COI Around the World
Photo by MalariaGEN.
Malaria remains a leading cause of death worldwide—in 2020, there were an estimated 241 million cases and 627,000 deaths around the globe [1]. Despite the considerable burden of malaria, these numbers represent substantial progress that has been made globally to control malaria in the last two decades. However, there is evidence that progress has slowed and that there is a need for new approaches to capitalize on the gains made [2].
One approach is the use of computational methods to help inform eradication efforts, which often rely on recent advances in genetic sequencing and an increased understanding of malaria biology [3–5]. While determining the exact metrics that are most informative is an active field of investigation [6], one important metric is the complexity of infection (COI). The COI represents the number of genetically distinct malaria genomes or strains that can be identified in a particular individual and has been increasingly used for inferring malaria transmission intensity and evaluating malaria control interventions [7].
Using our newly developed software package, coiaf, we analysed samples from the MalariaGEN Plasmodium falciparum Community Project [8]. The MalariaGEN Plasmodium falciparum Community Project provides genomic data from over 7,000 P. falciparum samples from 28 malaria-endemic countries in Africa, Asia, South America, and Oceania from 2002-2015. Detailed information about the data release including brief descriptions of contributing partner studies and study locations is available in the supplementary of MalariaGEN et al.
References
1. Organization WH. World malaria report 2021. World Health Organization; 2021.
2. Organization WH et al. World malaria report 2020: 20 years of global progress and challenges. 2020.
3. Andrade BB, Reis-Filho A, Barros AM, Souza-Neto SM, Nogueira LL, Fukutani KF, et al. Towards a precise test for malaria diagnosis in the Brazilian Amazon: Comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks. Malaria Journal. 2010;9: 117. doi:10.1186/1475-2875-9-117
4. Band G, Le QS, Clarke GM, Kivinen K, Hubbart C, Jeffreys AE, et al. Insights into malaria susceptibility using genome-wide data on 17,000 individuals from Africa, Asia and Oceania. Nature Communications. 2019;10: 5732. doi:10.1038/s41467-019-13480-z
5. Timmann C, Thye T, Vens M, Evans J, May J, Ehmen C, et al. Genome-wide association study indicates two novel resistance loci for severe malaria. Nature. 2012;489: 443–446. doi:10.1038/nature11334
6. Watson OJ, Okell LC, Hellewell J, Slater HC, Unwin HJT, Omedo I, et al. Evaluating the Performance of Malaria Genetics for Inferring Changes in Transmission Intensity Using Transmission Modeling. Molecular Biology and Evolution. 2021;38: 274–289. doi:10.1093/molbev/msaa225
7. Daniels RF, Schaffner SF, Wenger EA, Proctor JL, Chang H-H, Wong W, et al. Modeling malaria genomics reveals transmission decline and rebound in Senegal. Proceedings of the National Academy of Sciences. 2015;112: 7067–7072. doi:10.1073/pnas.1505691112
8. MalariaGEN, Ahouidi A, Ali M, Almagro-Garcia J, Amambua-Ngwa A, Amaratunga C, et al. An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples. Wellcome Open Research. 2021;6: 42. doi:10.12688/wellcomeopenres.16168.1
Aris Paschalidis
Medical Student
My research interests include health analytics, infectious diseases, and artificial intelligence.