Malaria and lymphatic filariasis (LF) continue to cause a considerable public health burden globally and are co-endemic in many regions of sub-Saharan Africa. These infections are transmitted by the ...same mosquito species which raises important questions about optimal vector control strategies in co-endemic regions, as well as the effect of the presence of each infection on endemicity of the other; there is currently little consensus on the latter. The need for comprehensive modelling studies to address such questions is therefore significant, yet very few have been undertaken to date despite the recognised explanatory power of reliable dynamic mathematical models. Here, we develop a malaria-LF co-infection modelling framework that accounts for two key interactions between these infections, namely the increase in vector mortality as LF mosquito prevalence increases and the antagonistic Th1/Th2 immune response that occurs in co-infected hosts. We consider the crucial interplay between these interactions on the resulting endemic prevalence when introducing each infection in regions where the other is already endemic (e.g. due to regional environmental change), and the associated timescale for such changes, as well as effects on the basic reproduction number R₀ of each disease. We also highlight potential perverse effects of vector controls on human infection prevalence in co-endemic regions, noting that understanding such effects is critical in designing optimal integrated control programmes. Hence, as well as highlighting where better data are required to more reliably address such questions, we provide an important framework that will form the basis of future scenario analysis tools used to plan and inform policy decisions on intervention measures in different transmission settings.
To date schistosomiasis control programmes based on chemotherapy have largely aimed at controlling morbidity in treated individuals rather than at suppressing transmission. In this study, a ...mathematical modelling approach was used to estimate reductions in the rate of Schistosoma mansoni reinfection following annual mass drug administration (MDA) with praziquantel in Uganda over four years (2003-2006). In doing this we aim to elucidate the benefits of MDA in reducing community transmission.
Age-structured models were fitted to a longitudinal cohort followed up across successive rounds of annual treatment for four years (Baseline: 2003, TREATMENT: 2004-2006; n = 1,764). Instead of modelling contamination, infection and immunity processes separately, these functions were combined in order to estimate a composite force of infection (FOI), i.e., the rate of parasite acquisition by hosts.
MDA achieved substantial and statistically significant reductions in the FOI following one round of treatment in areas of low baseline infection intensity, and following two rounds in areas with high and medium intensities. In all areas, the FOI remained suppressed following a third round of treatment.
This study represents one of the first attempts to monitor reductions in the FOI within a large-scale MDA schistosomiasis morbidity control programme in sub-Saharan Africa. The results indicate that the Schistosomiasis Control Initiative, as a model for other MDA programmes, is likely exerting a significant ancillary impact on reducing transmission within the community, and may provide health benefits to those who do not receive treatment. The results obtained will have implications for evaluating the cost-effectiveness of schistosomiasis control programmes and the design of monitoring and evaluation approaches in general.
Progress towards controlling and eliminating parasitic worms, including schistosomiasis, onchocerciasis, and lymphatic filariasis, is advancing rapidly as national governments, multinational NGOs, ...and pharmaceutical companies launch collaborative chemotherapeutic control campaigns. Critical questions remain regarding the potential for achieving elimination of these infections, and analytical methods can help to quickly estimate progress towards-and the probability of achieving-elimination over specific timeframes. Here, we propose the effective reproduction number, Reff, as a proxy of elimination potential for sexually reproducing worms that are subject to poor mating success at very low abundance (positive density dependence, or Allee effects). Reff is the number of parasites produced by a single reproductive parasite at a given stage in the transmission cycle, over the parasite's lifetime-it is the generalized form of the more familiar basic reproduction number, R0, which only applies at the beginning of an epidemic-and it can be estimated in a 'model-free' manner by an estimator ('ε'). We introduce ε, demonstrate its estimation using simulated data, and discuss how it may be used in planning and evaluation of ongoing elimination efforts for a range of parasitic diseases.
Using data from travelers to 4 countries in the Middle East, we estimated 3,250 (95% CI 1,300-6,600) severe cases of Middle East respiratory syndrome occurred in this region during September ...2012-January 2016. This number is 2.3-fold higher than the number of laboratory-confirmed cases recorded in these countries.
Australia is the only high-income country in which endemic trachoma persists. In response, the Australian Government has recently invested heavily towards the nationwide control of the disease.
A ...novel simulation model was developed to reflect the trachoma epidemic in Australian Aboriginal communities. The model, which incorporates demographic, migration, mixing, and biological heterogeneities, was used to evaluate recent intervention measures against counterfactual past scenarios, and also to assess the potential impact of a series of hypothesized future intervention measures relative to the current national strategy and intensity. The model simulations indicate that, under the current intervention strategy and intensity, the likelihood of controlling trachoma to less than 5% prevalence among 5-9 year-old children in hyperendemic communities by 2020 is 31% (19%-43%). By shifting intervention priorities such that large increases in the facial cleanliness of children are observed, this likelihood of controlling trachoma in hyperendemic communities is increased to 64% (53%-76%). The most effective intervention strategy incorporated large-scale antibiotic distribution programs whilst attaining ambitious yet feasible screening, treatment, facial cleanliness and housing construction targets. Accordingly, the estimated likelihood of controlling trachoma in these communities is increased to 86% (76%-95%).
Maintaining the current intervention strategy and intensity is unlikely to be sufficient to control trachoma across Australia by 2020. However, by shifting the intervention strategy and increasing intensity, the likelihood of controlling trachoma nationwide can be significantly increased.
Highlights • A dynamic model of norovirus transmission and impacts of vaccination was developed. • This approach captured the impact of vaccination on the disease transmission process. • Model ...predicted greater population benefits of pediatric versus elderly vaccination. • Vaccine immunity and transmission parameters strongly influence vaccine impact.
Trachoma programs rely on guidelines made in large part using expert opinion of what will happen with and without intervention. Large community-randomized trials offer an opportunity to actually ...compare forecasting methods in a masked fashion.
The Program for the Rapid Elimination of Trachoma trials estimated longitudinal prevalence of ocular chlamydial infection from 24 communities treated annually with mass azithromycin. Given antibiotic coverage and biannual assessments from baseline through 30 months, forecasts of the prevalence of infection in each of the 24 communities at 36 months were made by three methods: the sum of 15 experts' opinion, statistical regression of the square-root-transformed prevalence, and a stochastic hidden Markov model of infection transmission (Susceptible-Infectious-Susceptible, or SIS model). All forecasters were masked to the 36-month results and to the other forecasts. Forecasts of the 24 communities were scored by the likelihood of the observed results and compared using Wilcoxon's signed-rank statistic.
Regression and SIS hidden Markov models had significantly better likelihood than community expert opinion (p = 0.004 and p = 0.01, respectively). All forecasts scored better when perturbed to decrease Fisher's information. Each individual expert's forecast was poorer than the sum of experts.
Regression and SIS models performed significantly better than expert opinion, although all forecasts were overly confident. Further model refinements may score better, although would need to be tested and compared in new masked studies. Construction of guidelines that rely on forecasting future prevalence could consider use of mathematical and statistical models.
Clinicaltrials.gov NCT00792922.
Trachoma, the worldwide leading infectious cause of blindness, is due to repeated conjunctival infection with Chlamydia trachomatis. The effects of control interventions on population levels of ...infection and active disease can be promptly measured, but the effects on severe ocular sequelae require long-term monitoring. We present an age-structured mathematical model of trachoma transmission and disease to predict the impact of interventions on the prevalence of blinding trachoma.
The model is based on the concept of multiple reinfections leading to progressive conjunctival scarring, trichiasis, corneal opacity and blindness. It also includes aspects of trachoma natural history, such as an increasing rate of recovery from infection and a decreasing chlamydial load with subsequent infections that depend upon a (presumed) acquired immunity that clears infection with age more rapidly. Parameters were estimated using maximum likelihood by fitting the model to pre-control infection prevalence data from hypo-, meso- and hyperendemic communities from The Gambia and Tanzania. The model reproduces key features of trachoma epidemiology: 1) the age-profile of infection prevalence, which increases to a peak at very young ages and declines at older ages; 2) a shift in this prevalence peak, toward younger ages in higher force of infection environments; 3) a raised overall profile of infection prevalence with higher force of infection; and 4) a rising profile, with age, of the prevalence of the ensuing severe sequelae (trachomatous scarring, trichiasis), as well as estimates of the number of infections that need to occur before these sequelae appear.
We present a framework that is sufficiently comprehensive to examine the outcomes of the A (antibiotic) component of the SAFE strategy on disease. The suitability of the model for representing population-level patterns of infection and disease sequelae is discussed in view of the individual processes leading to these patterns.
Abstract The World Health Organization and its partners are aiming to eliminate trachoma as a public health problem by 2020. In this study, we compare forecasts of TF prevalence in 2011 for 7 ...different statistical and mechanistic models across 9 de-identified trachoma endemic districts, representing 4 unique trachoma endemic countries. We forecast TF prevalence between 1–6 years ahead in time and compare the 7 different models to the observed 2011 data using a log-likelihood score. An SIS model, including a district-specific random effect for the district-specific transmission coefficient, had the highest log-likelihood score across all 9 districts and was therefore the best performing model. While overall the deterministic transmission model was the least well performing model, although it did comparably well to the other models for 8 of 9 districts. We perform a statistically rigorous comparison of the forecasting ability of a range of mathematical and statistical models across multiple endemic districts between 1 and 6 years ahead of the last collected TF prevalence data point in 2011, assessing results against surveillance data. This study is a step towards making statements about likelihood and time to elimination with regard to the WHO GET2020 goals.
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