Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19.

BACKGROUND: During the early stages of the COVID-19 pandemic, there was considerable uncertainty surrounding epidemiological and clinical aspects of SARS-CoV-2. Governments around the world, starting from varying levels of pandemic preparedness, needed to make decisions about how to respond to SARS-CoV-2 with only limited information about transmission rates, disease severity and the likely effectiveness of public health interventions. In the face of such uncertainties, formal approaches to quantifying the value of information can help decision makers to prioritise research efforts. METHODS: In this study we use Value of Information (VoI) analysis to quantify the likely benefit associated with reducing three key uncertainties present in the early stages of the COVID-19 pandemic: the basic reproduction number ([Formula: see text]), case severity (CS), and the relative infectiousness of children compared to adults (CI). The specific decision problem we consider is the optimal level of investment in intensive care unit (ICU) beds. Our analysis incorporates mathematical models of disease transmission and clinical pathways in order to estimate ICU demand and disease outcomes across a range of scenarios. RESULTS: We found that VoI analysis enabled us to estimate the relative benefit of resolving different uncertainties about epidemiological and clinical aspects of SARS-CoV-2. Given the initial beliefs of an expert, obtaining more information about case severity had the highest parameter value of information, followed by the basic reproduction number [Formula: see text]. Resolving uncertainty about the relative infectiousness of children did not affect the decision about the number of ICU beds to be purchased for any COVID-19 outbreak scenarios defined by these three parameters. CONCLUSION: For the scenarios where the value of information was high enough to justify monitoring, if CS and [Formula: see text] are known, management actions will not change when we learn about child infectiousness. VoI is an important tool for understanding the importance of each disease factor during outbreak preparedness and can help to prioritise the allocation of resources for relevant information.

The Expected Value of Perfect Information of COVID-19 Treatment Using Discrete Event Simulation

The Value of Perfect Information (EVPI) and also Sample Information (EVSI) are necessary for calculating the expected economic benefit of a research based on evidence about the cost and efficacy of novel therapies. The EVPI determines the maximum value resulting from soliciting data to decrease the uncertainties and the expected loss in case of providing ineffective treatment. In general, an inefficient decision will waste health resources that may be better spent elsewhere, thereby deteriorating health outcomes. In this article, the value of information resulting from reducing uncertainty will be applied in assessing two COVID-19 treatments, namely, the standard care and vaccines. A discrete event simulation model is introduced to expand the usage of EVPI calculations to medical applications with various sources of uncertainty as the case of COVID-19. Our simulation results show that further testing and vaccine validation will be of insignificant value if the response rate on vaccine is higher than 85%. The purpose of this study is to provide a step-by-step guide to the computation of the value pre-testing in the context of healthcare decision-making. Worked scenarios were presented for COVID-19 in UAE. The study can serve as a useful template for various decision-making problems in medical settings. © 2022 The Authors.

A Bayesian Network Model for Supporting School Managers Decisions in the Pandemic Era.

Due to the dramatic health situation caused by the COVID-19 pandemic, in Italy the emergency remote teaching lasted longer than in other countries. The mandatory teaching modalities have required digital transformation processes in a framework where digital-divide is one of the limitations to school modernization. We believe that the experience can promote a deeper formatting of organizational process. The paper shows results of a multitarget research carried out during the Italian lockdown aiming at animating the debate around school from multi-actors perspectives and at supporting policies. The paper aims at showing the potentiality of a multivariate statistical method as a tool supporting school managers in identifying those challenges they have to face to improve the setting up of internal processes. The main result is a model supporting the decision making process at orienting school managers strategies.

Modelling: Understanding pandemics and how to control them.

New disease challenges, societal demands and better or novel types of data, drive innovations in the structure, formulation and analysis of epidemic models. Innovations in modelling can lead to new insights into epidemic processes and better use of available data, yielding improved disease control and stimulating collection of better data and new data types. Here we identify key challenges for the structure, formulation, analysis and use of mathematical models of pathogen transmission relevant to current and future pandemics.

Publishing during a pandemic: comparison of scientific knowledge between COVID-19 and Swine Flu

Scientific knowledge has a well-established cycle of generating hypotheses, testing them in experiments with proper discussion, and submitting it to the scientific community analysis through publications. It takes time to establish sample size for biomedical studies, especially concerning the effect of medicines and vaccines. The World Health Organization's protocol estimates that more than 19 months of experiments are necessary to approve a vaccine, for example. As the world has witnessed, a pandemic with immediate impact on human lives urges scientific methods to speed up finding solutions. Here it was assessed the speed and volume of information generated by the Academia to tackle the COVID-19 compared to the Swine Flu pandemic. Were considered papers published in journals indexed in PubMed, the most comprehensive biomedical scientific database available online. The number of publications about COVID-19 was 11 times higher than the number of publications about Swine Flu in a one-year timeframe. Though the expectation were finding more international collaborations and studies focusing on vaccines for COVID-19, papers were mostly concentrated in China and studying symptoms, managing the pandemic, reviewing knowledge, or establishing clinical trials. For sure, science is working faster every day for solutions in biomedical critical situations. However, the fast volume of information might blurry decisions on public health management. This paper's results show it is mandatory before using papers to take actions, waiting for the scientific community to first progress on its scientific knowledge cycle and mature discussions on the generated knowledge.

Publicando durante uma pandemia: comparação do conhecimento científico durante as crises causadas pela COVID-19 e pela Gripe Suína

O conhecimento científico tem um ciclo bem estabelecido de criação de hipóteses, testando-as em experimentos e submetendo-as à análise da comunidade científica por meio de publicações. Leva-se tempo para atingir suficiência amostral em estudos biomédicos, especialmente sobre o efeito de medicamentos e vacinas. O protocolo da Organização Mundial da Saúde estima que sejam necessários mais de 19 meses de experimentos para aprovar uma vacina, por exemplo. Uma pandemia com impacto imediato em vidas humanas exige que estudos científicos acelerem a busca de soluções. No presente trabalho, avaliamos a velocidade e o volume de informações geradas pela Academia para enfrentar a COVID-19 em comparação com a Gripe Suína. Foram considerados artigos de periódicos indexados na plataforma PubMed. O número de publicações sobre a COVID-19 foi 11 vezes maior que o número de publicações sobre a Gripe Suína no período de um ano. Embora esperássemos mais colaborações e estudos internacionais com foco em vacinas para a COVID-19, os artigos se concentraram na China e no estudo de sintomas, gerenciamento da pandemia, revisões do conhecimento ou em ensaios clínicos. Com certeza, a Ciência está trabalhando mais rápido para soluções em situações biomédicas críticas. No entanto, o grande volume de informações gerado em pouco tempo pode dificultar a tomada de decisões em diversas áreas, incluindo na gestão da saúde. Os resultados deste artigo mostram que antes de usar artigos para realizar ações, os tomadores de decisão devem filtrar as informações recebidas e aguardar que a comunidade científica amadureça as discussões sobre o conhecimento gerado.Alternate : Scientific knowledge has a well-established cycle of generating hypotheses, testing them in experiments with proper discussion, and submitting it to the scientific community analysis through publications. It takes time to establish sample size for biomedical studies, especially concerning the effect of medicines and vaccines. The World Health Organization’s protocol estimates that more than 19 months of experiments are necessary to approve a vaccine, for example. As the world has witnessed, a pandemic with immediate impact on human lives urges scientific methods to speed up finding solutions. Here it was assessed the speed and volume of information generated by the Academia to tackle the COVID-19 compared to the Swine Flu pandemic. Were considered papers published in journals indexed in PubMed, the most comprehensive biomedical scientific database available online. The number of publications about COVID-19 was 11 times higher than the number of publications about Swine Flu in a one-year timeframe. Though the expectation were finding more international collaborations and studies focusing on vaccines for COVID-19, papers were mostly concentrated in China and studying symptoms, managing the pandemic, reviewing knowledge, or establishing clinical trials. For sure, science is working faster every day for solutions in biomedical critical situations. However, the fast volume of information might blurry decisions on public health management. This paper’s results show it is mandatory before using papers to take actions, waiting for the scientific community to first progress on its scientific knowledge cycle and mature discussions on the generated knowledge.

On Ambiguity Reduction and the Role of Decision Analysis during the Pandemic.

The COVID-19 pandemic has created a multitude of decision problems for a variety of fields. Questions from the seriousness and breadth of the problem to the effectiveness of proposed mitigation measures have been raised. We assert that the decision sciences have a crucial role to play here, as the questions requiring answers involve complex decision making under both uncertainty and ambiguity. The collection, processing, and analysis of data is critical in providing a useful response-especially as information of fundamental importance to such decision making (base rates and transmission rates) is lacking. We propose that scarce testing resources should be diverted away from confirmatory analysis of symptomatic people, as laboratory diagnosis appears to have little decision value in treatment choice over clinical diagnosis in patients presenting with symptoms. In contrast, the exploratory use of testing resources to reduce ambiguity in estimates of the base rate of infection appears to have significant value and great practical import for public policy purposes. As these stances may be at odds with triage practices among medical practitioners, they highlight the important role the decision analyst can play in responding to the challenges of the COVID-19 pandemic.

Open government data, uncertainty and coronavirus: An infodemiological case study.

Governments around the world have made data on COVID-19 testing, case numbers, hospitalizations and deaths openly available, and a breadth of researchers, media sources and data scientists have curated and used these data to inform the public about the state of the coronavirus pandemic. However, it is unclear if all data being released convey anything useful beyond the reputational benefits of governments wishing to appear open and transparent. In this analysis we use Ontario, Canada as a case study to assess the value of publicly available SARS-CoV-2 positive case numbers. Using a combination of real data and simulations, we find that daily publicly available test results probably contain considerable error about individual risk (measured as proportion of tests that are positive, population based incidence and prevalence of active cases) and that short term variations are very unlikely to provide useful information for any plausible decision making on the part of individual citizens. Open government data can increase the transparency and accountability of government, however it is essential that all publication, use and re-use of these data highlight their weaknesses to ensure that the public is properly informed about the uncertainty associated with SARS-CoV-2 information.