Effective vaccine allocation strategies, balancing economy with infection control against COVID-19 in Japan.
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ABSTRACT:
SUBMITTER: Sunohara S
PROVIDER: S-EPMC8412346 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background: Global tobacco control is a major public health issue, as smoking-related disease burden remains high worldwide. The World Bank and the World Health Organization (WHO) are the driving forces in global tobacco control. However, little research has focused on their development, financing, decision-making, and accountability structures. Methods: We used two strategies to identify the development and structure of global tobacco control initiatives. First, we reviewed the published literature through electronic databases. Second, we conducted grey literature searching. Results: We identified four periods in the Bank's involvement in global tobacco control, from creation of the evidence base in the 1990s to the implementation of tax reforms. We identified three phases in the WHO's efforts, from its early recognition of the link between tobacco and health risks in the 1970s to its implementation of the Framework Convention on Tobacco Control. Both organisations are financed by a handful of private philanthropies, and face similar risks for effective tobacco control: reduced accountability and resource mobilisation, poor decision-making authority due to specific donor influence, and difficulty in monitoring and evaluation. Conclusions: Continued attention should be paid not only to the primary health-related outcomes of tobacco use, but also to the decision-making and financing structures to promote tobacco control activities.
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Project description:Individuals are prioritized based on their risk profiles when allocating limited vaccine stocks during an influenza pandemic. Computationally expensive but realistic agent-based simulations and fast but stylized compartmental models are typically used to derive effective vaccine allocation strategies. A detailed comparison of these two approaches, however, is often omitted. We derive age-specific vaccine allocation strategies to mitigate a pandemic influenza outbreak in Seattle by applying derivative-free optimization to an agent-based simulation and also to a compartmental model. We compare the strategies derived by these two approaches under various infection aggressiveness and vaccine coverage scenarios. We observe that both approaches primarily vaccinate school children, however they may allocate the remaining vaccines in different ways. The vaccine allocation strategies derived by using the agent-based simulation are associated with up to 70% decrease in total cost and 34% reduction in the number of infections compared to the strategies derived by using the compartmental model. Nevertheless, the latter approach may still be competitive for very low and/or very high infection aggressiveness. Our results provide insights about potential differences between the vaccine allocation strategies derived by using agent-based simulations and those derived by using compartmental models.
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Project description:Public policy and academic debates regarding pandemic control strategies note disease-economy trade-offs, often prioritizing one outcome over the other. Using a calibrated, coupled epi-economic model of individual behavior embedded within the broader economy during a novel epidemic, we show that targeted isolation strategies can avert up to 91% of economic losses relative to voluntary isolation strategies. Unlike widely-used blanket lockdowns, economic savings of targeted isolation do not impose additional disease burdens, avoiding disease-economy trade-offs. Targeted isolation achieves this by addressing the fundamental coordination failure between infectious and susceptible individuals that drives the recession. Importantly, we show testing and compliance frictions can erode some of the gains from targeted isolation, but improving test quality unlocks the majority of the benefits of targeted isolation.
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Project description:We develop a model to retrospectively evaluate age-dependent counterfactual vaccine allocation strategies against the coronavirus disease 2019 (COVID-19) pandemic. To estimate the effect of allocation on the expected severe-case incidence, we employ a simulation-assisted causal modeling approach that combines a compartmental infection-dynamics simulation, a coarse-grained causal model, and literature estimates for immunity waning. We compare Israel's strategy, implemented in 2021, with counterfactual strategies such as no prioritization, prioritization of younger age groups, or a strict risk-ranked approach; we find that Israel's implemented strategy was indeed highly effective. We also study the impact of increasing vaccine uptake for given age groups. Because of its modular structure, our model can easily be adapted to study future pandemics. We demonstrate this by simulating a pandemic with characteristics of the Spanish flu. Our approach helps evaluate vaccination strategies under the complex interplay of core epidemic factors, including age-dependent risk profiles, immunity waning, vaccine availability, and spreading rates.
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Project description:BackgroundThe world is currently confronting the first influenza pandemic of the 21(st) century. Influenza vaccination is an effective preventive measure, but the unique epidemiological features of swine-origin influenza A (H1N1) (pH1N1) introduce uncertainty as to the best strategy for prioritization of vaccine allocation. We sought to determine optimal prioritization of vaccine distribution among different age and risk groups within the Canadian population, to minimize influenza-attributable morbidity and mortality.Methodology/principal findingsWe developed a deterministic, age-structured compartmental model of influenza transmission, with key parameter values estimated from data collected during the initial phase of the epidemic in Ontario, Canada. We examined the effect of different vaccination strategies on attack rates, hospitalizations, intensive care unit admissions, and mortality. In all scenarios, prioritization of high-risk individuals (those with underlying chronic conditions and pregnant women), regardless of age, markedly decreased the frequency of severe outcomes. When individuals with underlying medical conditions were not prioritized and an age group-based approach was used, preferential vaccination of age groups at increased risk of severe outcomes following infection generally resulted in decreased mortality compared to targeting vaccine to age groups with higher transmission, at a cost of higher population-level attack rates. All simulations were sensitive to the timing of the epidemic peak in relation to vaccine availability, with vaccination having the greatest impact when it was implemented well in advance of the epidemic peak.Conclusions/significanceOur model simulations suggest that vaccine should be allocated to high-risk groups, regardless of age, followed by age groups at increased risk of severe outcomes. Vaccination may significantly reduce influenza-attributable morbidity and mortality, but the benefits are dependent on epidemic dynamics, time for program roll-out, and vaccine uptake.
| S-EPMC2865540 | biostudies-literature
Project description:Sudden outbreak of a new pathogen in numbers of pneumonic patients in Wuhan province during December 2019 has threatened the world population within a short period of its occurrence. This respiratory tract-isolated pathogen was initially named as novel coronavirus 2019 (nCoV-2019), but later termed as SARS-CoV-2. The rapid spreading of this infectious disease received the label of pandemic by the World Health Organization within 4 months of its occurrence, which still seeks continuous attention of the researchers to prevent the spread and for cure of the infected patients. The propagation of the disease has been recorded in 215 countries, with more than 25.5 million cases and a death toll of more than 0.85 million. Several measures are taken to control the disease transmission, and researchers are actively engaged in finding suitable therapeutics to effectively control the disease to minimize the mortality and morbidity rates. Several existing potential candidates were explored in the prevention and treatment of worsening condition of COVID-19 patients; however, none of the formulation has been approved for the treatment but used under medical supervision. In this article, a focus has been made to highlight on current epidemiology on the COVID-19 infection, clinical features, diagnosis, and transmission, with special emphasis on treatment measures of the disease at different stages of clinical research and the global economic influence due to this pandemic situation. Progress in the development on vaccine against COVID-19 has also been explored as important measures to immunize people. Moreover, this article is expected to provide information to the researchers, who are constantly combating in the management against this outbreak.
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Project description:The problem of optimally allocating a limited supply of vaccine to control a communicable disease has broad applications in public health and has received renewed attention during the COVID-19 pandemic. This allocation problem is highly complex and nonlinear. Decision makers need a practical, accurate, and interpretable method to guide vaccine allocation. In this paper we develop simple analytical conditions that can guide the allocation of vaccines over time. We consider four objectives: minimize new infections, minimize deaths, minimize life years lost, or minimize quality-adjusted life years lost due to death. We consider an SIR model with interacting population groups. We approximate the model using Taylor series expansions, and develop simple analytical conditions characterizing the optimal solution to the resulting problem for a single time period. We develop a solution approach in which we allocate vaccines using the analytical conditions in each time period based on the state of the epidemic at the start of the time period. We illustrate our method with an example of COVID-19 vaccination, calibrated to epidemic data from New York State. Using numerical simulations, we show that our method achieves near-optimal results over a wide range of vaccination scenarios. Our method provides a practical, intuitive, and accurate tool for decision makers as they allocate limited vaccines over time, and highlights the need for more interpretable models over complicated black box models to aid in decision making.
| S-EPMC9288241 | biostudies-literature
Project description:BackgroundThe new waves of COVID-19 outbreaks caused by the SARS-CoV-2 Omicron variant are developing rapidly and getting out of control around the world, especially in highly populated regions. The healthcare capacity (especially the testing resources, vaccination coverage, and hospital capacity) is becoming extremely insufficient as the demand will far exceed the supply. To address this time-critical issue, we need to answer a key question: How can we effectively infer the daily transmission risks in different districts using machine learning methods and thus lay out the corresponding resource prioritization strategies, so as to alleviate the impact of the Omicron outbreaks?MethodsWe propose a computational method for future risk mapping and optimal resource allocation based on the quantitative characterization of spatiotemporal transmission patterns of the Omicron variant. We collect the publicly available data from the official website of the Hong Kong Special Administrative Region (HKSAR) Government and the study period in this paper is from December 27, 2021 to July 17, 2022 (including a period for future prediction). First, we construct the spatiotemporal transmission intensity matrices across different districts based on infection case records. With the constructed cross-district transmission matrices, we forecast the future risks of various locations daily by means of the Gaussian process. Finally, we develop a transmission-guided resource prioritization strategy that enables effective control of Omicron outbreaks under limited capacity.ResultsWe conduct a comprehensive investigation of risk mapping and resource allocation in Hong Kong, China. The maps of the district-level transmission risks clearly demonstrate the irregular and spatiotemporal varying patterns of the risks, making it difficult for the public health authority to foresee the outbreaks and plan the responses accordingly. With the guidance of the inferred transmission risks, the developed prioritization strategy enables the optimal testing resource allocation for integrative case management (including case detection, quarantine, and further treatment), i.e., with the 300,000 testing capacity per day; it could reduce the infection peak by 87.1% compared with the population-based allocation strategy (case number reduces from 20,860 to 2689) and by 24.2% compared with the case-based strategy (case number reduces from 3547 to 2689), significantly alleviating the burden of the healthcare system.ConclusionsComputationally characterizing spatiotemporal transmission patterns allows for the effective risk mapping and resource prioritization; such adaptive strategies are of critical importance in achieving timely outbreak control under insufficient capacity. The proposed method can help guide public-health responses not only to the Omicron outbreaks but also to the potential future outbreaks caused by other new variants. Moreover, the investigation conducted in Hong Kong, China provides useful suggestions on how to achieve effective disease control with insufficient capacity in other highly populated countries and regions.
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Project description:Tuberculosis (TB) caused by Mycobacterium tuberculosis infection is responsible for the most deaths by a single infectious agent worldwide, with 1.6 million deaths in 2017 alone. The World Health Organization, through its "End TB" strategy, aims to reduce TB deaths by 95% by 2035. In order to reach this goal, a more effective vaccine than the Bacillus Calmette-Guerin (BCG) vaccine currently in use is needed. Subunit TB vaccines are ideal candidates, because they can be used as booster vaccinations for individuals who have already received BCG and would also be safer for use in immunocompromised individuals in whom BCG is contraindicated. However, subunit TB vaccines will almost certainly require formulation with a potent adjuvant. As the correlates of vaccine protection against TB are currently unclear, there are a variety of adjuvants currently being used in TB vaccines in preclinical and clinical development. This review describes the various adjuvants in use in TB vaccines, their effectiveness, and their proposed mechanisms of action. Notably, adjuvants with less inflammatory and reactogenic profiles that can be administered safely via mucosal routes, may have the biggest impact on future directions in TB vaccine design.
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Project description:The COVID-19 pandemic has become a long-term crisis that calls for long-term solutions. We combined an augmented SEIR simulation model with real-time human mobility data to decompose the effects of lockdown, travel bans and effective testing measures in the curtailment of COVID-19 spread in China over different time horizons. Our analysis reveals that the significant growth in the detection rate of infectious cases, thanks to the expansion in testing efficiency, were as effective as city lockdowns in explaining the reduction in new infections up to mid-March. However, as we extended the analysis to July, increasing the detection rate to at least 50% is the only reliable way to bring the spread under control.
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