Using Passive Surveillance to Maintain Elimination as a Public Health Problem for Neglected Tropical Diseases: A Model-Based Exploration.

BACKGROUND: Great progress is being made toward the goal of elimination as a public health problem for neglected tropical diseases such as leprosy, human African trypanosomiasis, Buruli ulcer, and visceral leishmaniasis, which relies on intensified disease management and case finding. However, strategies for maintaining this goal are still under discussion. Passive surveillance is a core pillar of a long-term, sustainable surveillance program. METHODS: We use a generic model of disease transmission with slow epidemic growth rates and cases detected through severe symptoms and passive detection to evaluate under what circumstances passive detection alone can keep transmission under control. RESULTS: Reducing the period of infectiousness due to decreasing time to treatment has a small effect on reducing transmission. Therefore, to prevent resurgence, passive surveillance needs to be very efficient. For some diseases, the treatment time and level of passive detection needed to prevent resurgence is unlikely to be obtainable. CONCLUSIONS: The success of a passive surveillance program crucially depends on what proportion of cases are detected, how much of their infectious period is reduced, and the underlying reproduction number of the disease. Modeling suggests that relying on passive detection alone is unlikely to be enough to maintain elimination goals.

Estimation of Seasonal Influenza Attack Rates and Antibody Dynamics in Children Using Cross-Sectional Serological Data.

Directly measuring evidence of influenza infections is difficult, especially in low-surveillance settings such as sub-Saharan Africa. Using a Bayesian model, we estimated unobserved infection times and underlying antibody responses to influenza A/H3N2, using cross-sectional serum antibody responses to 4 strains in children aged 24-60 months. Among the 242 individuals, we estimated a variable seasonal attack rate and found that most children had ≥1 infection before 2 years of age. Our results are consistent with previously published high attack rates in children. The modeling approach highlights how cross-sectional serological data can be used to estimate epidemiological dynamics.

What Can Modeling Tell Us About Sustainable End Points for Neglected Tropical Diseases?

As programs move closer toward the World Health Organization (WHO) goals of reduction in morbidity, elimination as a public health problem or elimination of transmission, countries will be faced with planning the next stages of surveillance and control in low prevalence settings. Mathematical models of neglected tropical diseases (NTDs) will need to go beyond predicting the effect of different treatment programs on these goals and on to predicting whether the gains can be sustained. One of the most important challenges will be identifying the policy goal and the right constraints on interventions and surveillance over the long term, as a single policy option will not achieve all aims-for example, minimizing morbidity and minimizing costs cannot both be achieved. As NTDs move toward 2030 and beyond, more nuanced intervention choices will be informed by quantitative analyses which are adapted to national context.

An open source tool to infer epidemiological and immunological dynamics from serological data: serosolver.

We present a flexible, open source R package designed to obtain biological and epidemiological insights from serological datasets. Characterising past exposures for multi-strain pathogens poses a specific statistical challenge: observed antibody responses measured in serological assays depend on multiple unobserved prior infections that produce cross-reactive antibody responses. We provide a general modelling framework to jointly infer infection histories and describe immune responses generated by these infections using antibody titres against current and historical strains. We do this by linking latent infection dynamics with a mechanistic model of antibody kinetics that generates expected antibody titres over time. Our aim is to provide a flexible package to identify infection histories that can be applied to a range of pathogens. We present two case studies to illustrate how our model can infer key immunological parameters, such as antibody titre boosting, waning and cross-reaction, as well as latent epidemiological processes such as attack rates and age-stratified infection risk.

The helminth community of a population of Rattus norvegicus from an urban Brazilian slum and the threat of zoonotic diseases.

Urban slums provide suitable conditions for infestation by rats, which harbour and shed a wide diversity of zoonotic pathogens including helminths. We aimed to identify risk factors associated with the probability and intensity of infection of helminths of the digestive tract in an urban slum population of Rattus norvegicus. Among 299 rats, eleven species/groups of helminths were identified, of which Strongyloides sp., Nippostrongylus brasiliensis and, the human pathogen, Angiostrongylus cantonensis were the most frequent (97, 41 and 39%, respectively). Sex interactions highlighted behavioural differences between males and females, as eg males were more likely to be infected with N. brasiliensis where rat signs were present, and males presented more intense infections of Strongyloides sp. Moreover, rats in poor body condition had higher intensities of N. brasiliensis. We describe a high global richness of parasites in R. norvegicus, including five species known to cause disease in humans. Among these, A. cantonensis was found in high prevalence and it was ubiquitous in the study area - knowledge which is of public health importance. A variety of environmental, demographic and body condition variables were associated with helminth species infection of rats, suggesting a comparable variety of risk factors for humans.

A Comparative Assessment of Track Plates to Quantify Fine Scale Variations in the Relative Abundance of Norway Rats in Urban Slums.

Norway rats (Rattus norvegicus) living in urban environments are a critical public health and economic problem, particularly in urban slums where residents are at a higher risk for rat borne diseases, yet convenient methods to quantitatively assess population sizes are lacking. We evaluated track plates as a method to determine rat distribution and relative abundance in a complex urban slum environment by correlating the presence and intensity of rat-specific marks on track plates with findings from rat infestation surveys and trapping of rats to population exhaustion. To integrate the zero-inflated track plate data we developed a two-component mixture model with one binary and one censored continuous component. Track plate mark-intensity was highly correlated with signs of rodent infestation (all coefficients between 0.61 and 0.79 and all p-values < 0.05). Moreover, the mean level of pre-trapping rat-mark intensity on plates was significantly associated with the number of rats captured subsequently (Odds ratio1.38; 95% CI 1.19-1.61) and declined significantly following trapping (Odds ratio 0.86; 95% CI 0.78-0.95). Track plates provided robust proxy measurements of rat abundance and distribution and detected rat presence even when populations appeared 'trapped out'. Tracking plates are relatively easy and inexpensive methods that can be used to intensively sample settings such as urban slums, where traditional trapping or mark-recapture studies are impossible to implement, and therefore the results can inform and assess the impact of targeted urban rodent control campaigns.

The Importance of Livestock Demography and Infrastructure in Driving Foot and Mouth Disease Dynamics.

Transboundary animal diseases, such as foot and mouth disease (FMD) pose a significant and ongoing threat to global food security. Such diseases can produce large, spatially complex outbreaks. Mathematical models are often used to understand the spatio-temporal dynamics and create response plans for possible disease introductions. Model assumptions regarding transmission behavior of premises and movement patterns of livestock directly impact our understanding of the ecological drivers of outbreaks and how to best control them. Here, we investigate the impact that these assumptions have on model predictions of FMD outbreaks in the U.S. using models of livestock shipment networks and disease spread. We explore the impact of changing assumptions about premises transmission behavior, both by including within-herd dynamics, and by accounting for premises type and increasing the accuracy of shipment predictions. We find that the impact these assumptions have on outbreak predictions is less than the impact of the underlying livestock demography, but that they are important for investigating some response objectives, such as the impact on trade. These results suggest that demography is a key ecological driver of outbreaks and is critical for making robust predictions but that understanding management objectives is also important when making choices about model assumptions.

Competition between RSV and influenza: Limits of modelling inference from surveillance data.

Respiratory Syncytial Virus (RSV) and Influenza cause a large burden of disease. Evidence of their interaction via temporary cross-protection implies that prevention of one could inadvertently lead to an increase in the burden of the other. However, evidence for the public health impact of such interaction is sparse and largely derives from ecological analyses of peak shifts in surveillance data. To test the robustness of estimates of interaction parameters between RSV and Influenza from surveillance data we conducted a simulation and back-inference study. We developed a two-pathogen interaction model, parameterised to simulate RSV and Influenza epidemiology in the UK. Using the infection model in combination with a surveillance-like stochastic observation process we generated a range of possible RSV and Influenza trajectories and then used Markov Chain Monte Carlo (MCMC) methods to back-infer parameters including those describing competition. We find that in most scenarios both the strength and duration of RSV and Influenza interaction could be estimated from the simulated surveillance data reasonably well. However, the robustness of inference declined towards the extremes of the plausible parameter ranges, with misleading results. It was for instance not possible to tell the difference between low/moderate interaction and no interaction. In conclusion, our results illustrate that in a plausible parameter range, the strength of RSV and Influenza interaction can be estimated from a single season of high-quality surveillance data but also highlights the importance to test parameter identifiability a priori in such situations.

Transmission modelling of environmentally persistent zoonotic diseases: a systematic review.

Transmission of many infectious diseases depends on interactions between humans, animals, and the environment. Incorporating these complex processes in transmission dynamic models can help inform policy and disease control interventions. We identified 20 diseases involving environmentally persistent pathogens (ie, pathogens that survive for more than 48 h in the environment and can cause subsequent human infections), of which indirect transmission can occur from animals to humans via the environment. Using a systematic approach, we critically appraised dynamic transmission models for environmentally persistent zoonotic diseases to quantify traits of models across diseases. 210 transmission modelling studies were identified and most studies considered diseases of domestic animals or high-income settings, or both. We found that less than half of studies validated their models to real-world data, and environmental data on pathogen persistence was rarely incorporated. Model structures varied, with few studies considering the animal-human-environment interface of transmission in the context of a One Health framework. This Review highlights the need for more data-driven modelling of these diseases and a holistic One Health approach to model these pathogens to inform disease prevention and control strategies.

Approximate Bayesian Computation for infectious disease modelling.

Approximate Bayesian Computation (ABC) techniques are a suite of model fitting methods which can be implemented without a using likelihood function. In order to use ABC in a time-efficient manner users must make several design decisions including how to code the ABC algorithm and the type of ABC algorithm to use. Furthermore, ABC relies on a number of user defined choices which can greatly effect the accuracy of estimation. Having a clear understanding of these factors in reducing computation time and improving accuracy allows users to make more informed decisions when planning analyses. In this paper, we present an introduction to ABC with a focus of application to infectious disease models. We present a tutorial on coding practice for ABC in R and three case studies to illustrate the application of ABC to infectious disease models.

A model for leptospire dynamics and control in the Norway rat (Rattus norvegicus) the reservoir host in urban slum environments.

Leptospirosis is a zoonosis that humans can contract via contact with animal reservoirs directly or with water contaminated with their urine. The primary reservoir of pathogenic leptospires within urban slum environments is the Norway rat (Rattus norvegicus). Motivated by the annual outbreaks of human leptospirosis in slum urban settings, the within population infection dynamics of the Norway rat were investigated in Pau da Lima, an community in Salvador, Brazil. A mechanistic model of the dynamics of leptospire infection was informed by extensive field and laboratory data was developed and explored analytically. To identify the intraspecific transmission route of most importance, a global sensitivity analysis of the basic reproduction number to its components was performed. In addition, different methods of rodent control were investigated by calculating target reproduction numbers. Our results suggest environmental transmission plays an important role in the maintenance of infection in the rodent population. To control numbers of wild Norway rats, combinations of controls are recommended but environmental control should also be investigated to reduce prevalence of infection in rats.

A Two-Year Ecological Study of Norway Rats (Rattus norvegicus) in a Brazilian Urban Slum.

The Norway or brown rat (Rattus norvegicus) is among the most ubiquitous of rodents. However, the lack of studies describing Norway rat populations from tropical areas have limited our understanding regarding their demography and seasonal dynamics. In this study, we describe seasonal pattern in the abundance, reproductive parameters, and morphometrics of Norway rat populations in Salvador, Brazil. Rodents were trapped over four seasonal trapping periods (2013-2014) from three valleys. A total of 802 Norway rats were trapped over the course of the study over 7653 trap-nights. Norway rat abundance was high, but there was no significant differences between seasons. The reproductive parameters (e.g. frequency of pregnant and lactating females) did not show statistical differences between seasons. Female rats collected in the rainy season were heavier and older than females from the dry season. Salvador rats had a high incidence of pregnancy and birth rate (estimated birth rate of 79 young per year) compared to previous studies. The information generated is critical for the understanding of the ecology of Norway rat, the main reservoir of Leptospira in Salvador. However, future studies examining the effect of rodent control programs aimed at reducing populations, and determining rates of recovery, will further clarify our understanding of population dynamics.

windex: Analyzing Convergent Evolution Using the Wheatsheaf Index in R.

SUMMARY: windex is a package developed for the R statistical environment to provide novel tools for the analysis of convergent evolution. The recently described Wheatsheaf index provides quantitative measures of the strength of convergence and opens up new possibilities for exploring this evolutionary phenomenon. The windex package allows implementation of this method with additional functions that can be used to create plots and perform statistical tests. R provides compatibility with other packages, and the R environment is familiar to many researchers. AVAILABILITY: The windex package is freely available from CRAN: http://cran.r-project.org/web/packages/windex/. Consequently, windex can be installed directly from R and is distributed under the GNU General Public License 2.0.