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

Abstract

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.

Tracking progress towards malaria elimination in China: Individual-level estimates of transmission and its spatiotemporal variation using a diffusion network approach

“…This information could be used to weight certain connections. Genetic data are also increasingly available, and provide useful information about movement of parasites [26,27], the likelihood of two cases being linked by transmission, and can provide useful information to help distinguish imported from local cases and chains of transmission resulting from importation from on-going local transmission [21]. Such data were not available in this context; however, a similar methodological framework or approach could incorporate information such as genetic distance.…”

A new twenty-first century science for effective epidemic response

“…Such data would not only include genomic information, but also satellite imaging, mobile phone data, social media use data and so on. For example, a study published in 2019 combined epidemiological surveillance data, travel surveys, parasite genetics and anonymized mobile phone data to measure the spread of malaria parasites in southeast Bangladesh 92 . A retrospective analysis of mobile phone call data in Sierra Leone from 2015 showed how it might have been used to assess the impact of travel restrictions on mobility during the Ebola epidemic 46 .…”

Modelling the epidemiology of residual Plasmodium vivax malaria in a heterogeneous host population: a case study in the Amazon Basin

“…Product of exposure to infectious mosquitoes and susceptibility to infection given exposure, each individual’s risk is determined by numerous interacting factors. Despites notorious efforts being invested in characterising specific determinants, such as individual mobility to and from hotspots [39], parasite genetics [40] and human genetics [41], a complete catalogue of risk factors and respective measures is not on the horizon. Smith [42] suggested that individual-level variation in susceptibility to malaria given exposure can be inferred by modelling malaria incidence as a function of EIR measured in the same population.…”