Total: 139
Exploring fine-scale human and livestock movement in western Kenya
One Health Volume 7, June 2019, 100081.
Author(s): Jessica R. Floyd, Nick W. Ruktanonchai, Nicola Wardrop, Andrew J. Tatem, Joseph Ogola, Eric M.Fèvre
Type: method. Year: 2019
DOI: 10.1016/j.onehlt.2019.100081.

Abstract: Human and livestock mobility are key factors in the transmission of several high-burden zoonoses such as rift valley fever and trypanosomiasis, yet our knowledge of this mobility is relatively poor due to difficulty in quantifying population-level movement patterns. Significant variation in the movement patterns of individual hosts means it is necessary to capture their fine-scale mobility in order to gain useful knowledge that can be extrapolated to a population level. Here we explore how the movements of people and their ruminants, and their exposure to various types of land cover, correlate with ruminant ownership and other demographic factors which could affect individual exposure to zoonoses. The study was conducted in Busia County, western Kenya, where the population are mostly subsistence farmers operating a mixed crop/livestock farming system. We used GPS trackers to collect movement data from 26 people and their ruminants for 1 week per individual in July/August 2016, and the study was repeated at the end of the same year to compare movement patterns between the short rainy and dry seasons respectively. We found that during the dry season, people and their ruminants travelled further on trips outside of the household, and that people spent less time on swampland compared to the short rainy season. Our findings also showed that ruminant owners spent longer and travelled further on trips outside the household than non-ruminant owners, and that people and ruminants from poorer households travelled further than people from relatively wealthier households. These results indicate that some individual-level mobility may be predicted by season and by household characteristics such as ruminant ownership and household wealth, which could have practical uses for assessing individual risk of exposure to some zoonoses and for future modelling studies of zoonosis transmission in similar rural areas.
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Identifying residential neighbourhood types from settlement points in a machine learning approach
Computers, Environment and Urban Systems (2018).
Author(s): Warren C. Jochem, , Tomas J. Bird, Andrew J. Tatem
Type: method. Year: 2018
DOI: 10.1016/j.compenvurbsys.2018.01.004.

Abstract: Remote sensing techniques are now commonly applied to map and monitor urban land uses to measure growth and to assist with development and planning. Recent work in this area has highlighted the use of textures and other spatial features that can be measured in very high spatial resolution imagery. Far less attention has been given to using geospatial vector data (i.e. points, lines, polygons) to map land uses. This paper presents an approach to distinguish residential settlement types (regular vs. irregular) using an existing database of settlement points locating structures. Nine data features describing the density, distance, angles, and spacing of the settlement points are calculated at multiple spatial scales. These data are analysed alone and with five common remote sensing measures on elevation, slope, vegetation, and nighttime lights in a supervised machine learning approach to classify land use areas. The method was tested in seven provinces of Afghanistan (Balkh, Helmand, Herat, Kabul, Kandahar, Kunduz, Nangarhar). Overall accuracy ranged from 78% in Kandahar to 90% in Nangarhar. This research demonstrates the potential to accurately map land uses from even the simplest representation of structures.
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High resolution age-structured mapping of childhood vaccination coverage in low and middle income countries
Vaccine, (2018).
Author(s): C. Edson Utazi, Julia Thorley, Victor A. Alegana, Matthew J. Ferrari, Saki Takahashi, C. Jessica E. Metcalf, Justin Lessler, Andrew J. Tatem
Type: method. Year: 2018
DOI: 10.1016/j.vaccine.2018.02.020.

Abstract: The expansion of childhood vaccination programs in low and middle income countries has been a substantial public health success story. Indicators of the performance of intervention programmes such as coverage levels and numbers covered are typically measured through national statistics or at the scale of large regions due to survey design, administrative convenience or operational limitations. These mask heterogeneities and 'coldspots' of low coverage that may allow diseases to persist, even if overall coverage is high. Hence, to decrease inequities and accelerate progress towards disease elimination goals, fine-scale variation in coverage should be better characterized.
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Productive disruption: opportunities and challenges for innovation in infectious disease surveillance.
BMJ Global Health, (2018) .
Author(s): Caroline O. Buckee, Maria I E Cardenas, June Corpuz, Arpita Ghosh, Farhana Haque, Jahirul Karim, Ayesha S. Mahmud, Richard J Maude, Keitly Mensah, Nkengafac Villyen Motaze, Maria Nabaggala, Charlotte Jessica Eland Metcalf, Sedera Aurélien Mioramalala, Frank Mubiru, Corey M. Peak, Santanu Pramanik, Jean Marius Rakotondramanga, Eric Remera, Ipsita Sinha, Siv Sovannaroth, Andrew J Tatem, Win Zaw
Type: method. Year: 2018
DOI: 10.1136/bmjgh-2017-000538.

Abstract: Infectious diseases place an unacceptable and disproportionate social and economic burden on low-income countries. National disease control programmes have the difficult task of allocating limited budgets for interventions across regions of their countries, based on often disparate datasets of varying quality from a range of sources including clinics, hospitals, village health workers, the private sector and non-governmental organisations (NGOs). Every stage of the data collection and analysis pipeline for surveillance systems may be affected by a lack of capacity as well as by biases and misaligned incentives for reporting and managing data. Addressing these issues will be essential for effective reduction in the burden of endemic infectious diseases globally as well as to preparing for emerging epidemic threats. Meanwhile, academic researchers—often in high-income settings—are developing increasingly sophisticated methods to collect and analyse data to improve spatial estimates of disease burden using new Big Data sources, mobile-Health or m-Health approaches or mechanistic and statistical modelling techniques. While these advances leap ahead, however, many remain most useful for estimating global disease distribution,1 rather than for national control programme prioritisation. Translating these new techniques to inform policy in endemic settings remains challenging. The pronounced disconnect between health systems and academia may limit the utility of new approaches. The high burden of work placed on healthcare workers in low-income settings further limits their scope and time available for engagement with methodological developments. Despite ongoing challenges to implementation, however, there are promising analytical approaches that can leverage even patchy and low-quality data and diverse new data streams that can be productively harnessed to strengthen strategies for resource allocation when integrated with existing surveillance systems. We detail the data and analysis challenges faced by national disease control programmes, outline possible solutions offered by analytical approaches and new data-streams and conclude by outlining barriers to implementation.
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