficacy should be administered to teenagers and adults aged 50-59 years. Consideration of age-specific contact rates and vaccine efficacy is critical to optimize vaccine allocation. New recommendations for COVID-19 vaccines under consideration by the Korean Centers for Disease Control and Prevention are mainly based on a mortality-minimizing allocation strategy.

Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty.

Populations of invasive species often spread heterogeneously across a landscape, consisting of local populations that cluster in space but are connected by dispersal. A fundamental dilemma for invasive species control is how to optimally allocate limited fiscal resources across local populations. Theoretical work based on perfect knowledge of demographic connectivity suggests that targeting local populations from which migrants originate (sources) can be optimal. However, demographic processes such as abundance and dispersal can be highly uncertain, and the relationship between local population density and damage costs (damage function) is rarely known. We used a metapopulation model to understand how budget and uncertainty in abundance, connectivity, and the damage function, together impact return on investment (ROI) for optimal control strategies. Budget, observational uncertainty, and the damage function had strong effects on the optimal resource allocation strategy. Uncertainty in dispersal probability was the least important determinant of ROI. The damage function determined which resource prioritization strategy was optimal when connectivity was symmetric but not when it was asymmetric. When connectivity was asymmetric, prioritizing source populations had a higher ROI than allocating effort equally across local populations, regardless of the damage function, but uncertainty in connectivity structure and abundance reduced ROI of the optimal prioritization strategy by 57% on average depending on the control budget. With low budgets (monthly removal rate of 6.7% of population), there was little advantage to prioritizing resources, especially when connectivity was high or symmetric, and observational uncertainty had only minor effects on ROI. Allotting funding for improved monitoring appeared to be most important when budgets were moderate (monthly removal of 13-20% of the population). Our result showed that multiple sources of observational uncertainty should be considered concurrently for optimizing ROI. Accurate estimates of connectivity direction and abundance were more important than accurate estimates of dispersal rates. Developing cost-effective surveillance methods to reduce observational uncertainties, and quantitative frameworks for determining how resources should be spatially apportioned to multiple monitoring and control activities are important and challenging future directions for optimizing ROI for invasive species control programs.

Spatial regulation of resource allocation in response to nutritional availability.

It is critical for a living organism to appropriately allocate resources among its organs, or within a specific organ, because available resources are generally limited. For example, in response to the nutritional environment of their soil, plants regulate resource allocation in their roots in order to plastically change their root system architecture (RSA) for efficiently absorbing nutrients. However, it is still not understood why and how RSA is adaptively controlled. Therefore, we modeled and investigated the spatial regulation of resource allocation, focusing on RSA in response to nutrient availability, and provided analytical solutions to the optimal strategy in the case of simple fitness functions. We first showed that our model could explain the experimental evidence where root growth is maximized at the optimal nutrient concentration under the homogeneous condition. Next, we extended our model to incorporate the spatial heterogeneity of nutrient availability. This extended model revealed that growth suppression by systemic control is required for adapting to high nutrient conditions, whereas growth promotion by local control is sufficient for adaptation to low-nutrient environments. This evidence predicts that systemic control can be evolved in the presence of excessive amounts of nutrition, consistent with the 'N-supply' systemic signal that is observed experimentally. Furthermore, our model can also explain various experimental results using nitrogen nutrition. Our model provides a theoretical basis for understanding the spatial regulation of adaptive resource allocation in response to nutritional environment.

Frequentist operating characteristics of Bayesian optimal designs via simulation.

Bayesian adaptive designs have become popular because of the possibility of increasing the number of patients treated with more beneficial treatments, while still providing sufficient evidence for treatment efficacy comparisons. It can be essential, for regulatory and other purposes, to conduct frequentist analyses both before and after a Bayesian adaptive trial, and these remain challenging. In this paper, we propose a general simulation-based approach to compare frequentist designs with Bayesian adaptive designs based on frequentist criteria such as power and to compute valid frequentist p-values. We illustrate our approach by comparing the power of an equal randomization (ER) design with that of an optimal Bayesian adaptive (OBA) design. The Bayesian design considered here is the dynamic programming solution of the optimization of a specific utility function defined by the number of successes in a patient horizon, including patients whose treatment will be affected by the trial's results after the end of the trial. While the power of an ER design depends on treatment efficacy and the sample size, the power of the OBA design also depends on the patient horizon size. Our results quantify the trade-off between power and the optimal assignment of patients to treatments within the trial. We show that, for large patient horizons, the two criteria are in agreement, while for small horizons, differences can be substantial. This has implications for precision medicine, where patient horizons are decreasing as a result of increasing stratification of patients into subpopulations defined by molecular markers.