Reliable inference in infectious disease modeling requires careful treatment of both model structure and the relationship between latent infection dynamics and observed data. Likelihood functions, which link model parameters to empirical observations, can be formulated either to explicitly represent underlying disease transmission and reporting processes (process-based) or to summarize statistical patterns in aggregated outcomes (observation-based). Stochastic models capture inherent variability in transmission and detection, whereas deterministic models describe average system behavior and often rely on statistical assumptions to account for residual uncertainty. Using two neglected tropical disease (NTD) models, we compare parameter estimation based on complete individual-level events with that based on aggregated counts. By generating synthetic outbreak data from stochastic simulations and analyzing it under alternative modeling frameworks, we show how different combinations of model formulation and likelihood structure influence both point estimates and uncertainty quantification. Our findings indicate that, even when detailed process information is unavailable, observation-based likelihoods can produce robust parameter estimates and credible uncertainty intervals, highlighting their usefulness for practical decision-making in contexts with limited or aggregated surveillance data.

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.

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.

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.

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.

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.

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.

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.

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.

Leptospirosis is a zoonotic disease for which rats are the primary reservoir in urban environments. It is transmitted from rats to people via urine, and is responsible for significant human morbidity and mortality in under-resourced settings. To mitigate the risks posed to people, it is important to understand the ecology of the causative agent Leptospira interrogans. The overarching objective of this study was to compare L. interrogans carriage in urban Norway rats in two ecologically distinct urban environments. We trapped Norway rats (Rattus norvegicus) in Vancouver, Canada (N = 525) and Salvador, Brazil (N = 433) to evaluate whether rat characteristics (i.e., sex, weight, sexual maturity, pregnancy, and the presence of wounds) and location of capture were associated with L. interrogans status. Using generalized linear mixed models to control for clustering by trapping location, we found a greater prevalence of L. interrogans in Salvador (79%) than in Vancouver (12%), and greater spatial heterogeneity in pathogen prevalence in Vancouver than in Salvador. In both locations, we found that older rats and rats with more bite wounds had greater odds of L. interrogans carriage, although wounding influenced pathogen status more for younger animals. Additionally, we found that juvenile rats in Salvador were more likely to leave the nest infected with L. interrogans than were rats in Vancouver, suggesting that potential differences in early-life transmission dynamics exist between the two locations. Together, these results elucidate both general L. interrogans ecology, as well as the importance of geographical location in determining transmission among rats.

Humans acquire leptospirosis through direct contact with animal reservoirs, or more commonly, contact with the environment contaminated with leptospires shed in animal urine. Reservoir populations can be difficult to control through rodenticide application, and resource reduction via habitat management is costly and logistically complicated to implement. When resources are limited, simulation of different combinations of control methods can inform their application in the field. Here we present a framework to find time-dependent control measures for rodent-borne leptospirosis using optimal control mathematical model theory. An age-structured model for leptospire infection in a Norway rat (Pettus norvegicus) population was developed, informed by empirical analyses of data from the city of Salvador, Brazil. We extended this model to include two temporary control measures, rodenticide, and resource reduction, and two permanent control measures, reducing rat carrying capacity and leptospire lifespan in the environment. Optimal control theory seeks the optimum time-dependent controls while taking into account both the cost of the control measures and the "cost" of infection. Multiple control scenarios and the predicted effect of the optimal controls on the population and infection dynamics are presented to illustrate the applications of combinations of temporary and permanent controls. Permanent controls lead to a reduction in prevalence of leptospiral carriage in the rodent population. However, temporary controls can also achieve a reduction in the number of infected rats low enough to reduce risk to humans. Although we focus our modeling on a well-studied species, the Norway rat, our approach can be applied to other disease systems with animal and environmental reservoirs to inform decisions to reduce the risk of human infection.

AbstractWe examine coinfection between helminth species and the bacterium Leptospira interrogans in a natural population of the Norway rat, Rattus norvegicus. We ask whether coinfection can influence the probability and intensity of infection of these enzootic and zoonotic parasites in urban rats, which may affect the loads of parasites excreted into the environment. Rodent sampling was carried out during two seasons in 2014 in a Brazilian urban slum. We sampled rats’ feces, kidney imprints, and urine to identify and quantify helminth eggs/larvae and infection by L. interrogans. Eleven species/groups of helminths and L. interrogans were identified among 299 captured rats. Simple correlation tests and generalized linear models were performed to identify general patterns of coinfection and potential direction of effects, respectively, after controlling for consolidated environmental and host biotic variables. Significant associations were illustrated in an interaction network. Focusing on parasites with the potential to cause zoonoses among humans, we observed that coinfection between L. interrogans and the nematode Angiostrongylus cantonensis was significantly more frequent than expected. Reduced prevalence of A. cantonensis was found in the presence of Nippostrongylus brasiliensis, and N. brasiliensis intensities (eggs per gram of feces) were increased with greater intensities of both L. interrogans and Strongyloides sp., the latter of which was, in turn, found to increase the intensities of A. cantonensis. A higher probability of finding L. interrogans was associated with infection by Strongyloides sp. Our study provides a novel perspective on evaluating helminth coinfection profiles in populations of naturally infected urban rats, moving beyond previous studies which have been limited to descriptions of co‐occurrence. Noticeably, infection risk was dependent on coinfection and this should be accounted for when targeting the control of zoonotic pathogens in natural populations.

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 schematic illustration of direct and indirect eff ects of helminths and their products on host immune cells.As shown here

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.

SUMMARY Infectious diseases frequently have multiple potential routes of intraspecific transmission of pathogens within wildlife and other populations. For pathogens causing zoonotic diseases, knowing whether these transmission routes occur in the wild and their relative importance, is critical for understanding maintenance, improving control measures and ultimately preventing human disease. The Norway rat ( Rattus norvegicus ) is the primary reservoir of leptospirosis in the urban slums of Salvador, Brazil. There is biological evidence for potentially three different transmission routes of leptospire infection occurring in the rodent population. Using newly obtained prevalence data from rodents trapped at an urban slum field site, we present changes in cumulative risk of infection in relation to age-dependent transmission routes to infer which intra-specific transmission routes occur in the wild. We found that a significant proportion of animals leave the nest with infection and that the risk of infection increases throughout the lifetime of Norway rats. We did not observe a significant effect of sexual maturity on the risk of infection. In conclusion, our results suggest that vertical and environmental transmission of leptospirosis both occur in wild populations of Norway rats.

SUMMARYUrban slum environments in the tropics are conducive to the proliferation and the spread of rodent-borne zoonotic pathogens to humans. Calodium hepaticum (Brancroft, 1893) is a zoonotic nematode known to infect a variety of mammalian hosts, including humans. Norway rats (Rattus norvegicus) are considered the most important mammalian host of C. hepaticum and are therefore a potentially useful species to inform estimates of the risk to humans living in urban slum environments. There is a lack of studies systematically evaluating the role of demographic and environmental factors that influence both carriage and intensity of infection of C. hepaticum in rodents from urban slum areas within tropical regions. Carriage and the intensity of infection of C. hepaticum were studied in 402 Norway rats over a 2-year period in an urban slum in Salvador, Brazil. Overall, prevalence in Norway rats was 83% (337/402). Independent risk factors for C. hepaticum carriage in R. norvegicus were age and valley of capture. Of those infected the proportion with gross liver involvement (i.e. >75% of the liver affected, a proxy for a high level intensity of infection), was low (8%, 26/337). Sixty soil samples were collected from ten locations to estimate levels of environmental contamination and provide information on the potential risk to humans of contracting C. hepaticum from the environment. Sixty percent (6/10) of the sites were contaminated with C. hepaticum. High carriage levels of C. hepaticum within Norway rats and sub-standard living conditions within slum areas may increase the risk to humans of exposure to the infective eggs of C. hepaticum. This study supports the need for further studies to assess whether humans are becoming infected within this community and whether C. hepaticum is posing a significant risk to human health.