scholarly journals Seasonal Influenza Forecasting in Real Time Using the Incidence Decay With Exponential Adjustment Model

2017 ◽  
Vol 4 (3) ◽  
Author(s):  
Tahmina Nasserie ◽  
Ashleigh R Tuite ◽  
Lindsay Whitmore ◽  
Todd Hatchette ◽  
Steven J Drews ◽  
...  
Keyword(s):  
Nova Scotia ◽  
Real Time ◽  
Seasonal Dynamics ◽  
Seasonal Influenza ◽  
Influenza Season ◽  
Model Performance ◽  
Control Measures ◽  
Parameter Estimates ◽  
Final Size ◽  
Seasonal Characteristics

AbstractBackgroundSeasonal influenza epidemics occur frequently. Rapid characterization of seasonal dynamics and forecasting of epidemic peaks and final sizes could help support real-time decision-making related to vaccination and other control measures. Real-time forecasting remains challenging.MethodsWe used the previously described “incidence decay with exponential adjustment” (IDEA) model, a 2-parameter phenomenological model, to evaluate the characteristics of the 2015–2016 influenza season in 4 Canadian jurisdictions: the Provinces of Alberta, Nova Scotia and Ontario, and the City of Ottawa. Model fits were updated weekly with receipt of incident virologically confirmed case counts. Best-fit models were used to project seasonal influenza peaks and epidemic final sizes.ResultsThe 2015–2016 influenza season was mild and late-peaking. Parameter estimates generated through fitting were consistent in the 2 largest jurisdictions (Ontario and Alberta) and with pooled data including Nova Scotia counts (R0 approximately 1.4 for all fits). Lower R0 estimates were generated in Nova Scotia and Ottawa. Final size projections that made use of complete time series were accurate to within 6% of true final sizes, but final size was using pre-peak data. Projections of epidemic peaks stabilized before the true epidemic peak, but these were persistently early (~2 weeks) relative to the true peak.ConclusionsA simple, 2-parameter influenza model provided reasonably accurate real-time projections of influenza seasonal dynamics in an atypically late, mild influenza season. Challenges are similar to those seen with more complex forecasting methodologies. Future work includes identification of seasonal characteristics associated with variability in model performance.

scholarly journals Seasonal influenza during the COVID-19 pandemic in Bangladesh

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255646
Author(s):  
Zubair Akhtar ◽  
Fahmida Chowdhury ◽  
Mahmudur Rahman ◽  
Probir Kumar Ghosh ◽  
Md. Kaousar Ahmmed ◽  
...  
Keyword(s):  
Seasonal Influenza ◽  
Web Applications ◽  
Influenza Season ◽  
Tertiary Care ◽  
Age Groups ◽  
Influenza Viruses ◽  
Control Measures ◽  
Mitigation Measures ◽  
Influenza Surveillance ◽  
Severe Acute Respiratory Infection

Introduction During the 2019 novel coronavirus infectious disease (COVID-19) pandemic in 2020, limited data from several countries suggested reduced seasonal influenza viruses’ circulation. This was due to community mitigation measures implemented to control the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We used sentinel surveillance data to identify changes in the 2020 influenza season compared with previous seasons in Bangladesh. Methods We used hospital-based influenza surveillance (HBIS) data of Bangladesh that are generated year-round and are population-representative severe acute respiratory infection (SARI) data for all age groups from seven public and two private tertiary care level hospitals data from 2016 to 2019. We applied the moving epidemic method (MEM) by using R language (v4.0.3), and MEM web applications (v2.14) on influenza-positive rates of SARI cases collected weekly to estimate an average seasonal influenza curve and establish epidemic thresholds. Results The 2016–2019 average season started on epi week 18 (95% CI: 15–25) and lasted 12.5 weeks (95% CI: 12–14 weeks) until week 30.5. The 2020 influenza season started on epi week 36 and ended at epi week 41, lasting for only five weeks. Therefore, influenza epidemic started 18 weeks later, was 7.5 weeks shorter, and was less intense than the average epidemic of the four previous years. The 2020 influenza season started on the same week when COVID-19 control measures were halted, and 13 weeks after the measures were relaxed. Conclusion Our findings suggest that seasonal influenza circulation in Bangladesh was delayed and less intense in 2020 than in previous years. Community mitigation measures may have contributed to this reduction of seasonal influenza transmission. These findings contribute to a limited but growing body of evidence that influenza seasons were altered globally in 2020.

scholarly journals Moving epidemic method (MEM) applied to virology data as a novel real time tool to predict peak in seasonal influenza healthcare utilisation. The Scottish experience of the 2017/18 season to date

2018 ◽  
Vol 23 (11) ◽  
Author(s):  
Josephine L K Murray ◽  
Diogo F P Marques ◽  
Ross L Cameron ◽  
Alison Potts ◽  
Jennifer Bishop ◽  
...  
Keyword(s):  
Real Time ◽  
Influenza A ◽  
Seasonal Influenza ◽  
Influenza Season ◽  
Healthcare Utilisation ◽  
Healthcare Services ◽  
Activity Period ◽  
Peak Activity ◽  
Current Season ◽  
Potential Use

Scotland observed an unusual influenza A(H3N2)-dominated 2017/18 influenza season with healthcare services under significant pressure. We report the application of the moving epidemic method (MEM) to virology data as a tool to predict the influenza peak activity period and peak week of swab positivity in the current season. This novel MEM application has been successful locally and is believed to be of potential use to other countries for healthcare planning and building wider community resilience.

Evaluating a Sewershed Urban Storm Water Model for Variability in Parameter Sensitivity and Resolution Effects

2020 ◽  
Author(s):  
Zhaokai Dong ◽  
Daniel Bain ◽  
Murat Akcakaya ◽  
Carla Ng
Keyword(s):  
Pipe Flow ◽  
Peak Flow ◽  
Model Performance ◽  
Parameter Sensitivity ◽  
Geometric Parameters ◽  
Storm Water ◽  
Parameter Estimates ◽  
Kappa Statistics ◽  
Thiessen Polygon ◽  
Urban Storm

A high-quality parameter set is essential for reliable stormwater models. Model performance can be improved by optimizing initial parameter estimates. Parameter sensitivity analysis is a robust way to distinguish the influence of parameters on model output and efficiently target the most important parameters to modify. This study evaluates efficient construction of a sewershed model using relatively low-resolution (e.g., 30 meter DEM) data and explores model sensitivity to parameters and regional characteristics using the EPA’s Storm Water Management Model (SWMM). A SWMM model was developed for a sewershed in the City of Pittsburgh, where stormwater management is a critical concern. We assumed uniform or log-normal distributions for parameters and used Monte Carlo simulations to explore and rank the influence of parameters on predicted surface runoff, peak flow, maximum pipe flow and model performance, as measured using the Nash–Sutcliffe efficiency metric. By using the Thiessen polygon approach for sub-catchment delineations, we substantially simplified the parameterization of the areas and hydraulic parameters. Despite this simplification, our approach provided good agreement with monitored pipe flow (Nash–Sutcliffe efficiency: 0.41 – 0.85). Total runoff and peak flow were very sensitive to the model discretization. The size of the polygons (modeled subcatchment areas) and imperviousness had the most influence on both outputs. The imperviousness, infiltration and Manning’s roughness (in the pervious area) contributed strongly to the Nash-Sutcliffe efficiency (70%), as did pipe geometric parameters (92%). Parameter rank sets were compared by using kappa statistics between any two model elements to identify generalities. Within our relatively large (9.7 km^2) sewershed, optimizing parameters for the highly impervious (>50%) areas and larger pipes lower in the network contributed most to improving Nash–Sutcliffe efficiency. The geometric parameters influence the water quantity distribution and flow conveyance, while imperviousness determines the subcatchment subdivision and influences surface water generation. Application of the Thiessen polygon approach can simplify the construction of large-scale urban storm water models, but the model is sensitive to the sewer network configuration and care must be taken in parameterizing areas (polygons) with heterogenous land uses.

scholarly journals Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

2021 ◽  
Vol 13 (12) ◽  
pp. 2405
Author(s):  
Fengyang Long ◽  
Chengfa Gao ◽  
Yuxiang Yan ◽  
Jinling Wang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Model Performance ◽  
Global Scale ◽  
Radiosonde Data ◽  
Model Parameters ◽  
Measured Temperature ◽  
Weighted Mean ◽  
Weighted Mean Temperature ◽  
Application Scope

Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

A new hybrid model combining EMD and neural network for multi-step ahead load forecasting

2021 ◽  
pp. 1-16
Author(s):  
Hasmat Malik ◽  
Majed A. Alotaibi ◽  
Abdulaziz Almutairi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Hybrid Model ◽  
Storage System ◽  
Load Forecasting ◽  
Model Performance ◽  
Energy Storage System ◽  
Electric Load ◽  
Real Time Analysis ◽  
Model Combining

The electric load forecasting (ELF) is a key area of the modern power system (MPS) applications and also for the virtual power plant (VPP) analysis. The ELF is most prominent for the distinct applications of MPS and VPP such as real-time analysis of energy storage system, distributed energy resources, demand side management and electric vehicles etc. To manage the real-time challenges and map the stable power demand, in different time steps, the ELF is evaluated in yearly, monthly, weekly, daily, and hourly, etc. basis. In this study, an intelligent load predictor which is able to forecast the electric load for next month or day or hour is proposed. The proposed approach is a hybrid model combining empirical mode decomposition (EMD) and neural network (NN) for multi-step ahead load forecasting. The model performance is demonstrated by suing historical dataset collected form GEFCom2012 and GEFCom2014. For the demonstration of the performance, three case studies are analyzed into two categories. The demonstrated results represents the higher acceptability of the proposed approach with respect to the standard value of MAPE (mean absolute percent error).

scholarly journals Real-time Communication With Health Care Providers Through an Online Respiratory Pathogen Laboratory Report

2018 ◽  
Vol 5 (12) ◽  
Author(s):  
Paul A Christensen ◽  
Randall J Olsen ◽  
Katherine K Perez ◽  
Patricia L Cernoch ◽  
S Wesley Long
Keyword(s):  
Real Time ◽  
Health Care Providers ◽  
Influenza Season ◽  
Respiratory Pathogen ◽  
Observation Data ◽  
Care Providers ◽  
Laboratory Observation ◽  
Web Browser ◽  
Hospital System ◽  
Laboratory Report

Abstract We implemented a real-time report to distribute respiratory pathogen data for our 8-hospital system to anyone with an Internet connection and a web browser. Real-time access to accurate regional laboratory observation data during an epidemic influenza season can guide diagnostic and therapeutic strategies.

scholarly journals Real time controlled sustainable urban drainage systems in dense urban areas

2019 ◽  
Vol 69 (3) ◽  
pp. 238-247 ◽  
Author(s):  
Nils Kändler ◽  
Ivar Annus ◽  
Anatoli Vassiljev ◽  
Raido Puust
Keyword(s):  
Urban Development ◽  
Real Time ◽  
Best Management Practices ◽  
Urban Areas ◽  
Management Practices ◽  
Low Impact Development ◽  
Economic Feasibility ◽  
Control Measures ◽  
Real Time Control ◽  
Urban Catchments

Abstract Stormwater runoff from urban catchments is affected by the changing climate and rapid urban development. Intensity of rainstorms is expected to increase in Northern Europe, and sealing off surfaces reduces natural stormwater management. Both trends increase stormwater peak runoff volume that urban stormwater systems (UDS) have to tackle. Pipeline systems have typically limited capacity, therefore measures must be foreseen to reduce runoff from new developed areas to existing UDS in order to avoid surcharge. There are several solutions available to tackle this challenge, e.g. low impact development (LID), best management practices (BMP) or stormwater real time control measures (RTC). In our study, a new concept of a smart in-line storage system is developed and evaluated on the background of traditional in-line and off-line detention solutions. The system is operated by real time controlled actuators with an ability to predict rainfall dynamics. This solution does not need an advanced and expensive centralised control system; it is easy to implement and install. The concept has been successfully tested in a 12.5 ha urban development area in Tallinn, the Estonian capital. Our analysis results show a significant potential and economic feasibility in the reduction of peak flow from dense urban areas with limited free construction space.

scholarly journals Development of specific immunity in laboratory animals after co-immunization against seasonal influenza and COVID-19

2022 ◽  
Vol 98 (6) ◽  
pp. 648-656
Author(s):  
G. M. Ignatyev ◽  
I. A. Leneva ◽  
A. V. Atrasheuskaya ◽  
L. I. Kozlovskaya ◽  
N. P. Kartashova ◽  
...  
Keyword(s):  
Neutralizing Antibodies ◽  
Seasonal Influenza ◽  
Neutralization Test ◽  
Elisa Test ◽  
Control Measures ◽  
Laboratory Animals ◽  
Enzyme Linked Immunosorbent Assay ◽  
Post Vaccination ◽  
Specific Immunity ◽  
Influenza Prevention

Introduction. In clinical practice, the differential diagnosis of COVID-19 can be challenging during the flu season, entailing serious consequences such as delays in appropriate control measures against the SARS-CoV-2 pandemic. Another problem is posed by co-infection of SARS-CoV-2 and influenza virus (IV), which significantly contributes to the severity of the COVID-19 disease. This study was aimed to explore the cross-impact of co-administration of Russian influenza and COVID-19 vaccines on development of specific immunity in laboratory animals.Materials and methods. The study was conducted on BALB/c mice. The animals were inoculated intramuscularly with the vaccine for COVID-19 prevention (CoviVac) and the vaccine for influenza prevention (Flu-M). The sera from the immunized animals were examined separately. Three IV strains were used in the hemagglutination inhibition assay. Antibodies (Abs) against SARS-CoV-2 were detected by an enzyme-linked immunosorbent assay (ELISA). The neutralization test was performed to detect virus neutralizing antibodies against SARS-CoV-2 and IV.Results. Relatively high titers of specific Abs were found in the groups of animals inoculated with one vaccine and with two vaccines concurrently. In the groups of animals inoculated with CoviVac and with two vaccines concurrently, both in the ELISA test and in the neutralization test, the average titers of specific Abs against SARSCoV- 2 did not demonstrate any statistical difference. The group of animals inoculated concurrently with two vaccines demonstrated statistically higher titers of Abs against IV after the second immunization compared to the group of animals inoculated with Flu-M.Discussion. The study has shown that post-vaccination immunity both to IV and to SARS-CoV-2 develops after co-vaccination with two vaccines. The observed enhanced post-vaccination immune response to IV in the coimmunized laboratory animals needs further research.Conclusion. The performed studies suggest the possibility of co-administration of two vaccines to prevent influenza and COVID-19.

scholarly journals Towards assimilation of crowdsourced observations for different levels of citizen engagement: the flood event of 2013 in the Bacchiglione catchment

2017 ◽  
Author(s):  
Maurizio Mazzoleni ◽  
Vivian Juliette Cortes Arevalo ◽  
Uta Wehn ◽  
Leonardo Alfonso ◽  
Daniele Norbiato ◽  
...  
Keyword(s):  
Real Time ◽  
Hydrological Model ◽  
Population Distribution ◽  
Model Performance ◽  
Flood Event ◽  
Target Point ◽  
Citizen Engagement ◽  
Physical Sensors ◽  
Extreme Flood ◽  
Social Sensors

Abstract. Accurate flood predictions are essential to reduce the risk and damages over large urbanized areas. To improve prediction capabilities, hydrological measurements derived by traditional physical sensors are integrated in real-time within mathematic models. Recently, traditional sensors are complemented with low-cost social sensors. However, measurements derived by social sensors (i.e. crowdsourced observations) can be more spatially distributed but less accurate. In this study, we assess the usefulness for model performance of assimilating crowdsourced observations from a heterogeneous network of static physical, static social and dynamic social sensors. We assess potential effects on the model predictions to the extreme flood event occurred in the Bacchiglione catchment on May 2013. Flood predictions are estimated at the target point of Ponte degli Angeli (Vicenza), outlet of the Bacchiglione catchment, by means of a semi-distributed hydrological model. The contribution of the upstream sub-catchment is calculated using a conceptual hydrological model. The flow is propagated along the river reach using a hydraulic model. In both models, a Kalman filter is implemented to assimilate the real-time crowdsourced observations. We synthetically derived crowdsourced observations for either static social or dynamic social sensors because crowdsourced measures were not available. We consider three sets of experiments: (1) only physical sensors are available; (2) probability of receiving crowdsourced observations and (3) realistic scenario of citizen engagement based on population distribution. The results demonstrated the importance of integrating crowdsourced observations. Observations from upstream sub-catchments assimilated into the hydrological model ensures high model performance for high lead time values. Observations next to the outlet of the catchments provide good results for short lead times. Furthermore, citizen engagement level scenarios moved by a feeling of belonging to a community of friends indicated flood prediction improvements when such small communities are located upstream a particular target point. Effective communication and feedback is required between water authorities and citizens to ensure minimum engagement levels and to minimize the intrinsic low-variable accuracy of crowdsourced observations.

Control of Influenza A on a Bone Marrow Transplant Unit

2000 ◽  
Vol 21 (11) ◽  
pp. 730-732 ◽  
Author(s):  
David M. Weinstock ◽  
Janet Eagan ◽  
Sharp Abdel Malak ◽  
Maureen Rogers ◽  
Holly Wallace ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Infection Control ◽  
Bone Marrow Transplant ◽  
Influenza A ◽  
Influenza Season ◽  
Marrow Transplant ◽  
Control Measures ◽  
Rigorous Approach ◽  
Infection Control Measures ◽  
Adult Bone

AbstractIn January 1998, an outbreak of influenza A occurred on our adult bone marrow transplant unit. Aggressive infection control measures were instituted to halt further nosocomial spread. A new, more rigorous approach was implemented for the 1998/99 influenza season and was extremely effective in preventing nosocomial influenza at our institution.

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