The Hidden Hand of Asymptomatic Infection Hinders Control of Neglected Tropical Diseases: A Modeling Analysis.

BACKGROUND: Neglected tropical diseases are responsible for considerable morbidity and mortality in low-income populations. International efforts have reduced their global burden, but transmission is persistent and case-finding-based interventions rarely target asymptomatic individuals. METHODS: We develop a generic mathematical modeling framework for analyzing the dynamics of visceral leishmaniasis in the Indian sub-continent (VL), gambiense sleeping sickness (gHAT), and Chagas disease and use it to assess the possible contribution of asymptomatics who later develop disease (pre-symptomatics) and those who do not (non-symptomatics) to the maintenance of infection. Plausible interventions, including active screening, vector control, and reduced time to detection, are simulated for the three diseases. RESULTS: We found that the high asymptomatic contribution to transmission for Chagas and gHAT and the apparently high basic reproductive number of VL may undermine long-term control. However, the ability to treat some asymptomatics for Chagas and gHAT should make them more controllable, albeit over relatively long time periods due to the slow dynamics of these diseases. For VL, the toxicity of available therapeutics means the asymptomatic population cannot currently be treated, but combining treatment of symptomatics and vector control could yield a quick reduction in transmission. CONCLUSIONS: Despite the uncertainty in natural history, it appears there is already a relatively good toolbox of interventions to eliminate gHAT, and it is likely that Chagas will need improvements to diagnostics and their use to better target pre-symptomatics. The situation for VL is less clear, and model predictions could be improved by additional empirical data. However, interventions may have to improve to successfully eliminate this disease.

Demographic, environmental and physiological predictors of gastrointestinal parasites in urban raccoons.

Raccoons are host to diverse gastrointestinal parasites, but little is known about the ecology of these parasites in terms of their interactions with each other during coinfections, their interactions with host physiology and environmental factors, and their impact on raccoon health and survival. As a first step, we investigated the patterns of parasite infection and their demographic distribution in an urban-suburban population of raccoons trapped in the summers and autumns of 2018 and 2019. We collected faecal samples, demographic data, morphometric measurements, and blood smears, and used GPS data to classify trapping location by land cover type. Faecal floats were performed to detect and quantify gastrointestinal nematode eggs and coccidia oocysts, and white blood cell differentials were performed on blood smears to characterise white blood cell distributions. Data were analysed cross-sectionally and, where possible, longitudinally, using generalised linear models. Overall, 62.6% of sampled raccoons were infected with gastrointestinal nematodes, and 82.2% were infected with gastrointestinal coccidia. We analysed predictors of infection status and faecal egg count for three different morphotypes of nematode-Baylisascaris, strongyle, and capillariid nematodes-and found that infection status and egg count varied with Year, Month, Age class, Land cover, and coinfection status, though the significance of these predictors varied between nematode types. Gastrointestinal coccidia prevalence varied with Year, Month, Age class, strongyle infection status, and capillariid infection status. Coccidia oocyst counts were lower in adults and in October, but higher in females and in raccoons trapped in areas with natural land cover; furthermore, coccidia oocysts were positively associated with capillariid faecal egg counts. We found no evidence that gastrointestinal parasites influenced raccoon body condition or overwinter mortality, and so conclude that raccoons, though harbouring diverse and abundant gastrointestinal parasites, may be relatively tolerant of these parasites.

From serological surveys to disease burden: a modelling pipeline for Chagas disease.

In 2012, the World Health Organization (WHO) set the elimination of Chagas disease intradomiciliary vectorial transmission as a goal by 2020. After a decade, some progress has been made, but the new 2021-2030 WHO roadmap has set even more ambitious targets. Innovative and robust modelling methods are required to monitor progress towards these goals. We present a modelling pipeline using local seroprevalence data to obtain national disease burden estimates by disease stage. Firstly, local seroprevalence information is used to estimate spatio-temporal trends in the Force-of-Infection (FoI). FoI estimates are then used to predict such trends across larger and fine-scale geographical areas. Finally, predicted FoI values are used to estimate disease burden based on a disease progression model. Using Colombia as a case study, we estimated that the number of infected people would reach 506 000 (95% credible interval (CrI) = 395 000-648 000) in 2020 with a 1.0% (95%CrI = 0.8-1.3%) prevalence in the general population and 2400 (95%CrI = 1900-3400) deaths (approx. 0.5% of those infected). The interplay between a decrease in infection exposure (FoI and relative proportion of acute cases) was overcompensated by a large increase in population size and gradual population ageing, leading to an increase in the absolute number of Chagas disease cases over time. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.

Disease outbreaks select for mate choice and coat color in wolves.

We know much about pathogen evolution and the emergence of new disease strains, but less about host resistance and how it is signaled to other individuals and subsequently maintained. The cline in frequency of black-coated wolves (Canis lupus) across North America is hypothesized to result from a relationship with canine distemper virus (CDV) outbreaks. We tested this hypothesis using cross-sectional data from wolf populations across North America that vary in the prevalence of CDV and the allele that makes coats black, longitudinal data from Yellowstone National Park, and modeling. We found that the frequency of CDV outbreaks generates fluctuating selection that results in heterozygote advantage that in turn affects the frequency of the black allele, optimal mating behavior, and black wolf cline across the continent.

Too few, too late: U.S. Endangered Species Act undermined by inaction and inadequate funding.

This year, the Conference of Parties to the Convention on Biological Diversity will meet to finalize a post 2020-framework for biodiversity conservation, necessitating critical analysis of current barriers to conservation success. Here, we tackle one of the enduring puzzles about the U.S. Endangered Species Act, often considered a model for endangered species protection globally: Why have so few species been successfully recovered? For the period of 1992-2020, we analyzed trends in the population sizes of species of concern, trends in the time between when species are first petitioned for listing and when they actually receive protection, and trends in funding for the listing and recovery of imperiled species. We find that small population sizes at time of listing, coupled with delayed protection and insufficient funding, continue to undermine one of the world's strongest laws for protecting biodiversity.

Evidence gaps and diversity among potential win-win solutions for conservation and human infectious disease control.

As sustainable development practitioners have worked to "ensure healthy lives and promote well-being for all" and "conserve life on land and below water", what progress has been made with win-win interventions that reduce human infectious disease burdens while advancing conservation goals? Using a systematic literature review, we identified 46 proposed solutions, which we then investigated individually using targeted literature reviews. The proposed solutions addressed diverse conservation threats and human infectious diseases, and thus, the proposed interventions varied in scale, costs, and impacts. Some potential solutions had medium-quality to high-quality evidence for previous success in achieving proposed impacts in one or both sectors. However, there were notable evidence gaps within and among solutions, highlighting opportunities for further research and adaptive implementation. Stakeholders seeking win-win interventions can explore this Review and an online database to find and tailor a relevant solution or brainstorm new solutions.

Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease.

BACKGROUND: Chagas disease is a long-lasting disease with a prolonged asymptomatic period. Cumulative indices of infection such as prevalence do not shed light on the current epidemiological situation, as they integrate infection over long periods. Instead, metrics such as the Force-of-Infection (FoI) provide information about the rate at which susceptible people become infected and permit sharper inference about temporal changes in infection rates. FoI is estimated by fitting (catalytic) models to available age-stratified serological (ground-truth) data. Predictive FoI modelling frameworks are then used to understand spatial and temporal trends indicative of heterogeneity in transmission and changes effected by control interventions. Ideally, these frameworks should be able to propagate uncertainty and handle spatiotemporal issues. METHODOLOGY/PRINCIPAL FINDINGS: We compare three methods in their ability to propagate uncertainty and provide reliable estimates of FoI for Chagas disease in Colombia as a case study: two Machine Learning (ML) methods (Boosted Regression Trees (BRT) and Random Forest (RF)), and a Linear Model (LM) framework that we had developed previously. Our analyses show consistent results between the three modelling methods under scrutiny. The predictors (explanatory variables) selected, as well as the location of the most uncertain FoI values, were coherent across frameworks. RF was faster than BRT and LM, and provided estimates with fewer extreme values when extrapolating to areas where no ground-truth data were available. However, BRT and RF were less efficient at propagating uncertainty. CONCLUSIONS/SIGNIFICANCE: The choice of FoI predictive models will depend on the objectives of the analysis. ML methods will help characterise the mean behaviour of the estimates, while LM will provide insight into the uncertainty surrounding such estimates. Our approach can be extended to the modelling of FoI patterns in other Chagas disease-endemic countries and to other infectious diseases for which serosurveys are regularly conducted for surveillance.

The costs and benefits of primary prevention of zoonotic pandemics.

The lives lost and economic costs of viral zoonotic pandemics have steadily increased over the past century. Prominent policymakers have promoted plans that argue the best ways to address future pandemic catastrophes should entail, "detecting and containing emerging zoonotic threats." In other words, we should take actions only after humans get sick. We sharply disagree. Humans have extensive contact with wildlife known to harbor vast numbers of viruses, many of which have not yet spilled into humans. We compute the annualized damages from emerging viral zoonoses. We explore three practical actions to minimize the impact of future pandemics: better surveillance of pathogen spillover and development of global databases of virus genomics and serology, better management of wildlife trade, and substantial reduction of deforestation. We find that these primary pandemic prevention actions cost less than 1/20th the value of lives lost each year to emerging viral zoonoses and have substantial cobenefits.

Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study.

Age-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty.In Colombia, 76 serosurveys (1980-2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia.A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions.Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease.

Dietary abundance distributions: Dominance and diversity in vertebrate diets.

Diet composition is among the most important yet least understood dimensions of animal ecology. Inspired by the study of species abundance distributions (SADs), we tested for generalities in the structure of vertebrate diets by characterising them as dietary abundance distributions (DADs). We compiled data on 1167 population-level diets, representing >500 species from six vertebrate classes, spanning all continents and oceans. DADs near-universally (92.5%) followed a hollow-curve shape, with scant support for other plausible rank-abundance-distribution shapes. This strong generality is inherently related to, yet incompletely explained by, the SADs of available food taxa. By quantifying dietary generalisation as the half-saturation point of the cumulative distribution of dietary abundance (sp50, minimum number of foods required to account for 50% of diet), we found that vertebrate populations are surprisingly specialised: in most populations, fewer than three foods accounted for at least half the diet. Variation in sp50 was strongly associated with consumer type, with carnivores being more specialised than herbivores or omnivores. Other methodological (sampling method and effort, taxonomic resolution), biological (body mass, frugivory) and biogeographic (latitude) factors influenced sp50 to varying degrees. Future challenges include identifying the mechanisms underpinning the hollow-curve DAD, its generality beyond vertebrates, and the biological determinants of dietary generalisation.

A solution scan of societal options to reduce transmission and spread of respiratory viruses: SARS-CoV-2 as a case study.

Societal biosecurity - measures built into everyday society to minimize risks from pests and diseases - is an important aspect of managing epidemics and pandemics. We aimed to identify societal options for reducing the transmission and spread of respiratory viruses. We used SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) as a case study to meet the immediate need to manage the COVID-19 pandemic and eventually transition to more normal societal conditions, and to catalog options for managing similar pandemics in the future. We used a 'solution scanning' approach. We read the literature; consulted psychology, public health, medical, and solution scanning experts; crowd-sourced options using social media; and collated comments on a preprint. Here, we present a list of 519 possible measures to reduce SARS-CoV-2 transmission and spread. We provide a long list of options for policymakers and businesses to consider when designing biosecurity plans to combat SARS-CoV-2 and similar pathogens in the future. We also developed an online application to help with this process. We encourage testing of actions, documentation of outcomes, revisions to the current list, and the addition of further options.

Towards an ecosystem model of infectious disease.

Increasingly intimate associations between human society and the natural environment are driving the emergence of novel pathogens, with devastating consequences for humans and animals alike. Prior to emergence, these pathogens exist within complex ecological systems that are characterized by trophic interactions between parasites, their hosts and the environment. Predicting how disturbance to these ecological systems places people and animals at risk from emerging pathogens-and the best ways to manage this-remains a significant challenge. Predictive systems ecology models are powerful tools for the reconstruction of ecosystem function but have yet to be considered for modelling infectious disease. Part of this stems from a mistaken tendency to forget about the role that pathogens play in structuring the abundance and interactions of the free-living species favoured by systems ecologists. Here, we explore how developing and applying these more complete systems ecology models at a landscape scale would greatly enhance our understanding of the reciprocal interactions between parasites, pathogens and the environment, placing zoonoses in an ecological context, while identifying key variables and simplifying assumptions that underly pathogen host switching and animal-to-human spillover risk. As well as transforming our understanding of disease ecology, this would also allow us to better direct resources in preparation for future pandemics.

Host population dynamics in the face of an evolving pathogen.

Interactions between hosts and pathogens are dynamic at both ecological and evolutionary levels. In the resultant 'eco-evolutionary dynamics' ecological and evolutionary processes affect each other. For example, the house finch Haemorhous mexicanus and its recently emerged pathogen, the bacterium Mycoplasma gallisepticum, form a system in which evidence suggests that changes in bacterial virulence through time enhance levels of host immunity in ways that drive the evolution of virulence in an arms race. We use data from two associated citizen science projects in order to determine whether this arms race has had any detectable effect at the population level in the north-eastern United States. We used data from two citizen science projects, based on observations of birds at bird feeders, which provide information on the long-term changes in sizes of aggregations of house finches (host population density), and the probabilities that these house finches have observable disease (disease prevalence). The initial emergence of M. gallisepticum caused a rapid halving of house finch densities; this was then followed by house finch populations remaining stable or slowly declining. Disease prevalence also decreased sharply after the initial emergence and has remained low, although with fluctuations through time. Surprisingly, while initially higher local disease prevalence was found at sites with higher local densities of finches, this relationship has reversed over time. The ability of a vertebrate host species, with a generation time of at least 1 year, to maintain stable populations in the face of evolved higher virulence of a bacterium, with generation times measurable in minutes, suggests that genetic changes in the host are insufficient to explain the observed population-level patterns. We suggest that acquired immunity plays an important role in the observed interaction between house finches and M. gallisepticum.

A metapopulation model of social group dynamics and disease applied to Yellowstone wolves.

The population structure of social species has important consequences for both their demography and transmission of their pathogens. We develop a metapopulation model that tracks two key components of a species' social system: average group size and number of groups within a population. While the model is general, we parameterize it to mimic the dynamics of the Yellowstone wolf population and two associated pathogens: sarcoptic mange and canine distemper. In the initial absence of disease, we show that group size is mainly determined by the birth and death rates and the rates at which groups fission to form new groups. The total number of groups is determined by rates of fission and fusion, as well as environmental resources and rates of intergroup aggression. Incorporating pathogens into the models reduces the size of the host population, predominantly by reducing the number of social groups. Average group size responds in more subtle ways: infected groups decrease in size, but uninfected groups may increase when disease reduces the number of groups and thereby reduces intraspecific aggression. Our modeling approach allows for easy calculation of prevalence at multiple scales (within group, across groups, and population level), illustrating that aggregate population-level prevalence can be misleading for group-living species. The model structure is general, can be applied to other social species, and allows for a dynamic assessment of how pathogens can affect social structure and vice versa.

COVID-Clarity demands unification of health and environmental policy.

Spillover of novel pathogens from wildlife to people, such as the virus responsible for the COVID-19 pandemic, is increasing and this trend is most strongly associated with tropical deforestation driven by agricultural expansion. This same process is eroding natural capital, reducing forest-associated health co-benefits, and accelerating climate change. Protecting and promoting tropical forests is one of the most immediate steps we can take to simultaneously mitigate climate change while reducing the risk of future pandemics; however, success in this undertaking will require greater connectivity of policy initiatives from local to global, as well as unification of health and environmental policy.

Preliminary Characterization of Triatomine Bug Blood Meals on the Island of Trinidad Reveals Opportunistic Feeding Behavior on Both Human and Animal Hosts.

Chagas disease is a neglected tropical disease caused by infection with Trypanosoma cruzi. The parasite is endemic to the Americas, including the Caribbean, where it is vectored by triatomine bugs. Although Chagas disease is not considered a public health concern in the Caribbean islands, studies in Trinidad have found T. cruzi-seropositive humans and T. cruzi-infected triatomine bugs. However, little is known about triatomine bug host preferences in Trinidad, making it difficult to evaluate local risk of vector-borne T. cruzi transmission to humans. To investigate this question, we collected triatomine bugs in Trinidad and diagnosed each one for T. cruzi infection (microscopy and PCR). We then carried out a blood meal analysis using DNA extracted from each bug (PCR and sequencing). Fifty-five adult bugs (54 Panstrongylus geniculatus and one Rhodnius pictipes) were collected from five of 21 sample sites. All successful collection sites were residential. Forty-six out of the 55 bugs (83.6%) were infected with T. cruzi. Fifty-three blood meal hosts were successfully analyzed (one per bug), which consisted of wild birds (7% of all blood meals), wild mammals (17%), chickens (19%), and humans (57%). Of the 30 bugs with human blood meals, 26 (87%) were from bugs infected with T. cruzi. Although preliminary, our results align with previous work in which P. geniculatus in Trinidad had high levels of T. cruzi infection. Furthermore, our findings suggest that P. geniculatus moves between human and animal environments in Trinidad, feeding opportunistically on a wide range of species. Our findings highlight a critical need for further studies of Chagas disease in Trinidad in order to estimate the public health risk and implement necessary preventative and control measures.

Investigating the Dynamics of Elk Population Size and Body Mass in a Seasonal Environment Using a Mechanistic Integral Projection Model.

Environmentally mediated changes in body size often underlie population responses to environmental change, yet this is not a universal phenomenon. Understanding when phenotypic change underlies population responses to environmental change is important for obtaining insights and robust predictions of population dynamics in a changing world. We develop a dynamic integral projection model that mechanistically links environmental conditions to demographic rates and phenotypic traits (body size) via changes in resource availability and individual energetics. We apply the model to the northern Yellowstone elk population and explore population responses to changing patterns of seasonality, incorporating the interdependence of growth, demography, and density-dependent processes operating through population feedback on available resources. We found that small changes in body size distributions can have large impacts on population dynamics but need not cause population responses to environmental change. Environmental changes that altered demographic rates directly, via increasing or decreasing resource availability, led to large population impacts in the absence of substantial changes to body size distributions. In contrast, environmentally driven shifts in body size distributions could occur with little consequence for population dynamics when the effect of environmental change on resource availability was small and seasonally restricted and when strong density-dependent processes counteracted expected population responses. These findings highlight that a robust understanding of how associations between body size and demography influence population responses to environmental change will require knowledge of the shape of the relationship between phenotypic distributions and vital rates, the population status with regard to its carrying capacity, and importantly the nature of the environmentally driven change in body size and carrying capacity.

Do parasite infections interfere with immunisation? A review and meta-analysis.

Immune responses to vaccination are heterogeneous between individuals; the same vaccine that provides protection in one circumstance may be ineffective in another. One factor that could influence the response to vaccination is concurrent or prior infection with unrelated parasites. Here, we review both the experimental and epidemiological literature on parasite-vaccine interactions, and present a meta-analysis of the published data. In total, our review returned 101 relevant articles, 50 of which met criteria for meta-analysis. Parasite factors potentially affecting vaccination include the type of parasite involved, the stage of infection, and the timing of infection relative to vaccination. Vaccine factors affecting likelihood of interference by parasites include vaccine formulation, route of administration, and the type of immune response required to provide protection against the target antigen. Our meta-analysis of these data show three key things: (1) parasite infections at the time of vaccination result in worse immunisation outcomes, (2) chronic helminth infections are more likely to negatively impact immunisation than acute helminth infections, and (3) thymus-dependent vaccines are more susceptible to parasite interference than thymus-independent vaccines. Our findings highlight the importance of considering and mitigating parasite infections: by taking parasites into account, it should be possible to more effectively immunise individuals and populations.