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Analysis of research intensity on infectious disease by disease burden reveals which infectious diseases are neglected by researchers

Author

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  • Yuki Furuse

    (Institute for Frontier Life and Medical Sciences, Kyoto University, 606-8507 Kyoto, Japan, Hakubi Center for Advanced Research, Kyoto University, 606-8501 Kyoto, Japan)

Abstract

Infectious diseases are associated with considerable morbidity and mortality worldwide. Although human, financial, substantial, and time resources are limited, it is unknown whether such resources are used effectively in research to manage diseases. The correlation between the disability-adjusted life years to represent disease burden and number of publications as a surrogate for research activity was investigated to measure burden-adjusted research intensity for 52 infectious diseases at global and country levels. There was significantly low research intensity for paratyphoid fever and high intensity for influenza, HIV/acquired immunodeficiency syndrome, hepatitis C, and tuberculosis considering their disease burden. We identified the infectious diseases that have received the most attention from researchers and those that have been relatively disregarded. Interestingly, not all so-called neglected tropical diseases were subject to low burden-adjusted research intensity. Analysis of the intensity of infectious disease research at a country level revealed characteristic patterns. These findings provided a basis for further discussion of the more appropriate allocation of resources for research into infectious diseases.

Suggested Citation

  • Yuki Furuse, 2019. "Analysis of research intensity on infectious disease by disease burden reveals which infectious diseases are neglected by researchers," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(2), pages 478-483, January.
    • Handle: RePEc:nas:journl:v:116:y:2019:p:478-483
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    Cited by:

    1. C Marijn Hazelbag & Jonathan Dushoff & Emanuel M Dominic & Zinhle E Mthombothi & Wim Delva, 2020. "Calibration of individual-based models to epidemiological data: A systematic review," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-17, May.
    2. Arif M. Tanmoy & Yogesh Hooda & Mohammad S. I. Sajib & Kesia E. Silva & Junaid Iqbal & Farah N. Qamar & Stephen P. Luby & Gordon Dougan & Zoe A. Dyson & Stephen Baker & Denise O. Garrett & Jason R. An, 2022. "Paratype: a genotyping tool for Salmonella Paratyphi A reveals its global genomic diversity," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

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