Use of data on vectors of human disease, Flying fox edition


#1

Thanks to the quick work of @dnoesgaard, we’ve got what amounts to a rapid response feature out about a just-published paper, which modelled high-risk zones in South and Southeast Asia of Nipah virus (NiV).

I see this as yet another fascinating example of how research on vectors of human disease can a) make use of primary biodiversity data mobilized through the GBIF network, b) inform or reinforce public health decision-making around vector-borne disease, and c) offer potential taxonomic and geographic targets, focused on data-poor taxa and geographies, for additional biodiversity data mobilization and monitoring, including through citizen science expeditions in (potentially) affected regions. As the authors themselves note,

this study can be used as a geospatial guide to identify areas of high disease transmission risk and to inform national public health surveillance programs.

All of this serves to underscore the fact that bringing together open data for biodiversity is not some simple archival exercise aimed at compiling species range maps.

On 3 May, Mark A. Deka and Niaz Morshed, two PhD students at Texas State University submitted a manuscript to Tropical Medicine and Infectious Diseases. They cited their use of species occurrence data from GBIF.org (although not with a download DOI), having downloaded all records for Pteropus, the genus that contains 60-odd species of flying fox. Previous research (including some enabled by GBIF-mediated data, like Martin et al. (2016) and Walsh, Wiethoelter & Haseeb (2017)) has identified Pteropus spp. as a vector of Nipah virus in human communities. In their paper, Deka and Morshed started with 28,418 records, using them to model potential hotspots for #Nipah spillover across 24 countries in South and Southeast Asia. Note, however, that the data available for Pteropus species are comparatively sparse—particularly outside of Australia, where, thanks to the efforts of the Atlas of Living Australia, iNaturalist.org and QuestaGame, we likely have a much clearer past and present picture where Pteropus populations live.

Less than three weeks later, on 21 May, Nipah virus appeared among patients on the eastern coast of Kerala, India—as it happens, one of the eight hotspots for spillover risk that Deka and Morshed identified with ‘strong’ confidence.

We don’t know the publishing and review backstory here, which would probably be interesting, but to the authors’ and journal editors’ credit, revisions were received from Deka & Morshed on 24 May, and on 25 May—four days after first reports of the outbreak in Kerala—the paper appeared in Tropical Medicine and Infectious Diseases.

Despite any limitations of the data currently available, it clearly still carried a sufficiently strong signal (at least in this example) to identify one of the main high-risk areas accurately.

And what goes for NiV in this example holds equally true for other vector-based diseases: the mobilization of existing information as freely and openly available data, combined with field research and Citizen Science campaigns that target critical geographic and taxonomic gaps, can guide public health policy and increase preparedness in areas at high risk of a given disease.

If you feel this sounds a bit ‘Captain Obvious’ (h/ t @dschigel), note that the New York Times’ coverage of NiV outbreak in India details further risks of transmission only with regard to flight-pattern data. This emphasis on the potential for world travellers to carry and transmit the infection elsewhere from human to human, while perhaps understandable, seems unconcerned about the state of on-the-ground knowledge for people who live in—and don’t travel out of—Kerala or any of the other proposed regional hotspots.

While this may not provide a disease-curing vaccine, funders of research into human health and disease may also want to take note of the potential value of mobilizing open data on species occurrences of vectors of human disease.


What we're reading: May/June 2018