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Parasite associations predict infection risk: incorporating co-infections in predictive models for neglected tropical diseases.

Nicholas J ClarkKei OwadaEugene RuberanzizaGiuseppina OrtuIrenee UmulisaUrsin BayisengeJean Bosco MbonigabaJean Bosco MucacaWarren LancasterAlan FenwickRicardo J Soares MagalhãesAimable Mbituyumuremyi
Published in: Parasites & vectors (2020)
Monitoring studies routinely screen for multiple parasites, yet statistical models generally ignore this multivariate data when assessing risk factors and designing treatment guidelines. Multivariate approaches can be instrumental in the global effort to reduce and eventually eliminate neglected helminth infections in developing countries.
Keyphrases
  • risk factors
  • data analysis
  • plasmodium falciparum
  • high throughput
  • climate change
  • electronic health record
  • big data
  • clinical practice
  • machine learning
  • artificial intelligence
  • replacement therapy