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Tomlinson, Sean, South, Andy and Longbottom, Joshua (2019) 'Malaria Data by District: An open-source web application for increasing access to malaria information'. Wellcome Open Research, Vol 4, Issue 151.
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8280f8aa-eb90-4854-8732-3a396034af9e_15495_-_sean_tomlinson.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
In recent years, the mapping of diseases has improved considerably in extent, resolution and accuracy (Kraemer et al., 2016). Increasingly, data and related spatial outputs are being made publicly available (Briand et al., 2018; Flueckiger et al., 2015). However, the full potential of associated modelled outputs will only be realised if data are accessed and used to inform local decision making. Recent reviews have suggested that data repositories are mainly targeted toward researchers rather than decision makers and that there is a need to improve indicator data use in low- and middle-income countries (Briand et al., 2018; Omumbo et al., 2013). We describe the development of an open-source web application, MaDD (Malaria Data by District) (Tomlinson et al., 2019), that enables disease distribution data to be more accessible at a local level.
The Malaria Atlas Project (MAP) is an international consortium which provides geographical information on diverse aspects of malaria epidemiology (Hay & Snow, 2006). The open-access data generated by MAP have the potential to influence policy at the national and subnational level (Hay & Snow, 2006; Moyes et al., 2013). The project includes sophisticated interpolation models that allow inference of malaria prevalence, as detailed in national and regional indicator surveys, at non-sampled locations (Giorgi et al., 2018; Hay & Snow, 2006). Getting contemporary estimates of malaria metrics to policy makers is essential, but barriers to acceptance exist, notably for modelled predictions; these include the complexity of the statistics described within output reports, and the description of assumptions made during the modelling process (Whitty, 2015). Additional barriers include the sheer wealth of data available, making it difficult to find and choose data surfaces despite central repositories that may be easily navigable. These factors have contributed towards a general lack of modelled outputs being used by local-level implementation programmes in Africa (Omumbo et al., 2013).
Most modelled MAP data are provided as spatial estimates, presented as 5 × 5 km gridded surfaces, for example, estimates of Plasmodium falciparum prevalence and mortality, estimates of indoor residual spraying coverage and estimates of dominant vector species distributions and abundance (Bhatt et al., 2015; Gething et al., 2016; Sinka et al., 2016). Though data generated at this spatial resolution provides a visual indication of subnational disparities, it is not immediately clear how these data may be used directly in operational decision-making. For modelled data to be utilised by operational staff at a local level, there is a requirement for additional tools and the ability to convert such data into operationally useful metrics at the level of administrative units (Knight et al., 2016; Omumbo et al., 2013; Whitty, 2015).
Data curated by MAP can already be accessed via online interactive maps (Malaria Atlas Project, 2019), an online country profiles tool and the malariaAtlas R package (Pfeffer et al., 2018). These are powerful tools enabling access to MAP generated data that do include data summaries by administrative units. However, because of the wealth of data and functionality it is not straightforward to find and use these tools to perform district-level comparisons. Here, we present an application that allows rapid generation and comparison of summary statistics for a select suite of malaria indicator variables at the sub-national administrative level. MaDD is open-source and coded in R, so it can easily be modified to address local needs (R Core Team, 2019). This is a step towards developing tools for local decision makers to inform questions such as, “where should we prioritise the targeting of IRS rounds this season?”.
| Item Type: | Article |
|---|---|
| Subjects: | W General Medicine. Health Professions > W 83 Telemedicine (General) WA Public Health > WA 30 Socioeconomic factors in public health (General) WA Public Health > Statistics. Surveys > WA 900 Public health statistics WC Communicable Diseases > Tropical and Parasitic Diseases > WC 750 Malaria |
| Faculty: Department: | Biological Sciences > Department of Tropical Disease Biology Biological Sciences > Vector Biology Department Education |
| Digital Object Identifer (DOI): | https://doi.org/10.12688/wellcomeopenres.15495.1 |
| Depositing User: | Samantha Sheldrake |
| Date Deposited: | 29 Oct 2019 15:14 |
| Last Modified: | 29 Oct 2019 15:14 |
| URI: | https://archive.lstmed.ac.uk/id/eprint/12872 |
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