About: Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building
Creator	Chowell, Gerardo Grenfell, Bryan Rambaut, Andrew Simonsen, Lone Viboud, Cécile Spiro, David Lloyd-Smith, James Nelson, Martha Miller, Mark Holmes, Edward
topic	covid:8c0e2ac9d7085f613692fbce4f20972f718fc8b0#this
source	Elsevier; Medline; PMC
abstract	Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the ‘big data’ revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (h-index = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications.
has issue date	2018-10-23(xsd:dateTime)
bibo:doi	10.1016/j.epidem.2018.10.004
bibo:pmid	30446431
has license	no-cc
sha1sum (hex)	8c0e2ac9d7085f613692fbce4f20972f718fc8b0
schema:url	https://doi.org/10.1016/j.epidem.2018.10.004
resource representing a document's title	Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building
has PubMed Central identifier	PMC7105018
has PubMed identifier	30446431
schema:publication	Epidemics
resource representing a document's body	covid:8c0e2ac9d7085f613692fbce4f20972f718fc8b0#body_text
is http://vocab.deri.ie/void#inDataset of	https://covidontheweb.inria.fr:4443/about/id/http/ns.inria.fr/covid19/8c0e2ac9d7085f613692fbce4f20972f718fc8b0
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