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1.
PLoS One ; 17(3): e0266096, 2022.
Article in English | MEDLINE | ID: covidwho-1765543

ABSTRACT

BACKGROUND: A combined forecast from multiple models is typically more accurate than an individual forecast, but there are few examples of studies of combining in infectious disease forecasting. We investigated the accuracy of different ways of combining interval forecasts of weekly incident and cumulative coronavirus disease-2019 (COVID-19) mortality. METHODS: We considered weekly interval forecasts, for 1- to 4-week prediction horizons, with out-of-sample periods of approximately 18 months ending on 8 January 2022, for multiple locations in the United States, using data from the COVID-19 Forecast Hub. Our comparison involved simple and more complex combining methods, including methods that involve trimming outliers or performance-based weights. Prediction accuracy was evaluated using interval scores, weighted interval scores, skill scores, ranks, and reliability diagrams. RESULTS: The weighted inverse score and median combining methods performed best for forecasts of incident deaths. Overall, the leading inverse score method was 12% better than the mean benchmark method in forecasting the 95% interval and, considering all interval forecasts, the median was 7% better than the mean. Overall, the median was the most accurate method for forecasts of cumulative deaths. Compared to the mean, the median's accuracy was 65% better in forecasting the 95% interval, and 43% better considering all interval forecasts. For all combining methods except the median, combining forecasts from only compartmental models produced better forecasts than combining forecasts from all models. CONCLUSIONS: Combining forecasts can improve the contribution of probabilistic forecasting to health policy decision making during epidemics. The relative performance of combining methods depends on the extent of outliers and the type of models in the combination. The median combination has the advantage of being robust to outlying forecasts. Our results support the Hub's use of the median and we recommend further investigation into the use of weighted methods.


Subject(s)
COVID-19 , COVID-19/epidemiology , Forecasting , Humans , Reproducibility of Results , United States/epidemiology
2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-322581

ABSTRACT

The COVID-19 pandemic has placed forecasting models at the forefront of health policy making. Predictions of mortality and hospitalization help governments meet planning and resource allocation challenges. In this paper, we consider the weekly forecasting of the cumulative mortality due to COVID-19 at the national and state level in the U.S. Optimal decision-making requires a forecast of a probability distribution, rather than just a single point forecast. Interval forecasts are also important, as they can support decision making and provide situational awareness. We consider the case where probabilistic forecasts have been provided by multiple forecasting teams, and we aggregate the forecasts to extract the wisdom of the crowd. With only limited information available regarding the historical accuracy of the forecasting teams, we consider aggregation (i.e. combining) methods that do not rely on a record of past accuracy. In this empirical paper, we evaluate the accuracy of aggregation methods that have been previously proposed for interval forecasts and predictions of probability distributions. These include the use of the simple average, the median, and trimming methods, which enable robust estimation and allow the aggregate forecast to reduce the impact of a tendency for the forecasting teams to be under- or overconfident. We use data that has been made publicly available from the COVID-19 Forecast Hub. While the simple average performed well for the high mortality series, we obtained greater accuracy using the median and certain trimming methods for the low and medium mortality series. It will be interesting to see if this remains the case as the pandemic evolves.

4.
Alzheimers Dement ; 17 Suppl 7: e054996, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1680257

ABSTRACT

BACKGROUND: Alzheimer's disease and related dementias (ADRD) have an enormous impact on persons living with dementia and their care partners. Care partners of people with dementia are more likely to have depression, anxiety, and be isolated, heightened by events like the COVID-19 pandemic. Connecting individuals to research has been challenging, especially in diverse populations who are disproportionately impacted by ADRD and health and socioeconomic inequities. The Memory Advocate Peers (MAP) Program aims to address these challenges by building and piloting a sustainable, replicable, volunteer peer mentor program to support individuals newly diagnosed with dementia and their care partners, and help to connect them to services. METHOD: The MAP program has been developed and is led by people living with dementia, care partners, community-based experts in dementia care, and healthcare leaders. The program will give people with dementia and care partners the opportunity to share experiences with a volunteer advocate who has lived experience with dementia, obtain valuable education about how to live well with dementia, connect to community services, and access clinical trials or other research opportunities. Partnerships have been established with New York University-affiliated neurologists to identify and refer newly diagnosed patients to the program, and with the leading local research programs. RESULT: MAP will recruit and train up to 25 advocates to provide twelve months of post-diagnostic support to 50 clients and care partners (where applicable), with a focus on under-represented communities. A research study is embedded within the program to evaluate feasibility, as well as participant quality of life, health resource use, impact on psychological wellbeing, and value of research participation. CONCLUSION: This pilot will be critical in understanding the impact peer-to-peer mentorship can have for both the individual with ADRD and the care partner. It will also be critical to develop best practices to recruit, train and support volunteers serving as these peer advocates. Results from the pilot will be used to improve the program prior to expansion to other regions.

5.
2021.
Preprint in English | Other preprints | ID: ppcovidwho-296133

ABSTRACT

Background Forecasting models have played a pivotal role in health policy decision making during the coronavirus disease-2019 (COVID-19) pandemic. A combined forecast from multiple models will be typically more accurate than an individual forecast, but there are few examples of studies of combined forecasts of COVID-19 data, focusing mainly on simple mean and median ‘ensembles’ and involving short forecast evaluation periods. We aimed to investigate the accuracy of different ways of combining probabilistic forecasts of weekly COVID-19 mortality data, including two weighted methods that we developed previously, on an extended dataset and new dataset, and evaluate over a period of 52 weeks. Methods We considered 95% interval and point forecasts of weekly incident and cumulative COVID-19 mortalities between 16 May 2020 and 8 May 2021 in multiple locations in the United States. We compared the accuracy of simple and more complex combining methods, as well as individual models. Results The average of the forecasts from the individual models was consistently more accurate than the average performance of these models (the mean combination), which provides a fundamental motivation for combining. Weighted combining performed well for both incident and cumulative mortalities, and for both interval and point forecasting. Our inverse score with tuning method was the most accurate overall. The median combination was a leading method in the last quarter for both mortalities, and it was consistently more accurate than the mean combination for point forecasting. For interval forecasts of cumulative mortality, the mean performed better than the median. The best performance of the leading individual model was in point forecasting. Conclusions Combining forecasts can improve the contribution of probabilistic forecasting to health policy decision making during epidemics, and, when there are sufficient historical data on forecast accuracy, weighted combining provides the most accurate method.

6.
Telemed Rep ; 2(1): 171-178, 2021.
Article in English | MEDLINE | ID: covidwho-1305407

ABSTRACT

Background: Social determinants of health directly affect cancer survival. Driven by advances in technology and recent demands due to COVID-19, telemedicine has the ability to improve patient access to care, lower health care costs, and increase workflow efficiency. The role of telemedicine in radiation oncology is not established. Materials and Methods: We conducted an IRB-approved pilot trial using a telehealth platform for the first post-radiation visit in patients with any cancer diagnosis. The primary endpoint was feasibility of using telehealth defined by completion of five telehealth visits per month in a single department. Secondary endpoints included the ability to assess patients appropriately, patient and physician satisfaction. Physicians were surveyed again during the pandemic to determine whether viewpoints changed. Results: Between May 27, 2016 and August 1, 2018, 37 patients were enrolled in the Telehealth in Post-operative Radiation Therapy (TelePORT) trial, with 24 evaluable patients who completed their scheduled telehealth visit. On average, 1.4 patients were accrued per month. All patients were satisfied with their care, had enough time with their physician and 85.7% believed the telehealth communication was excellent. All physicians were able to accurately assess the patient's symptoms via telehealth, whereas 82.3% felt they could accurately assess treatment-related toxicity. Physicians assessing skin toxicity from breast radiation were those who did not feel they were able to assess toxicity. Discussion and Conclusions: Both health care providers and patients have identified telemedicine as a suitable platform for radiation oncology visits. Although there are limitations, telemedicine has significant potential for increasing access of cancer care delivery in radiation oncology.

7.
Eur J Oper Res ; 2021 Jun 28.
Article in English | MEDLINE | ID: covidwho-1284065

ABSTRACT

The COVID-19 pandemic has placed forecasting models at the forefront of health policy making. Predictions of mortality, cases and hospitalisations help governments meet planning and resource allocation challenges. In this paper, we consider the weekly forecasting of the cumulative mortality due to COVID-19 at the national and state level in the U.S. Optimal decision-making requires a forecast of a probability distribution, rather than just a single point forecast. Interval forecasts are also important, as they can support decision making and provide situational awareness. We consider the case where probabilistic forecasts have been provided by multiple forecasting teams, and we combine the forecasts to extract the wisdom of the crowd. We use a dataset that has been made publicly available from the COVID-19 Forecast Hub. A notable feature of the dataset is that the availability of forecasts from participating teams varies greatly across the 40 weeks in our study. We evaluate the accuracy of combining methods that have been previously proposed for interval forecasts and predictions of probability distributions. These include the use of the simple average, the median, and trimming methods. In addition, we propose several new weighted combining methods. Our results show that, although the median was very useful for the early weeks of the pandemic, the simple average was preferable thereafter, and that, as a history of forecast accuracy accumulates, the best results can be produced by a weighted combining method that uses weights that are inversely proportional to the historical accuracy of the individual forecasting teams.

8.
Cureus ; 13(2): e13354, 2021 Feb 15.
Article in English | MEDLINE | ID: covidwho-1145697

ABSTRACT

Background The COVID-19 pandemic challenges our ability to safely treat breast cancer patients and requires revisiting current techniques to evaluate optimal strategies. Potential long-term sequelae of breast radiation have been addressed by deep inspiration breath-hold (DIBH), prone positioning, and four-dimensional computed tomography (4DCT) average intensity projection (AveIP)-based planning techniques. Dosimetric comparisons to determine the optimal technique to minimize the normal tissue dose for left-sided breast cancers have not been performed. Methods Ten patients with left-sided, early-stage breast cancer undergoing whole breast radiation were simulated in the prone position, supine with DIBH, and with a free-breathing 4DCT scan. The target and organs at risk (OAR) contours were delineated in all scans. Target volume coverage and OAR doses were assessed. One-way analysis of variance (ANOVA) and Kruskal-Wallis one-way ANOVA were used to detect differences in dosimetric parameters among the different treatment plans. Significance was set as p < 0.05. Results We demonstrate differences in heart and lung dose by the simulation technique. The mean heart doses in the prone, DIBH, and AveIP plans were 129 cGy, 154 cGy, and 262 cGy, respectively (p=0.02). The lung V20 in the prone, DIBH, and AveIP groups was 0.5%, 10.3% and 9.5%, respectively (p <0.001). Regardless of technique, lumpectomy planning target volume (PTV) coverage did not differ between the three plans with 95% of the lumpectomy PTV volume covered by 100.4% in prone plans, 98.5% in AveIP plans, and 99.3% in DIBH plans (p=0.7). Conclusions Prone positioning provides dosimetric advantages as compared to DIBH. When infection risks are considered as in the current coronavirus disease 2019 (COVID-19) pandemic, prone plans have advantages in reducing the risk of disease transmission. In instances where prone positioning is not feasible, obtaining an AveIP simulation may be useful in more accurately assessing heart and lung toxicity and informing a risk/benefit discussion of DIBH vs free breath-hold techniques.

10.
Acta Otolaryngol ; 141(3): 299-302, 2021 Mar.
Article in English | MEDLINE | ID: covidwho-1023995

ABSTRACT

Background: It has been noted that olfactory and gustatory disturbances may precede or accompany the typical features of COVID-19, such as fever and cough. Hence, a high index of suspicion is required when patients report sudden loss of smell or taste, in order to facilitate timely diagnosis and isolation.Aims/objectives: The aim of this study was to assess the frequency of olfactory and gustatory disturbances in COVID-19 positive patients from a cohort representative of Melbourne, Australia.Methods: A retrospective descriptive study was conducted on patients who tested positive for COVID-19. Standardised phone consultations and online follow-up questionnaires were performed to assess clinical features of COVID-19, with a focus on smell and taste disorders.Results: The most frequent symptoms experienced were taste and smell disturbances with 74% experiencing either smell or taste disturbance or both. Post-recovery, 34% of patients continued to experience ongoing hyposmia and 2% anosmia, whereas 28% continued to suffer from hypogeusia or ageusia.Conclusion and significance: This study presents the high rates of improvement of both olfactory and gustatory disturbance in a short-lived period. It also highlights the importance of these symptoms in prompting appropriate testing, quarantine precautions, initiate early olfactory retraining and the potential for continued sensory disturbance.


Subject(s)
COVID-19/complications , Olfaction Disorders/epidemiology , Risk Assessment/methods , Smell/physiology , Taste Disorders/epidemiology , Taste/physiology , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , Female , Humans , Male , Middle Aged , Olfaction Disorders/etiology , Olfaction Disorders/physiopathology , Pandemics , Retrospective Studies , SARS-CoV-2 , Surveys and Questionnaires , Taste Disorders/etiology , Taste Disorders/physiopathology , Victoria/epidemiology , Young Adult
11.
PLoS Pathog ; 16(8): e1008643, 2020 08.
Article in English | MEDLINE | ID: covidwho-712942

ABSTRACT

The current state of much of the Wuhan pneumonia virus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) research shows a regrettable lack of data sharing and considerable analytical obfuscation. This impedes global research cooperation, which is essential for tackling public health emergencies and requires unimpeded access to data, analysis tools, and computational infrastructure. Here, we show that community efforts in developing open analytical software tools over the past 10 years, combined with national investments into scientific computational infrastructure, can overcome these deficiencies and provide an accessible platform for tackling global health emergencies in an open and transparent manner. Specifically, we use all SARS-CoV-2 genomic data available in the public domain so far to (1) underscore the importance of access to raw data and (2) demonstrate that existing community efforts in curation and deployment of biomedical software can reliably support rapid, reproducible research during global health crises. All our analyses are fully documented at https://github.com/galaxyproject/SARS-CoV-2.


Subject(s)
Betacoronavirus/pathogenicity , Coronavirus Infections/virology , Pneumonia, Viral/virology , Public Health , Severe Acute Respiratory Syndrome/virology , COVID-19 , Data Analysis , Humans , Pandemics , SARS-CoV-2
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