Expert Forecasts of COVID-19 Vaccine Development Timelines

Kane, Patrick Bodilly; Moyer, Hannah; MacPherson, Amanda; Papenburg, Jesse; Ward, Brian J.; Broomell, Stephen B.; Kimmelman, Jonathan

doi:10.1007/s11606-020-06244-9

Cited by 11 publications

(9 citation statements)

References 3 publications

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“…Another work [6] developed two COVID-19 prediction models based on genetic programming and found that both models were highly reliable for prediction of COVID-19 cases in India. The researchers in [7] reviewed the most recent COVID-19 forecasting models and identified the most predominant factors to be hospitalization [8][9][10][11], intensive care units [12][13][14], vaccination [15,16], and age groups [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases

et al. 2021

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The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and the United Kingdom is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and the United Kingdom, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS ( L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases.

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Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases

et al. 2021

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“…If alcohol consumption increases as a response to COVID-19, both short-and long-term health effects may increase as well. The epidemic has persisted in the United States into late 2020, and widely available vaccines are not expected in the United States until late in 2021 (Goldhill, 2020;Kane et al, 2020). Social and economic disruptions may therefore continue for some time, in which case epidemic-related increases in alcohol consumption could be anticipated to lead to a variety of morbidity and mortality outcomes including heart disease, metabolic diseases, liver disease, and cancers (Bagnardi et al, 2015;Kuypers et al, 2012;Mellinger, 2019;Rehm and Imtiaz, 2016;Skog, 2001;Tramacere et al, ,2010Tramacere et al, , , 2011.…”

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confidence: 99%

Trends in US Alcohol Consumption Frequency During the First Wave of the SARS‐CoV‐2 Pandemic

McKetta

Morrison

Keyes

2021

Alcoholism Clin & Exp Res

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Background The SARS‐CoV‐2 pandemic created disruptions and stressors which may have influenced alcohol consumption frequency trends. Varying COVID‐19 health burden and alcohol policies may have contributed to different consumption trends between states. The aim of this study is to assess trends in alcohol consumption and moderation by state of residence. Methods We examined trends in adult drinking days, during the first wave of the pandemic (March 10 to June 8) using longitudinal data from the Understanding America Study ( N = 6,172 unique participants; N = 28,059 observations). Because state mandates were responsive to disease burden, we modeled the interaction of time by COVID‐19 burden, defined as whether the state had the median (or higher) daily incidence of COVID‐19 cases on the survey date, and state random effects. We controlled for individual sociodemographics, perceived personal/familial COVID‐19 burden, mental health symptomology, and risk avoidance. Results Drinking days increased throughout the duration (incidence risk ratio [IRR] for drinking per increase in one calendar day = 1.003, 95% CI 1.001, 1.004); trends were heterogeneous by disease burden, with individuals living in states with lower COVID‐19 burden increasing (IRR = 1.005, 95% CI 1.003, 1.007) faster than those living in states with higher COVID‐19 burden (IRR = 1.000, 95% CI 0.998, 1.002). Trends were heterogeneous between states, but there was no evidence of systematic geographic clustering of state trends. Conclusions Drinking days increased during the first months of the COVID‐19 pandemic, particularly among residents of states with lower disease burden.

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“…This contact list was supplemented with corresponding authors identified by a literature search for review articles on vaccine development in major journals. Details of our expert sample are reported in the Supplement and elsewhere [ 14 ].…”

Section: Methodsmentioning

confidence: 99%

“…In what follows, we report a comparison between the expectations of US and Canadian lay people and the previously published predictions of group of experts regarding timelines and potential setbacks for SARS-CoV-2 vaccine development as of the summer of 2020 [ 14 ]. This comparison allows us to explore to what extent the beliefs of experts at the time were being successfully communicated to lay people, and possibly whether there is room for improvement in the communication process.…”

Section: Introductionmentioning

confidence: 99%

Relationship between lay and expert perceptions of COVID-19 vaccine development timelines in Canada and USA

et al. 2022

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Objectives Compare lay expectations of medical development to those of experts in the context of SARS-CoV-2 vaccine development. Methods A short online survey of experts and lay people measuring when participants believe important vaccine milestones would occur and how likely potential setbacks were. Samples of US and Canadian lay people recruited through Qualtrics. The expert sample was created through a contact network in vaccine development and supplemented with corresponding authors of recent scholarly review articles on vaccine development. Results In aggregate, lay people gave responses that were within 3 months of experts, tending to be later than experts for early milestones and earlier for later milestones. Median lay best estimates for when a vaccine would be available to the public were 08/2021 and 09/2021 for the US and Canadian samples, compared with 09-10/2021 for the experts. However, many individual lay responses showed more substantial disagreement with expert opinions, with 54% of lay best estimates of when a vaccine would be available to the public being before the median expert soonest estimate or after the median expert latest estimate. Lay people were much more pessimistic about vaccine development encountering setbacks than experts (median probability 59% of boxed warning compared with only 30% for experts). Misalignment between layperson and expert expectations was not explained by any demographic variables collected in our survey. Conclusion Median lay expectations were generally similar to experts. At the individual level, however, lay people showed substantial variation with many believing milestones would occur much sooner than experts. Lay people were in general much more pessimistic about the prospect of setbacks than were experts.

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Expert Forecasts of COVID-19 Vaccine Development Timelines

Cited by 11 publications

References 3 publications

Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases

Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases

Trends in US Alcohol Consumption Frequency During the First Wave of the SARS‐CoV‐2 Pandemic

Relationship between lay and expert perceptions of COVID-19 vaccine development timelines in Canada and USA

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