Stochastic Transmission Research Articles

Stochastic Analysis for the Dual Virus Parallel Transmission Model with Immunity Delay.

In this article, the qualitative properties of a stochastic dual virus parallel transmission model with immunity delay are analyzed. First, we use Lyapunov theory to study the existence and uniqueness of the global positive solution of the proposed model. Second, the threshold values of the persistence and extinction of two viruses were obtained. Finally, the numerical simulation verifies the theoretical results. The results show that the immunity delay and the intensity of noise have important effects on the two diseases spreading in parallel.

One-size measures do not fit all areas: Evaluation of area-specific control of foot and mouth disease in Thailand using bioeconomic modelling

Information on the epidemiological and economic consequences of control measures is fundamental to design effective foot and mouth disease (FMD) control measures. One approach to obtaining this information is through bioeconomic modelling. In this study, a bioeconomic model was used to evaluate FMD control in two different study areas in Thailand: a high farm density area predominantly consisting of dairy farms and a low farm density area with mixed farm types. The bioeconomic model consists of an epidemiological part and an economic part. For the epidemiological part, a stochastic between-farm transmission model was constructed with transmission parameters estimated from FMD outbreaks in Thailand. The outputs from the epidemiological model, i.e. the number of infected farms, the number of affected farms and the outbreak duration, are used as inputs for economic model to calculate the economic consequences. We applied the simulation model with four FMD control measures: culling the animals of infected farms, ring vaccination, animal movement restrictions and isolation of infected farms. Furthermore, we included effect of farmers' compliance to asses its effect on control measures. The simulated FMD outbreaks in the low farm density area were small, thus control measures did not greatly affect the size of outbreaks and, therefore, did not have a positive economic return. In contrast, in the high farm density area, FMD outbreaks were large without control measures. All measures reduced the size of the outbreaks but resulted in different total costs. In terms of outbreak control, culling infectious farms was the best option, but its total cost was higher than ring vaccination or isolation of infected farms. In terms of cost-effectiveness, ring vaccination was the best measure. If farmers' compliance were low, all control measures would be ineffective, resulting in high total cost of the outbreak. The cost distribution between compliant and non-compliant farms showed that non-compliant farms paid more than compliant farms, except for the ring vaccination scenario. The results emphasize the economic significance to customize control measures specific to the area's conditions and highlight the importance of farmers' compliance when designing control measures.

Role of the dengue vaccine TAK-003 in an outbreak response: Modeling the Sri Lanka experience.

Outbreaks of dengue can overburden hospital systems, drastically reducing capacity for other care. The 2017 dengue serotype 2 (DENV-2) outbreak in Sri Lanka coincided with vaccination in an ongoing phase 3 efficacy trial of a tetravalent dengue vaccine, TAK-003 (NCT02747927). Here, we present data on the efficacy of TAK-003 following two doses of the vaccine administered 3 months apart in participants aged 4-16 years in Sri Lanka. In addition, we have used the 2017 outbreak dynamics to model the potential impact of TAK-003 on virologically confirmed dengue (VCD) cases and hospitalizations during an outbreak situation. Modeling was performed using an age-structured, host-vector, spatial and stochastic transmission model, assuming 65% vaccine coverage and 30 days until initiation of vaccination. Efficacy of TAK-003 against VCD and hospitalized VCD cases was based on data against DENV-2 from the first year of the phase 3 trial. Vaccine efficacy and safety findings in Sri Lanka were in line with those of the overall trial population. The efficacy estimates in Sri Lanka up to the first 12 months after the second dose of TAK-003 were 94.7% and 95.7% against VCD and hospitalized VCD cases, respectively. Modeling of the trial data over an extended geographic area showed a substantial reduction in cases and a flattening of outbreak curves from TAK-003 use. The baseline vaccination scenario (initiation at 30 days, 65% target coverage, vaccine effective at 14 days, 70% hospitalization rate, VE of 95% for VCD and 97% for hospitalized VCD, and 47% for asymptomatic) resulted in a 69.1% reduction in VCD cases and 72.7% reduction in VCD hospitalizations compared with no vaccination. An extreme high scenario (vaccination initiated at Day 15, 80% coverage rate, baseline VE) resulted in 80.3% and 82.3% reduction in VCD and VCD hospitalizations, respectively. Vaccine performance, speed of vaccination campaign initiation, and vaccine coverage were key drivers in reducing VCD cases and hospitalizations. Overall, the study and modeling results indicate that TAK-003 has the potential of meaningful utility in dengue outbreaks in endemic areas.

Damping wings in the Lyman-α forest: A model-independent measurement of the neutral fraction at 5.4 < z < 6.1

Context. Recent observations have positioned the end point of the Epoch of Reionization (EoR) at a redshift of z ∼ 5.3. However, observations of the Lyman-α forest have not yet been able to discern whether reionization occurred slowly and late, with substantial neutral hydrogen persisting at a redshift of ∼6, or rapidly and earlier, with the apparent late end driven by the fluctuating ultraviolet background. Gunn-Peterson (GP) absorption troughs are solid indicators that reionization is not complete until z = 5.3, but whether they contain significantly neutral gas has not yet been proven. Aims. We aim to answer this question by directly measuring, for the first time, the neutral hydrogen fraction (xHI) at the end of the EoR (5 ≲ z ≲ 6) in high-redshift quasar spectra. Methods. For high neutral fractions, xHI ≳ 0.1, GP troughs exhibit damping wing (DW) absorption extending over 1000 km s−1 beyond the troughs. While conclusively detected in Lyman-α emission lines of quasars at z ≥ 7, DWs are challenging to observe in the general Lyman-α forest due to absorption complexities and small-scale stochastic transmission features. Results. We report the first successful identification of the stochastic DW signal adjacent to GP troughs at redshifts of z = 5.6 through careful stacking of the dark gaps in the Lyman-α forest (S/N = 6.3). We use the signal to present a measurement of the corresponding global xHI = 0.19 ± 0.07 (−0.16+0.11) at 1σ (2σ) at z = 5.6 and a limit of xHI < 0.44 at z = 5.9. Conclusions. The detection of this signal demonstrates the existence of substantially neutral islands near the conclusion of the EoR, unequivocally signaling a late-and-slow reionization scenario.

Modelling onchocerciasis-associated epilepsy and the impact of ivermectin treatment on its prevalence and incidence

Retrospective cohort studies in Cameroon found an association between Onchocerca volvulus microfilarial load in childhood (measured in 1991–1993) and risk of developing epilepsy later in life (measured in 2017). We parameterised and integrated this relationship (across children aged 3–15 years) into the previously published, stochastic transmission model, EPIONCHO-IBM, for Simulium damnosum sensu lato-transmitted onchocerciasis. We simulated 19 years (1998–2017) of annual ivermectin mass drug administration (MDA) reflecting coverage in the study area, and modelled epilepsy prevalence and incidence. Scenario-based simulations of 25 years of (annual and biannual) MDA in hyper- and holoendemic settings, with 65% and 80% therapeutic coverage, were also conducted. EPIONCHO-IBM predicted 7.6% epilepsy prevalence (compared to 8.2% in the Cameroon study) and incidence of 317 cases/100,000 person-years (compared to 350). In hyperendemic areas, 25 years of biannual MDA (80% coverage) eliminated onchocerciasis-associated epilepsy (OAE) and protected untreated under-fives from its development. Strengthening onchocerciasis programmes, implementing alternative strategies, and evaluating treatment for under-fives and school-age children are crucial to prevent OAE in highly-endemic settings.

Phase synchronization between culture and climate forcing.

Over the history of humankind, cultural innovations have helped improve survival and adaptation to environmental stress. This has led to an overall increase in human population size, which in turn further contributed to cumulative cultural learning. During the Anthropocene, or arguably even earlier, this positive sociodemographic feedback has caused a strong decline in important resources that, coupled with projected future transgression of planetary boundaries, may potentially reverse the long-term trend in population growth. Here, we present a simple consumer/resource model that captures the coupled dynamics of stochastic cultural learning and transmission, population growth and resource depletion in a changing environment. The idealized stochastic mathematical model simulates boom/bust cycles between low-population subsistence, high-density resource exploitation and subsequent population decline. For slow resource recovery time scales and in the absence of climate forcing, the model predicts a long-term global population collapse. Including a simplified periodic climate forcing, we find that cultural innovation and population growth can couple with climatic forcing via nonlinear phase synchronization. We discuss the relevance of this finding in the context of cultural innovation, the anthropological record and long-term future resilience of our own predatory species.

How Does the Proportion of Never Treatment Influence the Success of Mass Drug Administration Programs for the Elimination of Lymphatic Filariasis?

Mass drug administration (MDA) is the cornerstone for the elimination of lymphatic filariasis (LF). The proportion of the population that is never treated (NT) is a crucial determinant of whether this goal is achieved within reasonable time frames. Using 2 individual-based stochastic LF transmission models, we assess the maximum permissible level of NT for which the 1% microfilaremia (mf) prevalence threshold can be achieved (with 90% probability) within 10 years under different scenarios of annual MDA coverage, drug combination and transmission setting. For Anopheles-transmission settings, we find that treating 80% of the eligible population annually with ivermectin + albendazole (IA) can achieve the 1% mf prevalence threshold within 10 years of annual treatment when baseline mf prevalence is 10%, as long as NT <10%. Higher proportions of NT are acceptable when more efficacious treatment regimens are used. For Culex-transmission settings with a low (5%) baseline mf prevalence and diethylcarbamazine + albendazole (DA) or ivermectin + diethylcarbamazine + albendazole (IDA) treatment, elimination can be reached if treatment coverage among eligibles is 80% or higher. For 10% baseline mf prevalence, the target can be achieved when the annual coverage is 80% and NT ≤15%. Higher infection prevalence or levels of NT would make achieving the target more difficult. The proportion of people never treated in MDA programmes for LF can strongly influence the achievement of elimination and the impact of NT is greater in high transmission areas. This study provides a starting point for further development of criteria for the evaluation of NT.

An Updated Economic Assessment of Moxidectin Treatment Strategies for Onchocerciasis Elimination.

Concerns that annual mass administration of ivermectin, the predominant strategy for onchocerciasis control and elimination, may not lead to elimination of parasite transmission (EoT) in all endemic areas have increased interest in alternative treatment strategies. One such strategy is moxidectin. We performed an updated economic assessment of moxidectin- relative to ivermectin-based strategies. We investigated annual and biannual community-directed treatment with ivermectin (aCDTI, bCDTI) and moxidectin (aCDTM, bCDTM) with minimal or enhanced coverage (65% or 80% of total population taking the drug, respectively) in intervention-naive areas with 30%, 50%, or 70% microfilarial baseline prevalence (representative of hypo-, meso-, and hyperendemic areas). We compared programmatic delivery costs for the number of treatments achieving 90% probability of EoT (EoT90), calculated with the individual-based stochastic transmission model EPIONCHO-IBM. We used the costs for 40 years of program delivery when EoT90 was not reached earlier. The delivery costs do not include drug costs. aCDTM and bCDTM achieved EoT90 with lower programmatic delivery costs than aCDTI with 1 exception: aCDTM with minimal coverage did not achieve EoT90 in hyperendemic areas within 40 years. With minimal coverage, bCDTI delivery costs as much or more than aCDTM and bCDTM. With enhanced coverage, programmatic delivery costs for aCDTM and bCDTM were lower than for aCDTI and bCDTI. Moxidectin-based strategies could accelerate progress toward EoT and reduce programmatic delivery costs compared with ivermectin-based strategies. The costs of moxidectin to national programs are needed to quantify whether delivery cost reductions will translate into overall program cost reduction.

Scenarios of future mpox outbreaks among men who have sex with men: a modelling study based on cross-sectional seroprevalence data from the Netherlands, 2022.

BackgroundFollowing the 2022-2023 mpox outbreak, crucial knowledge gaps exist regarding orthopoxvirus-specific immunity in risk groups and its impact on future outbreaks.AimWe combined cross-sectional seroprevalence studies in two cities in the Netherlands with mathematical modelling to evaluate scenarios of future mpox outbreaks among men who have sex with men (MSM).MethodsSerum samples were obtained from 1,065 MSM attending Centres for Sexual Health (CSH) in Rotterdam or Amsterdam following the peak of the Dutch mpox outbreak and the introduction of vaccination. For MSM visiting the Rotterdam CSH, sera were linked to epidemiological and vaccination data. An in-house developed ELISA was used to detect vaccinia virus (VACV)-specific IgG. These observations were combined with published data on serial interval and vaccine effectiveness to inform a stochastic transmission model that estimates the risk of future mpox outbreaks.ResultsThe seroprevalence of VACV-specific antibodies was 45.4% and 47.1% in Rotterdam and Amsterdam, respectively. Transmission modelling showed that the impact of risk group vaccination on the original outbreak was likely small. However, assuming different scenarios, the number of mpox cases in a future outbreak would be markedly reduced because of vaccination. Simultaneously, the current level of immunity alone may not prevent future outbreaks. Maintaining a short time-to-diagnosis is a key component of any strategy to prevent new outbreaks.ConclusionOur findings indicate a reduced likelihood of large future mpox outbreaks among MSM in the Netherlands under current conditions, but emphasise the importance of maintaining population immunity, diagnostic capacities and disease awareness.

An infectious disease epidemic model with migration and stochastic transmission in deterministic and stochastic environments

Understanding population migration is essential for controlling highly infectious diseases. This paper studies the global dynamics of an infectious disease epidemic model incorporating population migration and a stochastic transmission rate. Our findings demonstrate that in deterministic and stochastic environments, the models exhibit global Lyapunov stability near the disease-free equilibrium point, determined by a threshold parameter. Furthermore, we analyze the effect of migration on infectious diseases. We discover that the number of infections and the peak value of the infection curve increase with a higher level of population migration. These results are supported by numerical illustrations that hold epidemiological relevance. Additionally, the disease-free equilibrium of the associated time delay model is linearly asymptotically stable, and the endemic equilibrium exhibits more bifurcation for larger time delay values.

Synchronization of Stochastic Neural Networks Using Looped-Lyapunov Functional and Its Application to Secure Communication.

This study aims to investigate the synchronization of user-controlled and uncontrolled neural networks (NNs) that exhibit chaotic solutions. The idea behind focusing on synchronization problems is to design the user-desired NNs by emulating the dynamical properties of traditional NNs rather than redefining them. Besides, instead of conventional NNs, this study considers NNs with significant factors such as time-dependent delays and uncertainties in the neural coefficients. In addition, information transmission over transmission may experience stochastic disturbances and network transmission. These factors will result in a stochastic differential NN model. Analyzing the NNs without these factors may be incompatible during the implementation. Theoretically, the model with stochastic disturbances can be considered a stochastic differential model, and the stability conditions are derived by employing Itô's formula and appropriate integral inequalities. To achieve synchronization, the sampled-data-based control scheme is proposed because it is more effective while information is being transmitted over networks. In contrast to the existing studies, this study contributes in terms of handling stochastic disturbances, effects of time-varying delays, and uncertainties in the system parameters via looped-type Lyapunov functional. Besides this, in the application view, delayed NNs are employed as a cryptosystem that helps to secure the transmission between the sender and the receiver, which is explored by illustrating the statistical measures evaluated for the standard images. From the simulation results, the proposed control and derived sufficient conditions can provide better synchronization and the proposed delayed NNs give a better cryptosystem.

Hybrid-scheduling-based sliding mode control for Markovian jump systems under transmission constraints

This paper presents a new hybrid-scheduling-based design of sliding mode control for Markovian jump systems against transmission constraints.The hybrid scheduling scheme integrates stochastic communication protocol and event-triggering mechanism. At each transmission instant, a discrete-time Markovian chain is employed to orchestrate the stochastic transmission of multiple sensor nodes, by which only one sensor node has the priority to be evaluated whether it meets the triggering condition to access the network. This hybrid scheduling scheme aims to generate a sparser sampling pattern than the existing one while improving channel resource utilization. Based on which a feasible compensator and token-dependent sliding mode controller is constructed. Moreover, a genetic algorithm-based optimization problem is given to achieve a smaller convergent domain. Finally, a numerical example is shown to verify the present control strategy.

A beta-Poisson model for infectious disease transmission.

Outbreaks of emerging and zoonotic infections represent a substantial threat to human health and well-being. These outbreaks tend to be characterised by highly stochastic transmission dynamics with intense variation in transmission potential between cases. The negative binomial distribution is commonly used as a model for transmission in the early stages of an epidemic as it has a natural interpretation as the convolution of a Poisson contact process and a gamma-distributed infectivity. In this study we expand upon the negative binomial model by introducing a beta-Poisson mixture model in which infectious individuals make contacts at the points of a Poisson process and then transmit infection along these contacts with a beta-distributed probability. We show that the negative binomial distribution is a limit case of this model, as is the zero-inflated Poisson distribution obtained by combining a Poisson-distributed contact process with an additional failure probability. We assess the beta-Poisson model's applicability by fitting it to secondary case distributions (the distribution of the number of subsequent cases generated by a single case) estimated from outbreaks covering a range of pathogens and geographical settings. We find that while the beta-Poisson mixture can achieve a closer to fit to data than the negative binomial distribution, it is consistently outperformed by the negative binomial in terms of Akaike Information Criterion, making it a suboptimal choice on parsimonious grounds. The beta-Poisson performs similarly to the negative binomial model in its ability to capture features of the secondary case distribution such as overdispersion, prevalence of superspreaders, and the probability of a case generating zero subsequent cases. Despite this possible shortcoming, the beta-Poisson distribution may still be of interest in the context of intervention modelling since its structure allows for the simulation of measures which change contact structures while leaving individual-level infectivity unchanged, and vice-versa.

Stochastic transmission in epidemiological models.

Recent empirical evidence suggests that the transmission coefficient in susceptible-exposed-infected-removed-like (SEIR-like) models evolves with time, presenting random patterns, and some stylized facts, such as mean-reversion and jumps. To address such observations we propose the use of jump-diffusion stochastic processes to parameterize the transmission coefficient in an SEIR-like model that accounts for death and time-dependent parameters. We provide a detailed theoretical analysis of the proposed model proving the existence and uniqueness of solutions as well as studying its asymptotic behavior. We also compare the proposed model with some variations possibly including jumps. The forecast performance of the considered models, using reported COVID-19 infections from New York City, is then tested in different scenarios. Despite the simplicity of the epidemiological model, by considering stochastic transmission, the forecasted scenarios were fairly accurate.

Saturated H∞ control of networked systems under stochastic transmission delays: The random sampling periods case

AbstractIn this paper, the saturated control problems of networked systems with random sampling periods and stochastic transmission delays are investigated. The main issue is to design an saturated controller such that the controlled close‐loop system is exponentially mean‐square stable with a desired performance. A matrix exponential which is different from some existing literature is used to reconstruct the discrete‐time stochastic system to an equivalent yet tractable augmented model without any additional conditional constraint. In virtue of reduced‐order confluent Vandermonde matrix approach and Kronecker product operation, the stability condition in terms of linear matrix inequality is derived and then the desired saturated controller is designed. It should be emphasized that the improved method can also be used for the stability analysis and controller design when the system matrix contains complex roots. Finally, two numerical examples and a three‐axis milling machine system are utilized to manifest the reliability and practicality of the designed strategy.

Computational Performance Enhancement Strategies for Risk-Averse Two-Stage Stochastic Generation and Transmission Network Expansion Planning

This paper proposes a new acceleration technique and a representative day aggregation procedure for the risk-averse two-stage stochastic generation and transmission network expansion planning problem, in which the conditional value-at-risk is used. We use a finite set of scenarios to model uncertainty in the peak demand level of loads, along with the capacity and marginal production cost of generating units. Moreover, we use representative days to model the operational variability of the electrical demand and renewable generation. The combination of scenarios and representative days involves many variables and constraints, which may lead to computationally intractable problems. Therefore, we propose a new relaxed version of the constraint generation-based algorithm that reduces the computational time of the problem. We additionally present a two-stage aggregation procedure that combines the modified maximum dissimilarity algorithm and the priority chronological time-period clustering in order to reduce the resolution of the representative days and to pay attention to extreme conditions. The numerical results of modified versions of the IEEE 24-bus Reliability Test System and the IEEE 118-bus Test System show reductions in the computational time of more than 89% for the relaxed constraint generation-based algorithm, and of more than 94% for the two-stage aggregation procedure.

Simulation of Premovement Active Surveillance Protocols for Moving Finishing Pigs to a Harvest Facility from a Control Area during an Outbreak of African Swine Fever in the United States

Movement restrictions are a critical component of response plans for an African swine fever (ASF) outbreak in the United States. These restrictions are likely to include requiring permits to move animals and products within, into, and out of 5‐km control areas (CAs) established around confirmed positive farms. For quarantined finishing farms located within a CA, diagnostic testing is an expected criterion for receival of a permit to move pigs to a harvest facility or removal of quarantine. A stochastic disease transmission and active surveillance model were used to evaluate premovement active surveillance protocols varying by the number of samples and timing of sample collection before movement. Surveillance protocol scenarios were evaluated for several different sampling prioritization schemes; virus strains of medium or high virulence; barn sizes of 1,200, 2,400, and 4,800 pigs; and farms with average to high mortality and morbidity during routine production. Surveillance protocols that included prioritization schemes targeting dead pigs and pigs with clinical signs resulted in the highest probabilities of detection and the lowest numbers of infectious pigs at the time of movement in barns that went undetected. There was some evidence that targeting sick pigs prior to dead pigs may be more effective for moderately virulent strains. However, in most scenarios, including all highly virulent strain scenarios and moderately virulent strain scenarios in barn sizes of 1,200 with average farm performance, prioritization of dead versus sick pigs first did not have a large impact on the predicted outcomes. Increasing sample sizes improved outcomes, though only marginal gains were achieved once the available dead and sick were sampled. Predicted outcomes may be further improved by sampling the available dead and sick pigs in a barn across multiple days, though the associated increase in the probability of detection was minor.

Stochastic Transmission Planning: Latent Factor Approximation of AC Costs With DC Subproblems

Transmission planning requires solving an optimal power flow problem for each considered investment plan for multiple operating conditions. AC power flow formulations are more accurate, but for transmission planning on large systems may be computationally intractable and can converge to local optima. Regional transmission studies usually rely on linearized DC power flow formulations, which reduce computation time but sometimes produce solutions that are inferior when the AC flows are resolved. We propose an approach to regional-scale transmission planning that approximates the AC power flow results using the faster DC formulation. An initial sample of investment plans is simulated with both AC and DC power flows, and then the singular value decomposition is applied to characterize the error between the two formulations and form a projection that estimates the AC costs from the DC costs. The estimation is used in a DC formulation of the transmission investment problem. We show that the proposed approach accurately estimates the AC operation costs and returns expansion plans that are consistently reliable with AC power flows. We also demonstrate that traditional DC formulations can produce low-cost, AC-feasible investment plans if enough representative hours are selected and weighted effectively.

Financial arbitrage in electricity markets via virtual bidding

The concept of virtual bidding serves as a pure financial tool in electricity markets, aiming at enhancing their performance. Although virtual bidding helps reduce price gaps between different trading floors, there is evidence suggesting that this does not necessarily lead to improved welfare for market consumers. To gain a deeper understanding of virtual bidding and its impact on electricity market prices, this work delves into the strategic behavior of a financial player engaged in an electricity pool-based market. To this end, a bi-level model is formulated. The upper level optimizes the expected revenues of the virtual bidder while the lower-level represents the clearing of the market under network-constrained economic dispatch through a two-stage stochastic programming. The proposed model derives optimal arbitrage strategies in both scenarios of explicit and implicit virtual bidding. It also assesses the performance of virtual bidding under stochastic generation and transmission contingencies, including considerations of up-to-congestion virtual bidding.

2814. The Impact of Novel Antibiotics on Curbing Antimicrobial Resistance and Infection Spread a Dynamic Modeling Study

Abstract Background Antimicrobial resistance (AMR) is a growing global concern. Novel antibiotics (ABXs) target resistant pathogens causing gram-negative (GN) bacterial infections. This study assesses the broader impact of novel ABX use on the US healthcare system, including preventing the onward spread of infections and reducing the level of high-threat AMR. Methods A stochastic dynamic transmission model was developed to simulate the interaction between infection transmissibility, bacterial resistance development, and multiple lines of ABX. Included infection types were intra-abdominal infections, urinary tract infections and hospital-acquired /ventilator-associated pneumonia. Included AMR types were carbapenem-resistant and ESBL-producing enterobacterales (CRE and ESBLE), and multi-drug resistant pseudomonas aeruginosa (MDRP). The model has compartments for non-infected, colonized, or infected patients, with a sensitive or resistant strain in a US hospital. Infected patients receive antibiotics resulting in infection clearance or use of an alternate ABX class. Sensitive pathogens gain resistance due to drug pressure or conversely could lose resistance. Ordinary differential equations were used to estimate compartment membership. The primary data source was the Premier healthcare database (172 hospitals, 2016-2020). The model examined the potential reduction in AMR levels and avoided hospital infections, by comparing two scenarios: targeted use of novel ABX for specific AMR types and the status quo with limited novel ABX use. Results When even novel ABXs are reserved as the 2nd or 3rd line of treatment, compared to the status quo, it could result in a ∼1.5% reduction in hospital infections and a change in CRE, ESBLE, and MDRP by −2.5%, −3.6%, and −2.1%, respectively, on an annual basis. The cumulative impact of novel ABXs over 5-10 years was much larger but subject to higher prediction uncertainty. The primary driver of results are infection clearance rates, risk of resistance gain on various ABX, and uptake of novel ABX. Conclusion Targeted use of novel ABXs for resistant pathogens could lead to improved, earlier infection clearance with most types of infections and pathogens, thereby reducing infection transmission and rise of high-threat AMR in the long-term. Disclosures Apoorva Ambavane, PhD, Merck &amp; Co., Inc: Grant/Research Support Michal Litkiewicz, MSc, Merck &amp; Co., Inc: Grant/Research Support Aditya Sardesai, MSc, CSL Seqirus: Advisor/Consultant|Merck &amp; Co. Inc: Grant/Research Support|Merck &amp; Co. Inc: Grant/Research Support Diana Teloian, MA, Merck &amp; Co., Inc: Grant/Research Support Engels N. Obi, PhD, Merck &amp; Co Inc: Employee Joe Yang, PhD, Merck &amp; Co., Inc: Employee|Merck &amp; Co., Inc: Stocks/Bonds