Abstract Predicting how increasing intensity of human–environment interactions affects pathogen transmission is essential to anticipate changing disease risks and identify appropriate mitigation strategies. Vector-borne diseases (VBDs) are highly responsive to environmental changes, but such responses are notoriously difficult to isolate because pathogen transmission depends on a suite of ecological and social responses in vectors and hosts that may differ across species. Here we use the emerging tools of cumulative pressure mapping and machine learning to better understand how the occurrence of six medically important VBDs, differing in ecology from sylvatic to urban, respond to multidimensional effects of human pressure. We find that not only is human footprint—an index of human pressure, incorporating built environments, energy and transportation infrastructure, agricultural lands and human population density—an important predictor of VBD occurrence, but there are clear thresholds governing the occurrence of different VBDs. Across a spectrum of human pressure, diseases associated with lower human pressure, including malaria, cutaneous leishmaniasis and visceral leishmaniasis, give way to diseases associated with high human pressure, such as dengue, chikungunya and Zika. These heterogeneous responses of VBDs to human pressure highlight thresholds of land-use transitions that may lead to abrupt shifts in infectious disease burdens and public health needs.
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The influence of vector‐borne disease on human history: socio‐ecological mechanisms
Vector‐borne diseases (VBDs) are embedded within complex socio‐ecological systems. While research has traditionally focused on the direct effects of VBDs on human morbidity and mortality, it is increasingly clear that their impacts are much more pervasive. VBDs are dynamically linked to feedbacks between environmental conditions, vector ecology, disease burden, and societal responses that drive transmission. As a result, VBDs have had profound influence on human history. Mechanisms include: (1) killing or debilitating large numbers of people, with demographic and population‐level impacts; (2) differentially affecting populations based on prior history of disease exposure, immunity, and resistance; (3) being weaponised to promote or justify hierarchies of power, colonialism, racism, classism and sexism; (4) catalysing changes in ideas, institutions, infrastructure, technologies and social practices in efforts to control disease outbreaks; and (5) changing human relationships with the land and environment. We use historical and archaeological evidence interpreted through an ecological lens to illustrate how VBDs have shaped society and culture, focusing on case studies from four pertinent VBDs: plague, malaria, yellow fever and trypanosomiasis. By comparing across diseases, time periods and geographies, we highlight the enormous scope and variety of mechanisms by which VBDs have influenced human history.
more » « less- PAR ID:
- 10372462
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Ecology Letters
- Volume:
- 24
- Issue:
- 4
- ISSN:
- 1461-023X
- Format(s):
- Medium: X Size: p. 829-846
- Size(s):
- p. 829-846
- Sponsoring Org:
- National Science Foundation
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Abstract Background Vector-borne diseases (VBDs) are important contributors to the global burden of infectious diseases due to their epidemic potential, which can result in significant population and economic impacts. Oropouche fever, caused by Oropouche virus (OROV), is an understudied zoonotic VBD febrile illness reported in Central and South America. The epidemic potential and areas of likely OROV spread remain unexplored, limiting capacities to improve epidemiological surveillance.
Methods To better understand the capacity for spread of OROV, we developed spatial epidemiology models using human outbreaks as OROV transmission-locality data, coupled with high-resolution satellite-derived vegetation phenology. Data were integrated using hypervolume modeling to infer likely areas of OROV transmission and emergence across the Americas.
Results Models based on one-support vector machine hypervolumes consistently predicted risk areas for OROV transmission across the tropics of Latin America despite the inclusion of different parameters such as different study areas and environmental predictors. Models estimate that up to 5 million people are at risk of exposure to OROV. Nevertheless, the limited epidemiological data available generates uncertainty in projections. For example, some outbreaks have occurred under climatic conditions outside those where most transmission events occur. The distribution models also revealed that landscape variation, expressed as vegetation loss, is linked to OROV outbreaks.
Conclusions Hotspots of OROV transmission risk were detected along the tropics of South America. Vegetation loss might be a driver of Oropouche fever emergence. Modeling based on hypervolumes in spatial epidemiology might be considered an exploratory tool for analyzing data-limited emerging infectious diseases for which little understanding exists on their sylvatic cycles. OROV transmission risk maps can be used to improve surveillance, investigate OROV ecology and epidemiology, and inform early detection.
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Our world is undergoing rapid planetary changes driven by human activities, often mediated by economic incentives and resource management, affecting all life on Earth. Concurrently, many infectious diseases have recently emerged or spread into new populations. Mounting evidence suggests that global change—including climate change, land-use change, urbanization, and global movement of individuals, species, and goods—may be accelerating disease emergence by reshaping ecological systems in concert with socioeconomic factors. Here, we review insights, approaches, and mechanisms by which global change drives disease emergence from a disease ecology perspective. We aim to spur more interdisciplinary collaboration with economists and identification of more effective and sustainable interventions to prevent disease emergence. While almost all infectious diseases change in response to global change, the mechanisms and directions of these effects are system specific, requiring new, integrated approaches to disease control that recognize linkages between environmental and economic sustainability and human and planetary health. Expected final online publication date for the Annual Review of Resource Economics, Volume 14 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.more » « less
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Abstract Understanding seasonal patterns of activity, or phenology, of vector species is fundamental to determining seasonality of disease risk and epidemics of vector‐borne disease. Spatiotemporal variation in abiotic conditions can influence variation in phenological patterns and life history events, which can dramatically influence the ecological role and human impact of a species. For arthropod vectors of human diseases such as ticks, these phenological patterns determine human exposure risk, yet how abiotic conditions interact to determine suitable conditions for host‐seeking of vector species is difficult to disentangle.
Here, we use MaxEnt to model spatial patterns and differences in host‐seeking phenology of the western blacklegged tick (
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Synthesis and applications . Modelling the seasonal activity of the western blacklegged tick, we find both a longer window for host‐feeding and more protracted risk of human exposure to this vector species in northern than southern California. We further identify key environmental factors associated with these patterns, including precipitation and temperature that are otherwise challenging to elucidate in field and laboratory studies over large spatial scales. Moreover, we illustrate how species distribution models, in combination with temporally refined species occurrence and environmental data, can be used to investigate environmental factors predictive of geographic variation in seasonality or phenology of vector species. This produces not only novel ecological insight, but key information for public health practitioners in managing vector‐borne disease transmission and targeting public outreach and interventions. -
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Abstract 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.
