In search for the hotspots of Disease X: A biogeographic approach to mapping the predictive risk of WHO's blueprint priority diseases
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SciScore for 10.1101/2020.03.27.20044156: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:As with all mathematical models, our study has its limitations. The size of the environmental predictor raster layers limited our spatial extent of the models. We chose the quality of the satellite imagery and resolution over a …
SciScore for 10.1101/2020.03.27.20044156: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:As with all mathematical models, our study has its limitations. The size of the environmental predictor raster layers limited our spatial extent of the models. We chose the quality of the satellite imagery and resolution over a global perspective with poor or outdated data. Our study does not have a temporal component in the form of times series, which would be interesting especially with the climatic covariates. We mitigated this by choosing recent raster data corresponding to the period of the study and linking the spatiotemporal presence and pseudo-absence points to corresponding climatic monthly covariates. Despite these limitations, our study is the first to confirm the validity and effectiveness of using SDMs and other mathematical models to predict and identify the potential hotspots for BPDs. The use of a biogeographic approach in disease modelling offers a wider perspective on the environmental drivers and highlights the importance of climate change in the context of disease emergence. Most of all, our study results observed that the potential hotspots for an unknown disease X is located in Uganda and China (Figure 6). It is interesting to note that the associated predictive risk map includes the region of Wuhan, the epicenter of the ongoing COVID-19 outbreak.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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