Estimating malaria transmission through mathematical models

Stuckey, Erin M.; Smith, T.; Chitnis, Nakul

doi:10.1016/j.pt.2013.08.001

Cited by 25 publications

(13 citation statements)

References 57 publications

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“…The OpenMalaria platform supports an ensemble of stochastic, individual-based, simulation models of malaria in humans [13, 17, 18] that can be used for calibrating different malariological indices against each other [19]. This includes sub-models of infection of humans [20], incidence of morbidity including severe and in-patient cases and mortality [14].…”

Section: Methodsmentioning

confidence: 99%

Incidence and admission rates for severe malaria and their impact on mortality in Africa

Camponovo

Bever

Galactionova

et al. 2017

Malar J

234

256

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BackgroundAppropriate treatment of life-threatening Plasmodium falciparum malaria requires in-patient care. Although the proportion of severe cases accessing in-patient care in endemic settings strongly affects overall case fatality rates and thus disease burden, this proportion is generally unknown. At present, estimates of malaria mortality are driven by prevalence or overall clinical incidence data, ignoring differences in case fatality resulting from variations in access. Consequently, the overall impact of preventive interventions on disease burden have not been validly compared with those of improvements in access to case management or its quality.MethodsUsing a simulation-based approach, severe malaria admission rates and the subsequent severe malaria disease and mortality rates for 41 malaria endemic countries of sub-Saharan Africa were estimated. Country differences in transmission and health care settings were captured by use of high spatial resolution data on demographics and falciparum malaria prevalence, as well as national level estimates of effective coverage of treatment for uncomplicated malaria. Reported and modelled estimates of cases, admissions and malaria deaths from the World Malaria Report, along with predicted burden from simulations, were combined to provide revised estimates of access to in-patient care and case fatality rates.ResultsThere is substantial variation between countries’ in-patient admission rates and estimated levels of case fatality rates. It was found that for many African countries, most patients admitted for in-patient treatment would not meet strict criteria for severe disease and that for some countries only a small proportion of the total severe cases are admitted. Estimates are highly sensitive to the assumed community case fatality rates. Re-estimation of national level malaria mortality rates suggests that there is substantial burden attributable to inefficient in-patient access and treatment of severe disease.ConclusionsThe model-based methods proposed here offer a standardized approach to estimate the numbers of severe malaria cases and deaths based on national level reporting, allowing for coverage of both curative and preventive interventions. This makes possible direct comparisons of the potential benefits of scaling-up either category of interventions. The profound uncertainties around these estimates highlight the need for better data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-016-1650-6) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Incidence and admission rates for severe malaria and their impact on mortality in Africa

Camponovo

Bever

Galactionova

et al. 2017

Malar J

234

256

View full text Add to dashboard Cite

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“…Country specific were obtained from the collated data, including intervention deployment and coverage, case management levels, as well as vector composition, resistance and biting behaviour. Details on OpenMalaria development and parameterisations are available in previous literature [26,66,[68][69][70] and online [71]. A comprehensive comparison of the model with other models was published by Smith et al [23].…”

Section: Simulation Modelsmentioning

confidence: 99%

Simulating the council-specific impact of anti-malaria interventions: A tool to support malaria strategic planning in Tanzania

et al. 2020

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“…Malaria risk mapping is traditionally obtained through various statistical techniques and datadriven modeling [5,12,13,16,[21][22][23][24][25][26]. Such models are often either very specific or very general, as they are generated from data characterizing either local scale contexts at high resolutions, preventing obtaining reproducible results and to describe or predict large scale phenomena [23], or large scale contexts at low resolutions, preventing the ability to precisely describe disease transmission mechanisms [5,12,16].…”

Section: Introductionmentioning

confidence: 99%

Mapping a Knowledge-Based Malaria Hazard Index Related to Landscape Using Remote Sensing: Application to the Cross-Border Area between French Guiana and Brazil

Roux

Dessay

et al. 2016

Remote Sensing

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Malaria remains one of the most common vector-borne diseases in the world and the definition of novel control strategies can benefit from the modeling of transmission processes. However, data-driven models are often difficult to build, as data are very often incomplete, heterogeneous in nature and in quality, and/or biased. In this context, a knowledge-based approach is proposed to build a robust and general landscape-based hazard index for malaria transmission that is tailored to the Amazonian region. A partial knowledge-based model of the risk of malaria transmission in the Amazonian region, based on landscape features and extracted from a systematic literature review, was used. Spatialization of the model was obtained by generating land use and land cover maps of the cross-border area between French Guiana and Brazil, followed by computing and combining landscape metrics to build a set of normalized landscape-based hazard indices. An empirical selection of the best index was performed by comparing the indices in terms of adequacy with the knowledge-based model, intelligibility and correlation with P. falciparum incidence rates. The selected index is easy to interpret and successfully represents the current knowledge about the role played by landscape patterns in malaria transmission within the study area. It was significantly associated with P. falciparum incidence rates, using the Pearson and Spearman correlation coefficients (up to 0.79 and 0.75, respectively; p-value < 0.001), and the linear regression coefficient of determination (reaching 0.63; p-values < 0.001). This study establishes a spatial knowledge-driven, landscape-based hazard malaria index using remote sensing that can be easily produced on a regular basis and might be useful for malaria prediction, surveillance, and control.Keywords: remote sensing; land use and land cover; landscape metric; knowledge-based hazard modeling; malaria; cross-border area between French Guiana and Brazil

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Estimating malaria transmission through mathematical models

Cited by 25 publications

References 57 publications

Incidence and admission rates for severe malaria and their impact on mortality in Africa

Incidence and admission rates for severe malaria and their impact on mortality in Africa

Simulating the council-specific impact of anti-malaria interventions: A tool to support malaria strategic planning in Tanzania

Mapping a Knowledge-Based Malaria Hazard Index Related to Landscape Using Remote Sensing: Application to the Cross-Border Area between French Guiana and Brazil

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