Literature DB >> 31292726

Enhancing Situational Awareness to Prevent Infectious Disease Outbreaks from Becoming Catastrophic.

Marc Lipsitch1, Mauricio Santillana2,3.   

Abstract

Catastrophic epidemics, if they occur, will very likely start from localized and far smaller (non-catastrophic) outbreaks that grow into much greater threats. One key bulwark against this outcome is the ability of governments and the health sector more generally to make informed decisions about control measures based on accurate understanding of the current and future extent of the outbreak. Situation reporting is the activity of periodically summarizing the state of the outbreak in a (usually) public way. We delineate key classes of decisions whose quality depends on high-quality situation reporting, key quantities for which estimates are needed to inform these decisions, and the traditional and novel sources of data that can aid in estimating these quantities. We emphasize the important role of situation reports as providing public, shared planning assumptions that allow decision makers to harmonize the response while making explicit the uncertainties that underlie the scenarios outlined for planning. In this era of multiple data sources and complex factors informing the interpretation of these data sources, we describe four principles for situation reporting: (1) Situation reporting should be thematic, concentrating on essential areas of evidence needed for decisions. (2) Situation reports should adduce evidence from multiple sources to address each area of evidence, along with expert assessments of key parameters. (3) Situation reports should acknowledge uncertainty and attempt to estimate its magnitude for each assessment. (4) Situation reports should contain carefully curated visualizations along with text and tables.

Year:  2019        PMID: 31292726     DOI: 10.1007/82_2019_172

Source DB:  PubMed          Journal:  Curr Top Microbiol Immunol        ISSN: 0070-217X            Impact factor:   4.291


  9 in total

1.  Detecting Early-Warning Signals in Time Series of Visits to Points of Interest to Examine Population Response to COVID-19 Pandemic.

Authors:  Qingchun Li; Zhiyuan Tang; Natalie Coleman; Ali Mostafavi
Journal:  IEEE Access       Date:  2021-02-10       Impact factor: 3.476

2.  Forecasting new diseases in low-data settings using transfer learning.

Authors:  Kirstin Roster; Colm Connaughton; Francisco A Rodrigues
Journal:  Chaos Solitons Fractals       Date:  2022-06-23       Impact factor: 9.922

3.  Infectious disease pandemic planning and response: Incorporating decision analysis.

Authors:  Freya M Shearer; Robert Moss; Jodie McVernon; Joshua V Ross; James M McCaw
Journal:  PLoS Med       Date:  2020-01-09       Impact factor: 11.069

4.  Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data.

Authors:  P M De Salazar; F Lu; J A Hay; D Gómez-Barroso; P Fernández-Navarro; E Martínez; J Astray-Mochales; R Amillategui; A García-Fulgueiras; M D Chirlaque; A Sánchez-Migallón; A Larrauri; M J Sierra; M Lipsitch; F Simón; M Santillana; M A Hernán
Journal:  medRxiv       Date:  2021-01-26

5.  Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile.

Authors:  Gonzalo E Mena; Pamela P Martinez; Caroline O Buckee; Mauricio Santillana; Ayesha S Mahmud; Pablo A Marquet
Journal:  Science       Date:  2021-04-27       Impact factor: 47.728

6.  Interval forecasts of weekly incident and cumulative COVID-19 mortality in the United States: A comparison of combining methods.

Authors:  Kathryn S Taylor; James W Taylor
Journal:  PLoS One       Date:  2022-03-29       Impact factor: 3.240

7.  Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data.

Authors:  Pablo M De Salazar; Fred Lu; James A Hay; Diana Gómez-Barroso; Pablo Fernández-Navarro; Elena V Martínez; Jenaro Astray-Mochales; Rocío Amillategui; Ana García-Fulgueiras; Maria D Chirlaque; Alonso Sánchez-Migallón; Amparo Larrauri; María J Sierra; Marc Lipsitch; Fernando Simón; Mauricio Santillana; Miguel A Hernán
Journal:  PLoS Comput Biol       Date:  2022-03-31       Impact factor: 4.475

8.  Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models.

Authors:  Dianbo Liu; Leonardo Clemente; Canelle Poirier; Xiyu Ding; Matteo Chinazzi; Jessica Davis; Alessandro Vespignani; Mauricio Santillana
Journal:  J Med Internet Res       Date:  2020-08-17       Impact factor: 5.428

9.  Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches.

Authors:  Fred S Lu; Andre T Nguyen; Nicholas B Link; Mathieu Molina; Jessica T Davis; Matteo Chinazzi; Xinyue Xiong; Alessandro Vespignani; Marc Lipsitch; Mauricio Santillana
Journal:  PLoS Comput Biol       Date:  2021-06-17       Impact factor: 4.475

  9 in total

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