Guinea is confronted to the increasing risks of bushfires that destroy thousands of hectares of vegetation cover every year. Very little research is devoted to the variability of those fires, which makes it a serious threat to both wildlife and human habitats. The current study investigates the spatial and temporal distribution of bushfires in the period from 2003 to 2016. The method used is the geospatial technology: we first filter pixels corresponding with active light supplied by MODIS images (Moderate Resolution Imaging Spectroradiometer) and estimate their densities following the square meshing procedure. Burned areas are deducted from the estimated pixel densities by calculations. The results highlight great occurrence of fires: 4 to 48 pixels of active fire per year and per 100 km² depending on the location; 2 to 5 million hectares per year of burned areas (20,000 to 50,000 sqkm). Almost 8 to 24% the size of the whole country. The prefectures of Beyla, Siguiri, Kouroussa, Kankan, Dinguiraye, Mali and Tougué are the most exposed areas. Every year, fire activities are observed as from October and between May and June. They are however mitigated according to the regions (or the geographical domains). Summits of bushfires activities are generally reached between December and January.

This study investigates the variability and the predictability of bush fire on inter-annual and multi-year timescales in Guinea (latitudes 7° 05 and 12° 51 N and longitudes 7° 30 and 15° 10 W). Using Moderate Resolution Imaging Spectroradiometer (MODIS) with a spatial resolution of 231 m × 231 m and 16 days composite temporal resolution between 2001 and 2016, two brush fire hazard indices are calculated based on the NDVI variability. Results show that both indices could be considered as good indicators of NDVI deficiency corresponding to the drought of vegetation. Multiple linear regression model using these risk indices as predictors and burned areas as predictands has shown a non-significant model skill of 0.33 (lower than the significant threshold equals to 0.42), at the inter-annual scale, while at the multi-year timescale (>5 years), the model's skill rise up to 0.89. These indices can therefore be used as predictors of Guinea burned areas on multi-year timescale. This novel finding improved our understanding on the forecasting of burned area in Guinea, and could therefore help for successful adaptation strategies.

Bush fires are increasingly becoming a threat to Guinean ecosystems and their understanding is a big challenge to scientists and environmental managers. Using the FARSITE model (Fire Area Simulator), this paper presents the prediction of the spread and characteristics of bush fires in the savannahs of Northeastern Guinea, specifically in Malea/Siguiri prefecture. Inputs, vegetation and topography dataset from satellite imagery (30 m resolution, Landsat and SRTM respectively) as well as in-situ meteorological data (wind, temperature and humidity) were used. These data obtained from the boundaries of the area of study were prepared using Geographic Information System (GIS). The burning time and the ignition points were fixed while admitting 3 scenarios: spread of fire in a plain without wind; spread on hills without wind effect and spread with wind effect. The results show that, in these savannahs, the intensity of the fire lines can reach 4133,3 KW/m under the effect of winds. Without winds, a decrease of 69% over plains and 68% over hills is noted. The amount of heat released could go up to 38000 KJ/m 2 with the wind effect. While, without the wind effect a decrease of 10 and 9% over the plains and hills is observed respectively. Eventually, the speed of propagation reaches 8 m/min (0.5 km/h), but without the wind, it would decrease up to 73 and 61% over plains and hills, respectively. This study could be improved to serve as a decision support tool for the management of ecosystems Northeastern Guinea.

The coronavirus disease 2019 , caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), dates back to December 29, 2019, in Wuhan, China. It quickly spreads like wildfire to all continents in the following months. In Guinea, the first case of COVID-19 and death were all reported respectively on March 12 and April 16, 2020. Since then, several studies have found a relationship between certain environmental conditions such as the meteorological factors to have the potential of contributing to the spread of the virus. Thus, this study aims at examining the extent to which observed meteorological factors might have contributed to the spread of the coronavirus disease 2019 (COVID-19) cases in Conakry, from March 1 to May 31, 2020. Meteorological factors such as temperature (T min , T mean and T max ) and relative humidity (RH min , RH mean and RH max ) were analyzed together with the data on the COVID-19. The dynamic of the COVID-19 in Guinea was analyzed along with that of some west African countries. The analysis on the dynamic of the COVID-19 pandemic in West Africa indicated Guinea as one of the most affected countries by the pandemic after Nigeria and Ghana. The study found that in general an increase in the temperature is linked to a decline in the COVID-19 number of cases and deaths, while an increase in the humidity is positively correlated to the number of cases and deaths. Nevertheless, from this study it was also observed that low temperature, mild di-