ArticlePLoS computational biology2014
Seasonally dependent relationships between indicators of malaria transmission and disease provided by mathematical model simulations.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Malaria, relationship with climatic variables and deforestation in Colombia, Latin America and the Caribbean from 2000 to 2020: a systematic review.Malaria journal · 2024Pooled it
- Space-time clusters and co-occurrence of Plasmodium vivax and Plasmodium falciparum malaria in West Bengal, India.Malaria journal · 2024Article
- Article
- Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions.Infectious diseases of poverty · 2022Article
- Evaluation of different deployment strategies for larviciding to control malaria: a simulation study.Malaria journal · 2021Article
- Space-Time Clustering Characteristics of Malaria in Bhutan at the End Stages of Elimination.International journal of environmental research and public health · 2021Article
- Mapping malaria seasonality in Madagascar using health facility data.BMC medicine · 2020Article
- Mathematical analysis to prioritise strategies for malaria elimination.Journal of theoretical biology · 2018Article
- An analysis of the influence of the local effects of climatic and hydrological factors affecting new malaria cases in riverine areas along the Rio Negro and surrounding Puraquequara Lake, Amazonas, Brazil.Environmental monitoring and assessment · 2018Article
- Impact of malaria interventions on child mortality in endemic African settings: comparison and alignment between LiST and Spectrum-Malaria model.BMC public health · 2017Article
- Malaria intervention scale-up in Africa: effectiveness predictions for health programme planning tools, based on dynamic transmission modelling.Malaria journal · 2016Article
- Distribution of malaria exposure in endemic countries in Africa considering country levels of effective treatment.Malaria journal · 2015Article
- Defining the relationship between infection prevalence and clinical incidence of Plasmodium falciparum malaria.Nature communications · 2015Article
- Article
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3 authors.
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Abstract
Evaluating the effectiveness of malaria control interventions on the basis of their impact on transmission as well as impact on morbidity and mortality is becoming increasingly important as countries consider pre-elimination and elimination as well as disease control. Data on prevalence and transmission are traditionally obtained through resource-intensive epidemiological and entomological surveys that become difficult as transmission decreases. This work employs mathematical modeling to examine the relationships between malaria indicators allowing more easily measured data, such as routine health systems data on case incidence, to be translated into measures of transmission and other malaria indicators. Simulations of scenarios with different levels of malaria transmission, patterns of seasonality and access to treatment were run with an ensemble of models of malaria epidemiology and within-host dynamics, as part of the OpenMalaria modeling platform. For a given seasonality profile, regression analysis mapped simulation results of malaria indicators, such as annual average entomological inoculation rate, prevalence, incidence of uncomplicated and severe episodes, and mortality, to an expected range of values of any of the other indicators. Results were validated by comparing simulated relationships between indicators with previously published data on these same indicators as observed in malaria endemic areas. These results allow for direct comparisons of malaria transmission intensity estimates made using data collected with different methods on different indicators. They also address key concerns with traditional methods of quantifying transmission in areas of differing transmission intensity and sparse data. Although seasonality of transmission is often ignored in data compilations, the models suggest it can be critically important in determining the relationship between transmission and disease. Application of these models could help public health officials detect changes of disease dynamics in a population and plan and assess the impact of malaria control interventions.
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