The Experts below are selected from a list of 225 Experts worldwide ranked by ideXlab platform

Jeffrey Shaman - One of the best experts on this subject based on the ideXlab platform.

  • optimizing respiratory virus surveillance networks using uncertainty propagation
    Nature Communications, 2021
    Co-Authors: Sen Pei, Xian Teng, Paul Lewis, Jeffrey Shaman
    Abstract:

    Infectious Disease Prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory Diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked Disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for Diseases with limited records. The  proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens - human metapneumovirus and seasonal coronavirus - from 35 US states and can be used to guide systemic allocation of surveillance efforts.

  • optimizing respiratory virus surveillance networks using uncertainty propagation
    Nature Communications, 2021
    Co-Authors: Sen Pei, Xian Teng, Paul Lewis, Jeffrey Shaman
    Abstract:

    Infectious Disease Prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory Diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked Disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for Diseases with limited records. The  proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens – human metapneumovirus and seasonal coronavirus – from 35 US states and can be used to guide systemic allocation of surveillance efforts. Lack of a widespread surveillance network hampers accurate Infectious Disease forecasting. Here the authors provide a framework to optimize the selection of surveillance site locations and show that accurate forecasting of respiratory Diseases for locations without surveillance is feasible.

Sen Pei - One of the best experts on this subject based on the ideXlab platform.

  • optimizing respiratory virus surveillance networks using uncertainty propagation
    Nature Communications, 2021
    Co-Authors: Sen Pei, Xian Teng, Paul Lewis, Jeffrey Shaman
    Abstract:

    Infectious Disease Prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory Diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked Disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for Diseases with limited records. The  proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens - human metapneumovirus and seasonal coronavirus - from 35 US states and can be used to guide systemic allocation of surveillance efforts.

  • optimizing respiratory virus surveillance networks using uncertainty propagation
    Nature Communications, 2021
    Co-Authors: Sen Pei, Xian Teng, Paul Lewis, Jeffrey Shaman
    Abstract:

    Infectious Disease Prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory Diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked Disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for Diseases with limited records. The  proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens – human metapneumovirus and seasonal coronavirus – from 35 US states and can be used to guide systemic allocation of surveillance efforts. Lack of a widespread surveillance network hampers accurate Infectious Disease forecasting. Here the authors provide a framework to optimize the selection of surveillance site locations and show that accurate forecasting of respiratory Diseases for locations without surveillance is feasible.

Paul Lewis - One of the best experts on this subject based on the ideXlab platform.

  • optimizing respiratory virus surveillance networks using uncertainty propagation
    Nature Communications, 2021
    Co-Authors: Sen Pei, Xian Teng, Paul Lewis, Jeffrey Shaman
    Abstract:

    Infectious Disease Prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory Diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked Disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for Diseases with limited records. The  proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens - human metapneumovirus and seasonal coronavirus - from 35 US states and can be used to guide systemic allocation of surveillance efforts.

  • optimizing respiratory virus surveillance networks using uncertainty propagation
    Nature Communications, 2021
    Co-Authors: Sen Pei, Xian Teng, Paul Lewis, Jeffrey Shaman
    Abstract:

    Infectious Disease Prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory Diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked Disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for Diseases with limited records. The  proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens – human metapneumovirus and seasonal coronavirus – from 35 US states and can be used to guide systemic allocation of surveillance efforts. Lack of a widespread surveillance network hampers accurate Infectious Disease forecasting. Here the authors provide a framework to optimize the selection of surveillance site locations and show that accurate forecasting of respiratory Diseases for locations without surveillance is feasible.

Xian Teng - One of the best experts on this subject based on the ideXlab platform.

  • optimizing respiratory virus surveillance networks using uncertainty propagation
    Nature Communications, 2021
    Co-Authors: Sen Pei, Xian Teng, Paul Lewis, Jeffrey Shaman
    Abstract:

    Infectious Disease Prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory Diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked Disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for Diseases with limited records. The  proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens - human metapneumovirus and seasonal coronavirus - from 35 US states and can be used to guide systemic allocation of surveillance efforts.

  • optimizing respiratory virus surveillance networks using uncertainty propagation
    Nature Communications, 2021
    Co-Authors: Sen Pei, Xian Teng, Paul Lewis, Jeffrey Shaman
    Abstract:

    Infectious Disease Prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory Diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked Disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for Diseases with limited records. The  proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens – human metapneumovirus and seasonal coronavirus – from 35 US states and can be used to guide systemic allocation of surveillance efforts. Lack of a widespread surveillance network hampers accurate Infectious Disease forecasting. Here the authors provide a framework to optimize the selection of surveillance site locations and show that accurate forecasting of respiratory Diseases for locations without surveillance is feasible.

Ole Wichmann - One of the best experts on this subject based on the ideXlab platform.

  • use of existing systematic reviews for evidence assessments in Infectious Disease Prevention a comparative case study
    Systematic Reviews, 2016
    Co-Authors: Thomas Harder, Cornelius Remschmidt, Sebastian Haller, Tim Eckmanns, Ole Wichmann
    Abstract:

    Given limited resources and time constraints, the use of existing systematic reviews (SR) for the development of evidence-based public health recommendations has become increasingly important. Recently, a five-step approach for identifying, analyzing, appraising and using existing SRs based on recent guidance by the US Agency for Healthcare Research and Quality (AHRQ) was proposed within the Project on a Framework for Rating Evidence in Public Health (PRECEPT). However, case studies are needed to test whether this approach is useful, what challenges arise and how problems can be solved. In two case studies, the five-step approach was applied to integrate existing SRs in the development of evidence-based public health recommendations. Case study A focused on the role of neonatal sepsis as a risk factor for adverse neurodevelopmental outcome. Case study B examined the efficacy, effectiveness and safety of influenza vaccination during pregnancy. For each step, we report the approach of the review team, discuss challenges and describe solutions. For case study A, one existing SR was identified, while in case study B four SRs were eligible for analysis. We found that comparison of inclusion criteria alone was sufficient to judge on relevance of SRs in case study A, but not B. Although methodological quality of all identified SRs was acceptable, risk of bias assessments of individual studies included in the SRs had to be repeated in both case studies. Particular challenges appeared in case study B where multiple SRs addressed the same research question. With the help of spreadsheets comparing the characteristics of the existing SR we decided to use the most comprehensive one for our evidence synthesis and supplemented the results with those from the other SRs. In both case studies using the complete SR was not possible. The five-step approach provided useful and structured guidance and should be routinely applied when using existing SRs as a basis for evidence-based recommendations in public health. In situations where more than one SR has to be considered, the development of spreadsheets comparing characteristics, inclusion criteria, risk of bias, included studies and outcomes seems useful.