312 research outputs found
Co-Selection of Metal- and Antibiotic-Resistance Following Nanoparticle Exposure
No full textThis research was supported by the Undergraduate Research Opportunities Program (UROP).Wesolowski, Amy R.; Mitchell, Stephanie. (2020). Co-Selection of Metal- and Antibiotic-Resistance Following Nanoparticle Exposure. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/213302
The use of census migration data to approximate human movement patterns across temporal scales
No full textHuman movement plays a key role in economies and development, the delivery of services, and the spread of infectious diseases. However, it remains poorly quantified partly because reliable data are often lacking, particularly for low-income countries. The most widely available are migration data from human population censuses, which provide valuable information on relatively long timescale relocations across countries, but do not capture the shorter-scale patterns, trips less than a year, that make up the bulk of human movement. Census-derived migration data may provide valuable proxies for shorter-term movements however, as substantial migration between regions can be indicative of well connected places exhibiting high levels of movement at finer time scales, but this has never been examined in detail. Here, an extensive mobile phone usage data set for Kenya was processed to extract movements between counties in 2009 on weekly, monthly, and annual time scales and compared to data on change in residence from the national census conducted during the same time period. We find that the relative ordering across Kenyan counties for incoming, outgoing and between-county movements shows strong correlations. Moreover, the distributions of trip durations from both sources of data are similar, and a spatial interaction model fit to the data reveals the relationships of different parameters over a range of movement time scales. Significant relationships between census migration data and fine temporal scale movement patterns exist, and results suggest that census data can be used to approximate certain features of movement patterns across multiple temporal scales, extending the utility of census-derived migration data
Capoeira Connections A Memoir in Motion
No full textThis ethnographic memoir weaves together the history of capoeira, recent transformations in the practice, and personal insights from author Katya Wesolowski's thirty years of experience as a capoeirista
Quantifying the impact of accessibility on preventive healthcare in Sub-Saharan Africa using mobile phone data
No full textBackground: Poor physical access to health facilities has been identified as an important contributor to reduced uptake of preventive health services and is likely to be most critical in low-income settings. However, the relation among physical access, travel behavior, and the uptake of healthcare is difficult to quantify. Methods: Using anonymized mobile phone data from 2008 to 2009, we analyze individual and spatially aggregated travel patterns of 14,816,521 subscribers across Kenya and compare these measures to (1) estimated travel times to health facilities and (2) data on the uptake of 2 preventive healthcare interventions in an area of western Kenya: childhood immunizations and antenatal care. Results: We document that long travel times to health facilities are strongly correlated with increased mobility in geographically isolated areas. Furthermore, we found that in areas with equal physical access to healthcare, mobile phone-derived measures of mobility predict which regions are lacking preventive care. Conclusions: Routinely collected mobile phone data provide a simple and low-cost approach to mapping the uptake of preventive healthcare in low-income settings. <br/
Evaluating spatial interaction models for regional mobility in sub-Saharan Africa
No full textSimple spatial interaction models of human mobility based on physical laws have been used extensively in the social, biological, and physical sciences, and in the study of the human dynamics underlying the spread of disease. Recent analyses of commuting patterns and travel behavior in high-income countries have led to the suggestion that these models are highly generalizable, and as a result, gravity and radiation models have become standard tools for describing population mobility dynamics for infectious disease epidemiology. Communities in Sub-Saharan Africa may not conform to these models, however; physical accessibility, availability of transport, and cost of travel between locations may be variable and severely constrained compared to high-income settings, informal labor movements rather than regular commuting patterns are often the norm, and the rise of mega-cities across the continent has important implications for travel between rural and urban areas. Here, we first review how infectious disease frameworks incorporate human mobility on different spatial scales and use anonymous mobile phone data from nearly 15 million individuals to analyze the spatiotemporal dynamics of the Kenyan population. We find that gravity and radiation models fail in systematic ways to capture human mobility measured by mobile phones; both severely overestimate the spatial spread of travel and perform poorly in rural areas, but each exhibits different characteristic patterns of failure with respect to routes and volumes of travel. Thus, infectious disease frameworks that rely on spatial interaction models are likely to misrepresent population dynamics important for the spread of disease in many African populations
Capoeira Connections: A Memoir in Motion
No full textOriginating in the Black Atlantic world as a fusion of dance and martial art, capoeira was a marginalized practice for much of its history. Today it is globally popular. This ethnographic memoir weaves together the history of capoeira, recent transformations in the practice, and personal insights from author Katya Wesolowski’s thirty years of experience as a capoeirista.
Capoeira Connections follows Wesolowski’s journey from novice to instructor while drawing on her decades of research as an anthropologist in Brazil, Angola, Europe, and the United States. In a story of local practice and global flow, Wesolowski offers an intimate portrait of the game and what it means in people’s lives. She reveals camaraderie and conviviality in the capoeira ring as well as tensions and ruptures involving race, gender, and competing claims over how this artful play should be practiced. Capoeira brings people together and yet is never free of histories of struggle, and these too play out in the game’s encounters.
In her at once clear-sighted and hopeful analysis, Wesolowski ultimately argues that capoeira offers opportunities for connection, dialogue, and collaboration in a world that is increasingly fractured. In doing so, capoeira can transform lives, create social spheres, and shape mobile futures.</p
Social, mobility and contact networks in shaping health behaviours and infectious disease dynamics: a scoping review
No full textBackground: the interconnectedness of human society in this modern world can transform localised outbreaks into global pandemics, underscoring the pivotal roles of social, mobility and contact networks in shaping infectious disease dynamics. Although these networks share analogous contagion principles, they are often studied in isolation, hindering the incorporation of behavioural, informational, and epidemiological processes into disease models. This review synthesises current research on the interplay between social, mobility and contact networks in health behaviour contagion and infectious disease transmission.Methods: we searched Web-of-Science and PubMed from January 2000 to June 2025 for research on health behaviour contagion and information dissemination in social networks, pathogen spread through mobility and contact networks, and their joint impacts on epidemic dynamics. This was first done by a preliminary literature screening based on predefined criteria. With potentially relevant publications retained, we performed keyword co-occurrence network analysis to identify the most common themes in studies. The results guide us to narrow down the reviewing scope to the social, mobility and contact network impacts on informational, behavioural, and epidemiological dynamics. We then further identified and reviewed the literature on these multidimensional network influences.Results: our review finds that each network type plays a distinct yet interconnected role in shaping behaviours and disease dynamics. Social networks, comprising both online and offline interpersonal relationships, facilitate the dissemination of health information and influence behavioural responses to public health interventions. Concurrently, mobility and contact networks govern the spatiotemporal pathways of pathogen transmission, as demonstrated in recent pandemics. While traditional population-level models often overlook individual discrepancies and social network effects, significant efforts have been made through developing individual-level simulation-based models that integrate behavioural dynamics. With emerging new data sources and advanced computational techniques, two promising approaches-multiplex network analysis and generative agent-based modelling-offer frameworks for integrating the complex interdependencies among social, mobility and contact networks into epidemic dynamics estimation.Conclusions: this review highlights the theoretical and methodological advances in network-based infectious disease modelling and identifies critical knowledge and research gaps. Future research should prioritise integrating multi-source behavioural and spatial data, unifying modelling strategies, and developing scalable approaches for incorporating multilayer network data. The integrated approach will strengthen public health strategies, enabling equitable and effective interventions against emerging infections.</p
Measles outbreak risk in Pakistan: exploring the potential of combining vaccination coverage and incidence data with novel data-streams to strengthen control
No full textAlthough measles incidence has reached historic lows in many parts of the world, the disease still causes substantial morbidity globally. Even where control programs have succeeded in driving measles locally extinct, unless vaccination coverage is maintained at extremely high levels, susceptible numbers may increase sufficiently to spark large outbreaks. Human mobility will drive potentially infectious contacts and interact with the landscape of susceptibility to determine the pattern of measles outbreaks. These interactions have proved difficult to characterise empirically. We explore the degree to which new sources of data combined with existing public health data can be used to evaluate the landscape of immunity and the role of spatial movement for measles introductions by retrospectively evaluating our ability to predict measles outbreaks in vaccinated populations. Using inferred spatial patterns of accumulation of susceptible individuals and travel data, we predicted the timing of epidemics in each district of Pakistan during a large measles outbreak in 2012–2013 with over 30 000 reported cases. We combined these data with mobility data extracted from over 40 million mobile phone subscribers during the same time frame in the country to quantify the role of connectivity in the spread of measles. We investigate how different approaches could contribute to targeting vaccination efforts to reach districts before outbreaks started. While some prediction was possible, accuracy was low and we discuss key uncertainties linked to existing data streams that impede such inference and detail what data might be necessary to robustly infer timing of epidemics.</p
Dataset on the dental morphology and occlusal dental wear of pre-colonial societies of the South and Southeast Coast of Brazil
No full textThis dataset compiles information on dental morphology and occlusal dental wear of 431 individuals exhumed from coastal and riverine sites of the South and Southeast Coast of Brazil, dated between approximately 10,000 to 1,000 years before present. Dental traits were scored according to the Arizona State University Dental Anthropology System (ASUDAS) (Scott and Irish, 2017; Turner II et al., 1991). Few additional mandibular traits were added following Hauser and Stefano (1989). Occlusal dental wear was scored according to the method described in Smith (1984).
Sex and age at death estimations derive from previous studies (Estevam, 2020; Fischer, 2012; Neves et al., 2005; Silva, 2005; Tognoli, 2016; Wesolowski, 2007). When this information was not available from previous studies, it was assessed by the first author (Fidalgo, 2021) using standard protocol methods (Buikstra and Ubelaker, 1994). Further detailed information and description of each variable can be consulted within the “description” and “dental grades” sheets in the excel file.
The dataset is part of a PhD project developed at the Museu de Arqueologia e Etnologia da Universidade de São Paulo, carried by Daniel Fidalgo and advised by Veronica Wesolowski and Mark Hubbe (Fidalgo, 2021). Manuscripts have already been published using this data (Fidalgo et al., 2021a, 2021b). All research was funded by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), grants 17/20637-4 and 19/18289-3. It was also supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) grant 001
Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa.
No full textForecasting the risk of pathogen spillover from reservoir populations of wild or domestic animals is essential for the effective deployment of interventions such as wildlife vaccination or culling. Due to the sporadic nature of spillover events and limited availability of data, developing and validating robust, spatially explicit, predictions is challenging. Recent efforts have begun to make progress in this direction by capitalizing on machine learning methodologies. An important weakness of existing approaches, however, is that they generally rely on combining human and reservoir infection data during the training process and thus conflate risk attributable to the prevalence of the pathogen in the reservoir population with the risk attributed to the realized rate of spillover into the human population. Because effective planning of interventions requires that these components of risk be disentangled, we developed a multi-layer machine learning framework that separates these processes. Our approach begins by training models to predict the geographic range of the primary reservoir and the subset of this range in which the pathogen occurs. The spillover risk predicted by the product of these reservoir specific models is then fit to data on realized patterns of historical spillover into the human population. The result is a geographically specific spillover risk forecast that can be easily decomposed and used to guide effective intervention. Applying our method to Lassa virus, a zoonotic pathogen that regularly spills over into the human population across West Africa, results in a model that explains a modest but statistically significant portion of geographic variation in historical patterns of spillover. When combined with a mechanistic mathematical model of infection dynamics, our spillover risk model predicts that 897,700 humans are infected by Lassa virus each year across West Africa, with Nigeria accounting for more than half of these human infections
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