Search

Additional file 3. Results table as included in the revised strategic plan.

Additional file 1. Strategic planning questions for modelling in mainland Tanzania.

Additional file 2. Historical trends per strata and fitting performance.

Additional file 1. Additional tables, text and figures.

Additional file 1: A simple periodically forced difference equation model for mosquito population dynamics.

Additional file 3: Flowchart from operational to effective larviciding coverage.

Additional file 4: Re-simulated larviciding study in Mbita, western Kenya between 2002 and 2006.

Additional file 1. Details of the main interactive activities between 2016 and 2018

Additional file 1. Bionomics data sources.

BACKGROUND:Understanding pattern of antimalarials use at large scale helps ensuring appropriate use of treatments and preventing the spread of resistant parasites. We estimated the proportion of individuals in community surveys with residual antimalarials in their blood and identified the factors associated with the presence of the most commonly detected drugs, lumefantrine and/or desbutyl-lumefantrine (LF/DLF) or sulfadoxine-pyrimethamine (SP). METHODS:A cross-sectional survey was conducted in 2015 in three regions of Tanzania with different levels of malaria endemicity. Interviews were conducted and blood samples collected through household surveys for further antimalarial measurements using liquid chromatography coupled to tandem mass spectrometry. In addition, diagnosis and treatment availability was investigated through outlet surveys. Multilevel mixed effects logistic regression models were used to estimate odds ratios for having LF/DLF or SP in the blood. RESULTS:Amongst 6391 participants, 12.4% (792/6391) had LF/DLF and 8.0% (510/6391) SP in the blood. Factors associated with higher odds of detecting LF/DLF in the blood included fever in the previous two weeks (OR = 2.6, p

Infection with the Plasmodium falciparum malaria parasite results in an immune response which includes the production of antibodies against the blood-stage of infection. In recent years there has been an increase in the use of serological data to monitor malaria transmission intensity. Traditionally, EIR and parasite prevalence were the preferred tools for measuring malaria transmission intensity. Serology has been shown to be particularly useful in areas of low endemicity where traditional measures (EIR and parasite prevalence) are problematic. Transmission intensity in this case is usually described by the seroconversion rate obtained from fitting a catalytic model to age-stratified serological responses. The aim of my thesis was to better utilise the continuous measurements of antibody responses provided by serology studies to obtain improved estimates of transmission intensity. To do this, I developed a series of biologically motivated models to mimic the acquisition and decay in blood-stage antibody responses. In the first part of the thesis, I developed a discrete model as a direct extension of the catalytic model and fitted this to cross-sectional data from several sites in Cambodia to obtain an estimate of the exposure rate. In the second part of the thesis a series of continuous density models were developed to mimic antibody acquisition and loss for P. falciparum infections. These models were fitted to both the Cambodian data and separately validated by fitting to data from Tanzanian villages at a wider range of transmission intensities. In the final section I applied and extended the model to encompass a wider range of endemic transmission in Somalia, Bioko Island, Gambia and Uganda in order to assess the robustness of the method. My results show that estimates of the exposure rate obtained by fitting the density model are highly correlated with classic malariometric indices and that a key advantage of this approach is the increased precision in the estimates compared to estimates of the seroconversion rate, especially in areas of low transmission. This method could therefore be a useful alternative framework for quantifying transmission intensity which makes more complete use of serological data and shows potentials for detecting heterogeneity in malaria exposure. Open Access

Additional file 1: Figure S1. Administrative boundaries and distribution of urban and rural councils in mainland Tanzania. Figure S2. Locations of sampled schools for SMPS in 2015 & 2017 (N = 711). Figure S3. Location of operational health facilities by ownership in mainland Tanzania (N = 7620). Table S2. The cumulative proportion of health facilities submitting between 3 and 12 monthly facility reports from OPD, ANC and laboratory in 2015 and 2017 (N = Total number of facilities). Figure S4. Malaria risk stratification using health facilities with >50% reporting rates.

Additional file 1: Figure S1. Administrative boundaries and distribution of urban and rural councils in mainland Tanzania. Figure S2. Locations of sampled schools for SMPS in 2015 & 2017 (N = 711). Figure S3. Location of operational health facilities by ownership in mainland Tanzania (N = 7620). Table S2. The cumulative proportion of health facilities submitting between 3 and 12 monthly facility reports from OPD, ANC and laboratory in 2015 and 2017 (N = Total number of facilities). Figure S4. Malaria risk stratification using health facilities with >50% reporting rates.