Dataset Information

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.

SUBMITTER: Chang HH

PROVIDER: S-EPMC6478433 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Chang Hsiao-Han HH Wesolowski Amy A Sinha Ipsita I Jacob Christopher G CG Mahmud Ayesha A Uddin Didar D Zaman Sazid Ibna SI Hossain Md Amir MA Faiz M Abul MA Ghose Aniruddha A Sayeed Abdullah Abu AA Rahman M Ridwanur MR Islam Akramul A Karim Mohammad Jahirul MJ Rezwan M Kamar MK Shamsuzzaman Abul Khair Mohammad AKM Jhora Sanya Tahmina ST Aktaruzzaman M M MM Drury Eleanor E Gonçalves Sonia S Kekre Mihir M Dhorda Mehul M Vongpromek Ranitha R Miotto Olivo O Engø-Monsen Kenth K Kwiatkowski Dominic D Maude Richard J RJ Buckee Caroline C

eLife 20190402

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 bein ...[more]

PMID: 30938289

Similar Datasets

Project description:BackgroundMany malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions. Identifying location-specific seasonality characteristics is useful for planning interventions. While most existing maps of malaria seasonality use fixed thresholds of rainfall, temperature, and/or vegetation indices to identify suitable transmission months, we construct a statistical modelling framework for characterising the seasonal patterns derived directly from monthly health facility data.MethodsWith data from 2669 of the 3247 health facilities in Madagascar, a spatiotemporal regression model was used to estimate seasonal patterns across the island. In the absence of catchment population estimates or the ability to aggregate to the district level, this focused on the monthly proportions of total annual cases by health facility level. The model was informed by dynamic environmental covariates known to directly influence seasonal malaria trends. To identify operationally relevant characteristics such as the transmission start months and associated uncertainty measures, an algorithm was developed and applied to model realisations. A seasonality index was used to incorporate burden information from household prevalence surveys and summarise 'how seasonal' locations are relative to their surroundings.ResultsPositive associations were detected between monthly case proportions and temporally lagged covariates of rainfall and temperature suitability. Consistent with the existing literature, model estimates indicate that while most parts of Madagascar experience peaks in malaria transmission near March-April, the eastern coast experiences an earlier peak around February. Transmission was estimated to start in southeast districts before southwest districts, suggesting that indoor residual spraying should be completed in the same order. In regions where the data suggested conflicting seasonal signals or two transmission seasons, estimates of seasonal features had larger deviations and therefore less certainty.ConclusionsMonthly health facility data can be used to establish seasonal patterns in malaria burden and augment the information provided by household prevalence surveys. The proposed modelling framework allows for evidence-based and cohesive inferences on location-specific seasonal characteristics. As health surveillance systems continue to improve, it is hoped that more of such data will be available to improve our understanding and planning of intervention strategies.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets