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

ABSTRACT: INTRODUCTION:Uganda 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. METHODS:The 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. RESULTS:We 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. CONCLUSIONS:Despite 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.

SUBMITTER: Nyakarahuka L

PROVIDER: S-EPMC5614672 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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

Nyakarahuka Luke L Ayebare Samuel S Mosomtai Gladys G Kankya Clovice C Lutwama Julius J Mwiine Frank Norbert FN Skjerve Eystein E

PLoS currents 20170905

<h4>Introduction</h4>Uganda 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.<h4>Methods</h4>The MaxEnt software, a machine learning modeling approach that uses presence-only data was used to establish filovirus - environmental relationships. Presence-only data for filovirus out ...[more]

PMID: 29034123

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Project description:BackgroundUganda has reported five (5) Ebola virus disease outbreaks and three (3) Marburg virus disease outbreaks from 2000 to 2016. Peoples' knowledge and attitude towards Ebola and Marburg virus disease impact on control and prevention measures especially during outbreaks. We describe knowledge and attitude towards Ebola and Marburg virus outbreaks in two affected communities in Uganda to inform future outbreak responses and help in the design of health education and communication messages.MethodsThe study was a community survey done in Luweero, Ibanda and Kamwenge districts that have experienced outbreaks of Ebola and Marburg virus diseases. Quantitative data were collected using a structured questionnaire and triangulated with qualitative participatory epidemiology techniques to gain a communities' knowledge and attitude towards Ebola and Marburg virus disease.ResultsOut of 740 respondents, 48.5% (359/740) were categorized as being knowledgeable about Ebola and Marburg virus diseases, whereas 60.5% (448/740) were having a positive attitude towards control and prevention of Ebola and Marburg virus diseases. The mean knowledge and attitude percentage scores were 54.3 (SD = 23.5, 95%CI = 52.6-56.0) and 69.9 (SD = 16.9, 95%CI = 68.9-71.1) respectively. People educated beyond primary school were more likely to be knowledgeable about Ebola and Marburg virus disease than those who did not attain any formal education (OR = 3.6, 95%CI = 2.1-6.1). Qualitative data revealed that communities describe Ebola and Marburg virus diseases as very severe diseases with no cure and they believe the diseases spread so fast. Respondents reported fear and stigma suffered by survivors, their families and the broader community due to these diseases.ConclusionCommunities in Uganda affected by filovirus outbreaks have moderate knowledge about these diseases and have a positive attitude towards practices to prevent and control Ebola and Marburg viral diseases. The public health sector should enhance this community knowledge gap to empower them more by supplying educational materials for epidemic preparedness in future using appropriate communication channels as proposed by the communities.

Project description:Ebola and Marburg hemorrhagic fevers (EHF and MHF) are caused by the Filoviridae family, Ebolavirus and Marburgvirus (ebolavirus and marburgvirus), respectively. These severe diseases have high mortality rates in humans. Although EHF and MHF are endemic to sub-Saharan Africa. A novel filovirus, Lloviu virus, which is genetically distinct from ebolavirus and marburgvirus, was recently discovered in Spain where filoviral hemorrhagic fever had never been reported. The virulence of this virus has not been determined. Ebolavirus and marburgvirus are classified as biosafety level-4 (BSL-4) pathogens and Category A agents, for which the US government requires preparedness in case of bioterrorism. Therefore, preventive measures against these viral hemorrhagic fevers should be prepared, not only in disease-endemic regions, but also in disease-free countries. Diagnostics, vaccines, and therapeutics need to be developed, and therefore the establishment of animal models for EHF and MHF is invaluable. Several animal models have been developed for EHF and MHF using non-human primates (NHPs) and rodents, which are crucial to understand pathophysiology and to develop diagnostics, vaccines, and therapeutics. Rhesus and cynomolgus macaques are representative models of filovirus infection as they exhibit remarkably similar symptoms to those observed in humans. However, the NHP models have practical and ethical problems that limit their experimental use. Furthermore, there are no inbred and genetically manipulated strains of NHP. Rodent models such as mouse, guinea pig, and hamster, have also been developed. However, these rodent models require adaptation of the virus to produce lethal disease and do not mirror all symptoms of human filovirus infection. This review article provides an outline of the clinical features of EHF and MHF in animals, including humans, and discusses how the animal models have been developed to study pathophysiology, vaccines, and therapeutics.

Project description:BACKGROUND:Emerging and understudied pathogens often lack information that most commonly used analytical tools require, such as negative controls or baseline data; thus, new analytical strategies are needed to analyze transmission patterns and drivers of disease emergence. Zoonotic infections with Vaccinia virus (VACV) were first reported in Brazil in 1999, VACV is an emerging zoonotic Orthopoxvirus, which primarily infects dairy cattle and farmers in close contact with infected cows. Prospective studies of emerging pathogens could provide critical data that would inform public health planning and response to outbreaks. By using the location of 87-recorded outbreaks and publicly available bioclimatic data, we demonstrate one such approach. Using an ecological niche model (ENM) algorithm, we identify the environmental conditions under which VACV outbreaks have occurred, and determine additional locations in two affected countries that may be susceptible to transmission. Further, we show how suitability for the virus responds to different levels of various environmental factors and highlight the most important factors in determining its transmission. METHODS:A literature review was performed and the geospatial coordinates of 87 molecularly confirmed VACV outbreaks in Brazil were identified. An ENM was generated using MaxENT software by combining principal component analysis results of 19 bioclim spatial layers, and 25 randomly selected subsets of the original list of 87 outbreaks. RESULTS:The final ENM predicted all areas where Brazilian outbreaks occurred, one out of five of the Colombian outbreak regions and identified new regions within Brazil that are suitable for transmission based on bioclimatic factors. Further, the most important factors in determining transmission suitability are precipitation of the wettest quarter, annual precipitation, mean temperature of the coldest quarter and mean diurnal range. CONCLUSION:The analyses here provide a means by which to study patterns of an emerging infectious disease and identify regions that are potentially suitable for its transmission, in spite of the paucity of high-quality critical data. Policy and methods for the control of infectious diseases often use a reactionary model, addressing diseases only after significant impact on human health has ensued. The methodology used in the present work allows the identification of areas where disease is likely to appear, which could be used for directed intervention.

Dataset Information

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

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

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