Person: Wesolowski, Amy
Loading...
Email Address
AA Acceptance Date
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
Wesolowski
First Name
Amy
Name
Wesolowski, Amy
5 results
Search Results
Now showing 1 - 5 of 5
Publication Evaluating Spatial Interaction Models for Regional Mobility in Sub-Saharan Africa(Public Library of Science, 2015) Wesolowski, Amy; O’Meara, Wendy Prudhomme; Eagle, Nathan; Tatem, Andrew J.; Buckee, Caroline O.Simple 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.Publication Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones(Nature Publishing Group, 2014) Wesolowski, Amy; Stresman, Gillian; Eagle, Nathan; Stevenson, Jennifer; Owaga, Chrispin; Marube, Elizabeth; Bousema, Teun; Drakeley, Christopher; Cox, Jonathan; Buckee, Caroline O.Human travel impacts the spread of infectious diseases across spatial and temporal scales, with broad implications for the biological and social sciences. Individual data on travel patterns have been difficult to obtain, particularly in low-income countries. Travel survey data provide detailed demographic information, but sample sizes are often small and travel histories are hard to validate. Mobile phone records can provide vast quantities of spatio-temporal travel data but vary in spatial resolution and explicitly do not include individual information in order to protect the privacy of subscribers. Here we compare and contrast both sources of data over the same time period in a rural area of Kenya. Although both data sets are able to quantify broad travel patterns and distinguish regional differences in travel, each provides different insights that can be combined to form a more detailed picture of travel in low-income settings to understand the spread of infectious diseases.Publication Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates(BioMed Central, 2016) zu Erbach-Schoenberg, Elisabeth; Alegana, Victor A.; Sorichetta, Alessandro; Linard, Catherine; Lourenço, Christoper; Ruktanonchai, Nick W.; Graupe, Bonita; Bird, Tomas J.; Pezzulo, Carla; Wesolowski, Amy; Tatem, Andrew J.Background: Reliable health metrics are crucial for accurately assessing disease burden and planning interventions. Many health indicators are measured through passive surveillance systems and are reliant on accurate estimates of denominators to transform case counts into incidence measures. These denominator estimates generally come from national censuses and use large area growth rates to estimate annual changes. Typically, they do not account for any seasonal fluctuations and thus assume a static denominator population. Many recent studies have highlighted the dynamic nature of human populations through quantitative analyses of mobile phone call data records and a range of other sources, emphasizing seasonal changes. In this study, we use mobile phone data to capture patterns of short-term human population movement and to map dynamism in population densities. Methods: We show how mobile phone data can be used to measure seasonal changes in health district population numbers, which are used as denominators for calculating district-level disease incidence. Using the example of malaria case reporting in Namibia we use 3.5 years of phone data to investigate the spatial and temporal effects of fluctuations in denominators caused by seasonal mobility on malaria incidence estimates. Results: We show that even in a sparsely populated country with large distances between population centers, such as Namibia, populations are highly dynamic throughout the year. We highlight how seasonal mobility affects malaria incidence estimates, leading to differences of up to 30 % compared to estimates created using static population maps. These differences exhibit clear spatial patterns, with likely overestimation of incidence in the high-prevalence zones in the north of Namibia and underestimation in lower-risk areas when compared to using static populations. Conclusion: The results here highlight how health metrics that rely on static estimates of denominators from censuses may differ substantially once mobility and seasonal variations are taken into account. With respect to the setting of malaria in Namibia, the results indicate that Namibia may actually be closer to malaria elimination than previously thought. More broadly, the results highlight how dynamic populations are. In addition to affecting incidence estimates, these changes in population density will also have an impact on allocation of medical resources. Awareness of seasonal movements has the potential to improve the impact of interventions, such as vaccination campaigns or distributions of commodities like bed nets. Electronic supplementary material The online version of this article (doi:10.1186/s12963-016-0106-0) contains supplementary material, which is available to authorized users.Publication Enhancing disease surveillance with novel data streams: challenges and opportunities(2016) Althouse, Benjamin M; Scarpino, Samuel V; Meyers, Lauren Ancel; Ayers, John W; Bargsten, Marisa; Baumbach, Joan; Brownstein, John; Castro, Lauren; Clapham, Hannah; Cummings, Derek AT; Del Valle, Sara; Eubank, Stephen; Fairchild, Geoffrey; Finelli, Lyn; Generous, Nicholas; George, Dylan; Harper, David R; Hébert-Dufresne, Laurent; Johansson, Michael A; Konty, Kevin; Lipsitch, Marc; Milinovich, Gabriel; Miller, Joseph D; Nsoesie, Elaine O; Olson, Donald R; Paul, Michael; Polgreen, Philip M; Priedhorsky, Reid; Read, Jonathan M; Rodríguez-Barraquer, Isabel; Smith, Derek J; Stefansen, Christian; Swerdlow, David L; Thompson, Deborah; Vespignani, Alessandro; Wesolowski, AmyNovel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.Publication Introduction of rubella-containing-vaccine to Madagascar: implications for roll-out and local elimination(The Royal Society, 2016) Wesolowski, Amy; Mensah, Keitly; Brook, Cara E.; Andrianjafimasy, Miora; Winter, Amy; Buckee, Caroline O.; Razafindratsimandresy, Richter; Tatem, Andrew J.; Heraud, Jean-Michel; Metcalf, C. Jessica E.Few countries in Africa currently include rubella-containing vaccination (RCV) in their immunization schedule. The Global Alliance for Vaccines Initiative (GAVI) recently opened a funding window that has motivated more widespread roll-out of RCV. As countries plan RCV introductions, an understanding of the existing burden, spatial patterns of vaccine coverage, and the impact of patterns of local extinction and reintroduction for rubella will be critical to developing effective programmes. As one of the first countries proposing RCV introduction in part with GAVI funding, Madagascar provides a powerful and timely case study. We analyse serological data from measles surveillance systems to characterize the epidemiology of rubella in Madagascar. Combining these results with data on measles vaccination delivery, we develop an age-structured model to simulate rubella vaccination scenarios and evaluate the dynamics of rubella and the burden of congenital rubella syndrome (CRS) across Madagascar. We additionally evaluate the drivers of spatial heterogeneity in age of infection to identify focal locations where vaccine surveillance should be strengthened and where challenges to successful vaccination introduction are expected. Our analyses indicate that characteristics of rubella in Madagascar are in line with global observations, with an average age of infection near 7 years, and an impact of frequent local extinction with reintroductions causing localized epidemics. Modelling results indicate that introduction of RCV into the routine programme alone may initially decrease rubella incidence but then result in cumulative increases in the burden of CRS in some regions (and transient increases in this burden in many regions). Deployment of RCV with regular supplementary campaigns will mitigate these outcomes. Results suggest that introduction of RCV offers a potential for elimination of rubella in Madagascar, but also emphasize both that targeted vaccination is likely to be a lynchpin of this success, and the public health vigilance that this introduction will require.