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Climatic and ecological context of the 1994-1996 Ebola outbreaks
Abstract and Figures
Ebola hemorrhagic fever outbreaks occurred in 1975-1979 and 1994-1996 within tropical Africa. It was determined from Landsat satellite data that all outbreaks occurred in tropical forest with a range of human intrusions. Meteorological satellite data, spanning the 1981 to 2000 time period, showed that marked and sudden climate changes from drier to wetter conditions were associated with the Ebola outbreaks in the 1990s. The extent of the marked climate changes suggest that Ebola outbreaks are possible over large areas of equitorial Africa. Our analysis is limited by only having one Ebola hemorrhagic fever outbreak during our period of study.
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... Besides seasonal fluctuations, and despite annual bat migration and high rates of human consumption of bushmeat, Ebola virus disease does not emerge annually. The 1990s Ebola outbreaks were related to short-term changes in regional precipitation and Normalized Difference Vegetation Index (NDVI) (Tucker et al. 2002). Evapotranspiration and Enhanced Vegetation Index are predictive of the spatial environmental niche of Ebola virus (i.e. ...
... EMXT, the highest monthly daily maximum temperature, was a significant predictor for the number of Ebola spillover events in humans and animals. These results align to previous studies that have found a climatic dimension for Ebola spillover events (Tucker et al. 2002;Pigott et al. 2014;Schmidt et al. 2017). In our study, however, we could additionally show that plant phenology variables [including the anomaly of Normalized Difference Vegetation Index (NDVI) between July and December, the proportion of population fruiting in Kibale National Park, Uganda, and the flowering anomalies in Lope, Gabon] informed neural network models with a superior fit to the data than when climate or climate in conjunction with phenology variables were used as inputs. ...
... This seasonal association between Ebola spillover events and a multitude of phenology variables describing seasonality of flowering and fruiting was not previously reported and corroborates the newly found close association between inter-annual Ebola spillover events and inter-annual variation in vegetation and phenology variables. The seasonality in spillover events is thought to be associated with transitions between wet-todry and dry-to-wet conditions (Tucker et al. 2002;Pinzon et al. 2004;Groseth et al. 2007;Altizer et al. 2013;Schmidt et al. 2017), and thus may be associated with spatiotemporal fluctuations of the African monsoon (Cornforth 2013). While the September spike in human + other animal spillover events was associated with such a transition of climatic variables, the December spike was not. ...
Ebola virus disease outbreaks in animals (including humans and great apes) start with sporadic host switches from unknown reservoir species. The factors leading to such spillover events are little explored. Filoviridae viruses have a wide range of natural hosts and are unstable once outside hosts. Spillover events, which involve the physical transfer of viral particles across species, could therefore be directly promoted by conditions of host ecology and environment. In this report, we outline a proof of concept that temporal fluctuations of a set of ecological and environmental variables describing the dynamics of the host ecosystem are able to predict such events of Ebola virus spillover to humans and animals. We compiled a data set of climate and plant phenology variables and Ebola virus disease spillovers in humans and animals. We identified critical biotic and abiotic conditions for spillovers via multiple regression and neural network-based time series regression. Phenology variables proved to be overall better predictors than climate variables. African phenology variables are not yet available as a comprehensive online resource. Given the likely importance of phenology for forecasting the likelihood of future Ebola spillover events, our results highlight the need for cost-effective transect surveys to supply phenology data for predictive modelling efforts.
Electronic supplementary material
The online version of this article (10.1007/s10393-017-1288-z) contains supplementary material, which is available to authorized users.
... Environmental niche modeling (ie, the use of algorithms to predict the geographic distribution of organisms on the basis of their environmental distribution using meteorological and other data) indicates succinct distributions for filoviruses in the Afrotropic ecozone. [90][91][92][93][94][95] According to these models, ebolaviruses are endemic in humid rain forests in Western and Middle Africa and South-Eastern Asia, whereas marburgviruses circulate in caves located in arid woodlands in Middle, Eastern, and Southern Africa. 90,91 Filovirus emergence in human populations appears to be associated with the appearance of climate anomalies or drastic climate changes. ...
... 92 For instance, ebolavirus activity is suggested to be correlated with unusually heavy rainfalls subsequent to extended dry periods. 90,94,95 If these models prove correct, then filovirus disease outbreaks should be expected in numerous African countries that have thus far not experienced (or noticed) any outbreaks. 93 ...
... The transition from the dry to the rainy season has been identified as favorable for Ebolavirus spillover [20,41,42]. Seasonal climatic variability can be related to the risk of spillover to humans through factors that increase the likelihood of contact between maintenance, intermediate and target hosts on the one hand, and through factors that affect virus circulation and shedding in the maintenance host on the other. ...
The unexpected Ebola virus outbreak in West Africa in 2014 involving the Zaire ebolavirus made clear that other regions outside Central Africa, its previously documented niche, were at risk of future epidemics. The complex transmission cycle and a lack of epidemiological data make mapping areas at risk of the disease challenging. We used a Geographic Information System-based multicriteria evaluation (GIS-MCE), a knowledge-based approach, to identify areas suitable for Ebola virus spillover to humans in regions of Guinea, Congo and Gabon where Ebola viruses already emerged. We identified environmental, climatic and anthropogenic risk factors and potential hosts from a literature review. Geographical data layers, representing risk factors, were combined to produce suitability maps of Ebola virus spillover at the landscape scale. Our maps show high spatial and temporal variability in the suitability for Ebola virus spillover at a fine regional scale. Reported spillover events fell in areas of intermediate to high suitability in our maps, and a sensitivity analysis showed that the maps produced were robust. There are still important gaps in our knowledge about what factors are associated with the risk of Ebola virus spillover. As more information becomes available, maps produced using the GIS-MCE approach can be easily updated to improve surveillance and the prevention of future outbreaks.
... The transition from the dry to the rainy season has been identified as favorable for Ebolavirus spillover [20,41,42]. Seasonal climatic variability can be related to the risk of spillover to humans through factors that increase the likelihood of contact between maintenance, intermediate and target hosts on the one hand, and through factors that affect virus circulation and shedding in the maintenance host on the other. ...
The unexpected Ebola virus outbreak in West Africa in 2014 involving the Zaire ebolavirus made clear that other regions outside Central Africa, its previously documented niche, were at risk of future epidemics. The complex transmission cycle and a lack of epidemiological data make mapping areas at risk of the disease challenging. We used a Geographic Information System-based multicriteria evaluation (GIS-MCE), a knowledge-based approach, to identify areas suitable for Ebola virus spillover to humans in regions of Guinea, Congo and Gabon where Ebola viruses already emerged. We identified environmental, climatic and anthropogenic risk factors and potential hosts from a literature review. Geographical data layers, representing risk factors, were combined to produce suitability maps of Ebola virus spillover at the landscape scale. Our maps show high spatial and temporal variability in the suitability for Ebola virus spillover at a fine regional scale. Reported spillover events fell in areas of intermediate to high suitability in our maps, and a sensitivity analysis showed that the maps produced were robust. There are still important gaps in our knowledge about what factors are associated with the risk of Ebola virus spillover. As more information becomes available, maps produced using the GIS-MCE approach can be easily updated to improve surveillance and the prevention of future outbreaks.
... Ng et al reported that temperature and absolute humidity in the Democratic Republic of the Congo and ebolavirus outbreaks were highly positively correlated (13). Their studies and others suggest that Ebolavirus outbreaks emerge from their reservoir in a specific geotemporal and enviroclimatic context (14)(15). We have also observed the same coupling of temperature and rainfall with the occurrence of a filovirus outbreak when analyzing the data from 1960-2015 (Figures 1 and 2). ...
This study investigates the possible emergence of a known or novel filovirus in the New World by the presence of an endogenous filovirus elements footprint of prior infection in seven families of bats with a paleogeographic and phylogenetic origin to Gondwana.
... These predictions suggest that some filovirus reservoir hosts might react to climate anomalies in ways that further their interaction with humans. 440,589,590,[592][593][594][595][596]746 Finally, serological surveys indicate that humans are exposed to filoviruses in numerous countries that, thus far, have not reported disease outbreaks (e.g., certain African countries, Belarus, Ukraine). However, the results of most of these surveys are considered controversial because of the applied methodologies and the likelihood of serological cross-reactions. ...
... Several studies have linked climate-driven changes to patterns of disease occurrence at different spatial scales. Two groups have evaluated the relationship between temporal patterns of Normalized Difference Vegetation Index (NDVI) and occurrence of Ebola virus in West Africa (Pinzon et al. 2004;Tucker et al. 2002). They found that NDVI trajectories showed distinctive "trigger events" prior to occurrences of the disease in humans and apes, which they hypothesized might be used to forecast conditions conducive to outbreaks of Ebola hemorrhagic fever. ...
Changes in landscape and land use can drive the emergence of zoonoses, and hence, there has been great interest in understanding how land cover change and the cascade of ecological effect associated with it are associated with emerging infectious diseases. In this chapter, we review how a spatially hierarchical approach can be used to guide research into the links between landscape properties and zoonotic diseases. Methodological advances have played a role in the revival of landscape epidemiology and we introduce the role of methodologies such as geospatial analysis and mathematical modeling. Importantly, we discuss cross-scale analysis and how this would provide a richer perspective of the ecology of zoonotic diseases. Finally, we will provide an overview of how hierarchical research strategies and modeling might be generally used in analyses of infectious zoonoses originating in wildlife.
... While attributing any singular disease outbreak to climate change is not possible, the Ebola outbreak is given as an example of the complex feedback loop which can result in health impacts from climate-sensitive hazards escalating to emergencies and disasters. Here, Ebola is considered to be a potential climate-sensitive hazard as the proliferation of the virus is potentially influenced by climate-sensitive factors such as climatological and meteorological conditions including periods of unusual drought and rain [71][72][73]. This draws attention to the consideration of a sub-group of infectious diseases which are potentially climate-sensitive but have not yet been identified as such due to current spatial and temporal limitations in meteorological and baseline health data [72]. ...
Disasters and climate change have significant implications for human health worldwide. Both climate change and the climate-sensitive hazards that result in disasters, are discussed in terms of direct and indirect impacts on health. A growing body of literature has argued for the need to link disaster risk reduction and climate change adaptation. However, there is limited articulation of the commonalities between these health impacts. Understanding the shared risk pathways is an important starting point for developing joint strategies for adapting to, and reducing, health risks. Therefore, this article discusses the common aspects of direct and indirect health risks of climate change and climate-sensitive disasters. Based on this discussion a theoretical framework is presented for understanding these commonalities. As such, this article hopes to extend the current health impact frameworks and provide a platform for further research exploring opportunities for linked adaptation and risk reduction strategies.
In 2008, an outbreak of Reston ebolavirus (RESTV) in pigs in the Philippines expanded our understanding of the host range of ebolaviruses. Subsequent experimental infections with the human-pathogenic species Zaire ebolavirus (EBOV) confirmed that pigs are susceptible to African species of ebolaviruses. Pig keeping has become an increasingly important livelihood strategy throughout parts of sub-Saharan Africa, driven by increasing demand for pork. The growth in pig keeping is particularly rapid in Uganda, which has the highest per capita pork consumption in East Africa and a history of sporadic human outbreaks of Ebola virus disease (EVD). Using a systematic sampling protocol, we collected sera from 658 pigs presented for slaughter in Uganda between December 2015 and October 2016. Forty-six pigs (7%) were seropositive based on ELISA tests at two different institutions. Seropositive pigs had antibodies that bound to Sudan NP (n=27), Zaire NP (Kikwit; n=8) or both NPs (n=11). Sera from 4 of the ELISA-positive pigs reacted in Western blot (EBOV NP = 1; RESTV NP=2; both NPs=2) and one sample had full neutralizing antibody against Sudan ebolavirus (SUDV) in virus neutralization tests. Pigs sampled in June 2016 were significantly more likely to be seropositive than pigs sampled in October 2016 (p=0.03). Seropositive pigs were sourced from all regions except Western region. These observed temporal and spatial variations are suggestive of multiple introductions of ebolaviruses into the pig population in Uganda. This is the first report of exposure of pigs in Uganda to ebolaviruses and the first to employ systematic abattoir sampling for ebolavirus surveillance during a non-outbreak period. Future studies will be necessary to further define the role pigs play (if any) in ebolavirus maintenance and transmission so that potential risks can be mitigated.
Represented are the results of analysis of the on-going EVD epidemic, 2013-2015 in West Africa countries. Identified have been epidemiological peculiarities, the principal ones of which are: the scale of epidemic transmission; social factors of widespread occurrence; registration of EVD cases in the new territories of the African continent - West Africa (Guinea, Liberia, Sierra-Leone); genetic distinction between Ebola virus and the strains of the same virus, species Zaire ebolavirus, that caused previous outbreaks; prevalence of febrile syndrome over hemorrhagic; high risk of infection with EVD among the healthcare workers. Most probable carriers of Ebola virus may be fruit-bats of the three species - Hypsignathus monstrosus, Myonycteris torquata, and Epomops franqueti. Outlined are the key stages and factors of EVD epidemic development.
Rift Valley fever (RVF) outbreaks in East Africa are closely coupled with above normal rainfall that is associated with the occurrence of the warm phase of the El Niño/Southern Oscillation (ENSO) phenomenon. Outbreaks elsewhere in central and southern Africa are also linked to elevated rainfall patterns. Major RVF activity has been reported to occur throughout much of sub-Saharan Africa, except in areas with extensive tropical forest. In this study we used normalized difference vegetation index (NDVI) time-series data derived from the Advanced Very High Resolution Radiometer (AVHRR) instrument on polar orbiting National Oceanographic and Atmospheric Administration (NOAA) satellites to map areas with a potential for an RVF outbreak. A 19-year NDVI climatology was created and used to discriminate between areas with tropical forest, savanna, and desert. Because most RVF outbreaks have occurred in regions dominated by savanna vegetation, we created a mask to identify those areas where RVF would likely occur within the savanna ecosystems. NDVI anomalies were then calculated for the entire time series from July 1981 to the July 2000. Subsequently, we developed a methodology that detects areas with persistent positive NDVI anomalies (greater than + 0.1 NDVI units) using a three-month moving window to flag regions at greatest risk. Algorithms were designed to account for periods of extended above normal NDVI (by inference rainfall) and to consider the complex life cycle of mosquitoes that maintain and transmit RVF virus to domestic animals and people. We present results for different ENSO warm- and cold-event periods. The results indicate that regions of potential outbreaks have occurred predominantly during warm ENSO events in East Africa and during cold ENSO events in southern Africa. Results provide a likely historical reconstruction of areas where RVF may have occurred during the last 19 years. There is a close agreement between confirmed outbreaks between 1981 and 2000, particularly in East Africa, and the risk maps produced in this study. This technique is adaptable to near real-time monitoring on a monthly basis and may be a useful tool in RVF disease surveillance.
Some plant growth models require estimates of leaf area and absorbed radiation for simulating evapotranspiration and photosynthesis. Previous studies indicated that spectral reflectance, absorption of photosynthetically active radiation (PAR), and leaf area index (LAI) are interrelated. The objective of this study was to establish a procedure by which spectral reflectance can be used to simultaneously estimate PAR absorption and LAI. A method is presented for estimating the quantity of absorbed PAR by wheat (Triticum aestivum L.) plants and their LAI based on the normalized difference (ND), transformation of the near infrared (ρn = 800 to 1100 nm) and red (ρr = 600 to 700 nm) canopy reflectances. The results, from a theoretical analysis and field measurements, indicated that ND correlates with the fraction of PAR absorbed by wheat canopies. Bare soil reflectance and scattering of near infrared radiation by foliage elements were the major factors that influenced the relation between ND and PAR absorption. The estimated PAR absorption values, based on the ND, and four classes of assumed leaf angles (45°, 60°, 75°, and spherical), were used to indirectly evaluate LAI of wheat for three different geographical locations. The standard deviation on mean predicted to measured LAI's for the three locations varied from 0.5 to 0.9 for a range of 0 to 6 LAI. The method is considerably less sensitive in predicting LAI above 6.0 since the sensitivity of ND to changes in LAI becomes small (<0.01), due to small changes in canopy reflectance.
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An operational stratospheric correction scheme used after the Mount Pinatubo (Phillipines) eruption (Jun. 1991) is presented. The stratospheric aerosol distribution is assumed to be only variable with latitude. Each 9 days the latitudinal distribution of the optical thickness is computed by inverting radiances observed in the NOAA AVHRR channel 1 (0.63 micrometers) and channel 2 (0.83 micrometers) over the Pacific Ocean. This radiance data set is used to check the validity of model used for inversion by checking consistency of the optical thickness deduced from each channel as well as optical thickness deduced from different scattering angles. Using the optical thickness profile previously computed and radiative transfer code assuming Lambertian boundary condition, each pixel of channel 1 and 2 are corrected prior to computation of NDVI (Normalized Difference Vegetation Index). Comparison between corrected, non corrected, and years prior to Pinatubo eruption (1989 to 1990) NDVI composite, shows the necessity and the accuracy of the operational correction scheme.
The Global Historical Climatology Network version 2 temperature database was released in May 1997. This century-scale dataset consists of monthly surface observations from 7000 stations from around the world. This archive breaks considerable new ground in the field of global climate databases. The enhancements include 1) data for additional stations to improve regional-scale analyses, particularly in previously data-sparse areas; 2) the addition of maximum-minimum temperature data to provide climate information not available in mean temperature data alone; 3) detailed assessments of data quality to increase the confidence in research results; 4) rigorous and objective homogeneity adjustments to decrease the effect of nonclimatic factors on the time series; 5) detailed metadata (e.g., population, vegetation, topography) that allow more detailed analyses to be conducted; and 6) an infrastructure for updating the archive at regular intervals so that current climatic conditions can constantly be put into historical perspective. This paper describes these enhancements in detail.
The effect of sensor degradation in the Advanced Very High Resolution Radiometer (AVHRR) channels 1 and 2 on the Normalized Difference Vegetation Index (NDVI) has been established. Three models have been developed that adjust NDVI for sensor degradation without recourse to component channel 1 and 2 data. The models have been verified with data obtained by the AVHRR on board of NOAA-7, -9 and -11. Two models provide accurate results in some cases, but perform less well in others. A third model is applicable to all cases investigated, and estimates the effect of sensor degradation with a maximum RMS error of 0·002NDVI. The remaining error depends on surface characteristics and the magnitude of sensor degradation, and cannot be accounted for without the component channel 1 and 2 data.
The local observing time of the sensor on National Oceanic and Atmospheric Administration (NOAA) satellites is discussed and a strategy is recommended for maintaining relatively constant time of afternoon observations by the Advanced Very High Resolution Radiometer (AVHRR) over the satellite lifetime. The NOAA current orbital strategy is shown to be very conservative and a re-examination of the problem is recommended.
Satellite data and ground rainfall measurements have been used to study variations in the size of the Sahara Desert from 1980 to 1997. Through a combination of the satellite and ground data, the 200 mm yr-1 precipitation boundary was mapped for the Saharan-Sahelian region by year. Although highly significant year-to-year variation in the size of the Sahara Desert has occurred, no systematically increasing or decreasing trend from 1980 to 1997 was evident. The area of the Sahara Desert varied from 9 980 000 km2 in 1984 to km2 in 8 600 000 1994 and had an average 1980-1997 area of 9 150 000 km2.
Most models of crop growth and yield require an estimate of canopy leaf area index or absorption of radiation; however, direct measurement of LAI or light absorption can be tedious and time-consuming. The object of this study was to develop relationships between photosynthetically active radiation (PAR) absorbed by corn (Zea mays L.) canopies and the spectral reflectance of the canopies. Absorption of PAR was measured near solar noon in corn canopies planted in north-south rows at densities of 50,000 and 100,000 plants ha.−1 Reflectance factor data were acquired with a radiometer with spectral bands similar to the Landsat MSS. Three spectral vegetation indices (ratio of near infrared to red reflectance, normalized difference, and greenness) were associated with more than 95% of the variability in absorbed PAR from planting to silking. The relationships developed between absorbed PAR and the three indices were tested with reflectance factor data acquired from corn canopies planted in 1979–1982 that excluded those canopies from which the equations were developed. Treatments included in these data were two hybrids, four planting densities (25, 50, 75, and 100 thousand plantsha−1), three soil types (Typic Argiaquoll, Udollic Ochraqualf, and Aeric Ochraqualf), and several planting dates. Seasonal cumulations of measured LAI and each of the three indices were associated with greater than 50% of the variation in final grain yields from the test years. Seasonal cumulations of daily absorbed PAR, estimated indirectly from the multispectral reflectance of the canopies, were associated with up to 73% of the variation in final grain yields. Absorbed PAR, cumulated through the growing season, is a better indicator of yield than cumulated leaf area index.
Interception of photosynthetically active radiation (PAR) was evaluated relative to greenness and normalized difference [MSS (7 − 5)/(7 + 5)] for five planting dates of wheat for 1978–1979 and 1979–1980 at Phoenix, Arizona. Intercepted PAR was calculated from leaf area index and stage of growth. Linear relationships were found with greenness and normalized difference with separate relationships describing growth and senescence of the crop. Normalized difference was significantly better than greenness for all planting dates. For the leaf area growth portion of the season the relation between PAR interception and normalized difference was the same over years and planting dates. For the leaf senescence phase the relationships showed more variability due to the lack of data on light interception in sparse and senescing canopies. Normalized difference could be used to estimate PAR interception throughout a growing season.