Controlling Epidemics Through Optimal Allocation of Test Kits and Vaccine Doses Across Networks

Xia, Mingtao; Böttcher, Lucas; Chou, Tom

doi:10.1109/tnse.2022.3144624

Cited by 21 publications

(6 citation statements)

References 61 publications

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“…Our study opens up several avenues for future research. One worthwhile direction for future work is to combine our methods with control theory (Chehrazi et al 2019;Xia et al 2021;Asikis et al 2022;Böttcher et al 2022a) to study how many new antibiotics are needed on average in a certain time interval (e.g., 10-20 years) to create a stable supply of effective treatment options and to keep the emergence of antibiotic resistance at a minimum. Another important direction is to estimate the minimum size of the proposed funding scheme for different regions to make antibiotic R&D viable under current and/or modified market conditions.…”

Section: Discussionmentioning

confidence: 99%

A Refunding Scheme to Incentivize Narrow-Spectrum Antibiotic Development

Böttcher

Gersbach

2022

Bull Math Biol

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The rapid rise of antibiotic resistance is a serious threat to global public health. The situation is exacerbated by the “antibiotics dilemma”: Developing narrow-spectrum antibiotics against resistant bacteria is most beneficial for society, but least attractive for companies, since their usage and sales volumes are more limited than for broad-spectrum drugs. After developing a general mathematical framework for the study of antibiotic resistance dynamics with an arbitrary number of antibiotics, we identify efficient treatment protocols. Then, we introduce a market-based refunding scheme that incentivizes pharmaceutical companies to develop new antibiotics against resistant bacteria and, in particular, narrow-spectrum antibiotics that target specific bacterial strains. We illustrate how such a refunding scheme can solve the antibiotics dilemma and cope with various sources of uncertainty that impede antibiotic R &D. Finally, connecting our refunding approach to the recently established Antimicrobial Resistance (AMR) Action Fund, we discuss how our proposed incentivization scheme could be financed.

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Section: Discussionmentioning

confidence: 99%

A Refunding Scheme to Incentivize Narrow-Spectrum Antibiotic Development

Böttcher

Gersbach

2022

Bull Math Biol

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“…Previous research on infectious disease surveillance has focused primarily on developing models to identify sentinel sites or subpopulations, with the objective of classifying nodes in networks that could serve as observational units for monitoring disease spread (25)(26)(27). Since the COVID-19 pandemic, there has been growing interest in the design of optimal control measures to contain transmission (28), with some studies examining the cost-effectiveness of different strategies for testing and isolation (29)(30)(31)(32); one recent study also explored the impact of different air travel regulations on the likelihood of a local epidemic escalating into a global pandemic (33). However, the effectiveness of these interventions depends ultimately on the capacity of local authorities to conduct surveillance and to collectively provide an accurate assessment of overall disease distribution at any stage of an outbreak -a challenge which, to the best of our knowledge, has received little attention to date (34).…”

Section: Introductionmentioning

confidence: 99%

Optimal disease surveillance with graph-based Active Learning

Tsui,

Zhang,

Sambaturu

et al. 2024

Preprint

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Tracking the spread of emerging pathogens is critical to the design of timely and effective public health responses. Policymakers face the challenge of allocating finite resources for testing and surveillance across locations, with the goal of maximising the information obtained about the underlying trends in prevalence and incidence. We model this decision-making process as an iterative node classification problem on an undirected and unweighted graph, in which nodes represent locations and edges represent movement of infectious agents among them. To begin, a single node is randomly selected for testing and determined to be either infected or uninfected. Test feedback is then used to update estimates of the probability of unobserved nodes being infected and to inform the selection of nodes for testing at the next iterations, until a certain resource budget is exhausted. Using this framework we evaluate and compare the performance of previously developed Active Learning policies, including node-entropy and Bayesian Active Learning by Disagreement. We explore the performance of these policies under different outbreak scenarios using simulated outbreaks on both synthetic and empirical networks. Further, we propose a novel policy that considers the distance-weighted average entropy of infection predictions among the neighbours of each candidate node. Our proposed policy outperforms existing ones in most outbreak scenarios, leading to a reduction in the number of tests required to achieve a certain predictive accuracy. Our findings could inform the design of cost-effective surveillance strategies for emerging and endemic pathogens, and reduce the uncertainties associated with early risk assessments in resource-constrained situations.

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“…Ref. [13] uses control-theoretic methods to obtain a model for optimal vaccine allocation, targeting high-degree nodes and then lower-degree nodes. An optimal control model is also formulated in [14] to allocate vaccines while considering constraints such as disease transmission, vaccine supply, and distribution logistics.…”

Section: Introductionmentioning

confidence: 99%

“…𝐼 𝑖 (𝑡+1)−𝐼 𝑖 (𝑡) ∆𝑡 ≈ 𝑑𝐼 𝑖 (𝑡) 𝑑𝑡 = 𝑆 ̅ 𝑖 (𝑡) (∑ 𝛽 𝑖𝑗 𝑛 𝑗=1𝐼 𝑗 (𝑡)) − 𝛾 𝑖 𝐼 𝑖 (𝑡 − 𝑚 1 ) − 𝜇 𝑖 𝐼 𝑖 (𝑡 − 𝑚 2 ), for 𝑡 > 𝑚 2(13) where 𝛾 𝑖 and 𝜇 𝑖 are constants. Following the assumptions in (i) we know that unvaccinated but infected people will not recover or die when 𝑡 ≤ 𝑚 2 , and𝑑𝐼 𝑖 (𝑡) 𝑑𝑡 can be simplified as the following for 𝑡 ≤ 𝑚 2 : 𝐼 𝑖 (𝑡+1)−𝐼 𝑖 (𝑡) recovered population is related to those unvaccinated and infected at 𝑡 − 𝑚 1 and those vaccinated but infected at 𝑡 − 𝑚 1 .…”

mentioning

confidence: 99%

An Optimal Vaccine Allocation Model Considering Vaccine Hesitancy and Efficacy Rates Among Populations

Zhang¹

2023

IEEE Access

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Vaccines need to be urgently allocated in pandemics like the ongoing COVID-19 pandemic. In the literature, vaccines are optimally allocated using various mathematical models, including the extensively used Susceptible-Infected-Recovered epidemic model. However, these models do not account for the time duration concerning multi-dose vaccines, time duration from infection to recovery or death, the vaccine hesitancy (i.e., delay in acceptance or refusal of vaccination), and vaccine efficacy (i.e., the time-varying protection capability of the vaccine). To make the vaccine allocation model more applicable to reality, this paper presents an optimal model considering the above mentioned time duration concerning multi-dose vaccination, time duration from infection to recovery or death, hesitancy rates, efficacy levels, and also breakthrough rates-the rates at which individuals get infected after vaccination. This vaccine allocation model is constructed using a revised Susceptible-Infected-Recovered model. The concept of people*week infections is introduced to measure the number of infected people within a certain time duration, and in this paper, the amount of people*week infections is minimized by the proposed vaccine allocation model. Our case study of the New York State 2021 population of 19,840,000 shows that this optimal allocation method can avoid 0.05%~2.75% people*week infections than the baseline allocation method when 2 to 11 million vaccines are optimally allocated. In conclusion, the obtained optimal allocation method can effectively reduce people*week infections and avoid vaccine waste when more vaccines are available.

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Controlling Epidemics Through Optimal Allocation of Test Kits and Vaccine Doses Across Networks

Cited by 21 publications

References 61 publications

A Refunding Scheme to Incentivize Narrow-Spectrum Antibiotic Development

A Refunding Scheme to Incentivize Narrow-Spectrum Antibiotic Development

Optimal disease surveillance with graph-based Active Learning

An Optimal Vaccine Allocation Model Considering Vaccine Hesitancy and Efficacy Rates Among Populations

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