Amy Pinsent

Quantitative analyses and modelling to support achievement of the 2020 goals for nine neglected tropical diseases

Quantitative analysis and mathematical models are useful tools in informing strategies to control or eliminate disease. Currently, there is an urgent need to develop these tools to inform policy to achieve the 2020 goals for neglected tropical diseases (NTDs). In this paper we give an overview of a collection of novel model-based analyses which aim to address key questions on the dynamics of transmission and control of nine NTDs: Chagas disease, visceral leishmaniasis, human African trypanosomiasis, leprosy, soil-transmitted helminths, schistosomiasis, lymphatic filariasis, onchocerciasis and trachoma. Several common themes resonate throughout these analyses, including: the importance of epidemiological setting on the success of interventions; targeting groups who are at highest risk of infection or re-infection; and reaching populations who are not accessing interventions and may act as a reservoir for infection,. The results also highlight the challenge of maintaining elimination ‘as a public health problem’ when true elimination is not reached. The models elucidate the factors that may be contributing most to persistence of disease and discuss the requirements for eventually achieving true elimination, if that is possible. Overall this collection presents new analyses to inform current control initiatives. These papers form a base from which further development of the models and more rigorous validation against a variety of datasets can help to give more detailed advice. At the moment, the models’ predictions are being considered as the world prepares for a final push towards control or elimination of neglected tropical diseases by 2020.

Distinguishing Between Reservoir Exposure and Human-to-Human Transmission for Emerging Pathogens Using Case Onset Data

Pathogens such as MERS-CoV, influenza A/H5N1 and influenza A/H7N9 are currently generating sporadic clusters of spillover human cases from animal reservoirs. The lack of a clear human epidemic suggests that the basic reproductive number R0 is below or very close to one for all three infections. However, robust cluster-based estimates for low R0 values are still desirable so as to help prioritise scarce resources between different emerging infections and to detect significant changes between clusters and over time. We developed an inferential transmission model capable of distinguishing the signal of human-to-human transmission from the background noise of direct spillover transmission (e.g. from markets or farms). By simulation, we showed that our approach could obtain unbiased estimates of R0, even when the temporal trend in spillover exposure was not fully known, so long as the serial interval of the infection and the timing of a sudden drop in spillover exposure were known (e.g. day of market closure). Applying our method to data from the three largest outbreaks of influenza A/H7N9 outbreak in China in 2013, we found evidence that human-to-human transmission accounted for 13% (95% credible interval 1%–32%) of cases overall. We estimated R0 for the three clusters to be: 0.19 in Shanghai (0.01-0.49), 0.29 in Jiangsu (0.03-0.73); and 0.03 in Zhejiang (0.00-0.22). If a reliable temporal trend for the spillover hazard could be estimated, for example by implementing widespread routine sampling in sentinel markets, it should be possible to estimate sub-critical values of R0 even more accurately. Should a similar strain emerge with R0>1, these methods could give a real-time indication that sustained transmission is occurring with well-characterised uncertainty.

A systematic review of reported reassortant viral lineages of influenza A

BackgroundMost previous evolutionary studies of influenza A have focussed on genetic drift, or reassortment of specific gene segments, hosts or subtypes. We conducted a systematic literature review to identify reported claimed reassortant influenza A lineages with genomic data available in GenBank, to obtain 646 unique first-report isolates out of a possible 20,781 open-access genomes.ResultsAfter adjusting for correlations, only: swine as host, China, Europe, Japan and years between 1997 and 2002; remained as significant risk factors for the reporting of reassortant viral lineages. For swine H1, more reassortants were observed in the North American H1 clade compared with the Eurasian avian-like H1N1 clade. Conversely, for avian H5 isolates, a higher number of reported reassortants were observed in the European H5N2/H3N2 clade compared with the H5N2 North American clade.ConclusionsDespite unavoidable biases (publication, database choice and upload propensity) these results synthesize a large majority of the current literature on novel reported influenza A reassortants and are a potentially useful prerequisite to inform further algorithmic studies.

Possible changes in the transmissibility of trachoma following MDA and transmission reduction: implications for the GET2020 goals.

BackgroundThe role of mass drug administration (MDA) and the implementation of transmission reduction measures are essential to successfully control and eliminate a wide range of NTDs, including the ocular disease trachoma. Immunity to trachoma infection acts by reducing the duration of an individual’s infectious period and by reducing their infectivity with each successive infection. MethodsIn this study, we use a model of trachoma infection, which includes population immunity, to explore the impact of treatment and transmission reduction measures on trachoma prevalence. Specifically, we investigate the possibility of increasing transmissibility of trachoma arising as MDA and transmission reduction measures are scaled up in endemic settings.ResultsWe demonstrate this increase in transmissibility by calculating the effective reproduction number during several simulated control programmes and show that it is related to a decrease in the level of immunity in the population.ConclusionsThis effect should be studied in the field by measuring the rate of return of infection and disease in at least two separate age groups. If the decline of population immunity is operating, it should be accounted for when planning for the GET2020 goal of eliminating blinding trachoma by 2020.

Risk factors for UK Plasmodium falciparum cases.

BackgroundAn increasing proportion of malaria cases diagnosed in UK residents with a history of travel to malaria endemic areas are due to Plasmodium falciparum.MethodsIn order to identify travellers at most risk of acquiring malaria a proportional hazards model was used to estimate the risk of acquiring malaria stratified by purpose of travel and age whilst adjusting for entomological inoculation rate (EIR) and duration of stay in endemic countries.ResultsTravellers visiting friends and relatives and business travellers were found to have significantly higher hazard of acquiring malaria (adjusted hazard ratio (HR) relative to that of holiday makers 7.4, 95% CI 6.4–8.5, p < 0. 0001 and HR 3.4, 95% CI 2.9-3.8, p < 0. 0001, respectively). All age-groups were at lower risk than children aged 0–15 years.ConclusionsThese estimates of the increased risk for business travellers and those visiting friends and relatives should be used to inform programmes to improve awareness of the risks of malaria when travelling.

Probabilistic forecasts of trachoma transmission at the district level: A statistical model comparison

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.

Surveillance of low pathogenic novel H7N9 avian influenza in commercial poultry barns: detection of outbreaks and estimation of virus introduction time

BackgroundBoth high and low pathogenic subtype A avian influenza remain ongoing threats to the commercial poultry industry globally. The emergence of a novel low pathogenic H7N9 lineage in China presents itself as a new concern to both human and animal health and may necessitate additional surveillance in commercial poultry operations in affected regions.MethodsSampling data was simulated using a mechanistic model of H7N9 influenza transmission within commercial poultry barns together with a stochastic observation process. Parameters were estimated using maximum likelihood. We assessed the probability of detecting an outbreak at time of slaughter using both real-time polymerase chain reaction (rt-PCR) and a hemagglutinin inhibition assay (HI assay) before considering more intense sampling prior to slaughter. The day of virus introduction and R0 were estimated jointly from weekly flock sampling data. For scenarios where R0 was known, we estimated the day of virus introduction into a barn under different sampling frequencies.ResultsIf birds were tested at time of slaughter, there was a higher probability of detecting evidence of an outbreak using an HI assay compared to rt-PCR, except when the virus was introduced <2 weeks before time of slaughter. Prior to the initial detection of infection Nsample = 50 (1%) of birds were sampled on a weekly basis once, but after infection was detected, Nsample = 2000 birds (40%) were sampled to estimate both parameters. We accurately estimated the day of virus introduction in isolation with weekly and 2-weekly sampling.ConclusionsA strong sampling effort would be required to infer both the day of virus introduction and R0. Such a sampling effort would not be required to estimate the day of virus introduction alone once R0 was known, and sampling Nsample = 50 of birds in the flock on a weekly or 2 weekly basis would be sufficient.

Models of Trachoma Transmission and Their Policy Implications: From Control to Elimination

Abstract Despite great progress in eliminating trachoma from the majority of worldwide districts, trachoma control seems to have stalled in some endemic districts. Can mathematical models help suggest the way forward? We review specific achievements of models in trachoma control in the past. Models showed that, even with incomplete coverage, mass drug administration could eliminate disease through a spillover effect, somewhat analogous to how incomplete vaccine campaigns can eliminate disease through herd protection. Models also suggest that elimination can always be achieved if enough people are treated often enough with an effective enough drug. Other models supported the idea that targeting ages at highest risk or continued improvements in hygiene and sanitation can contribute meaningfully to trachoma control. Models of intensive targeting of a core group may point the way to final eradication even in areas with substantial transmission and within-community heterogeneity.

The persistence of multiple strains of avian influenza in live bird markets

Multiple subtypes of avian influenza (AI) and novel reassortants are frequently isolated from live bird markets (LBMs). However, our understanding of the drivers of persistence of multiple AI subtypes is limited. We propose a stochastic model of AI transmission within an LBM that incorporates market size, turnover rate and the balance of direct versus environmental transmissibility. We investigate the relationship between these factors and the critical community size (CCS) for the persistence of single and multiple AI strains within an LBM. We fit different models of seeding from farms to two-strain surveillance data collected from Shantou, China. For a single strain and plausible estimates for continuous turnover rates and transmissibility, the CCS was approximately 11 800 birds, only a 4.2% increase in this estimate was needed to ensure persistence of the co-infecting strains (two strains in a single host). Precise values of CCS estimates were sensitive to changes in market turnover rate and duration of the latent period. Assuming a gradual daily sell rate of birds the estimated CCS was higher than when an instantaneous selling rate was assumed. We were able to reproduce prevalence dynamics similar to observations from a single market in China with infection seeded every 5–15 days, and a maximum non-seeding duration of 80 days. Our findings suggest that persistence of co-infections is more likely to be owing to sequential infection of single strains rather than ongoing transmission of both strains concurrently. In any given system for a fixed set of ecological and epidemiological conditions, there is an LBM size below which the risk of sustained co-circulation is low and which may suggest a clear policy opportunity to reduce the frequency of influenza co-infection in poultry.

Optimising sampling regimes and data collection to inform surveillance for trachoma control

It is estimated that 190 million individuals are at risk of blindness from trachoma, and that control by mass drug administration (MDA) is reducing this risk in many populations. Programs are monitored using prevalence of follicular trachoma disease (TF) in children. However, as programs progress to low prevalence there are challenges interpreting this indirect measure of infection. PCR and sero-surveillance are being considered as complementary tools to monitor low-level transmission, but there are questions on how they can be most effectively used. We use a previously-published, mathematical model to explore the dynamic relationship between TF and PCR throughout a control program and a sero-catalytic model to evaluate the utility of two cross-sectional sero-surveys for estimating sero-conversion rates. The simulations show that whilst PCR is more sensitive than TF at detecting infection, the probability of detecting at least one positive individual declines during an MDA program more quickly for PCR than for TF (for the same sample size). Towards the end of a program there is a moderate chance of a random sample showing both low PCR prevalence and higher TF prevalence, which may contribute to the lack of correlation observed in epidemiological studies. We also show that conducting two cross-sectional sero-surveys 10 years apart can provide more precise and accurate estimation of epidemiological parameters than a single survey, supporting previous findings that whilst serology holds great promise, multiple cross-sections from the same community are needed to generate the most valuable information about transmission. These results highlight that the quantitative dynamics of infection and disease should be included alongside the many logistical and practical factors to be considered in designing a monitoring and evaluation strategy at the operational research level, in order to help subsequently inform data collection for individual country programs. Whilst our simulations provide some insight, they also highlight that some level of longitudinal, individual-level data on reinfection and disease may be needed to monitor elimination progress.