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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/455| Finding hotspots: development of an adaptive spatial sampling approach | |
| Aurelien Bibaut Ricardo Andrade Pacheco Jean Lemoine Leda Hernandez Aboulaye Meïté Benjamin Arnold Hugh JW Sturrock Francois Rerolle Mark van der Laan | |
| Novel Coronavirus | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| 10.1101/2020.01.10.20016964 | |
| The identification of disease hotspots is an increasingly important public health problem. While geospatial modeling offers an opportunity to predict the locations of hotspots using suitable environmental and climatological data, little attention has been paid to optimizing the design of surveys used to inform such models. Here we introduce an adaptive sampling scheme optimized to identify hotspot locations where prevalence exceeds a relevant threshold. Our approach incorporates ideas from Bayesian optimization theory to adaptively select sample batches. We present an experimental simulation study based on survey data of schistosomiasis and lymphatic filariasis across four countries. Results across all scenarios explored show that adaptive sampling produces superior results and suggest that similar performance to random sampling can be achieved with a fraction of the sample size. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work received financial support from the Coalition for Operational Research on Neglected Tropical Diseases, which is funded at The Task Force for Global Health primarily by the Bill & Melinda Gates Foundation, by the United States Agency for International Development through its Neglected Tropical Diseases Program, and with UK aid from the British people. ### Author Declarations All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All the simulated datasets and code developed as part of this study, including that used to conduct the simulation experiments, is available on a public GitHub repository. [https://github.com/disarm-platform/adaptive_sampling_simulation_r_functions][1] [1]: https://github.com/disarm-platform/adaptive_sampling_simulation_r_functions | |
| Cold Spring Harbor Laboratory Press | |
| 2020 | |
| Preimpreso | |
| https://www.medrxiv.org/content/10.1101/2020.01.10.20016964v1 | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| Finding hotspots.pdf | 1.36 MB | Adobe PDF | Visualizar/Abrir |