Assessing seasonal variations and age patterns in mortality during the first year of life in Tanzania.

Lack of birth and death registries in most of developing countries, particularly those in sub-Saharan Africa led to the establishment of Demographic Surveillance Systems (DSS) sites which monitor large population cohorts within defined geographical areas. DSS collects longitudinal data on migration, births, deaths and their causes via verbal autopsies. DSS data provide an opportunity to monitor many health indicators including mortality trends. Mortality rates in Sub-Sahara Africa show seasonal patterns due to high infant and child malaria-related mortality which is influenced by seasonal features present in environmental and climatic factors. However, it is unclear whether seasonal patterns differ by age in the first few months of life. This study provides an overview of approaches to assess, capture and detect seasonality peaks and patterns in mortality using the infant mortality data from the Rufiji DSS, Tanzania. Seasonality was best captured using Bayesian negative binomial models with time and cycle dependent seasonal parameters and autoregressive temporal error terms. Seasonal patterns are similar among different age groups during infancy and timing of their mortality peaks do not differ. Seasonality in mortality rates with two peaks per year is pronounced which corresponds to rainy seasons. Understanding of these trends is important for public health preparedness.

Modelling the geographical distribution of co-infection risk from single-disease surveys.

BACKGROUND: The need to deliver interventions targeting multiple diseases in a cost-effective manner calls for integrated disease control efforts. Consequently, maps are required that show where the risk of co-infection is particularly high. Co-infection risk is preferably estimated via Bayesian geostatistical multinomial modelling, using data from surveys screening for multiple infections simultaneously. However, only few surveys have collected this type of data. METHODS: Bayesian geostatistical shared component models (allowing for covariates, disease-specific and shared spatial and non-spatial random effects) are proposed to model the geographical distribution and burden of co-infection risk from single-disease surveys. The ability of the models to capture co-infection risk is assessed on simulated data sets based on multinomial distributions assuming light- and heavy-dependent diseases, and a real data set of Schistosoma mansoni-hookworm co-infection in the region of Man, Côte d'Ivoire. The data were restructured as if obtained from single-disease surveys. The estimated results of co-infection risk, together with independent and multinomial model results, were compared via different validation techniques. RESULTS: The results showed that shared component models result in more accurate estimates of co-infection risk than models assuming independence in settings of heavy-dependent diseases. The shared spatial random effects are similar to the spatial co-infection random effects of the multinomial model for heavy-dependent data. CONCLUSIONS: In the absence of true co-infection data geostatistical shared component models are able to estimate the spatial patterns and burden of co-infection risk from single-disease survey data, especially in settings of heavy-dependent diseases.

Spatial dispersion and characterisation of mosquito breeding habitats in urban vegetable-production areas of Abidjan, Côte d'Ivoire.

Although urban agriculture (UA) in the developing world may enhance nutrition and local economies, it may also lead to higher densities of mosquito breeding sites and, consequently, to increased transmission of malarial parasites. If targeted interventions against malaria vectors are to be successful in urban areas, the habitats that support Anopheles breeding need to be identified and detected. Mosquito breeding sites have recently been characterised, and the factors associated with productive Anopheles habitats identified, in market gardens of Abidjan, Côte d'Ivoire. Two surveys were conducted in seven vegetable-production areas, one towards the end of the rainy season and one during the dry season. A standardized methodology was used for habitat characterisation and the detection of Anopheles larvae and mosquito pupae. Overall, 454 and 559 potential mosquito-breeding sites were recorded in the rainy-season and dry-season surveys, respectively. In the rainy season, Anopheles larvae and mosquito pupae were found in 29.7% and 5.5% of the potential breeding sites, respectively, whereas the corresponding percentages in the dry season were 24.3% and 8.6%. The potential breeding sites in an agricultural zone on the periphery of Abidjan were those least likely to be positive for Anopheles larvae and mosquito pupae whereas 'agricultural trenches' between seedbeds were the sites most likely to be positive. In a spatially-explicit Bayesian multivariate logistic-regression model, although one out of every five such wells was also found to harbour Anopheles larvae, irrigation wells were found to be the least productive habitats, of those sampled, for pupae. In the study area, simple and cost-effective strategies of larval control should be targeted at agricultural trenches, ideally with the active involvement of local stakeholders (i.e. urban farmers and urban agricultural extension services).

Microscopic diagnosis of sodium acetate-acetic acid-formalin-fixed stool samples for helminths and intestinal protozoa: a comparison among European reference laboratories.

The present study aimed to compare the diagnostic performance of different European reference laboratories in diagnosing helminths and intestinal protozoa, using an ether-concentration method applied to sodium acetate-acetic acid-formalin (SAF)-preserved faecal samples. In total, 102 stool specimens were analysed during a cross-sectional parasitological survey in urban farming communities in Côte d'Ivoire. Five SAF-preserved faecal samples were prepared from each specimen and forwarded to the participating reference laboratories, processed and examined under a microscope adhering to a standard operating procedure (SOP). Schistosoma mansoni (cumulative prevalence: 51.0%) and hookworm (cumulative prevalence: 39.2%) were the predominant helminths. There was excellent agreement (kappa > 0.8; p < 0.001) among the reference laboratories for the diagnosis of S. mansoni, hookworm, Trichuris trichiura and Ascaris lumbricoides. Moderate agreement (kappa = 0.54) was found for Hymenolepis nana, and lesser agreement was observed for other, less prevalent helminths. The predominant intestinal protozoa were Entamoeba coli (median prevalence: 67.6%), Blastocystis hominis (median prevalence: 55.9%) and Entamoeba histolytica/Entamoeba dispar (median prevalence: 47.1%). Substantial agreement among reference laboratories was found for E. coli (kappa = 0.69), but only fair or moderate agreement was found for other Entamoeba species, Giardia intestinalis and Chilomastix mesnili. There was only poor agreement for B. hominis, Isospora belli and Trichomonas intestinalis. In conclusion, although common helminths were reliably diagnosed by European reference laboratories, there was only moderate agreement between centres for pathogenic intestinal protozoa. Continued external quality assessment and the establishment of a formal network of reference laboratories is necessary to further enhance both accuracy and uniformity in parasite diagnosis.

Remote sensing, geographical information system and spatial analysis for schistosomiasis epidemiology and ecology in Africa.

Beginning in 1970, the potential of remote sensing (RS) techniques, coupled with geographical information systems (GIS), to improve our understanding of the epidemiology and control of schistosomiasis in Africa, has steadily grown. In our current review, working definitions of RS, GIS and spatial analysis are given, and applications made to date with RS and GIS for the epidemiology and ecology of schistosomiasis in Africa are summarised. Progress has been made in mapping the prevalence of infection in humans and the distribution of intermediate host snails. More recently, Bayesian geostatistical modelling approaches have been utilized for predicting the prevalence and intensity of infection at different scales. However, a number of challenges remain; hence new research is needed to overcome these limitations. First, greater spatial and temporal resolution seems important to improve risk mapping and understanding of transmission dynamics at the local scale. Second, more realistic risk profiling can be achieved by taking into account information on people's socio-economic status; furthermore, future efforts should incorporate data on domestic access to clean water and adequate sanitation, as well as behavioural and educational issues. Third, high-quality data on intermediate host snail distribution should facilitate validation of infection risk maps and modelling transmission dynamics. Finally, more emphasis should be placed on risk mapping and prediction of multiple species parasitic infections in an effort to integrate disease risk mapping and to enhance the cost-effectiveness of their control.

Bayesian geostatistical modelling for mapping schistosomiasis transmission.

Progress has been made in mapping and predicting the risk of schistosomiasis using Bayesian geostatistical inference. Applications primarily focused on risk profiling of prevalence rather than infection intensity, although the latter is particularly important for morbidity control. In this review, the underlying assumptions used in a study mapping Schistosoma mansoni infection intensity in East Africa are examined. We argue that the assumption of stationarity needs to be relaxed, and that the negative binomial assumption might result in misleading inference because of a high number of excess zeros (individuals without an infection). We developed a Bayesian geostatistical zero-inflated (ZI) regression model that assumes a non-stationary spatial process. Our model is validated with a high-quality georeferenced database from western Côte d'Ivoire, consisting of demographic, environmental, parasitological and socio-economic data. Nearly 40% of the 3818 participating schoolchildren were infected with S. mansoni, and the mean egg count among infected children was 162 eggs per gram of stool (EPG), ranging between 24 and 6768 EPG. Compared to a negative binomial and ZI Poisson and negative binomial models, the Bayesian non-stationary ZI negative binomial model showed a better fit to the data. We conclude that geostatistical ZI models produce more accurate maps of helminth infection intensity than the spatial negative binomial ones.

Effectiveness of dog rabies vaccination programmes: comparison of owner-charged and free vaccination campaigns.

We investigated the percentage of dogs that could be vaccinated against rabies by conducting a pilot campaign in N'Djaména, Chad. Owners were charged US$4.13 per dog vaccinated, and 24% of all dogs in the three city districts covered by the campaign were vaccinated. Total campaign costs were US$7623, resulting in an average of US$19.40 per vaccinated dog. This is five times more expensive than the cost per animal vaccinated during a previous free vaccination campaign for dog-owners, conducted in the same districts. The free campaign, which vaccinated 2605 more dogs than this campaign, cost an additional US$1.45 per extra dog vaccinated. Campaigns in which owners are charged for vaccinations result in lower vaccination rates than in free campaigns. Public health officials can use these results when evaluating the costs and benefits of subsidizing dog rabies vaccination programmes.

The spatial distribution of Anopheles gambiae sensu stricto and An. arabiensis (Diptera: Culicidae) in Mali.

Variations in the biology and ecology and the high level of genetic polymorphism of malaria vectors in Africa highlight the value of mapping their spatial distribution to enhance successful implementation of integrated vector management. The objective of this study was to collate data on the relative frequencies of Anopheles gambiae s.s. and An. arabiensis mosquitoes in Mali, to assess their association with climate and environmental covariates, and to produce maps of their spatial distribution. Bayesian geostatistical logistic regression models were fitted to identify environmental determinants of the relative frequencies of An. gambiae s.s. and An. arabiensis species and to produce smooth maps of their geographical distribution. The frequency of An. arabiensis was positively associated with the normalized difference vegetation index, the soil water storage index, the maximum temperature and the distance to water bodies. It was negatively associated with the minimum temperature and rainfall. The predicted map suggests that, in West Africa, An. arabiensis is concentrated in the drier savannah areas, while An. gambiae s.s. prefers the southern savannah and land along the rivers, particularly the inner delta of Niger. Because the insecticide knockdown resistance (kdr) gene is reported only in An. gambiae s.s. in Mali, the maps provide valuable information for vector control. They may also be useful for planning future implementation of malaria control by genetically manipulated mosquitoes.

Prevalence of geohelminths in savana and forest areas of Côte d'Ivoire.

BACKGROUND: Côte d'Ivoire has large regional variation in intestinal helminth prevalence. STUDY DESIGN: In a large cross-sectional study conducted from 1997 to 1999, stool samples from 6952 children aged 4-15 years were examined for helminth eggs by the Kato/ Katz technique from 24 villages in the savanna (North) and from 21 villages in the forest (West) in Côte d'Ivoire. RESULTS AND CONCLUSION: Ascariasis lumbricoides (0.7%) was the only species present in the savana area. In the forest area, ascariasis occurred most frequently (18.9%), followed by trichuriasis (2%), Strongyloidiasis (0.1%) and oxyuriasis (0.03%). Ascariasis prevalence was not significantly associated with age within each ecological zone, but was significantly higher in boys in the forest zone. This study confirms that in the more arid savanna, the conditions are less suitable for helminth transmission than in the forest zone.

The reliability of diagnostic techniques in the diagnosis and management of malaria in the absence of a gold standard.

The accuracy of techniques for the diagnosis of malaria are usually compared with optical microscopy, which is considered to be a gold standard. However, microscopy is prone to error and therefore makes it difficult to assess the reliability of other diagnostic techniques. We did a systematic review to assess the specificity and sensitivity of diagnostic techniques in different settings, using a statistical method that avoided defining a gold standard. Performance varied depending on species of the malaria parasite, level of parasitaemia, and immunity. Overall, histidine-rich protein 2 (HRP2)-based dipsticks showed a high sensitivity (92.7%) and specificity (99.2%) for Plasmodium falciparum in endemic areas. The acridine orange test was more sensitive (97.1%) in detecting P falciparum in epidemiological studies, with a specificity of 97.9%. In the absence of a gold standard, HRP2 dipsticks and acridine orange could provide an alternative for detecting falciparum infections in endemic areas and epidemiological studies, respectively. Microscopy still remains more reliable in detecting non-falciparum infections.

Malaria mapping using transmission models: application to survey data from Mali.

Geographic mapping of the distribution of malaria is complicated by the limitations of the available data. The most widely available data are from prevalence surveys, but these surveys are generally carried out at arbitrary locations and include nonstandardized and overlapping age groups. To achieve comparability between different surveys, the authors propose the use of transmission models, particularly the Garki model, to convert heterogeneous age prevalence data to a common scale of estimated entomological inoculation rates, vectorial capacity, or force of infection. They apply this approach to the analysis of survey data from Mali, collected in 1965-1998, extracted from the Mapping Malaria Risk in Africa database. They use Bayesian geostatistical models to produce smooth maps of estimates of the entomological inoculation rates obtained from the Garki model, allowing for the effect of environmental covariates. Again using the Garki model, they convert kriged entomological inoculation rates values to age-specific malaria prevalence. The approach makes more efficient use of the available data than do previous malaria mapping methods, and it produces highly plausible maps of malaria distribution.

Bayesian modelling of geostatistical malaria risk data.

Bayesian geostatistical models applied to malaria risk data quantify the environment-disease relations, identify significant environmental predictors of malaria transmission and provide model-based predictions of malaria risk together with their precision. These models are often based on the stationarity assumption which implies that spatial correlation is a function of distance between locations and independent of location. We relax this assumption and analyse malaria survey data in Mali using a Bayesian non-stationary model. Model fit and predictions are based on Markov chain Monte Carlo simulation methods. Model validation compares the predictive ability of the non-stationary model with the stationary analogue. Results indicate that the stationarity assumption is important because it influences the significance of environmental factors and the corresponding malaria risk maps.

Towards empirical description of malaria seasonality in southern Africa: the example of Zimbabwe.

BACKGROUND: Quantitative description and mapping of malaria seasonality is important for timely spatial targeting of interventions and for modelling malaria risk. There is a need for seasonality models that predict quantitative variation in transmission between months. METHODS: We use Zimbabwe as an example for developing an empirical map of malaria seasonality. We describe the relationship between seasonality in malaria and environmental covariates for the period 1988--1999, by fitting a spatial-temporal regression model within a Bayesian framework to provide smoothed maps of the seasonal trend. We adapt a seasonality concentration index used previously for rainfall to quantify malaria case load during the peak transmission season based on monthly values. RESULTS: Combinations of mean monthly temperature (range 28--32 degrees C), maximum temperature (24--28 degrees C) and high rainfall provide suitable conditions for seasonal transmission. High monthly maximum and mean monthly minimum temperatures limit months of high transmission. The intensity of seasonal transmission was highest in the north western part of the country from February to May with the peak in April and lowest in the whole country from July to December. The north western lowlands had the highest concentration of malaria cases (>25%) followed by some districts in the north central and eastern part with a moderate concentration of cases (20-25%). The central highlands and south eastern part of the country had the lowest concentration of malaria cases (<20%). This pattern was closely associated to the geographic variation in the seasonality of climatic covariates particularly rainfall and temperature. Conclusions Our modelling approach quantifies the geographical variation in seasonal trend and the concentration of cases during the peak transmission season and therefore has potential application in malaria control. The use of a covariate adjusted empirical model may prove useful for predicting the seasonal risk pattern across southern Africa.

A potential impact of climate change and water resource development on the transmission of Schistosoma japonicum in China.

There is growing consensus among climate modellers that the unusual global warming observed in the last decades of the 20th century is primarily forced by human activities, namely greenhouse gas increases in the atmosphere. Global warming will trigger alterations in physical and biological systems, including shifts in the spatio-temporal distribution of disease vectors, but the nature and extent of these changes are poorly understood. The purpose of the present study was to assess the potential impact of climate change and water resource development on the distribution of Oncomelania hupensis, the intermediate host snail of Schistosoma japonicum. We employed two 30-year composite datasets comprising average monthly temperatures collected at 623 observing stations throughout China, spanning the periods 1961-1990 and 1971-2000. Temperature changes were assessed spatially between the 1960s and 1990s for January, as this is the critical month for survival of O. hupensis. Our database shows that January temperatures increased at 590 stations (94.7%), and that China's average January temperature in the 1990s was 0.96 degrees C higher than 30 years earlier. The historical 0-1 degrees C January isotherm, which was considered the approximate northern limit of S. japonicum transmission, has shifted from 33 degrees 15' N to 33 degrees 41' N, expanding the potential transmission area by 41,335 km2. This translates to an additional 20.7 million people at risk of schistosomiasis. Two lakes are located in this new transmission area that form part of the proposed South-North water transfer project. Climate change, coupled with water resource developments in China, may pose additional challenges for the control of schistosomiasis.

Spatial risk prediction and mapping of Schistosoma mansoni infections among schoolchildren living in western Côte d'Ivoire.

The objectives of this study were (1) to examine risk factors for Schistosoma mansoni infection among schoolchildren living in western Côte d'Ivoire, and (2) to carry forward spatial risk prediction and mapping at non-sampled locations. First, demographic and socio-economic data were obtained from 3818 children, aged 6-16 years, from 55 schools. Second, a single stool sample was examined from each child by the Kato-Katz technique to assess infection status of S. mansoni and its intensity. Third, remotely sensed environmental data were derived from satellite imagery and digitized ground maps. With these databases a comprehensive geographical information system was established. Bayesian variogram models were applied for spatial risk modelling and prediction. The infection prevalence of S. mansoni was 38.9%, ranging from 0% to 89.3% among schools. Results showed that age, sex, the richest wealth quintile, elevation and rainfall explained the geographical variation of the school prevalences of S. mansoni infection. The goodness of fit of different spatial models revealed that age, sex and socio-economic status had a stronger influence on infection prevalence than environmental covariates. The generated risk map can be used by decision-makers for the design and implementation of schistosomiasis control in this setting. If successfully validated elsewhere, this approach can guide control programmes quite generally.

A model of animal-human brucellosis transmission in Mongolia.

We developed a dynamic model of livestock-to-human brucellosis transmission in Mongolia. The compartmental model considers transmission within sheep and cattle populations and the transmission to humans as additive components. The model was fitted to demographic and seroprevalence data (Rose Bengal test) from livestock and annually reported new human brucellosis cases in Mongolia for 1991-1999 prior to the onset of a mass livestock-vaccination campaign (S19 Brucella abortus for cattle and Rev 1 Brucella melitensis for sheep and goat). The vaccination effect was fitted to livestock- and human-brucellosis data from the first 3 years of the vaccination campaign (2000-2002). Parameters were optimized on the basis of the goodness-of-fit (assessed by the deviance). The simultaneously fitted sheep-human and cattle-human contact rates show that 90% of human brucellosis was small-ruminant derived. Average effective reproductive ratios for the year 1999 were 1.2 for sheep and 1.7 for cattle.

Malaria transmission dynamics in central Côte d'Ivoire: the influence of changing patterns of irrigated rice agriculture.

The dynamics of malaria transmission was studied comparatively in the villages of Zatta and Tiemelekro, central Cote d'Ivoire, from February 2002 to August 2003. Prominent agroecosystems in these villages are irrigated rice growing and vegetable farming, respectively. Mosquitoes (Diptera: Culicidae) were collected on human bait at night and by pyrethrum knock-down spray sheet collections at four randomly selected sentinel sites in each village. In 2002, for a total of 96 man-nights per village, 7716 mosquitoes were collected in Zatta and 3308 in Tiemelekro. In 2003, with half the sampling effort, 859 and 2056 mosquitoes were collected in Zatta and Tiemelekro, respectively. Anopheles gambiae Giles s.l. was the predominant mosquito and the key malaria vector throughout, followed by An. funestus Giles. Anthropophily among adult female Anopheles exceeded 95% in both villages. Comparison between years revealed that the biting rate of An. gambiae s.l. in Zatta decreased several-fold from 49.3 bites per person per night (b/p/n) in 2002 to 7.9 b/p/n in 2003 (likelihood ratio test (LRT) = 1072.66; P < 0.001). Although the biting rate remained fairly constant in Tiemelekro, the difference between years was significant (16.1 vs. 18.2 b/p/n; LRT = 148.06; P < 0.001). These observations were paralleled by a marked decrease in the infective rate of An. gambiae s.l. in Zatta (4.6-1.2%), and an increase in Tiemelekro (3.1-7.6%). Meanwhile, the entomological inoculation rate of An. gambiae s.l. decreased 21-fold in Zatta, from 789 to 38 infective bites per person per year (ib/p/y), whereas it remained high in Tiemelekro (233 vs. 342 ib/p/y). The interruption of irrigated rice growing in Zatta in 2003, consequential to a farmers' conflict over land, might be the underlying cause for the significant reduction in malaria transmission, whereas more stable conditions occurred in Tiemelekro.

Spatial effects of the social marketing of insecticide-treated nets on malaria morbidity.

Randomized controlled trials have shown that insecticide-treated nets (ITNs) have an impact on both malaria morbidity and mortality. Uniformly high coverage of ITNs characterized these trials and this resulted in some protection of nearby non-users of ITNs. We have now assessed the coverage, distribution pattern and resultant spatial effects in one village in Tanzania where ITNs were distributed in a social marketing programme. The prevalence of parasitaemia, mild anaemia (Hb <11 g/dl) and moderate/severe anaemia (Hb <8 g/dl) in children under five was assessed cross-sectionally. Data on ownership of ITNs were collected and inhabitants' houses were mapped. One year after the start of the social marketing programme, 52% of the children were using a net which had been treated at least once. The ITNs were rather homogeneously distributed throughout the village at an average density of about 118 ITNs per thousand population. There was no evidence of a pattern in the distribution of parasitaemia and anaemia cases, but children living in areas of moderately high ITN coverage were about half as likely to have moderate/severe anaemia (OR 0.5, 95% CI: 0.2, 0.9) and had lower prevalence of splenomegaly, irrespective of their net use. No protective effects of coverage were found for prevalence of mild anaemia nor for parasitaemia. The use of untreated nets had neither coverage nor short distance effects. More efforts should be made to ensure high coverage in ITNs programmes to achieve maximum benefit.

Rice irrigation and schistosomiasis in savannah and forest areas of Côte d'Ivoire.

Prevalence and intensity of infection of Schistosoma haematobium and Schistosoma mansoni were studied in relation to irrigated rice cultivation in Côte d'Ivoire. Urine and stool samples were collected from 4 to 15-year-old children in 24 villages in the savannah zone and 21 villages in the forest zone. Villages were classified according to surrounding inland valleys into three agro-ecosystems: (R2) full or partial water control allowing two rice cycles per year; (R1) no or partial water control allowing one harvest per year and (R0) absence of rice growing. In the savannah zone, S. haematobium prevalence was 4.8%, 2.3% and 0.7% and S. mansoni prevalence was 16.1%, 11.9% and 2.1% in R2, R1 and R0, respectively. In the forest zone, S. haematobium prevalence was 0.9%, 4.4% and 1.7% and S. mansoni prevalence was 61.3%, 46.6% and 17.5% in R2, in R1 and R0, respectively. Prevalences of S. mansoni adjusted for village effects were significantly different between agro-ecosystems in both zones. Significance of differences between agro-ecosystems of S. haematobium infection were strongly influenced by outlying villages. In savannah rice growing villages, negative binomial regression on infection intensity of each species showed significant positive relations to the surface of rice cultivated inland valleys, whereas uncultivated inland valleys showed no significant relation. However, in forest rice growing villages, S. mansoni infection intensity showed significant positive relations to the surface of uncultivated inland valleys, whereas surface water on rice cultivated land showed significant negative relations with infection intensity of each schistosomiasis species.

Bayesian age-stage modelling of Plasmodium falciparum sequestered parasite loads in severe malaria patients.

A discrete-time age-stage model is proposed for estimating the number of sequestered parasites in severe malaria patients. A Bayesian Markov chain Monte Carlo (MCMC) approach is used to model the dynamics of Plasmodium falciparum parasitaemia in 107 paediatric patients in a randomized controlled trial of quinine and artemether in Kenya, in whom 4-hourly peripheral parasitaemia determinations were made. The MCMC approach allows the model to be fitted simultaneously to the entire dataset, providing point and interval estimates for both population and individual patient parameters. Analysis of a simulated dataset indicated that the models gave good estimates of the distribution of parasites between different stages on enrolment, for patients with a wide range of initial states. The analysis of the Kenyan patients suggested that there is considerable variation between patients within the same centre, in both the proportion of sequestered parasites and the intrinsic rate of increase of the parasite population in the absence of treatment. The resulting models should prove a useful tool for cross-validating biochemical approaches for estimating the sequestered load.