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Mapping imported malaria in Bangladesh using parasite genetic and human mobility data.

Hsiao-Han ChangAmy WesolowskiIpsita SinhaChristopher G JacobAyesha MahmudDidar UddinSazid Ibna ZamanMd Amir HossainM Abul FaizAniruddha GhoseAbdullah Abu SayeedM Ridwanur RahmanAkramul IslamMohammad Jahirul KarimM Kamar RezwanAbul Khair Mohammad ShamsuzzamanSanya Tahmina JhoraM M AktaruzzamanEleanor DrurySonia GonçalvesMihir KekreMehul DhordaRanitha VongpromekOlivo MiottoKenth Engø-MonsenDominic KwiatkowskiRichard J MaudeCaroline O Buckee
Published in: eLife (2019)
For countries aiming for malaria elimination, travel of infected individuals between endemic areas undermines local interventions. Quantifying parasite importation has therefore become a priority for national control programs. We analyzed epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data to measure the spatial spread of malaria parasites in southeast Bangladesh. We developed a genetic mixing index to estimate the likelihood of samples being local or imported from parasite genetic data and inferred the direction and intensity of parasite flow between locations using an epidemiological model integrating the travel survey and mobile phone calling data. Our approach indicates that, contrary to dogma, frequent mixing occurs in low transmission regions in the southwest, and elimination will require interventions in addition to reducing imported infections from forested regions. Unlike risk maps generated from clinical case counts alone, therefore, our approach distinguishes areas of frequent importation as well as high transmission.
Keyphrases
  • plasmodium falciparum
  • electronic health record
  • big data
  • genome wide
  • public health
  • gene expression
  • machine learning
  • endothelial cells
  • mass spectrometry
  • deep learning
  • life cycle