Optimal health and disease management using spatial uncertainty: a geographic characterization of emergent artemisinin-resistant Plasmodium falciparum distributions in Southeast Asia

Grist, Eric P. M.; Flegg, Jennifer A.; Humphreys, Georgina S; Mas, I.; Anderson, Tim; Ashley, Elizabeth A.; Day, Nicholas P. J.; Dhorda, Mehul; Dondorp, Arjen M.; Faiz, M. Abul; Gething, Peter W.; Hien, Tran Tinh; Hlaing, Tin Maung; Imwong, Mallika; Kindermans, Jean Marie; Maude, Richard J.; Mayxay, Mayfong; McDew-White, Marina; Ménard, Didier; Nair, Shalini; Nosten, François; Newton, Paul N.; Price, Ric N.; Pukrittayakamee, Sasithon; Takala-Harrison, Shannon; Smithuis, Frank; Nguyen, Nhien Thuy Thanh; Tun, Kyaw Myo; White, Nicholas J.; Witkowski, Benoît; Woodrow, Charles J.; Fairhurst, Rick M.; Sibley, Carol Hopkins; Guérin, Philippe J.

doi:10.1186/s12942-016-0064-6

Cited by 15 publications

(25 citation statements)

References 30 publications

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“…These results suggest that studies in many of these more rural areas may benefit from collection of GPS coordinates for each household or at least for clusters of households, instead of village-level coordinates, or sampling frameworks that are designed based on spatially stratified random sampling instead of solely on population proportional random sampling [ 67 ]. Finally, these uncertainty maps may help in the identification of areas in need of additional sampling [ 68 ].…”

Section: Discussionmentioning

confidence: 99%

Characterizing local-scale heterogeneity of malaria risk: a case study in Bunkpurugu-Yunyoo district in northern Ghana

Amratia

Psychas

Abuaku

et al. 2019

Malar J

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BackgroundBayesian methods have been used to generate country-level and global maps of malaria prevalence. With increasing availability of detailed malaria surveillance data, these methodologies can also be used to identify fine-scale heterogeneity of malaria parasitaemia for operational prevention and control of malaria.MethodsIn this article, a Bayesian geostatistical model was applied to six malaria parasitaemia surveys conducted during rainy and dry seasons between November 2010 and 2013 to characterize the micro-scale spatial heterogeneity of malaria risk in northern Ghana.ResultsThe geostatistical model showed substantial spatial heterogeneity, with malaria parasite prevalence varying between 19 and 90%, and revealing a northeast to southwest gradient of predicted risk. The spatial distribution of prevalence was heavily influenced by two modest urban centres, with a substantially lower prevalence in urban centres compared to rural areas. Although strong seasonal variations were observed, spatial malaria prevalence patterns did not change substantially from year to year. Furthermore, independent surveillance data suggested that the model had a relatively good predictive performance when extrapolated to a neighbouring district.ConclusionsThis high variability in malaria prevalence is striking, given that this small area (approximately 30 km × 40 km) was purportedly homogeneous based on country-level spatial analysis, suggesting that fine-scale parasitaemia data might be critical to guide district-level programmatic efforts to prevent and control malaria. Extrapolations results suggest that fine-scale parasitaemia data can be useful for spatial predictions in neighbouring unsampled districts and does not have to be collected every year to aid district-level operations, helping to alleviate concerns regarding the cost of fine-scale data collection.Electronic supplementary materialThe online version of this article (10.1186/s12936-019-2703-4) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Characterizing local-scale heterogeneity of malaria risk: a case study in Bunkpurugu-Yunyoo district in northern Ghana

Amratia

Psychas

Abuaku

et al. 2019

Malar J

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“…This evidence suggests that these factors along with other host–pathogen–environmental factors could have collectively experienced similar tipping points and adverse trends prior to the emergence of C. auris in specific regions of the world. Furthermore, as has been seen with other pathogens, geospatial topologies of host–pathogen–environmental interactions vary from region to region, exerting varied evolutionary pressures and triggering varied responses in a pathogen [98–100]. This could be a reason for the emergence of genetically diverse C. auris clades in different parts of the globe.…”

Section: Contrasting Geospatial Trends In Antimicrobial Usage and Hosmentioning

confidence: 99%

On the emergence, spread and resistance of Candida auris: host, pathogen and environmental tipping points

Chakrabarti

Sood

2021

Journal of Medical Microbiology

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Over a decade ago, a multidrug-resistant nosocomial fungus Candida auris emerged worldwide and has since become a significant challenge for clinicians and microbiologists across the globe. A resilient pathogen, C. auris survives harsh disinfectants, desiccation and high-saline environments. It readily colonizes the inanimate environment, susceptible patients and causes invasive infections that exact a high toll. Prone to misidentification by conventional microbiology techniques, C. auris rapidly acquires multiple genetic determinants that confer multidrug resistance. Whole-genome sequencing has identified four distinct clades of C. auris, and possibly a fifth one, in circulation. Even as our understanding of this formidable pathogen grows, the nearly simultaneous emergence of its distinct clades in different parts of the world, followed by their rapid global spread, remains largely unexplained. We contend that certain host–pathogen–environmental factors have been evolving along adverse trajectories for the last few decades, especially in regions where C. auris originally appeared, until these factors possibly reached a tipping point to compel the evolution, emergence and spread of C. auris. Comparative genomics has helped identify several resistance mechanisms in C. auris that are analogous to those seen in other Candida species, but they fail to fully explain how high-level resistance rapidly develops in this yeast. A better understanding of these unresolved aspects is essential not only for the effective management of C. auris patients, hospital outbreaks and its global spread but also for forecasting and tackling novel resistant pathogens that might emerge in the future. In this review, we discuss the emergence, spread and resistance of C. auris, and propose future investigations to tackle this resilient pathogen.

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“…Modelling has been used to investigate the effects of resistance [ 25 , 26 , 30 , 32 , 113 – 116 ], and there have been some studies examining risk factors for resistance and drug failure [ 114 , 117 – 119 ]. Geostatistical models are also being developed to predict localities where resistance might be present in order to target surveillance activities, for example, mapping artemisinin-resistance in Southeast Asia [ 120 ]. The biology and natural history of mosquito vectors and malaria parasites tells us that the development and evolution of resistance will continue, given the pressure of insecticides and drugs.…”

Section: Opportunitiesmentioning

confidence: 99%

malERA: An updated research agenda for combination interventions and modelling in malaria elimination and eradication

Modelling¹

2017

PLoS Med

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This paper summarises key advances and priorities since the 2011 presentation of the Malaria Eradication Research Agenda (malERA), with a focus on the combinations of intervention tools and strategies for elimination and their evaluation using modelling approaches. With an increasing number of countries embarking on malaria elimination programmes, national and local decisions to select combinations of tools and deployment strategies directed at malaria elimination must address rapidly changing transmission patterns across diverse geographic areas. However, not all of these approaches can be systematically evaluated in the field. Thus, there is potential for modelling to investigate appropriate ‘packages’ of combined interventions that include various forms of vector control, case management, surveillance, and population-based approaches for different settings, particularly at lower transmission levels. Modelling can help prioritise which intervention packages should be tested in field studies, suggest which intervention package should be used at a particular level or stratum of transmission intensity, estimate the risk of resurgence when scaling down specific interventions after local transmission is interrupted, and evaluate the risk and impact of parasite drug resistance and vector insecticide resistance. However, modelling intervention package deployment against a heterogeneous transmission background is a challenge. Further validation of malaria models should be pursued through an iterative process, whereby field data collected with the deployment of intervention packages is used to refine models and make them progressively more relevant for assessing and predicting elimination outcomes.

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Optimal health and disease management using spatial uncertainty: a geographic characterization of emergent artemisinin-resistant Plasmodium falciparum distributions in Southeast Asia

Cited by 15 publications

References 30 publications

Characterizing local-scale heterogeneity of malaria risk: a case study in Bunkpurugu-Yunyoo district in northern Ghana

Characterizing local-scale heterogeneity of malaria risk: a case study in Bunkpurugu-Yunyoo district in northern Ghana

On the emergence, spread and resistance of Candida auris: host, pathogen and environmental tipping points

malERA: An updated research agenda for combination interventions and modelling in malaria elimination and eradication

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