Aggregating Human Judgment Probabilistic Predictions of Coronavirus Disease 2019 Transmission, Burden, and Preventive Measures.
Ontology highlight
ABSTRACT:
SUBMITTER: Codi A
PROVIDER: S-EPMC9348614 | biostudies-literature | 2022 Aug
REPOSITORIES: biostudies-literature
Publications
Aggregating Human Judgment Probabilistic Predictions of Coronavirus Disease 2019 Transmission, Burden, and Preventive Measures.
Codi Allison A Luk Damon D Braun David D Cambeiro Juan J Besiroglu Tamay T Chen Eva E de Cesaris Luis Enrique Urtubey LEU Bocchini Paolo P McAndrew Thomas T
Open forum infectious diseases 20220725 8
Aggregated human judgment forecasts for coronavirus disease 2019 (COVID-19) targets of public health importance are accurate, often outperforming computational models. Our work shows that aggregated human judgment forecasts for infectious agents are timely, accurate, and adaptable, and can be used as a tool to aid public health decision making during outbreaks. ...[more]
Similar Datasets
Project description:Safe, efficacious vaccines were developed to reduce the transmission of SARS-CoV-2 during the COVID-19 pandemic. But in the middle of 2020, vaccine effectiveness, safety, and the timeline for when a vaccine would be approved and distributed to the public was uncertain. To support public health decision making, we solicited trained forecasters and experts in vaccinology and infectious disease to provide monthly probabilistic predictions from July to September of 2020 of the efficacy, safety, timing, and delivery of a COVID-19 vaccine. We found, that despite sparse historical data, a linear pool-a combination of human judgment probabilistic predictions-can quantify the uncertainty in clinical significance and timing of a potential vaccine. The linear pool underestimated how fast a therapy would show a survival benefit and the high efficacy of approved COVID-19 vaccines. However, the linear pool did make an accurate prediction for when a vaccine would be approved by the FDA. Compared to individual forecasters, the linear pool was consistently above the median of the most accurate forecasts. A linear pool is a fast and versatile method to build probabilistic predictions of a developing vaccine that is robust to poor individual predictions. Though experts and trained forecasters did underestimate the speed of development and the high efficacy of a SARS-CoV-2 vaccine, linear pool predictions can improve situational awareness for public health officials and for the public make clearer the risks, rewards, and timing of a vaccine.
| S-EPMC8882426 | biostudies-literature
Project description:Forecasts of the trajectory of an infectious agent can help guide public health decision making. A traditional approach to forecasting fits a computational model to structured data and generates a predictive distribution. However, human judgment has access to the same data as computational models plus experience, intuition, and subjective data. We propose a chimeric ensemble-a combination of computational and human judgment forecasts-as a novel approach to predicting the trajectory of an infectious agent. Each month from January, 2021 to June, 2021 we asked two generalist crowds, using the same criteria as the COVID-19 Forecast Hub, to submit a predictive distribution over incident cases and deaths at the US national level either two or three weeks into the future and combined these human judgment forecasts with forecasts from computational models submitted to the COVID-19 Forecasthub into a chimeric ensemble. We find a chimeric ensemble compared to an ensemble including only computational models improves predictions of incident cases and shows similar performance for predictions of incident deaths. A chimeric ensemble is a flexible, supportive public health tool and shows promising results for predictions of the spread of an infectious agent.
| S-EPMC9648897 | biostudies-literature
Project description:Structured protocols offer a transparent and systematic way to elicit and combine/aggregate, probabilistic predictions from multiple experts. These judgements can be aggregated behaviourally or mathematically to derive a final group prediction. Mathematical rules (e.g., weighted linear combinations of judgments) provide an objective approach to aggregation. The quality of this aggregation can be defined in terms of accuracy, calibration and informativeness. These measures can be used to compare different aggregation approaches and help decide on which aggregation produces the "best" final prediction. When experts' performance can be scored on similar questions ahead of time, these scores can be translated into performance-based weights, and a performance-based weighted aggregation can then be used. When this is not possible though, several other aggregation methods, informed by measurable proxies for good performance, can be formulated and compared. Here, we develop a suite of aggregation methods, informed by previous experience and the available literature. We differentially weight our experts' estimates by measures of reasoning, engagement, openness to changing their mind, informativeness, prior knowledge, and extremity, asymmetry or granularity of estimates. Next, we investigate the relative performance of these aggregation methods using three datasets. The main goal of this research is to explore how measures of knowledge and behaviour of individuals can be leveraged to produce a better performing combined group judgment. Although the accuracy, calibration, and informativeness of the majority of methods are very similar, a couple of the aggregation methods consistently distinguish themselves as among the best or worst. Moreover, the majority of methods outperform the usual benchmarks provided by the simple average or the median of estimates.
| S-EPMC8412308 | biostudies-literature
Project description:Objectives: We quantified adherence to COVID-19 preventive measures and explored associated factors, after the first and during the second Swiss epidemic waves. Methods: With an observational cohort study in a representative sample of individuals aged 15 years and more, we analysed the association between self-reported adherence to COVID-19 preventive measures (respect of simple hygiene rules; respect of social distancing rules; wearing a mask) and socio-demographic factors, the existence of a chronic disease, and the existence of a previous confirmed COVID-19 episode. Results: Highest adherence was to simple hygiene rules, followed by social distancing rules and mask wearing, with a slight decrease for simple hygiene rules and a strong increase for mask wearing between visits. Men were significantly less likely to respect simple hygiene rules and wear a mask in public. Participants aged 65 years and more (versus 25-64 years) and those with at least one chronic disease (versus none) were two times more likely to respect social distancing rules and wear a mask. Conclusion: Adherence to social distancing rules and mask wearing was rather poor, especially compared to other countries.
| S-EPMC9453818 | biostudies-literature
Project description:BackgroundWeeks after issuing social distancing orders to suppress severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and reduce growth in cases of severe coronavirus disease 2019 (COVID-19), all US states and the District of Columbia partially or fully relaxed these measures.MethodsWe identified all statewide social distancing measures that were implemented and/or relaxed in the United States between 10 March and 15 July 2020, triangulating data from state government and third-party sources. Using segmented linear regression, we estimated the extent to which relaxation of social distancing affected epidemic control, as indicated by the time-varying, state-specific effective reproduction number (Rt).ResultsIn the 8 weeks prior to relaxation, mean Rt declined by 0.012 units per day (95% confidence interval [CI], -.013 to -.012), and 46/51 jurisdictions achieved Rt < 1.0 by the date of relaxation. After relaxation of social distancing, Rt reversed course and began increasing by 0.007 units per day (95% CI, .006-.007), reaching a mean Rt of 1.16. Eight weeks later, the mean Rt was 1.16 and only 9/51 jurisdictions were maintaining an Rt < 1.0. Parallel models showed similar reversals in the growth of COVID-19 cases and deaths. Indicators often used to motivate relaxation at the time of relaxation (eg, test positivity rate <5%) predicted greater postrelaxation epidemic growth.ConclusionsWe detected an immediate and significant reversal in SARS-CoV-2 epidemic suppression after relaxation of social distancing measures across the United States. Premature relaxation of social distancing measures undermined the country's ability to control the disease burden associated with COVID-19.
| S-EPMC7797755 | biostudies-literature
Project description:AimsThe aim of this study is to determine the association between the coronavirus disease 2019 (COVID-19) pandemic and atrial fibrillation (AF) occurrence in individuals with cardiac implantable electronic devices (CIEDs).Method and resultsMulti-centre, observational, cohort study over a 100-day period during the COVID-19 pandemic (COVID-19) in the USA. Remote monitoring was used to assess AF episodes in patients with a CIED (pacemaker or defibrillator; 20 centres, 13 states). For comparison, the identical 100-day period in 2019 was used (Control). The primary outcomes were the AF burden during the COVID-19 pandemic, and the association of the pandemic with AF occurrence, as compared with 1 year prior. The secondary outcome was the association of AF occurrence with per-state COVID-19 prevalence. During COVID-19, 10 346 CIEDs with an atrial lead were monitored. There were 16 570 AF episodes of ≥6 min transmitted (16 events per 1000 patient days) with a significant increase in proportion of patients with AF episodes in high COVID-19 prevalence states compared with low prevalence states [odds ratio 1.34, 95% confidence interval (CI) 1.21-1.48, P < 0.001]. There were significantly more AF episodes during COVID-19 compared with Control [incident rate ratio (IRR) 1.33, 95% CI 1.25-1.40, P < 0.001]. This relationship persisted for AF episodes ≥1 h (IRR 1.65, 95% CI 1.53-1.79, P < 0.001) and ≥6 h (IRR 1.54, 95% CI 1.38-1.73, P < 0.001).ConclusionDuring the first 100 days of COVID-19, a 33% increase in AF episodes occurred with a 34% increase in the proportion of patients with AF episodes observed in states with higher COVID-19 prevalence. These findings suggest a possible association between pandemic-associated social disruptions and AF in patients with CIEDs.Clinical trial registrationAustralian New Zealand Clinical Trial Registry: ACTRN12620000692932.
| S-EPMC8195127 | biostudies-literature
Project description:This study aimed to evaluate Jordan citizens' awareness, knowledge, and practice concerning Coronavirus-19 (COVID-19) symptoms, routes of transmission, and preventive measures. An online self-administered questionnaire was filled out completely by participants (N = 328) from mainly four major cities in Jordan during the period beginning of May-end of September 2020. Participants' main sources of knowledge about COVID-19 were the government websites (87.8%), social media (87.5%), and Television (TV) (81.1%). The majority of participants valued the drizzle of cough from infected individuals (96.3%), direct contact with contaminated surfaces (91.5%), and direct contact with infected individuals (84.5%) as the highest rates of the route of transmission. The highest rates chosen as symptoms of COVID-19 viral infections were high-grade fever (99.1%), troublesome breathing (96.6%), coughing (92.7%), headache (91.2%), and loss of smell and taste (80.8%). The majority of participants (>92%) strongly agreed on the behavioral protective measures such as no face touching, wearing a mask, the use of alcoholic hand disinfectants, and the need for self-isolation. This study showed that the Jordanian citizens were aware of the epidemiology of COVID-19 and related infection preventive measures. This agrees well with the efforts done by the Ministry of health and governmental organizations to spread the necessary information about the virus among citizens.
| S-EPMC10022341 | biostudies-literature
Project description:The current outbreak of viral pneumonia in the city of Wuhan, China, was caused by a novel coronavirus designated 2019-nCoV by the World Health Organization, as determined by sequencing the viral RNA genome. Many initial patients were exposed to wildlife animals at the Huanan seafood wholesale market, where poultry, snake, bats, and other farm animals were also sold. To investigate possible virus reservoir, we have carried out comprehensive sequence analysis and comparison in conjunction with relative synonymous codon usage (RSCU) bias among different animal species based on the 2019-nCoV sequence. Results obtained from our analyses suggest that the 2019-nCoV may appear to be a recombinant virus between the bat coronavirus and an origin-unknown coronavirus. The recombination may occurred within the viral spike glycoprotein, which recognizes a cell surface receptor. Additionally, our findings suggest that 2019-nCoV has most similar genetic information with bat coronovirus and most similar codon usage bias with snake. Taken together, our results suggest that homologous recombination may occur and contribute to the 2019-nCoV cross-species transmission.
| S-EPMC7138088 | biostudies-literature
Project description:ObjectiveTo replicate and analyse the information available to UK policymakers when the lockdown decision was taken in March 2020 in the United Kingdom.DesignIndependent calculations using the CovidSim code, which implements Imperial College London's individual based model, with data available in March 2020 applied to the coronavirus disease 2019 (covid-19) epidemic.SettingSimulations considering the spread of covid-19 in Great Britain and Northern Ireland.PopulationAbout 70 million simulated people matched as closely as possible to actual UK demographics, geography, and social behaviours.Main outcome measuresReplication of summary data on the covid-19 epidemic reported to the UK government Scientific Advisory Group for Emergencies (SAGE), and a detailed study of unpublished results, especially the effect of school closures.ResultsThe CovidSim model would have produced a good forecast of the subsequent data if initialised with a reproduction number of about 3.5 for covid-19. The model predicted that school closures and isolation of younger people would increase the total number of deaths, albeit postponed to a second and subsequent waves. The findings of this study suggest that prompt interventions were shown to be highly effective at reducing peak demand for intensive care unit (ICU) beds but also prolong the epidemic, in some cases resulting in more deaths long term. This happens because covid-19 related mortality is highly skewed towards older age groups. In the absence of an effective vaccination programme, none of the proposed mitigation strategies in the UK would reduce the predicted total number of deaths below 200 000.ConclusionsIt was predicted in March 2020 that in response to covid-19 a broad lockdown, as opposed to a focus on shielding the most vulnerable members of society, would reduce immediate demand for ICU beds at the cost of more deaths long term. The optimal strategy for saving lives in a covid-19 epidemic is different from that anticipated for an influenza epidemic with a different mortality age profile.
| S-EPMC7536648 | biostudies-literature
Project description:AimsOur objective was to determine the ventricular arrhythmia burden in implantable cardioverter-defibrillator (ICD) patients during COVID-19.Methods and resultsIn this multicentre, observational, cohort study over a 100-day period during the COVID-19 pandemic in the USA, we assessed ventricular arrhythmias in ICD patients from 20 centres in 13 states, via remote monitoring. Comparison was via a 100-day control period (late 2019) and seasonal control period (early 2019). The primary outcome was the impact of COVID-19 on ventricular arrhythmia burden. The secondary outcome was correlation with COVID-19 incidence. During the COVID-19 period, 5963 ICD patients underwent remote monitoring, with 16 942 episodes of treated ventricular arrhythmias (2.8 events per 100 patient-days). Ventricular arrhythmia burden progressively declined during COVID-19 (P < 0.001). The proportion of patients with ventricular arrhythmias amongst the high COVID-19 incidence states was significantly reduced compared with those in low incidence states [odds ratio 0.61, 95% confidence interval (CI) 0.54-0.69, P < 0.001]. Comparing patients remotely monitored during both COVID-19 and control periods (n = 2458), significantly fewer ventricular arrhythmias occurred during COVID-19 [incident rate ratio (IRR) 0.68, 95% CI 0.58-0.79, P < 0.001]. This difference persisted when comparing the 1719 patients monitored during both the COVID-19 and seasonal control periods (IRR 0.69, 95% CI 0.56-0.85, P < 0.001).ConclusionsDuring COVID-19, there was a 32% reduction in ventricular arrhythmias needing device therapies, coinciding with measures of social isolation. There was a 39% reduction in the proportion of patients with ventricular arrhythmias in states with higher COVID-19 incidence. These findings highlight the potential role of real-life stressors in ventricular arrhythmia burden in individuals with ICDs.Trial registrationAustralian New Zealand Clinical Trial Registry; URL: https://www.anzctr.org.au/; Unique Identifier: ACTRN12620000641998.
| S-EPMC7953962 | biostudies-literature