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The Metropolis-Hastings random walk algorithm remains popular with practitioners due to the wide variety of situations in which it can be successfully applied and the extreme ease with which it can be implemented. Adaptive versions of the algorithm use information from the early iterations of the Markov chain to improve the efficiency of the proposal. The aim of this paper is to reduce the number of iterations needed to adapt the proposal to the target, which is particularly important when the likelihood is time-consuming to evaluate. First, the accelerated shaping algorithm is a generalisation of both the adaptive proposal and adaptive Metropolis algorithms. It is designed to remove misleading information from the start of the chain from the estimate of the covariance matrix of the target. Second, the accelerated scaling algorithm rapidly changes the scale of the proposal to achieve a target acceptance rate. The usefulness of these approaches is illustrated with a range of examples.

Background The gambiense human African trypanosomiasis (gHAT) elimination programme in the Democratic Republic of Congo (DRC) routinely collects case data through passive surveillance and active screening, with several regions reporting no cases for several years, despite being endemic in the early 2000s. Methods We use mathematical models fitted to longitudinal data to estimate the probability that selected administrative regions have already achieved elimination of transmission (EOT) of gHAT. We examine the impact of active screening coverage on the certainty of model estimates for transmission and therefore the role of screening in the measurement of EOT. Results In 3 example health zones of Sud-Ubangi province, we find there is a moderate (>40%) probability that EOT has been achieved by 2018, based on 2000–2016 data. Budjala and Mbaya reported zero cases during 2017–18, and this further increases our respective estimates to 99.9% and 99.6% (model S) and to 87.3% and 92.1% (model W). Bominenge had recent case reporting, however, that if zero cases were found in 2021, it would substantially raise our certainty that EOT has been met there (99.0% for model S and 88.5% for model W); this could be higher with 50% coverage screening that year (99.1% for model S and 94.0% for model W). Conclusions We demonstrate how routine surveillance data coupled with mechanistic modeling can estimate the likelihood that EOT has already been achieved. Such quantitative assessment will become increasingly important for measuring local achievement of EOT as 2030 approaches.

Infectious diseases remain one of the major causes of human mortality and suffering. Mathematical models have been established as an important tool for capturing the features that drive the spread of the disease, predicting the progression of an epidemic and hence guiding the development of strategies to control it. Another important area of epidemiological interest is the development of geostatistical methods for the analysis of data from spatially referenced prevalence surveys. Maps of prevalence are useful, not only for enabling a more precise disease risk stratification, but also for guiding the planning of more reliable spatial control programmes by identifying affected areas. Despite the methodological advances that have been made in each area independently, efforts to link transmission models and geostatistical maps have been limited. Motivated by this fact, we developed a Bayesian approach that combines fine-scale geostatistical maps of disease prevalence with transmission models to provide quantitative, spatially explicit projections of the current and future impact of control programs against a disease. These estimates can then be used at a local level to identify the effectiveness of suggested intervention schemes and allow investigation of alternative strategies. The methodology has been applied to lymphatic filariasis in East Africa to provide estimates of the impact of different intervention strategies against the disease., Comment: 48 pages

Bio-oils are precursors for biofuels but are highly corrosive necessitating further upgrading. Furthermore, bio-oil samples are highly complex and represent a broad range of chemistries. They are complex mixtures not simply because of the large number of poly-oxygenated compounds but because each composition can comprise many isomers with multiple functional groups. The use of hyphenated ultrahigh-resolution mass spectrometry affords the ability to separate isomeric species of complex mixtures. Here, we present for the first time, the use of this powerful analytical technique combined with chemical reactivity to gain greater insights into the reactivity of the individual isomeric species of bio-oils. A pyrolysis bio-oils and its esterified bio-oil were analyzed using gas chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry, and in-house software (KairosMS) was used for fast comparison of the hyphenated data sets. The data revealed a total of 10,368 isomers in the pyrolysis bio-oil and an increase to 18,827 isomers after esterification conditions. Furthermore, the comparison of the isomeric distribution before and after esterification provide new light on the reactivities within these complex mixtures; these reactivities would be expected to correspond with carboxylic acid, aldehyde, and ketone functional groups. Using this approach, it was possible to reveal the increased chemical complexity of bio-oils after upgrading and target detection of valuable compounds within the bio-oils. The combination of chemical reactions alongside with in-depth molecular characterization opens a new window for the understanding of the chemistry and reactivity of complex mixtures.\ud \ud

Significance Methods for analyzing individual-level geo-located disease data have existed for some time, but have rarely been used to analyze endemic human diseases. Here we apply such methods to nearly a decade’s worth of uniquely detailed epidemiological data on incidence of the deadly vector-borne disease visceral leishmaniasis (VL) and its secondary condition, post–kala-azar dermal leishmaniasis (PKDL), to quantify the spread of infection around cases in space and time by inferring who infected whom, and estimate the relative contribution of different infection states to transmission. Our findings highlight the key role long diagnosis delays and PKDL play in maintaining VL transmission. This detailed characterization of the spatiotemporal transmission of VL will help inform targeting of interventions around VL and PKDL cases., Understanding of spatiotemporal transmission of infectious diseases has improved significantly in recent years. Advances in Bayesian inference methods for individual-level geo-located epidemiological data have enabled reconstruction of transmission trees and quantification of disease spread in space and time, while accounting for uncertainty in missing data. However, these methods have rarely been applied to endemic diseases or ones in which asymptomatic infection plays a role, for which additional estimation methods are required. Here, we develop such methods to analyze longitudinal incidence data on visceral leishmaniasis (VL) and its sequela, post–kala-azar dermal leishmaniasis (PKDL), in a highly endemic community in Bangladesh. Incorporating recent data on VL and PKDL infectiousness, we show that while VL cases drive transmission when incidence is high, the contribution of PKDL increases significantly as VL incidence declines (reaching 55% in this setting). Transmission is highly focal: 85% of mean distances from inferred infectors to their secondary VL cases were

Gambiense human African trypanosomiasis (gHAT) has been targeted for elimination of transmission (EoT) to humans by 2030. Whilst this ambitious goal is rapidly approaching, there remain fundamental questions about the presence of non-human animal transmission cycles and their potential role in slowing progress towards, or even preventing, EoT. In this study we focus on the country with the most gHAT disease burden, the Democratic Republic of Congo (DRC), and use mathematical modelling to assess whether animals may contribute to transmission in specific regions, and if so, how their presence could impact the likelihood and timing of EoT.By fitting two model variants – one with, and one without animal transmission – to the human case data from 2000–2016 we estimate model parameters for 158 endemic health zones of DRC. We evaluate the statistical support for each model variant in each health zone and infer the contribution of animals to overall transmission and how this could impact predicted time to EoT.We conclude that there are 24/158 health zones where there is moderate or high statistical support for some animal transmission. However, – even in these regions – we estimate that animals would be extremely unlikely to maintain transmission on their own. Animal transmission could hamper progress towards EoT in some settings, with projections under continuing interventions indicating that the number of health zones expected to achieve EoT by 2030 reduces from 68 to 61 if animals are included in the model. With supplementary vector control (at a modest 60% tsetse reduction) added to medical screening and treatment interventions, the predicted number of health zones meeting the goal increases to 147/158 for the model including animals. This is due to the impact of vector reduction on transmission to and from all hosts.Author summaryElimination of African sleeping sickness by 2030 is an ambitious goal, not least because of the unclear role that animals might play in transmission. We use mathematical models, fitted to case data from DRC to assess and quantify the contribution of animals to the human case burden.We found that 24/158 geographic regions included in this study had statistical evidence of animal transmission, although it appears extremely unlikely that animals could maintain transmission on their own. Animals could, however, delay elimination; using our model without animal transmission we predicted that 68 regions are expected to achieve elimination by 2030, whereas this reduces to 61 with animals. If vector control to reduce fly populations (which transmit the disease to and from hosts) are controlled in addition to medical interventions, then 147 regions are predicted to reach elimination by 2030 even with animal transmission.

Gambiense human African trypanosomiasis is a deadly disease that has been declining in incidence since the start of the Century, primarily due to increased screening, diagnosis and treatment of infected people. The main treatment regimen currently in use requires a lumbar puncture as part of the diagnostic process to determine disease stage and hospital admission for drug administration. Fexinidazole is a new oral treatment for stage 1 and non-severe stage 2 human African trypanosomiasis. The World Health Organization has recently incorporated fexinidazole into its treatment guidelines for human African trypanosomiasis. The treatment does not require hospital admission or a lumbar puncture for all patients, which is likely to ease access for patients; however, it does require concomitant food intake, which is likely to reduce adherence. Here, we use a mathematical model calibrated to case and screening data from Mushie territory, in the Democratic Republic of the Congo, to explore the potential negative impact of poor compliance to an oral treatment, and potential gains to be made from increases in the rate at which patients seek treatment. We find that reductions in compliance in treatment of stage 1 cases are projected to result in the largest increase in further transmission of the disease, with failing to cure stage 2 cases also posing a smaller concern. Reductions in compliance may be offset by increases in the rate at which cases are passively detected. Efforts should therefore be made to ensure good adherence for stage 1 patients to treatment with fexinidazole and to improve access to care., Author summary Sleeping sickness is a parasitic disease present in parts of Central and West Africa that is fatal if left untreated. Current case management requires unpleasant procedures such as a lumbar puncture and intravenous drug administration, but has high compliance rates as the treatment is given by hospital staff to patients. In this study, we explore the impact of a new oral treatment on compliance rates for treatment using a mathematical model fitted to data on sleeping sickness cases and screening activities. We also look at the possibility of patients being more likely to seek and access treatment since the new treatment can be used without a lumbar puncture if the patient does not display clinically severe symptoms. We find that reduced compliance, especially from patients suffering from the first less severe stage of the disease, will lead to more sleeping sickness cases and delay elimination, but increases in the number of patients seeking treatment will likely counter effects of reduced compliance.

Mathematical models of vector-borne infections, including malaria, often assume age-independent mortality rates of vectors, despite evidence that many insects senesce. In this study we present survival data on insecticide-resistant Anopheles gambiae s.l. from experiments in Côte d’Ivoire. We fit a constant mortality function and two age-dependent functions (logistic and Gompertz) to the data from mosquitoes exposed (treated) and not exposed (control) to insecticide-treated nets (ITNs), to establish biologically realistic survival functions. This enables us to explore the effects of insecticide exposure on mosquito mortality rates, and the extent to which insecticide resistance might impact the effectiveness of ITNs. We investigate this by calculating the expected number of infectious bites a mosquito will take in its lifetime, and by extension the vectorial capacity. Our results show that the predicted vectorial capacity is substantially lower in mosquitoes exposed to ITNs, despite the mosquitoes in the experiment being highly insecticide-resistant. The more realistic age-dependent functions provide a better fit to the experimental data compared to a constant mortality function and, hence, influence the predicted impact of ITNs on malaria transmission potential. In models with age-independent mortality, there is a great reduction for the vectorial capacity under exposure compared to no exposure. However, the two age-dependent functions predicted an even larger reduction due to exposure, highlighting the impact of incorporating age in the mortality rates. These results further show that multiple exposures to ITNs had a considerable effect on the vectorial capacity. Overall, the study highlights the importance of including age dependency in mathematical models of vector-borne disease transmission and in fully understanding the impact of interventions.Author summaryInterventions against malaria are most commonly targeted on the adult mosquitoes, which transmit the infection from person to person. One of the most important interventions are bed-nets, treated with insecticides. Unfortunately, extensive exposure of mosquitoes to insecticide has led to widespread evolution of insecticide resistance, which might threaten control strategies. Piecing together the overall impact of resistance on the efficacy of insecticide-treated nets is complex, but can be informed by the use of mathematical models. However, there are some assumptions that the models frequently use which are not realistic in terms of the mosquito biology. In this paper, we formulate a model that includes age-dependent mortality rates, an important parameter in vector control since control strategies most commonly aim to reduce the lifespan of the mosquitoes. By using novel data collected using field-derived insecticide-resistant mosquitoes, we explore the effects that the presence of insecticides on nets have on the mortality rates, as well as the difference incorporating age dependency in the model has on the results. We find that including age-dependent mortality greatly alters the anticipated effects of insecticide-treated nets on mosquito transmission potential, and that ignoring this realism potentially overestimates the negative impact of insecticide resistance.

Gambiense human African trypanosomiasis is a deadly disease that has been declining in incidence since the start of the Century, primarily due to increased screening, diagnosis and treatment of infected people. The main treatment regimen currently in use requires a lumbar puncture as part of the diagnostic process to determine disease stage and hospital admission for drug administration. Fexinidazole is a new oral treatment for stage 1 and non-severe stage 2 human African trypanosomiasis. The World Health Organization has recently incorporated fexinidazole into its treatment guidelines for human African trypanosomiasis. The treatment does not require hospital admission or a lumbar puncture for all patients, which is likely to ease access for patients; however, it does require concomitant food intake, which is likely to reduce adherence. Here, we use a mathematical model calibrated to case and screening data from Mushie territory, in the Democratic Republic of the Congo, to explore the potential negative impact of poor compliance to an oral treatment, and potential gains to be made from increases in the rate at which patients seek treatment. We find that reductions in compliance in treatment of stage 1 cases are projected to result in the largest increase in further transmission of the disease, with failing to cure stage 2 cases also posing a smaller concern. Reductions in compliance may be offset by increases in the rate at which cases are passively detected. Efforts should therefore be made to ensure good adherence for stage 1 patients to treatment with fexinidazole and to improve access to passive care.

Bayesian inference for coupled hidden Markov models frequently relies on data augmentation techniques for imputation of the hidden state processes. Considerable progress has been made on developing such techniques, mainly using Markov chain Monte Carlo (MCMC) methods. However, as the dimensionality and complexity of the hidden processes increase some of these methods become inefficient, either because they produce MCMC chains with high autocorrelation or because they become computationally intractable. Motivated by this fact we developed a novel MCMC algorithm, which is a modification of the forward filtering backward sampling algorithm, that achieves a good balance between computation and mixing properties, and thus can be used to analyze models with large numbers of hidden chains. Even though our approach is developed under the assumption of a Markovian model, we show how this assumption can be relaxed leading to minor modifications in the algorithm. Our approach is particularly well suited to epidemic models, where the hidden Markov chains represent the infection status of an individual through time. The performance of our method is assessed on simulated data on epidemic models for the spread of Escherichia coli O157:H7 in cattle. Supplementary materials for this article are available online.

A new strategy has been developed for characterization of the most challenging complex mixtures to date, using a combination of custom-designed experiments and a new data pre-processing algorithm., A new strategy has been developed for characterization of the most challenging complex mixtures to date, using a combination of custom-designed experiments and a new data pre-processing algorithm. In contrast to traditional methods, the approach enables operation of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) with constant ultrahigh resolution at hitherto inaccessible levels (approximately 3 million FWHM, independent of m/z). The approach, referred to as OCULAR, makes it possible to analyze samples that were previously too complex, even for high field FT-ICR MS instrumentation. Previous FT-ICR MS studies have typically spanned a broad mass range with decreasing resolving power (inversely proportional to m/z) or have used a single, very narrow m/z range to produce data of enhanced resolving power; both methods are of limited effectiveness for complex mixtures spanning a broad mass range, however. To illustrate the enhanced performance due to OCULAR, we show how a record number of unique molecular formulae (244 779 elemental compositions) can be assigned in a single, non-distillable petroleum fraction without the aid of chromatography or dissociation (MS/MS) experiments. The method is equally applicable to other areas of research, can be used with both high field and low field FT-ICR MS instruments to enhance their performance, and represents a step-change in the ability to analyze highly complex samples.

Background Gambiense human African trypanosomiasis (gHAT) is a disease targeted for elimination of transmission (EOT) by 2030, however the likelihood of achieving it is unknown. We utilised modelling to study the impact of currently-available intervention methods on transmission across the Democratic Republic of Congo (DRC) – which accounts for ∼ 70% of global burden – and highlight regions requiring intensified interventions. Methods A model previously fitted to case data in DRC was used to predict cases and new infections under four future strategies in 168 health zones. The strategies comprise of medical interventions – active and passive screening (AS and PS) and some include large-scale vector control (VC). In each health zone, we estimate the median year of EOT and the probability of EOT by 2030 under each and compute the least ambitious strategy predicted to achieve EOT by 2030. Findings The model predicts 42 health zones are very likely to achieve EOT (> 90% probability) using medical-only strategies continued at mean coverage levels; this increases to 52 when AS coverage is increased to maximum previous coverage. In all VC strategies, health zones are predicted to meet EOT by 2030, although there are several where increasing low AS coverage could achieve this. Interpretation This analysis provides a priority list for consideration for supplementary VC implementation (Bagata, Bandundu, Bolobo, Kikongo, Kwamouth and Masi Manimba in former Bandundu province) in conjunction with the recent AS coverage. Funding Bill Melinda Gates Foundation [OPP1177824, OPP1184344, OPP1156227, OPP1186851, and OPP1155293] and Belgian Development Agency (ENABEL). Research in context Evidence before this study On 30th April 2020 we searched PubMed and ScienceDirect to identify previous predictive modelling studies of gHAT in DRC using the search terms “model” AND “Democratic Republic of Congo” OR “DRC” AND “try-panosomiasis” OR “sleeping sickness”. There are numerous modelling studies which have looked at estimating the impact of a variety of strategies on transmission and elimination, however many utilise infection prevalence categories for performing simulations rather than location-specific data. For DRC, modelling studies have made projections at a province-level (i.e. Bandundu), and for health zones (i.e all of Equateur and some in Bandundu), concluding that there is high heterogeneity in underlying transmission, consequently whether medical-only strategies will suffice to meet elimination of transmission (EOT) by 2030. They find that supplemental, large-scale vector control would be expected to result in rapid EOT across settings. Two high-endemicity, village-level studies suggest that regular, high-coverage screening is needed to achieve EOT within 15 years without additional interventions. Added value of this study This study presents predictions for EOT across the whole DRC for the first time. Since DRC has the highest disease burden it is critical to understand how far current tools might go towards achieving this 2030 target across the country, and how strategies may need to be adapted for specific locations in the endgame. It also provides a priority list for regions requiring intensified interventions. Implications of all the available evidence Our analysis suggests that, whilst many regions of DRC are expected to meet the EOT goal by 2030 with medical-only strategies, for some regions current strategies may need to be bolstered to achieve EOT within the next decade. Although some regions could consider increasing coverage of active screening, vector control appears a desirable supplemental intervention in several specific high-prevalence locations.

Due to the COVID-19 pandemic, many key neglected tropical disease (NTD) activities have been postponed. This hindrance comes at a time when the NTDs are progressing towards their ambitious goals for 2030. Mathematical modelling on several NTDs, namely gambiense sleeping sickness, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminthiases (STH), trachoma, and visceral leishmaniasis, shows that the impact of this disruption will vary across the diseases. Programs face a risk of resurgence, which will be fastest in high-transmission areas. Furthermore, of the mass drug administration diseases, schistosomiasis, STH, and trachoma are likely to encounter faster resurgence. The case-finding diseases (gambiense sleeping sickness and visceral leishmaniasis) are likely to have fewer cases being detected but may face an increasing underlying rate of new infections. However, once programs are able to resume, there are ways to mitigate the impact and accelerate progress towards the 2030 goals., The COVID-19 pandemic has disrupted neglected tropical disease programs. Modelling shows the impact of this will vary across diseases. Once interventions are reintroduced, there are opportunities for mitigating the delay and accelerating progress towards the 2030 goals.

Routinely collected public health surveillance data are often partially complete, yet remain a useful source by which to monitor incidence and track progress during disease intervention. In the 1970s, leptospirosis in New Zealand (NZ) was known as ‘dairy farm fever’ and the disease was frequently associated with serovars Hardjo and Pomona. To reduce infection, interventions such as vaccination of dairy cattle with these two serovars was implemented. These interventions have been associated with significant reduction in leptospirosis incidence, however, livestock-based occupations continue to predominate notifications. In recent years, diagnosis is increasingly made by nucleic acid detection which currently does not provide serovar information. Serovar information can assist in linking the recognized maintenance host, such as livestock and wildlife, to infecting serovars in human cases which can feed back into the design of intervention strategies. In this study, confirmed and probable leptospirosis notification data from 1 January 1999 to 31 December 2016 were used to build a model to impute the number of cases from different occupational groups based on serovar and month of occurrence. We imputed missing occupation and serovar data within a Bayesian framework assuming a Poisson process for the occurrence of notified cases. The dataset contained 1430 notified cases, of which 927 had a specific occupation (181 dairy farmers, 45 dry stock farmers, 454 meatworkers, 247 other) while the remaining 503 had non-specified occupations. Of the 1430 cases, 1036 had specified serovars (231 Ballum, 460 Hardjo, 249 Pomona, 96 Tarassovi) while the remaining 394 had an unknown serovar. Thus, 47% (674/1430) of observations had both a serovar and a specific occupation. The results show that although all occupations have some degree of under-reporting, dry stock farmers were most strongly affected and were inferred to contribute as many cases as dairy farmers to the burden of disease, despite dairy farmer being recorded much more frequently. Rather than discard records with some missingness, we have illustrated how mathematical modelling can be used to leverage information from these partially complete cases. Our finding provides important evidence for reassessing the current minimal use of animal vaccinations in dry stock. Improving the capture of specific farming type in case report forms is an important next step.

Many control programmes against neglected tropical diseases have been interrupted due to the coronavirus disease 2019 (COVID-19) pandemic, including those that rely on active case finding. In this study we focus on gambiense human African trypanosomiasis (gHAT), where active screening was suspended in the Democratic Republic of Congo (DRC) due to the pandemic. We use two independent mathematical models to predict the impact of COVID-19 interruptions on transmission and reporting and achievement of the 2030 elimination of transmission (EOT) goal for gHAT in two moderate-risk regions of the DRC. We consider different interruption scenarios, including reduced passive surveillance in fixed health facilities, and whether this suspension lasts until the end of 2020 or 2021. Our models predict an increase in the number of new infections in the interruption period only if both active screening and passive surveillance were suspended, and with a slowed reduction—but no increase—if passive surveillance remains fully functional. In all scenarios, the EOT may be slightly pushed back if no mitigation, such as increased screening coverage, is put in place. However, we emphasise that the biggest challenge will remain in the higher-prevalence regions where EOT is already predicted to be behind schedule without interruptions unless interventions are bolstered.

The persistence mechanisms of Rift Valley fever (RVF), a zoonotic arboviral haemorrhagic fever, at both local and broader geographical scales have yet to be fully understood and rigorously quantified. We developed a mathematical metapopulation model describing RVF virus transmission in livestock across the four islands of the Comoros archipelago, accounting for island-specific environments and inter-island animal movements. By fitting our model in a Bayesian framework to 2004–2015 surveillance data, we estimated the importance of environmental drivers and animal movements on disease persistence, and tested the impact of different control scenarios on reducing disease burden throughout the archipelago. Here we report that (i) the archipelago network was able to sustain viral transmission in the absence of explicit disease introduction events after early 2007, (ii) repeated outbreaks during 2004–2020 may have gone under-detected by local surveillance, and (iii) co-ordinated within-island control measures are more effective than between-island animal movement restrictions., Rift Valley fever is a zoonotic haemorrhagic fever with complex transmission dynamics influenced by environmental variables and animal movements. Here, the authors develop a metapopulation model incorporating these factors and use it to identify the main drivers of transmission in the Comoros archipelago.

For most pathogens, testing procedures can be used to distinguish between different strains with which individuals are infected. Due to the growing availability of such data, multistrain models have increased in popularity over the past few years. Quantifying the interactions between different strains of a pathogen is crucial in order to obtain a more complete understanding of the transmission process, but statistical methods for this type of problem are still in the early stages of development. Motivated by this demand, we construct a stochastic epidemic model that incorporates additional strain information and propose a statistical algorithm for efficient inference. The model improves upon existing methods in the sense that it allows for both imperfect diagnostic test sensitivities and strain misclassification. Extensive simulation studies were conducted in order to assess the performance of our method, while the utility of the developed methodology is demonstrated on data obtained from a longitudinal study of Escherichia coli O157:H7 strains in feedlot cattle.\ud \ud

Many control programmes against neglected tropical diseases have been interrupted due to COVID-19 pandemic, including those that rely on active case finding. In this study we focus on gambiense human African trypanosomiasis (gHAT), where active screening was suspended in the Democratic Republic of Congo (DRC) due to the pandemic. We use two independent mathematical models to predict the impact of COVID-19 interruptions on transmission and reporting, and the achievement of 2030 elimination of transmission (EOT) goal for gHAT in two moderate-risk regions of DRC. We consider different interruption scenarios, including reduced passive surveillance in fixed health facilities, and whether this suspension lasts until the end of 2020 or 2021. Our models predict an increase in the number of new infections in the interruption period only if both active screening and passive surveillance were suspended, and with slowed reduction - but no increase - if passive surveillance remains fully functional. In all scenarios, the EOT may be slightly pushed back if no mitigation such as increased screening coverage is put in place. However, we emphasise that the biggest challenge will remain in the higher prevalence regions where EOT is already predicted to be behind schedule without interruptions unless interventions are bolstered.

Infectious diseases such as avian influenza pose a global threat to human health. Mathematical and statistical models can provide key insights into the mechanisms that underlie the spread and persistence of infectious diseases, though their utility is linked to the ability to adequately calibrate these models to observed data. Performing robust inference for these systems is challenging. The fact that the underlying models exhibit complex non-linear dynamics, coupled with practical constraints to observing key epidemiological events such as transmission, requires the use of inference techniques that are able to numerically integrate over multiple hidden states and/or infer missing information. Simulation-based inference techniques such as Approximate Bayesian Computation (ABC) have shown great promise in this area, since they rely on the development of suitable simulation models, which are often easier to code and generalise than routines that require evaluations of an intractable likelihood function. In this manuscript we make some contributions towards improving the efficiency of ABC-based particle Markov chain Monte Carlo methods, and show the utility of these approaches for performing both model inference and model comparison in a Bayesian framework. We illustrate these approaches on both simulated data, as well as real data from an experimental transmission study of highly pathogenic avian influenza in genetically modi fied chickens., This work was supported by the Biotechnology and Biological Sciences Research Council (grants BB/G00479X/1, BBS/B/00239, and BBS/B/00301) and by the Cambridge Infectious Diseases Consortium Department for Environment, Food and Rural Affairs-Higher Education Funding Council for England (grant VT0105). AJKC was supported by BBSRC grant BB/I024550/1.

Gambiense human African trypanosomiasis (gHAT) is a virulent disease declining in burden but still endemic in West and Central Africa. Although it is targeted for elimination of transmission by 2030, there remain numerous questions about the drivers of infection and how these vary geographically. In this study we focus on the Democratic Republic of Congo (DRC), which accounted for 84% of the global case burden in 2016, to explore changes in transmission across the country and elucidate factors which may have contributed to the persistence of disease or success of interventions in different regions. We present a Bayesian fitting methodology, applied to 168 endemic health zones (∼100,000 population size), which allows for calibration of a mechanistic gHAT model to case data (from the World Health Organization HAT Atlas) in an adaptive and automated framework. It was found that the model needed to capture improvements in passive detection to match observed trends in the data within former Bandundu and Bas Congo provinces indicating these regions have substantially reduced time to detection. Health zones in these provinces generally had longer burn-in periods during fitting due to additional model parameters. Posterior probability distributions were found for a range of fitted parameters in each health zone; these included the basic reproduction number estimates for pre-1998 (R0) which was inferred to be between 1 and 1.14, in line with previous gHAT estimates, with higher median values typically in health zones with more case reporting in the 2000s. Previously, it was not clear whether a fall in active case finding in the period contributed to the declining case numbers. The modelling here accounts for variable screening and suggests that underlying transmission has also reduced greatly—on average 96% in former Equateur, 93% in former Bas Congo and 89% in former Bandundu—Equateur and Bandundu having had the highest case burdens in 2000. This analysis also sets out a framework to enable future predictions for the country., Author summary Gambiense human African trypanosomiasis (gHAT; sleeping sickness) is a deadly disease targeted for elimination of transmission by 2030, however there are still several unknowns about what factors influence continued transmission and how this changes with geographic location. In this study we focus on the Democratic Republic of Congo (DRC), which reported 84% of the global cases in 2016 to try and explain why some regions of the country have had more success than others in bringing down case burden. To achieve this we used a state-of-the-art statistical framework to match a mathematical gHAT model to reported case data for 168 regions with some case reporting during 2000–2016. The analysis indicates that two former provinces, Bandundu and Bas Congo had substantial improvements to case detection in fixed health facilities in the time period. Overall, all provinces were estimated to have reductions in (unobservable) transmission including ∼96% in former Equateur. This is reassuring as case finding effort has decreased in that region. The model fitting presented here will allow predictions of gHAT under alternative intervention strategies to be performed in future studies.

Plague, caused byYersinia pestisinfection, continues to threaten low- and middle-income countries throughout the world. The complex interactions between rodents and fleas with their respective environments challenge our understanding of human plague epidemiology. Historical long-term datasets of reported plague cases offer a unique opportunity to elucidate the effects of climate on plague outbreaks in detail. Here, we analyse monthly plague deaths and climate data from 25 provinces in British India from 1898 to 1949 to generate insights into the influence of temperature, rainfall and humidity on the occurrence, severity and timing of plague outbreaks. We find that moderate relative humidity levels of between 60% and 80% were strongly associated with outbreaks. Using wavelet analysis, we determine that the nationwide spread of plague was driven by changes in humidity, where, on average, a one-month delay in the onset of rising humidity translated into a one-month delay in the timing of plague outbreaks. This work can inform modern spatio-temporal predictive models for the disease and aid in the development of early-warning strategies for the deployment of prophylactic treatments and other control measures.

Between pandemics, the influenza virus exhibits periods of incremental evolution via a process known as antigenic drift. This process gives rise to a sequence of strains of the pathogen that are continuously replaced by newer strains, preventing a build up of immunity in the host population. In this paper, a parsimonious epidemic model is defined that attempts to capture the dynamics of evolving strains within a host population. The `evolving strains' epidemic model has many properties that lie in-between the Susceptible-Infected-Susceptible and the Susceptible-Infected-Removed epidemic models, due to the fact that individuals can only be infected by each strain once, but remain susceptible to reinfection by newly emerged strains. Coupling results are used to identify key properties, such as the time to extinction. A range of reproduction numbers are explored to characterize the model, including a novel quasi-stationary reproduction number that can be used to describe the re-emergence of the pathogen into a population with `average' levels of strain immunity, analogous to the beginning of the winter peak in influenza. Finally the quasi-stationary distribution of the evolving strains model is explored via simulation., 34 pages, 7 figures, in review

The use of hyphenated Fourier transform mass spectrometry (FTMS) methods affords additional information about complex chemical mixtures. Co-eluted components can be resolved thanks to the ultra-high resolving power, which also allows extracted ion chromatograms (EICs) to be used for the observation of isomers. As such datasets can be large and data analyses laborious, improved tools are needed for data analyses and extraction of key information. The typical work-flow for this type of data is based upon manually dividing the total ions chromatogram (TIC) into several windows of usually equal retention time, averaging the signal of each window to create a single mass spectrum, extracting a peak list, performing the compositional assignments, visualizing the results, and repeating the process for each window. By removing the need to manually divide a data set into many time windows and analyze each one, a time-consuming work-flow has been significantly simplified. An environmental sample from the oil sands region of Alberta, Canada, and dissolved organic matter samples from the Suwannee River Fulvic Acid (SRFA) and marine waters (Marine DOM) were used as a test-bed for the new method. A complete solution named KairosMS, was developed in the R language utilizing the Tidyverse packages and Shiny for the user interface. KairosMS imports raw data from common file types, processes it and exports a mass list for compositional assignments. KairosMS then incorporate those assignments for analysis and visualization. The present method increases the computational speed while reducing the manual work of the analysis when compared to other current methods. The algorithm subsequently incorporates the assignments into the processed data set, generating a series of interactive plots, EICs for individual components or entire compound classes, and can export raw data or graphics for off-line use. Using the example of petroleum related data, it is then visualized according to heteroatom class, carbon number, double bond equivalents (DBE), and retention time. The algorithm also gives the ability to screen for isomeric contributions and to follow homologous series or compound classes, instead of individual components, as a function of time.

Selecting between competing statistical models is a challenging problem especially when the competing models are non-nested. An effective algorithm is developed in a Bayesian framework for selecting between a parameter-driven autoregressive Poisson regression model and an observation-driven integer valued autoregressive model when modelling time series count data. In order to achieve this a particle MCMC algorithm for the autoregressive Poisson regression model is introduced. The particle filter underpinning the particle MCMC algorithm plays a key role in estimating the marginal likelihood of the autoregressive Poisson regression model via importance sampling and is also utilised to estimate the DIC. The performance of the model selection algorithms are assessed via a simulation study. Two real-life data sets, monthly US polio cases (1970–1983) and monthly benefit claims from the logging industry to the British Columbia Workers Compensation Board (1985–1994) are successfully analysed.\ud \ud

Background:Our understanding of pathogens and disease transmission has improved dramatically over the past 100 years, but coinfection, how different pathogens interact with each other, remains a challenge. Cross-sectional serological studies including multiple pathogens offer a crucial insight into this problem. Methods:We use data from three cross-sectional serological surveys (in 2003, 2007 and 2013) in a Baltimore emergency department to predict the prevalence for HIV, hepatitis C virus (HCV) and herpes simplex virus, type 2 (HSV2), in a fourth survey (in 2016). We develop a mathematical model to make this prediction and to estimate the incidence of infection and coinfection in each age and ethnic group in each year.Results:Overall we find a much stronger age cohort effect than a time effect, so that, while incidence at a given age may decrease over time, individuals born at similar times experience a more constant force of infection over time.Conclusions:These results emphasise the importance of age-cohort counselling and early intervention while people are young. Our approach adds value to data such as these by providing age- and time-specific incidence estimates which could not be obtained any other way, and allows forecasting to enable future public health planning.

Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) provides the resolution and mass accuracy needed to analyze complex mixtures such as crude oil. When mixtures contain many different components, a competitive effect within the ICR cell takes place that hampers the detection of a potentially large fraction of the components. Recently, a new data collection technique, which consists of acquiring several spectra of small mass ranges and assembling a complete spectrum afterward, enabled the observation of a record number of peaks with greater accuracy compared to broadband methods. There is a need for statistical methods to combine and preprocess segmented acquisition data. A particular challenge of quadrupole isolation is that near the window edges there is a drop in intensity, hampering the stitching of consecutive windows. We developed an algorithm called Rhapso to stitch peak lists corresponding to multiple different m/z regions from crude oil samples. Rhapso corrects potential edge effects to enable the use of smaller windows and reduce the required overlap between windows, corrects mass shifts between windows, and generates a single peak list for the full spectrum. Relative to a stitching performed manually, Rhapso increased the data processing speed and avoided potential human errors, simplifying the subsequent chemical analysis of the sample. Relative to a broadband spectrum, the stitched output showed an over 2-fold increase in assigned peaks and reduced mass error by a factor of 2. Rhapso is expected to enable routine use of this spectral stitching method for ultracomplex samples, giving a more detailed characterization of existing samples and enabling the characterization of samples that were previously too complex to analyze.

Since the turn of the century, the global community has made great progress towards the elimination of gambiense human African trypanosomiasis (HAT). Elimination programs, primarily relying on screening and treatment campaigns, have also created a rich database of HAT epidemiology. Mathematical models calibrated with these data can help to fill remaining gaps in our understanding of HAT transmission dynamics, including key operational research questions such as whether integrating vector control with current intervention strategies is needed to achieve HAT elimination. Here we explore, via an ensemble of models and simulation studies, which aspects of the available data and level of data aggregation, such as separation by disease stage, would be most useful for better understanding transmission dynamics and improving model reliability in making future predictions of control and elimination strategies.Author summaryHuman African tryposonomiasis (HAT), also known as sleeping sickness, is a parasitic disease with over 65 million people estimated to be living at risk of infection. Sleeping sickness consists of two stages: the first one is relatively mild but the second stage is usually fatal if untreated. The World Health Organization has targeted HAT for elimination as a public health problem by 2020 and for elimination of transmission by 2030. Regular monitoring updates indicate that 2020 elimination goals are likely to be achieved. This monitoring relies mainly on case report data that is collected through medical-based control activities — the main strategy employed so far in HAT control. This epidemiological data are also used to calibrate mathematical models that can be used to analyse current interventions and provide projections of potential intensified strategies.We investigated the role of the type and level of aggregation of this HAT case data on model calibrations and projections. We highlight that the lack of detailed epidemiological information, such as missing stage of disease or truncated time series data, impacts model recommendations for strategy choice: it can misrepresent the underlying HAT epidemiology (for example, the ratio of stage 1 to stage 2 cases) and increase uncertainty in predictions. Consistently including new data from control activities as well as enriching it through cross-sectional (e.g. demographic or behavioural data) and geo-located data is likely to improve modelling accuracy to support planning, monitoring and adapting HAT interventions.

International audience; Background: International and global organisations advocate targeting interventions to areas of high HIV prevalence (ie, hotspots). To better understand the potential benefits of geo-targeted control, we assessed the extent to which HIV hotspots along Lake Victoria sustain transmission in neighbouring populations in south-central Uganda.Methods: We did a population-based survey in Rakai, Uganda, using data from the Rakai Community Cohort Study. The study surveyed all individuals aged 15-49 years in four high-prevalence Lake Victoria fishing communities and 36 neighbouring inland communities. Viral RNA was deep sequenced from participants infected with HIV who were antiretroviral therapy-naive during the observation period. Phylogenetic analysis was used to infer partial HIV transmission networks, including direction of transmission. Reconstructed networks were interpreted through data for current residence and migration history. HIV transmission flows within and between high-prevalence and low-prevalence areas were quantified adjusting for incomplete sampling of the population.Findings: Between Aug 10, 2011, and Jan 30, 2015, data were collected for the Rakai Community Cohort Study. 25 882 individuals participated, including an estimated 75·7% of the lakeside population and 16·2% of the inland population in the Rakai region of Uganda. 5142 participants were HIV-positive (2703 [13·7%] in inland and 2439 [40·1%] in fishing communities). 3878 (75·4%) people who were HIV-positive did not report antiretroviral therapy use, of whom 2652 (68·4%) had virus deep-sequenced at sufficient quality for phylogenetic analysis. 446 transmission networks were reconstructed, including 293 linked pairs with inferred direction of transmission. Adjusting for incomplete sampling, an estimated 5·7% (95% credibility interval 4·4-7·3) of transmissions occurred within lakeside areas, 89·2% (86·0-91·8) within inland areas, 1·3% (0·6-2·6) from lakeside to inland areas, and 3·7% (2·3-5·8) from inland to lakeside areas.Interpretation: Cross-community HIV transmissions between Lake Victoria hotspots and surrounding inland populations are infrequent and when they occur, virus more commonly flows into rather than out of hotspots. This result suggests that targeted interventions to these hotspots will not alone control the epidemic in inland populations, where most transmissions occur. Thus, geographical targeting of high prevalence areas might not be effective for broader epidemic control depending on underlying epidemic dynamics.Funding: The Bill & Melinda Gates Foundation, the National Institute of Allergy and Infectious Diseases, the National Institute of Mental Health, the National Institute of Child Health and Development, the Division of Intramural Research of the National Institute for Allergy and Infectious Diseases, the World Bank, the Doris Duke Charitable Foundation, the Johns Hopkins University Center for AIDS Research, and the President's Emergency Plan for AIDS Relief through the Centers for Disease Control and Prevention.

Since the turn of the century, the global community has made great progress towards the elimination of gambiense human African trypanosomiasis (HAT). Elimination programs, primarily relying on screening and treatment campaigns, have also created a rich database of HAT epidemiology. Mathematical models calibrated with these data can help to fill remaining gaps in our understanding of HAT transmission dynamics, including key operational research questions such as whether integrating vector control with current intervention strategies is needed to achieve HAT elimination. Here we explore, via an ensemble of models and simulation studies, how including or not disease stage data, or using more updated data sets affect model predictions of future control strategies., Author summary Human African tryposonomiasis (HAT), also known as sleeping sickness, is a parasitic disease with over 65 million people estimated to be living at risk of infection. Sleeping sickness consists of two stages: the first one is relatively mild but the second stage is usually fatal if untreated. The World Health Organization has targeted HAT for elimination as a public health problem by 2020 and for elimination of transmission by 2030. Regular monitoring updates indicate that 2020 elimination goals are likely to be achieved. This monitoring relies mainly on case report data that is collected through medical-based control activities—the main strategy employed so far in HAT control. This epidemiological data are also used to calibrate mathematical models that can be used to analyse current interventions and provide projections of potential intensified strategies. We investigated the role of the type and level of aggregation of this HAT case data (staging data and truncated data) on model calibrations and projections. We highlight that the lack of detailed epidemiological information, such as missing stage of disease or truncated time series data, impacts model recommendations for strategy choice: it can misrepresent the underlying HAT epidemiology (for example, the ratio of stage 1 to stage 2 cases) and increase uncertainty in predictions. Consistently including new data from control activities as well as enriching it through cross-sectional (e.g. demographic or behavioural data) and geo-located data is likely to improve modelling accuracy to support planning, monitoring and adapting HAT interventions.

Selecting between competing statistical models is a challenging problem especially when the competing models are non-nested. In this paper we offer a simple solution by devising an algorithm which combines MCMC and importance sampling to obtain computationally efficient estimates of the marginal likelihood which can then be used to compare the models. The algorithm is successfully applied to a longitudinal epidemic data set, where calculating the marginal likelihood is made more challenging by the presence of large amounts of missing data. In this context, our importance sampling approach is shown to outperform existing methods for computing the marginal likelihood.

Fourier transform ion cyclotron resonance mass spectrometry affords the resolving power to determine an unprecedented number of components in complex mixtures, such as petroleum. The software tools required to also analyze these data struggle to keep pace with advancing instrument capabilities and increasing quantities of data, particularly in terms of combining information efficiently across multiple replicates. Improved confidence in data and the use of replicates is particularly important where strategic decisions will be based upon the analysis. We present a new algorithm named Themis, developed using R, to jointly preprocess replicate measurements of a sample with the aim of improving consistency as a preliminary step to assigning peaks to chemical compositions. The main features of the algorithm are quality control criteria to detect failed runs, ensuring comparable magnitudes across replicates, peak alignment, and the use of an adaptive mixture model-based strategy to help distinguish true peaks from noise. The algorithm outputs a list of peaks reliably observed across replicates and facilitates data handling by preprocessing all replicates in a single step. The processed data produced by our algorithm can subsequently be analyzed by use of relevant specialized software. While Themis has been demonstrated with petroleum as an example of a complex mixture, its basic framework will be useful for complex samples arising from a variety of other applications.\ud \ud

BackgroundThere is a need for greater understanding of the epidemiology of primary care patient safety in order to generate solutions to prevent future harm.AimTo estimate the rate of failures in processing actions requested in hospital discharge summaries, and to determine factors associated with these failures.Design and settingThe authors undertook a retrospective records review. The study population was emergency admissions for patients aged ≥75 years, drawn from 10 practices in three areas of England.MethodOne GP researcher reviewed the records for 300 patients after hospital discharge to determine the rate of compliance with actions requested in the discharge summary, and to estimate the rate of associated harm from non-compliance. In cases where GPs documented decision-making contrary to what was requested, these instances did not constitute failures. Data were also collected on time taken to process discharge communications.ResultsThere were failures in processing actions requested in 46% (112/246) of discharge summaries (95% confidence interval [CI] = 39 to 52%). Medications changes were not made in 17% (124/750) of requests (95% CI = 14 to 19%). Tests were not completed for 26% of requests (95% CI = 16 to 35%), and 27% of requested follow-ups were not arranged (95% CI = 20 to 33%). The harm rate associated with these failures was 8%. Increased risk of failure to process test requests was significantly associated with the type of clinical IT system, and male patients.ConclusionFailures occurred in the processing of requested actions in almost half of all discharge summaries, and with all types of action requested. Associated harms were uncommon and most were of moderate severity.

Quasi-stationary distributions (QSDs)arise from stochastic processes that exhibit transient equilibrium behaviour on the way to absorption QSDs are often mathematically intractable and even drawing samples from them is not straightforward. In this paper the framework of Sequential Monte Carlo samplers is utilized to simulate QSDs and several novel resampling techniques are proposed to accommodate models with reducible state spaces, with particular focus on preserving particle diversity on discrete spaces. Finally an approach is considered to estimate eigenvalues associated with QSDs, such as the decay parameter., 30 pages, 9 Figures

SUMMARYThe epidemiology of human campylobacteriosis is complex but in recent years understanding of this disease has advanced considerably. Despite being a major public health concern in many countries, the presence of multiple hosts, genotypes and transmission pathways has made it difficult to identify and quantify the determinants of human infection and disease. This has delayed the development of successful intervention programmes for this disease in many countries including New Zealand, a country with a comparatively high, yet until recently poorly understood, rate of notified disease. This study investigated the epidemiology of Campylobacter jejuni at the genotype-level over a 3-year period between 2005 and 2008 using multilocus sequence typing. By combining epidemiological surveillance and population genetics, a dominant, internationally rare strain of C. jejuni (ST474) was identified, and most human cases (65·7%) were found to be caused by only seven different genotypes. Source association of genotypes was used to identify risk factors at the genotype-level through multivariable logistic regression and a spatial model. Poultry-associated cases were more likely to be found in urban areas compared to rural areas. In particular young children in rural areas had a higher risk of infection with ruminant strains than their urban counterparts. These findings provide important information for the implementation of pathway-specific control strategies.

Integrated surveillance of infectious multi-source diseases using a combination of epidemiology, ecology, genetics and evolution can provide a valuable risk-based approach for the control of important human pathogens. This includes a better understanding of transmission routes and the impact of human activities on the emergence of zoonoses. Until recently New Zealand had extraordinarily high and increasing rates of notified human campylobacteriosis, and our limited understanding of the source of these infections was hindering efforts to control this disease. Genetic and epidemiological modeling of a 3-year dataset comprising multilocus sequence typed isolates from human clinical cases, coupled with concurrent data on food and environmental sources, enabled us to estimate the relative importance of different sources of human disease. Our studies provided evidence that poultry was the leading cause of human campylobacteriosis in New Zealand, causing an estimated 58-76% of cases with widely varying contributions by individual poultry suppliers. These findings influenced national policy and, after the implementation of poultry industry-specific interventions, a dramatic decline in human notified cases was observed in 2008. The comparative-modeling and molecular sentinel surveillance approach proposed in this study provides new opportunities for the management of zoonotic diseases.

A stochastic model of disease transmission among a population partitioned into groups is defined. The model is of SEIR (Susceptible-Exposed-Infective-Removed) type and features intervention in response to the progress of the disease, and moreover includes a random delay before the intervention occurs. A threshold parameter for the model, which can be used to assess the efficacy of the intervention, is defined. The threshold parameter can be calculated either in closed form or via recursion, for a number of different choices of exposed, infectious and delay period distributions, both for the epidemic model itself and also a large-group approximation. In particular both constant and Erlang-distributed delay periods are considered. Sufficient conditions under which a constant delay gives the least effective intervention are presented. For a given mean delay, it is shown that the two-point delay distribution provides the optimal intervention.

This paper applies a Bayesian hierarchical model designed to identify potential outbreaks of campylobacteriosis from a background of sporadic cases. We assume that such outbreaks are characterized by spatially-localised periods of increased incidence. As well as calculating an outbreak probability for each potential disease cluster, the model simultaneously estimates the underlying spatial and temporal distribution of sporadic cases. The model is applied to notification data from a region of New Zealand for the period 2001-2007 and correctly identifies known outbreaks, whilst highlighting an appropriate number of potential outbreaks for further investigation. Using simulated data, we show that if additional epidemiological information is included in the construction of the model then it can outperform an established method.

This paper is concerned with the definition and calculation of containment probabilities for emerging disease epidemics. A general multitype branching process is used to model an emerging infectious disease in a population of households. It is shown that the containment probability satisfies a certain fixed point equation which has a unique solution under certain conditions; the case of multiple solutions is also described. The extinction probability of the branching process is shown to be a special case of the containment probability. It is shown that Laplace transform ordering of the severity distributions of households in different epidemics yields an ordering on the containment probabilities. The results are illustrated with both standard epidemic models and a specific model for an emerging strain of influenza.

The national control programme for Salmonella in Danish swine herds introduced in 1993 has led to a large decrease in pork-associated human cases of salmonellosis. The pork industry is increasingly focused on the cost-effectiveness of surveillance while maintaining consumer confidence in the pork food supply. Using national control programme data from 2003 and 2004, we developed a zero-inflated binomial model to predict which farms were most at risk of Salmonella. We preferentially sampled these high-risk farms using two sampling schemes based on model predictions resulting from a farm’s covariate pattern and its random effect. Zero-inflated binomial modelling allows assessment of similarities and differences between factors that affect herd infection status (introduction), and those that affect the seroprevalence in infected herds (persistence and spread). Both large (producing greater than 5000 pigs per annum), and small herds (producing less than 2000 pigs per annum) were at significantly higher risk for infection and subsequent seroprevalence, when compared with medium sized herds (producing between 2000 and 5000 pigs per annum). When compared with herds being located elsewhere, being located in the south of Jutland significantly decreased the risk of herd infection, but increased the risk of a pig from an infected herd being seropositive. The model suggested that many of the herds where Salmonella was not detected were infected, but at a low prevalence. Using cost and sensitivity, we compared the results of our model based sampling schemes with those under the standard sampling scheme, based on herd size, and the recently introduced risk-based approach. Model-based results were less sensitive but show significant cost savings. Further model refinements, sampling schemes and the methods to evaluate their performance are important areas for future work, and these should continue to occur in direct consultation with Danish authorities.

A Bayesian approach was developed by Hald et al.((1)) to estimate the contribution of different food sources to the burden of human salmonellosis in Denmark. This article describes the development of several modifications that can be used to adapt the model to different countries and pathogens. Our modified Hald model has several advantages over the original approach, which include the introduction of uncertainty in the estimates of source prevalence and an improved strategy for identifiability. We have applied our modified model to the two major food-borne zoonoses in New Zealand, namely, campylobacteriosis and salmonellosis. Major challenges were the data quality for salmonellosis and the inclusion of environmental sources of campylobacteriosis. We conclude that by modifying the Hald model we have improved its identifiability, made it more applicable to countries with less intensive surveillance, and feasible for other pathogens, in particular with respect to the inclusion of nonfood sources. The wider application and better understanding of this approach is of particular importance due to the value of the model for decision making and risk management.

Graphical models are widely used to study biological networks. Interventions on network nodes are an important feature of many experimental designs for the study of biological networks. In this paper we put forward a causal variant of dynamic Bayesian networks (DBNs) for the purpose of modeling time-course data with interventions. The models inherit the simplicity and computational efficiency of DBNs but allow interventional data to be integrated into network inference. We show empirical results, on both simulated and experimental data, that demonstrate the need to appropriately handle interventions when interventions form part of the design., Comment: Published at http://dx.doi.org/10.1214/15-AOAS806 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Networks have become an important tool for infectious disease epidemiology. Most previous theoretical studies of transmission network models have either considered simple Markovian dynamics at the individual level, or have focused on the invasion threshold and final outcome of the epidemic. Here, we provide a general theory for early real-time behaviour of epidemics on large configuration model networks (i.e. static and locally unclustered), in particular focusing on the computation of the Malthusian parameter that describes the early exponential epidemic growth. Analytical, numerical and Monte-Carlo methods under a wide variety of Markovian and non-Markovian assumptions about the infectivity profile are presented. Numerous examples provide explicit quantification of the impact of the network structure on the temporal dynamics of the spread of infection and provide a benchmark for validating results of large scale simulations., 45 pages, 8 figures, submitted to Mathematical Biosciences on 29/11/2014; Version 2: resubmitted on 15/04/2015; accepted on 17/04/2015. Changes: better explanations in introduction; restructured section 3.3 (3.3.3 added); section 6.3.1 added; more precise terminology; typos corrected