Literature DB >> 25843394

Modelling challenges in context: lessons from malaria, HIV, and tuberculosis.

Lauren M Childs1, Nadia N Abuelezam2, Christopher Dye3, Sunetra Gupta4, Megan B Murray5, Brian G Williams6, Caroline O Buckee7.   

Abstract

Malaria, HIV, and tuberculosis (TB) collectively account for several million deaths each year, with all three ranking among the top ten killers in low-income countries. Despite being caused by very different organisms, malaria, HIV, and TB present a suite of challenges for mathematical modellers that are particularly pronounced in these infections, but represent general problems in infectious disease modelling, and highlight many of the challenges described throughout this issue. Here, we describe some of the unifying challenges that arise in modelling malaria, HIV, and TB, including variation in dynamics within the host, diversity in the pathogen, and heterogeneity in human contact networks and behaviour. Through the lens of these three pathogens, we provide specific examples of the other challenges in this issue and discuss their implications for informing public health efforts.
Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  HIV; Malaria; Modelling; Tuberculosis

Mesh:

Year:  2015        PMID: 25843394      PMCID: PMC4451070          DOI: 10.1016/j.epidem.2015.02.002

Source DB:  PubMed          Journal:  Epidemics        ISSN: 1878-0067            Impact factor:   4.396


  68 in total

1.  Theory of the eradication of malaria.

Authors:  G MACDONALD
Journal:  Bull World Health Organ       Date:  1956       Impact factor: 9.408

2.  Infectiousness of malaria-endemic human populations to vectors.

Authors:  Gerry F Killeen; Amanda Ross; Thomas Smith
Journal:  Am J Trop Med Hyg       Date:  2006-08       Impact factor: 2.345

3.  Risk factors of sexually transmitted infections among migrant and non-migrant sexual partnerships from rural South Africa.

Authors:  K Zuma; M N Lurie; B G Williams; D Mkaya-Mwamburi; G P Garnett; A W Sturm
Journal:  Epidemiol Infect       Date:  2005-06       Impact factor: 2.451

4.  Seven challenges for model-driven data collection in experimental and observational studies.

Authors:  J Lessler; W J Edmunds; M E Halloran; T D Hollingsworth; A L Lloyd
Journal:  Epidemics       Date:  2014-12-16       Impact factor: 4.396

Review 5.  Risk of progression to active tuberculosis following reinfection with Mycobacterium tuberculosis.

Authors:  Jason R Andrews; Farzad Noubary; Rochelle P Walensky; Rodrigo Cerda; Elena Losina; C Robert Horsburgh
Journal:  Clin Infect Dis       Date:  2012-01-19       Impact factor: 9.079

6.  Six challenges in measuring contact networks for use in modelling.

Authors:  K Eames; S Bansal; S Frost; S Riley
Journal:  Epidemics       Date:  2014-08-30       Impact factor: 4.396

7.  Plasmodium falciparum variant surface antigen expression patterns during malaria.

Authors:  Peter C Bull; Matthew Berriman; Sue Kyes; Michael A Quail; Neil Hall; Moses M Kortok; Kevin Marsh; Chris I Newbold
Journal:  PLoS Pathog       Date:  2005-11-18       Impact factor: 6.823

8.  Eight challenges in phylodynamic inference.

Authors:  Simon D W Frost; Oliver G Pybus; Julia R Gog; Cecile Viboud; Sebastian Bonhoeffer; Trevor Bedford
Journal:  Epidemics       Date:  2014-09-16       Impact factor: 4.396

9.  Five challenges for spatial epidemic models.

Authors:  Steven Riley; Ken Eames; Valerie Isham; Denis Mollison; Pieter Trapman
Journal:  Epidemics       Date:  2014-07-31       Impact factor: 4.396

10.  Four key challenges in infectious disease modelling using data from multiple sources.

Authors:  Daniela De Angelis; Anne M Presanis; Paul J Birrell; Gianpaolo Scalia Tomba; Thomas House
Journal:  Epidemics       Date:  2014-09-28       Impact factor: 4.396

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  5 in total

Review 1.  From within host dynamics to the epidemiology of infectious disease: Scientific overview and challenges.

Authors:  Juan B Gutierrez; Mary R Galinski; Stephen Cantrell; Eberhard O Voit
Journal:  Math Biosci       Date:  2015-10-16       Impact factor: 2.144

2.  Using a network-based approach and targeted maximum likelihood estimation to evaluate the effect of adding pre-exposure prophylaxis to an ongoing test-and-treat trial.

Authors:  Laura Balzer; Patrick Staples; Jukka-Pekka Onnela; Victor DeGruttola
Journal:  Clin Trials       Date:  2017-01-26       Impact factor: 2.486

3.  Thinking clearly about social aspects of infectious disease transmission.

Authors:  Caroline Buckee; Abdisalan Noor; Lisa Sattenspiel
Journal:  Nature       Date:  2021-06-30       Impact factor: 49.962

Review 4.  Vector-based genetically modified vaccines: Exploiting Jenner's legacy.

Authors:  Bahar Ramezanpour; Ingrid Haan; Ab Osterhaus; Eric Claassen
Journal:  Vaccine       Date:  2016-10-28       Impact factor: 3.641

5.  Analysis of COVID-19 using a modified SLIR model with nonlinear incidence.

Authors:  Md Abdul Kuddus; Azizur Rahman
Journal:  Results Phys       Date:  2021-06-21       Impact factor: 4.476

  5 in total

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