Comparing transmission potential networks based on social network surveys, close contacts and environmental overlap in rural Madagascar

Disentangling social, environmental, and zoonotic transmission pathways of a gastrointestinal protozoan (Blastocystis spp.) in northeast Madagascar

OBJECTIVES

Understanding disease transmission is a fundamental challenge in ecology. We used transmission potential networks to investigate whether a gastrointestinal protozoan (Blastocystis spp.) is spread through social, environmental, and/or zoonotic pathways in rural northeast Madagascar.

MATERIALS AND METHODS

We obtained survey data, household GPS coordinates, and fecal samples from 804 participants. Surveys inquired about social contacts, agricultural activity, and sociodemographic characteristics. Fecal samples were screened for Blastocystis using DNA metabarcoding. We also tested 133 domesticated animals for Blastocystis. We used network autocorrelation models and permutation tests (network k-test) to determine whether networks reflecting different transmission pathways predicted infection.

RESULTS

We identified six distinct Blastocystis subtypes among study participants and their domesticated animals. Among the 804 human participants, 74% (n = 598) were positive for at least one Blastocystis subtype. Close proximity to infected households was the most informative predictor of infection with any subtype (model averaged OR [95% CI]: 1.56 [1.33-1.82]), and spending free time with infected participants was not an informative predictor of infection (model averaged OR [95% CI]: 0.95 [0.82-1.10]). No human participant was infected with the same subtype as the domesticated animals they owned.

DISCUSSION

Our findings suggest that Blastocystis is most likely spread through environmental pathways within villages, rather than through social or animal contact. The most likely mechanisms involve fecal contamination of the environment by infected individuals or shared food and water sources. These findings shed new light on human-pathogen ecology and mechanisms for reducing disease transmission in rural, low-income settings.

How market integration impacts human disease ecology

Market integration (MI), or the shift from subsistence to market-based livelihoods, profoundly influences health, yet its impacts on infectious diseases remain underexplored. Here, we synthesize the current understanding of MI and infectious disease to stimulate more research, specifically aiming to leverage concepts and tools from disease ecology and related fields to generate testable hypotheses. Embracing a One Health perspective, we examine both human-to-human and zoonotic transmission pathways in their environmental contexts to assess how MI alters infectious disease exposure and susceptibility in beneficial, detrimental and mixed ways. For human-to-human transmission, we consider how markets expand contact networks in ways that facilitate infectious disease transmission while also increasing access to hygiene products and housing materials that likely reduce infections. For zoonotic transmission, MI influences exposures to pathogens through agricultural intensification and other market-driven processes that may increase or decrease human encounters with disease reservoirs or vectors in their shared environments. We also consider how MI-driven changes in noncommunicable diseases affect immunocompetence and susceptibility to infectious disease. Throughout, we identify statistical, survey and laboratory methods from ecology and the social sciences that will advance interdisciplinary research on MI and infectious disease.

HIARA study protocol: impacts of artificial coral reef development on fisheries, human livelihoods and health in southwestern Madagascar

The Health Impacts of Artificial Reef Advancement (HIARA; in the Malagasy language, "together") study cohort was set up in December 2022 to assess the economic and nutritional importance of seafood for the coastal Malagasy population living along the Bay of Ranobe in southwestern Madagascar. Over the course of the research, which will continue until at least 2026, the primary question we seek to answer is whether the creation of artificial coral reefs can rehabilitate fish biomass, increase fish catch, and positively influence fisher livelihoods, community nutrition, and mental health. Through prospective, longitudinal monitoring of the ecological and social systems of Bay of Ranobe, we aim to understand the influence of seasonal and long-term shifts in marine ecological resources and their benefits to human livelihoods and health. Fourteen communities (12 coastal and two inland) were enrolled into the study including 450 households across both the coastal (n = 360 households) and inland (n = 90 households) ecosystems. In the ecological component, we quantify the extent and health of coral reef ecosystems and collect data on the diversity and abundance of fisheries resources. In the social component, we collect data on the diets, resource acquisition strategies, fisheries and agricultural practices, and other social, demographic and economic indicators, repeated every 3 months. At these visits, clinical measures are collected including anthropometric measures, blood pressure, and mental health diagnostic screening. By analyzing changes in fish catch and consumption arising from varying distances to artificial reef construction and associated impacts on fish biomass, our cohort study could provide valuable insights into the public health impacts of artificial coral reef construction on local populations. Specifically, we aim to assess the impact of changes in fish catch (caused by artificial reefs) on various health outcomes, such as stunting, underweight, wasting, nutrient intake, hypertension, anxiety, and depression.

Sociodemographic Variables Can Guide Prioritized Testing Strategies for Epidemic Control in Resource-Limited Contexts

BACKGROUND

Targeted surveillance allows public health authorities to implement testing and isolation strategies when diagnostic resources are limited, and can be implemented via the consideration of social network topologies. However, it remains unclear how to implement such surveillance and control when network data are unavailable.

METHODS

We evaluated the ability of sociodemographic proxies of degree centrality to guide prioritized testing of infected individuals compared to known degree centrality. Proxies were estimated via readily available sociodemographic variables (age, gender, marital status, educational attainment, household size). We simulated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics via a susceptible-exposed-infected-recovered individual-based model on 2 contact networks from rural Madagascar to test applicability of these findings to low-resource contexts.

RESULTS

Targeted testing using sociodemographic proxies performed similarly to targeted testing using known degree centralities. At low testing capacity, using proxies reduced infection burden by 22%-33% while using 20% fewer tests, compared to random testing. By comparison, using known degree centrality reduced the infection burden by 31%-44% while using 26%-29% fewer tests.

CONCLUSIONS

We demonstrate that incorporating social network information into epidemic control strategies is an effective countermeasure to low testing capacity and can be implemented via sociodemographic proxies when social network data are unavailable.

Epidemiological connectivity between humans and animals across an urban landscape

Urbanization is predicted to be a key driver of disease emergence through human exposure to novel, animal-borne pathogens. However, while we suspect that urban landscapes are primed to expose people to novel animal-borne diseases, evidence for the mechanisms by which this occurs is lacking. To address this, we studied how bacterial genes are shared between wild animals, livestock, and humans (n = 1,428) across Nairobi, Kenya-one of the world's most rapidly developing cities. Applying a multilayer network framework, we show that low biodiversity (of both natural habitat and vertebrate wildlife communities), coupled with livestock management practices and more densely populated urban environments, promotes sharing of Escherichia coli-borne bacterial mobile genetic elements between animals and humans. These results provide empirical support for hypotheses linking resource provision, the biological simplification of urban landscapes, and human and livestock demography to urban dynamics of cross-species pathogen transmission at a landscape scale. Urban areas where high densities of people and livestock live in close association with synanthropes (species such as rodents that are more competent reservoirs for zoonotic pathogens) should be prioritized for disease surveillance and control.

Deriving spatially explicit direct and indirect interaction networks from animal movement data

Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems ["GPS"]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous-time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions-individuals occurring at the same location, but at different times-while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease-relevant interaction networks for two behaviorally differentiated species, wild pigs (Sus scrofa) that can host African Swine Fever and mule deer (Odocoileus hemionus) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30-min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM-Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine-scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer-resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers.

Multilayer networks of plasmid genetic similarity reveal potential pathways of gene transmission

Antimicrobial resistance (AMR) is a significant threat to public health. Plasmids are principal vectors of AMR genes, significantly contributing to their spread and mobility across hosts. Nevertheless, little is known about the dynamics of plasmid genetic exchange across animal hosts. Here, we use theory and methodology from network and disease ecology to investigate the potential of gene transmission between plasmids using a data set of 21 plasmidomes from a single dairy cow population. We constructed a multilayer network based on pairwise plasmid genetic similarity. Genetic similarity is a signature of past genetic exchange that can aid in identifying potential routes and mechanisms of gene transmission within and between cows. Links between cows dominated the transmission network, and plasmids containing mobility genes were more connected. Modularity analysis revealed a network cluster where all plasmids contained a mobM gene, and one where all plasmids contained a beta-lactamase gene. Cows that contain both clusters also share transmission pathways with many other cows, making them candidates for super-spreading. In support, we found signatures of gene super-spreading in which a few plasmids and cows are responsible for most gene exchange. An agent-based transmission model showed that a new gene invading the cow population will likely reach all cows. Finally, we showed that edge weights contain a non-random signature for the mechanisms of gene transmission, allowing us to differentiate between dispersal and genetic exchange. These results provide insights into how genes, including those providing AMR, spread across animal hosts.

On limitations of uniplex networks for modeling multiplex contagion

Many network contagion processes are inherently multiplex in nature, yet are often reduced to processes on uniplex networks in analytic practice. We therefore examine how data modeling choices can affect the predictions of contagion processes. We demonstrate that multiplex contagion processes are not simply the union of contagion processes over their constituent uniplex networks. We use multiplex network data from two different contexts-(1) a behavioral network to represent their potential for infectious disease transmission using a "simple" epidemiological model, and (2) users from online social network sites to represent their potential for information spread using a threshold-based "complex" contagion process. Our results show that contagion on multiplex data is not captured accurately in models developed from the uniplex networks even when they are combined, and that the nature of the differences between the (combined) uniplex and multiplex results depends on the specific spreading process over these networks.

Addressing the socioeconomic divide in computational modeling for infectious diseases

The COVID-19 pandemic has highlighted how structural social inequities fundamentally shape disease dynamics. Here, the authors provide a set of practical and methodological recommendations to address socioeconomic vulnerabilities in epidemic models.