No abstract

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.

The globalization of mosquito-borne arboviral diseases has placed more than half of the human population at risk. Understanding arbovirus ecology, including the role individual mosquito species play in virus transmission cycles, is critical for limiting disease. Canonical virus-vector groupings, such as Aedes- or Culex-associated flaviviruses, have historically been defined using virus detection in field-collected mosquitoes, mosquito feeding patterns, and vector competence, which quantifies the intrinsic ability of a mosquito to become infected with and transmit a virus during a subsequent blood feed. Herein, we quantitatively synthesize data from 68 laboratory-based vector competence studies of 111 mosquito-virus pairings of Australian mosquito species and viruses of public health concern to further substantiate existing canonical vector-virus groupings and quantify variation within these groupings. Our synthesis reinforces current canonical vector-virus groupings but reveals substantial variation within them. While Aedes species were generally the most competent vectors of canonical “Aedes-associated flaviviruses” (such as dengue, Zika, and yellow fever viruses), there are some notable exceptions; for example, Aedes notoscriptus is an incompetent vector of dengue viruses. Culex spp. were the most competent vectors of many traditionally Culex-associated flaviviruses including West Nile, Japanese encephalitis and Murray Valley encephalitis viruses, although some Aedes spp. are also moderately competent vectors of these viruses. Conversely, many different mosquito genera were associated with the transmission of the arthritogenic alphaviruses, Ross River, Barmah Forest, and chikungunya viruses. We also confirm that vector competence is impacted by multiple barriers to infection and transmission within the mesenteron and salivary glands of the mosquito. Although these barriers represent important bottlenecks, species that were susceptible to infection with a virus were often likely to transmit it. Importantly, this synthesis provides essential information on what species need to be targeted in mosquito control programs.

Species distribution models (SDMs) are increasingly popular tools for profiling disease risk in ecology, particularly for infectious diseases of public health importance that include an obligate non-human host in their transmission cycle. SDMs can create high-resolution maps of host distribution across geographical scales, reflecting baseline risk of disease. However, as SDM computational methods have rapidly expanded, there are many outstanding methodological questions. Here we address key questions about SDM application, using schistosomiasis risk in Brazil as a case study. Schistosomiasis—a debilitating parasitic disease of poverty affecting over 200 million people across Africa, Asia, and South America—is transmitted to humans through contact with the free-living infectious stage ofSchistosomaspp. parasites released from freshwater snails, the parasite’s obligate intermediate hosts. In this study, we compared snail SDM performance across machine learning (ML) approaches (MaxEnt, Random Forest, and Boosted Regression Trees), geographic extents (national, regional, and state), types of presence data (expert-collected and publicly-available), and snail species (Biomphalaria glabrata,B. tenagophilaandB. straminea). We used high-resolution (1km) climate, hydrology, land-use/land-cover (LULC), and soil property data to describe the snails’ ecological niche and evaluated models on multiple criteria. Although all ML approaches produced comparable spatially cross-validated performance metrics, their suitability maps showed major qualitative differences that required validation based on local expert knowledge. Additionally, our findings revealed varying importance of LULC and bioclimatic variables for different snail species at different spatial scales. Finally, we found that models using publicly-available data predicted snail distribution with comparable AUC values to models using expert-collected data. This work serves as an instructional guide to SDM methods that can be applied to a range of vector-borne and zoonotic diseases. In addition, it advances our understanding of the relevant environment and bioclimatic determinants of schistosomiasis risk in Brazil.

Background Understanding pathogen-specific relationships with climate is crucial to informing interventions under climate change. Methods We matched spatiotemporal temperature, precipitation, surface water, and humidity data to data from a trial in rural Bangladesh that measured diarrhea and enteropathogen prevalence in children 0-2 years from 2012-2016. We fit generalized additive models and estimated percent changes in prevalence using projected precipitation under Shared Socio-Economic pathways describing sustainable development (SSP1), middle of the road (SSP2), and fossil fuel development (SSP5) scenarios. Findings An increase from 15 degrees C to 30 degrees C in weekly average temperature was associated with 5.0% higher diarrhea, 6.4% higher Norovirus, and 13.0% higher STEC prevalence. Above-median precipitation was associated with 1.27-fold (95% CI 0.99, 1.61) higher diarrhea; higher Cryptosporidium, tEPEC, ST-ETEC, STEC, Shigella, EAEC, Campylobacter, Aeromonas, and Adenovirus 40/41; and lower aEPEC, Giardia, Sapovirus, and Norovirus prevalence. Other associations were weak or null. Compared to the study period, diarrhea prevalence was similar under SSP1 (7%), 3.4% (2.7%, 4.3%) higher under SSP2, and 5.7% (4.4%, 7.0%) higher under SSP5. Prevalence of pathogens responsible for a large share of moderate-to-severe diarrhea in this setting (Shigella, Aeromonas) were 13-20% higher under SSP2 and SSP5. Interpretation Higher temperatures and precipitation were associated with higher prevalence of diarrhea and multiple enteropathogens; higher precipitation was associated with lower prevalence of some enteric viruses. Under likely climate change scenarios, we projected increased prevalence of diarrhea and enteropathogens responsible for clinical illness. Our findings inform pathogen-specific adaptation and mitigation strategies and priorities for vaccine development.