Dataset Information

Testing a multi-malaria-model ensemble against 30 years of data in the Kenyan highlands.

ABSTRACT: BACKGROUND: Multi-model ensembles could overcome challenges resulting from uncertainties in models' initial conditions, parameterization and structural imperfections. They could also quantify in a probabilistic way uncertainties in future climatic conditions and their impacts. METHODS: A four-malaria-model ensemble was implemented to assess the impact of long-term changes in climatic conditions on Plasmodium falciparum malaria morbidity observed in Kericho, in the highlands of Western Kenya, over the period 1979-2009. Input data included quality controlled temperature and rainfall records gathered at a nearby weather station over the historical periods 1979-2009 and 1980-2009, respectively. Simulations included models' sensitivities to changes in sets of parameters and analysis of non-linear changes in the mean duration of host's infectivity to vectors due to increased resistance to anti-malarial drugs. RESULTS: The ensemble explained from 32 to 38% of the variance of the observed P. falciparum malaria incidence. Obtained R²-values were above the results achieved with individual model simulation outputs. Up to 18.6% of the variance of malaria incidence could be attributed to the +0.19 to +0.25°C per decade significant long-term linear trend in near-surface air temperatures. On top of this 18.6%, at least 6% of the variance of malaria incidence could be related to the increased resistance to anti-malarial drugs. Ensemble simulations also suggest that climatic conditions have likely been less favourable to malaria transmission in Kericho in recent years. CONCLUSIONS: Long-term changes in climatic conditions and non-linear changes in the mean duration of host's infectivity are synergistically driving the increasing incidence of P. falciparum malaria in the Kenyan highlands. User-friendly, online-downloadable, open source mathematical tools, such as the one presented here, could improve decision-making processes of local and regional health authorities.

SUBMITTER: Ruiz D

PROVIDER: S-EPMC4090176 | biostudies-other | 2014

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Testing a multi-malaria-model ensemble against 30 years of data in the Kenyan highlands.

Ruiz Daniel D Brun Cyrille C Connor Stephen J SJ Omumbo Judith A JA Lyon Bradfield B Thomson Madeleine C MC

Malaria journal 20140530

<h4>Background</h4>Multi-model ensembles could overcome challenges resulting from uncertainties in models' initial conditions, parameterization and structural imperfections. They could also quantify in a probabilistic way uncertainties in future climatic conditions and their impacts.<h4>Methods</h4>A four-malaria-model ensemble was implemented to assess the impact of long-term changes in climatic conditions on Plasmodium falciparum malaria morbidity observed in Kericho, in the highlands of Weste ...[more]

PMID: 24885824

Similar Datasets

Project description:In this study we introduce a hybrid ensemble consisting of air quality models operating at both the global and regional scale. The work is motivated by the fact that these different types of models treat specific portions of the atmospheric spectrum with different levels of detail, and it is hypothesized that their combination can generate an ensemble that performs better than mono-scale ensembles. A detailed analysis of the hybrid ensemble is carried out in the attempt to investigate this hypothesis and determine the real benefit it produces compared to ensembles constructed from only global-scale or only regional-scale models. The study utilizes 13 regional and 7 global models participating in the Hemispheric Transport of Air Pollutants phase 2 (HTAP2)-Air Quality Model Evaluation International Initiative phase 3 (AQMEII3) activity and focuses on surface ozone concentrations over Europe for the year 2010. Observations from 405 monitoring rural stations are used for the evaluation of the ensemble performance. The analysis first compares the modelled and measured power spectra of all models and then assesses the properties of the mono-scale ensembles, particularly their level of redundancy, in order to inform the process of constructing the hybrid ensemble. This study has been conducted in the attempt to identify that the improvements obtained by the hybrid ensemble relative to the mono-scale ensembles can be attributed to its hybrid nature. The improvements are visible in a slight increase of the diversity (4 % for the hourly time series, 10 % for the daily maximum time series) and a smaller improvement of the accuracy compared to diversity. Root mean square error (RMSE) improved by 13-16 % compared to G and by 2-3 % compared to R. Probability of detection (POD) and false-alarm rate (FAR) show a remarkable improvement, with a steep increase in the largest POD values and smallest values of FAR across the concentration ranges. The results show that the optimal set is constructed from an equal number of global and regional models at only 15 % of the stations. This implies that for the majority of the cases the regional-scale set of models governs the ensemble. However given the high degree of redundancy that characterizes the regional-scale models, no further improvement could be expected in the ensemble performance by adding yet more regional models to it. Therefore the improvement obtained with the hybrid set can confidently be attributed to the different nature of the global models. The study strongly reaffirms the importance of an in-depth inspection of any ensemble of opportunity in order to extract the maximum amount of information and to have full control over the data used in the construction of the ensemble.

Project description:Understanding diagnostic capacities is essential to addressing healthcare provision and inequity, particularly in low-income and middle-income countries. This study used routine data to assess trends in rapid diagnostic test (RDT) reporting, supplies and unmet needs across national and 47 subnational (county) levels in Kenya. We extracted facility-level RDT data for 19 tests (2018-2020) from the Kenya District Health Information System, linked to 13 373 geocoded facilities. Data quality was assessed for reporting completeness (ratio of reports received against those expected), reporting patterns and outliers. Supply assessment covered 12 RDTs reported by at least 50% of the reporting facilities (n=5251), with missing values imputed considering reporting trends. Supply was computed by aggregating the number of tests reported per facility. Due to data limitations, demand was indirectly estimated using healthcare-seeking rates (HIV, malaria) and using population data for venereal disease research laboratory test (VDRL), with unmet need computed as the difference between supply and demand. Reporting completeness was under 40% across all counties, with RDT-specific reporting ranging from 9.6% to 89.6%. Malaria RDTs showed the highest annual test volumes (6.3-8.0 million) while rheumatoid factor was the lowest (0.5-0.7 million). Demand for RDTs varied from 2.5 to 11.5 million tests, with unmet needs between 1.2 and 3.5 million. Notably, malaria testing and unmet needs were highest in Turkana County, as well as the western and coastal regions. HIV testing was concentrated in the western and central regions, with decreasing unmet needs from 2018 to 2020. VDRL testing showed high volumes and unmet needs in Nairobi and select counties, with minimal yearly variation. RDTs are crucial in enhancing diagnostic accessibility, yet their utilisation varies significantly by region. These findings underscore the need for targeted interventions to close testing gaps and improve data reporting completeness. Addressing these disparities is vital for equitably enhancing diagnostic services nationwide.

Project description:BackgroundVaccination against human papillomavirus (HPV), predominantly targeting young females, has been introduced in many countries. Decisions to implement programs, which have involved substantial investment by governments, have in part been based on findings from cost-effectiveness models. Now that vaccination programs have been in place for some years, it is becoming possible to observe their effects, and compare these with model effectiveness predictions made previously.FindingsAustralia introduced a publicly-funded HPV vaccination program in 2007. Recently reported Australian data from a repeat cross-sectional survey showed a substantial (77%) fall in HPV16 prevalence in women aged 18-24 years in 2010-2011, compared to pre-vaccination levels. We have previously published model predictions for the population-wide reduction in incident HPV16 infections post-vaccination in Australia. We compared prior predictions from the same model (including the same assumed uptake rates) for the reduction in HPV16 prevalence in women aged 18-24 years by the end of 2010 with the observed data. Based on modelled vaccine uptake which is consistent with recent data on three-dose uptake (78% at 12-13 years; lower uptake in older catch-up age cohorts), we had predicted a 70% reduction in prevalence in 18-24 year old females by the end of 2010. Based on modelled vaccine uptake consistent with recent national data for two-dose coverage and similar to that reported by women in the cross-sectional study, we had predicted a 79% reduction.ConclusionsA close correspondence was observed between the prior model predictions and the recently reported findings on the rapid drop in HPV prevalence in Australia. Because broadly similar effectiveness predictions have been reported from other models used for cost-effectiveness predictions, this provides reassurance that the substantial public investment in HPV vaccination has been grounded in valid estimates of the effects of vaccination.

Project description:BackgroundThe over-distributed pattern of malaria transmission has led to attempts to define malaria "hotspots" that could be targeted for purposes of malaria control in Africa. However, few studies have investigated the use of routine health facility data in the more stable, endemic areas of Africa as a low-cost strategy to identify hotspots. Here the objective was to explore the spatial and temporal dynamics of fever positive rapid diagnostic test (RDT) malaria cases routinely collected along the Kenyan Coast.MethodsData on fever positive RDT cases between March 2018 and February 2019 were obtained from patients presenting to six out-patients health-facilities in a rural area of Kilifi County on the Kenyan Coast. To quantify spatial clustering, homestead level geocoded addresses were used as well as aggregated homesteads level data at enumeration zone. Data were sub-divided into quarterly intervals. Kulldorff's spatial scan statistics using Bernoulli probability model was used to detect hotspots of fever positive RDTs across all ages, where cases were febrile individuals with a positive test and controls were individuals with a negative test.ResultsAcross 12 months of surveillance, there were nine significant clusters that were identified using the spatial scan statistics among RDT positive fevers. These clusters included 52% of all fever positive RDT cases detected in 29% of the geocoded homesteads in the study area. When the resolution of the data was aggregated at enumeration zone (village) level the hotspots identified were located in the same areas. Only two of the nine hotspots were temporally stable accounting for 2.7% of the homesteads and included 10.8% of all fever positive RDT cases detected.ConclusionTaking together the temporal instability of spatial hotspots and the relatively modest fraction of the malaria cases that they account for; it would seem inadvisable to re-design the sub-county control strategies around targeting hotspots.

Dataset Information

Testing a multi-malaria-model ensemble against 30 years of data in the Kenyan highlands.

Publications

Testing a multi-malaria-model ensemble against 30 years of data in the Kenyan highlands.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets