Evidence map›Paper›PMID 31541212›Full record

ReviewNature microbiology2019

Modelling microbial infection to address global health challenges.

Meagan C Fitzpatrick, Chris T Bauch, Jeffrey P Townsend, Alison P Galvani

Abstract readReview
In one paragraph

Review in Nature microbiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Contact tracing-induced Allee effect in disease dynamics.Journal of theoretical biology · 2022
    Article
  14. Article
  15. Review
  16. The healthier healthcare management models for COVID-19.Journal of infection and public health · 2021
    Review
  17. The collaboration between infectious disease modeling and public health decision-making based on the COVID-19.Journal of safety science and resilience = An quan ke xue yu ren xing (Ying wen) · 2021
    Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Meagan C FitzpatrickCenter for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-5248-2668
Chris T BauchDepartment of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada.
Jeffrey P TownsendDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, USA.ORCID http://orcid.org/0000-0002-9890-3907
Alison P GalvaniCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA. alison.galvani@yale.edu.ORCID http://orcid.org/0000-0002-2059-6716

Funding

Impacts of Individual and Social Behavior on Influenza Dynamics and ControlU01GM087719 · NIGMS · YALE UNIVERSITY · PI GALVANI, ALISON P, MEYERS, LAUREN ANCEL · 2009 to 2018
$6.3M
Mathematical modeling to optimize rotavirus vaccination in sub-Saharan AfricaK01AI141576 · NIAID · UNIVERSITY OF MARYLAND BALTIMORE · PI FITZPATRICK, MEAGAN COLLEEN · 2019 to 2023
$649k
NIAID NIH HHS K01 AI141576NIGMS NIH HHS U01 GM087719
6 · The paper itself

Abstract

The continued growth of the world's population and increased interconnectivity heighten the risk that infectious diseases pose for human health worldwide. Epidemiological modelling is a tool that can be used to mitigate this risk by predicting disease spread or quantifying the impact of different intervention strategies on disease transmission dynamics. We illustrate how four decades of methodological advances and improved data quality have facilitated the contribution of modelling to address global health challenges, exemplified by models for the HIV crisis, emerging pathogens and pandemic preparedness. Throughout, we discuss the importance of designing a model that is appropriate to the research question and the available data. We highlight pitfalls that can arise in model development, validation and interpretation. Close collaboration between empiricists and modellers continues to improve the accuracy of predictions and the optimization of models for public health decision-making.

Indexed as

Global HealthModels, BiologicalCommunicable Disease ControlCommunicable DiseasesCommunicable Diseases, EmergingData CollectionDecision MakingHumansPandemics

Identifiers

PMID31541212
PMCPMC6800015

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.