Evidence map›Paper›PMID 39617902›Full record

ReviewBMC infectious diseases2024

How mathematical modelling can inform outbreak response vaccination.

Manjari Shankar, Anna-Maria Hartner, Callum R K Arnold, Ezra Gayawan, Hyolim Kang, Jong-Hoon Kim, Gemma Nedjati Gilani, Anne Cori, Han Fu, Mark Jit and 4 more

Abstract readReview
In one paragraph

Review in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

14 authors.

Manjari ShankarMedical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, London, UK. m.shankar@imperial.ac.uk.
Anna-Maria HartnerMedical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
Callum R K ArnoldCenter for Infectious Disease Dynamics, Pennsylvania State University, University Park, 16802, PA, USA.
Ezra GayawanDepartment of Statistics, Federal University of Technology, Akure, Nigeria.
Hyolim KangDepartment of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
Jong-Hoon KimDepartment of Epidemiology, Public Health, Impact, International Vaccine Institute, Seoul, South Korea.
Gemma Nedjati GilaniMedical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
Anne CoriMedical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
Han FuDepartment of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
Mark JitDepartment of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
Rudzani MuloiwaDepartment of Paediatrics & Child Health, Faculty of Health Sciences, University of Cape Town, Red Cross War Memorial Children's Hospital, Cape Town, South Africa.
Allison PortnoyDepartment of Global Health, Boston University School of Public Health, Boston, United States.
Caroline TrotterMedical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
Katy A M GaythorpeMedical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.

Funding

Bill and Melinda Gates Foundation INV-034281Medical Research Council MR/X020258/1National Institute for Health and Care Research NIHR200908Wellcome TrustWellcome Trust 226727_Z_22_Z
6 · The paper itself

Abstract

Mathematical models are established tools to assist in outbreak response. They help characterise complex patterns in disease spread, simulate control options to assist public health authorities in decision-making, and longer-term operational and financial planning. In the context of vaccine-preventable diseases (VPDs), vaccines are one of the most-cost effective outbreak response interventions, with the potential to avert significant morbidity and mortality through timely delivery. Models can contribute to the design of vaccine response by investigating the importance of timeliness, identifying high-risk areas, prioritising the use of limited vaccine supply, highlighting surveillance gaps and reporting, and determining the short- and long-term benefits. In this review, we examine how models have been used to inform vaccine response for 10 VPDs, and provide additional insights into the challenges of outbreak response modelling, such as data gaps, key vaccine-specific considerations, and communication between modellers and stakeholders. We illustrate that while models are key to policy-oriented outbreak vaccine response, they can only be as good as the surveillance data that inform them.

Indexed as

Disease OutbreaksModels, TheoreticalVaccinationHumansVaccine-Preventable DiseasesVaccinesVaccinesImmunisationImpactMathematical modellingOutbreakVaccinationVaccine

Identifiers

PMID39617902
PMCPMC11608489

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.