Evidence map›Paper›PMID 29034123›Full record

ArticlePLoS currents2017

Ecological Niche Modeling for Filoviruses: A Risk Map for Ebola and Marburg Virus Disease Outbreaks in Uganda.

Luke Nyakarahuka, Samuel Ayebare, Gladys Mosomtai, Clovice Kankya, Julius Lutwama, Frank Norbert Mwiine, Eystein Skjerve

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Article in PLoS currents, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Luke Nyakarahuka1) Department of Food Safety and Infection Biology, Norwegian University of Life Sciences, Oslo, Norway; 2) Department of Biosecurity, Ecosystems and Veterinary Public Health, Makerere University, Kampala Uganda; 3) Department of Arbovirology, Emerging and Re-Emerging disease, Uganda Virus Research Institute, Entebbe, Uganda.
Samuel AyebareClimate Change and Biodiversity Unit, Wildlife Conservation Society, Bronx, New York, United States of America.
Gladys MosomtaiEarth Observation Unit, International Centre for Insect Physiology and Ecology, Nairobi, Kenya.
Clovice KankyaDepartment of Biosecurity, Ecosystems and Veterinary Public Health, Makerere University, Kampala, Uganda.
Julius LutwamaDepartment of Arbovirology, Emerging and Re-Emerging diseases, Uganda Virus Research Institute, Entebbe, Uganda.
Frank Norbert MwiineDepartment of Biomolecular Resources and Biolab Sciences, Makerere University, Kampala, Uganda.
Eystein SkjerveDepartment of Food Safety and Infection Biology, Norwegian University of Life Sciences, Oslo, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionUganda has reported eight outbreaks caused by filoviruses between 2000 to 2016, more than any other country in the world. We used species distribution modeling to predict where filovirus outbreaks are likely to occur in Uganda to help in epidemic preparedness and surveillance.

methodsThe MaxEnt software, a machine learning modeling approach that uses presence-only data was used to establish filovirus - environmental relationships. Presence-only data for filovirus outbreaks were collected from the field and online sources. Environmental covariates from Africlim that have been downscaled to a nominal resolution of 1km x 1km were used. The final model gave the relative probability of the presence of filoviruses in the study area obtained from an average of 100 bootstrap runs. Model evaluation was carried out using Receiver Operating Characteristic (ROC) plots. Maps were created using ArcGIS 10.3 mapping software.

resultsWe showed that bats as potential reservoirs of filoviruses are distributed all over Uganda. Potential outbreak areas for Ebola and Marburg virus disease were predicted in West, Southwest and Central parts of Uganda, which corresponds to bat distribution and previous filovirus outbreaks areas. Additionally, the models predicted the Eastern Uganda region and other areas that have not reported outbreaks before to be potential outbreak hotspots. Rainfall variables were the most important in influencing model prediction compared to temperature variables.

conclusionsDespite the limitations in the prediction model due to lack of adequate sample records for outbreaks, especially for the Marburg cases, the models provided risk maps to the Uganda surveillance system on filovirus outbreaks. The risk maps will aid in identifying areas to focus the filovirus surveillance for early detection and responses hence curtailing a pandemic. The results from this study also confirm previous findings that suggest that filoviruses are mainly limited by the amount of rainfall received in an area.

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disease model

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.