Panel stacking is a threat to consensus statement validity.

Consensus statements can be very influential in medicine and public health. Some of these statements use systematic evidence synthesis but others fail on this front. Many consensus statements use panels of experts to deduce perceived consensus through Delphi processes. We argue that stacking of panel members towards one particular position or narrative is a major threat, especially in absence of systematic evidence review. Stacking may involve financial conflicts of interest, but non-financial conflicts of strong advocacy can also cause major bias. Given their emerging importance, we describe here how such consensus statements may be misleading, by analysing in depth a recent high-impact Delphi consensus statement on COVID-19 recommendations as a case example. We demonstrate that many of the selected panel members and at least 35% of the core panel members had advocated towards COVID-19 elimination (zero-COVID) during the pandemic and were leading members of aggressive advocacy groups. These advocacy conflicts were not declared in the Delphi consensus publication, with rare exceptions. Therefore, we propose that consensus statements should always require rigorous evidence synthesis and maximal transparency on potential biases towards advocacy or lobbyist groups to be valid. While advocacy can have many important functions, its biased impact on consensus panels should be carefully avoided.

Business forecasting methods: Impressive advances, lagging implementation.

Reliable forecasts are key to decisions in areas ranging from supply chain management to capacity planning in service industries. It is encouraging then that recent decades have seen dramatic advances in forecasting methods which have the potential to significantly increase forecast accuracy and improve operational and financial performance. However, despite their benefits, we have evidence that many organizations have failed to take up systematic forecasting methods. In this paper, we provide an overview of recent advances in forecasting and then use a combination of survey data and in-depth semi-structured interviews with forecasters to investigate reasons for the low rate of adoption. Finally, we identify pathways that could lead to the greater and more widespread use of systematic forecasting methods.

COVID-19: Forecasting confirmed cases and deaths with a simple time series model.

Forecasting the outcome of outbreaks as early and as accurately as possible is crucial for decision-making and policy implementations. A significant challenge faced by forecasters is that not all outbreaks and epidemics turn into pandemics, making the prediction of their severity difficult. At the same time, the decisions made to enforce lockdowns and other mitigating interventions versus their socioeconomic consequences are not only hard to make, but also highly uncertain. The majority of modeling approaches to outbreaks, epidemics, and pandemics take an epidemiological approach that considers biological and disease processes. In this paper, we accept the limitations of forecasting to predict the long-term trajectory of an outbreak, and instead, we propose a statistical, time series approach to modelling and predicting the short-term behavior of COVID-19. Our model assumes a multiplicative trend, aiming to capture the continuation of the two variables we predict (global confirmed cases and deaths) as well as their uncertainty. We present the timeline of producing and evaluating 10-day-ahead forecasts over a period of four months. Our simple model offers competitive forecast accuracy and estimates of uncertainty that are useful and practically relevant.

Forecasting the novel coronavirus COVID-19.

What will be the global impact of the novel coronavirus (COVID-19)? Answering this question requires accurate forecasting the spread of confirmed cases as well as analysis of the number of deaths and recoveries. Forecasting, however, requires ample historical data. At the same time, no prediction is certain as the future rarely repeats itself in the same way as the past. Moreover, forecasts are influenced by the reliability of the data, vested interests, and what variables are being predicted. Also, psychological factors play a significant role in how people perceive and react to the danger from the disease and the fear that it may affect them personally. This paper introduces an objective approach to predicting the continuation of the COVID-19 using a simple, but powerful method to do so. Assuming that the data used is reliable and that the future will continue to follow the past pattern of the disease, our forecasts suggest a continuing increase in the confirmed COVID-19 cases with sizable associated uncertainty. The risks are far from symmetric as underestimating its spread like a pandemic and not doing enough to contain it is much more severe than overspending and being over careful when it will not be needed. This paper describes the timeline of a live forecasting exercise with massive potential implications for planning and decision making and provides objective forecasts for the confirmed cases of COVID-19.

Statistical and Machine Learning forecasting methods: Concerns and ways forward.

Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions.

The costs and benefits of positive illusions.

Positive illusions are associated with unrealistic optimism about the future and an inflated assessment of one's abilities. They are prevalent in normal life and are considered essential for maintaining a healthy mental state, although, there are disagreements to the extent to which people demonstrate these positive illusions and whether they are beneficial or not. But whatever the situation, it is hard to dismiss their existence and their positive and/or negative influence on human behavior and decision making in general. Prominent among illusions is that of control, that is "the tendency for people to overestimate their ability to control events." This paper describes positive illusions, their potential benefits but also quantifies their costs in five specific fields (gambling, stock and other markets, new firms and startups, preventive medicine and wars). It is organized into three parts. First the psychological reasons giving rise to positive illusions are described and their likely harm and benefits stated. Second, their negative consequences are presented and their costs are quantified in five areas seriously affected with emphasis to those related to the illusion of control that seems to dominate those of unrealistic optimism. The costs involved are huge and serious efforts must be undertaken to understand their enormity and steps taken to avoid them in the future. Finally, there is a concluding section where the challenges related to positive illusions are noted and directions for future research are presented.

Hypertension: empirical evidence and implications in 2014.

High blood pressure (HBP) or hypertension (HTN) is one of the leading causes of cardiovascular (CV) morbidity and mortality throughout the world. Despite this fact, there is widespread agreement that the treatment of HBP, over the last half century, has been a great achievement. However, after the release of the new Joint National Committee on Prevention, Detection, Evaluation and Treatment of High Blood Pressure-8 (JNC-8) guidelines, there have been heated debates with regard to what are the most evidence-based blood pressure goals. While JNC-8 claims that the goal blood pressure for otherwise healthy patients with mild hypertension (systolic blood pressure ≥140-159 mm Hg and diastolic blood pressure ≥90-99 mm Hg) should be <140/90 mm Hg; a recent Cochrane meta-analysis is in direct conflict with these recommendations. Indeed, a 2012 Cochrane meta-analysis indicated that there is no evidence that treating otherwise healthy mild hypertension patients with antihypertensive therapy will reduce CV events or mortality. Additionally, the Cochrane meta-analysis showed that antihypertensive therapy was associated with a significant increase in withdrawal due to adverse events. Thus, the current evidence in the literature does not support the goals set by the JNC-8 guidelines. In this review we discussed the strengths and limitations of both lines of evidence and why it takes an evidence-based medication to reduce CV events/mortality (eg, how a goal blood pressure is achieved is more important than getting to the goal). As medications inherently cause side effects and come at a cost to the patient, the practice of evidence-based medicine becomes exceedingly important. Although the majority of HTN studies claim great advantages by lowering HBP, this review finds severe conflicts in the findings among the various HTN studies, as well as serious epistemological, methodological and statistical problems that cast doubt to such claims.