medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2021, Volume and Issue: unknown
Published: April 20, 2021
Abstract With a mathematical method based on linear algebra, from open access data (data.gouv.fr, google, apple) we produce forecasts for the number of patients in intensive care France with an average error 4% at 7 days, 7% 14 8% 21 10% one month, 17% 2 months, and 31% 3 months. For other epidemic indicators, is 6% days 25%
Language: Английский
ACS Infectious Diseases, Journal Year: 2024, Volume and Issue: 10(3), P. 808 - 826
Published: Feb. 28, 2024
Recent pandemics, including the COVID-19 outbreak, have brought up growing concerns about transmission of zoonotic diseases from animals to humans. This highlights requirement for a novel approach discern and address escalating health threats. The One Health paradigm has been developed as responsive strategy confront forthcoming outbreaks through early warning, highlighting interconnectedness humans, animals, their environment. system employs several innovative methods such use advanced technology, global collaboration, data-driven decision-making come with an extraordinary solution improving worldwide disease responses. Review deliberates environmental, animal, human factors that influence risk, analyzes challenges advantages inherent in using surveillance system, demonstrates how these can be empowered by Big Data Artificial Intelligence. Holistic Surveillance Framework presented herein holds potential revolutionize our capacity monitor, understand, mitigate impact infectious on populations.
Language: Английский
Citations
19
PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(5), P. e1011200 - e1011200
Published: May 6, 2024
During the COVID-19 pandemic, forecasting trends to support planning and response was a priority for scientists decision makers alike. In United States, coordinated by large group of universities, companies, government entities led Centers Disease Control Prevention US Forecast Hub ( https://covid19forecasthub.org ). We evaluated approximately 9.7 million forecasts weekly state-level cases predictions 1–4 weeks into future submitted 24 teams from August 2020 December 2021. assessed coverage central prediction intervals weighted interval scores (WIS), adjusting missing relative baseline forecast, used Gaussian generalized estimating equation (GEE) model evaluate differences in skill across epidemic phases that were defined effective reproduction number. Overall, we found high variation individual models, with ensemble-based outperforming other approaches. generally higher larger jurisdictions (e.g., states compared counties). Over time, performed worst periods rapid changes reported (either increasing or decreasing phases) 95% dropping below 50% during growth winter 2020, Delta, Omicron waves. Ideally, case could serve as leading indicator transmission dynamics. However, while most outperformed naïve model, even accurate unreliable key phases. Further research improve indicators, like cases, leveraging additional real-time data, addressing performance phases, improving characterization forecast confidence, ensuring coherent spatial scales. meantime, it is critical users appreciate current limitations use broad set indicators inform pandemic-related making.
Language: Английский
Citations
11
BMC Medicine, Journal Year: 2022, Volume and Issue: 20(1)
Published: Feb. 21, 2022
Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time locations. During the COVID-19 pandemic England, it an ongoing concern that for hospital care patients England will exceed available resources.
Language: Английский
Citations
22
Epidemics, Journal Year: 2023, Volume and Issue: 46, P. 100738 - 100738
Published: Dec. 29, 2023
Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of burden in US to guide pandemic planning decision-making context high uncertainty. This effort was born out an attempt coordinate, synthesize effectively use unprecedented amount predictive modeling that emerged throughout pandemic. Here we describe history this massive collective research effort, process convening maintaining open hub active over multiple years, provide a blueprint for future efforts. We detail generating 17 rounds scenarios at different stages pandemic, disseminating results public health community lay public. also highlight how SMH expanded generate influenza during 2022-23 season. identify key impacts on draw lessons improve collaborative efforts, scenario projections, interface between models policy.
Language: Английский
Citations
12
JMIR Public Health and Surveillance, Journal Year: 2023, Volume and Issue: 9, P. e41329 - e41329
Published: Jan. 11, 2023
Background Influenza causes considerable disease burden each year, particularly in children. Monitoring school absenteeism has long been proposed as a surveillance tool of influenza activity the community, but practice could be varying, and potential such usage remains unclear. Objective The aim this paper is to determine monitoring influenza. Methods We conducted systematic review published literature on relationship between community. categorized types community correlation these data streams. also extracted with different lags using leading indicator activity. Results Among 35 identified studies, 22 (63%), 12 (34%), 8 (23%) studies monitored all-cause, illness-specific, influenza-like illness (ILI)–specific absents, respectively, 16 (46%) used quantitative approaches provided 33 estimates temporal pooled estimate without lag, 1-week 2-week lag were 0.44 (95% CI 0.34, 0.53), 0.29 0.15, 0.42), 0.21 0.11, 0.31), respectively. ILI-specific was higher than that all-cause absenteeism. 19 qualitative approaches, 15 (79%) concluded concordance with, coincided or associated surveillance. Of only 6 (17%) attempted predict from Conclusions There moderate smaller compared suggested careful application required use epidemics. monitor more closely, resource participation willingness may require consideration weight against costs. Further development optimize In particular, advanced statistical models validation predictions should explored.
Language: Английский
Citations
11
PLoS Computational Biology, Journal Year: 2022, Volume and Issue: 18(10), P. e1010602 - e1010602
Published: Oct. 6, 2022
We analyze an ensemble of n -sub-epidemic modeling for forecasting the trajectory epidemics and pandemics. These approaches, models that integrate sub-epidemics to capture complex temporal dynamics, have demonstrated powerful capability. This framework can characterize epidemic patterns, including plateaus, resurgences, waves characterized by multiple peaks different sizes. systematically assess their calibration short-term performance in forecasts COVID-19 pandemic USA from late April 2020 February 2022. compare with two commonly used statistical ARIMA models. The best fit sub-epidemic model three constructed using top-ranking consistently outperformed terms weighted interval score (WIS) coverage 95% prediction across 10-, 20-, 30-day forecasts. In our forecasts, average WIS ranged 377.6 421.3 models, whereas it 439.29 767.05 Across 98 incorporating top four ranking (Ensemble(4)) (log) 66.3% time, model, 69.4% time ahead WIS. Ensemble(4) yielded metrics account uncertainty predictions. be readily applied investigate spread pandemics beyond COVID-19, as well other dynamic growth processes found nature society would benefit
Language: Английский
Citations
18
International Journal of Medical Informatics, Journal Year: 2024, Volume and Issue: 189, P. 105527 - 105527
Published: June 15, 2024
The COVID-19 pandemic has highlighted the critical importance of robust healthcare capacity planning and preparedness for emerging crises. However, systems must also adapt to more gradual temporal changes in disease prevalence demographic composition over time. To support proactive planning, statistical forecasting models can provide valuable information planners. This systematic literature review evidence mapping aims identify describe studies that have used estimate needs within hospital settings.
Language: Английский
Citations
3
Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)
Published: May 30, 2023
As of January 2021, Australia had effectively controlled local transmission COVID-19 despite a steady influx imported cases and several local, but contained, outbreaks in 2020. Throughout 2020, state territory public health responses were informed by weekly situational reports that included an ensemble forecast daily for each jurisdiction. We present here analysis one forecasting model this across the variety scenarios experienced jurisdiction from May to October examine how successfully forecasts characterised future case incidence, subject variations data timeliness completeness, showcase we adapted these support decisions priority rapidly-evolving situations, evaluate impact key features on skill, demonstrate assess skill real-time before ground truth is known. Conditioning most recent, incomplete, improved emphasising importance developing strong quantitative models surveillance system characteristics, such as ascertainment delay distributions. Forecast was highest when there at least 10 reported per day, circumstances which authorities need aid planning response.
Language: Английский
Citations
8
PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(5), P. e1012124 - e1012124
Published: May 17, 2024
Projects such as the European Covid-19 Forecast Hub publish forecasts on national level for new deaths, cases, and hospital admissions, but not direct measurements of strain like critical care bed occupancy at sub-national level, which is particular interest to health professionals planning purposes. We present a French framework forecasting based non-Markovian compartmental model, its associated online visualisation tool retrospective evaluation real-time it provided from January December 2021 by comparing three baselines derived standard statistical methods (a naive auto-regression, an ensemble exponential smoothing ARIMA). In terms median absolute error unit two-week horizon, our model only outperformed baseline 4 out 14 geographical units underperformed compared 5 them 90% confidence ( n = 38). However, same week was never statistically any despite outperforming 10 times spanning 7 units. This implies modest utility longer horizons may justify application models in context hospital-strain surveillance future pandemics.
Language: Английский
Citations
2
medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: Aug. 13, 2024
Abstract This study examines the use and utility of infectious disease modelling in national international COVID-19 outbreak response. We investigate modelling-policy practices 13 countries, by carrying out expert interviews with a range modellers, decision makers, scientific advisors. The included countries span all six UN geographic regions. document experiences collate lessons learned during pandemic across four key themes: structures pathways to policy, communication, collaboration knowledge transfer, evaluation reflection. Full analysis interpretation breadth interview responses is presented, providing evidence for best practice on translation policy.
Language: Английский
Citations
2