Early geography of the coronavirus disease outbreak in Nigeria.

An understanding of the COVID-19 spread is growing around the world, yet little is known of Africa. This paper explores the time-space geographies of COVID-19 infection focusing on Africa and Nigeria especially. We appraise the global-to-local situation of confirmed cases, deaths and recoveries as well as measures engaged in response to the pandemic. Findings reveal that South Africa accounts for 40% of the total confirmed cases in Africa, followed by Egypt (18%), Nigeria (6.8%), Ghana (4.6%) and Algeria (3.6). Our study shows a significant relationship between population density and COVID-19 cases in Nigeria (R2 = 0.76; y = 2.43x - 268.7). This expression suggests that high population densities catalyze the spread of COVID-19. While Nigeria has only recorded about 25,964 cases as at the time of writing, the country is struggling to match pressures from rising cases as a result of existing disparities in health care systems.

An assessment of flood event along Lower Niger using Sentinel-1 imagery.

Flood incidence, especially in global south countries, is one of the most challenging natural disasters in the light of changing climates, especially in Africa. This is because African countries have a large sub-section of vulnerable people who either live within flood-prone areas or depend on flood-prone areas for their means of livelihood such as we have in Nigeria. Recent flood disasters in Nigeria have been of major concern to people, communities, and institutions. Several studies have been conducted on flood events and their impacts in Nigeria. However, most of these studies are on public perception, flood modeling (rainfall-runoff), and the provision of binary maps with few studies engaging in the use of satellite observations, especially the use of Synthetic Aperture Radar, SAR, to enhance flood early warning designs, especially in Sub-Saharan Africa. This study is aimed at assessing the 2018 flood event in Lokoja, Kogi State, Nigeria, using the Sentinel-1 imagery. The study confirmed that a total of 69 buildings out of 611 buildings were affected by the flood disaster with about 24,902 people displaced by this singular flood event. The study shows that backscattering from microwave sensors provides very useful information for highlighting inundated areas that could prove useful in forecasting, monitoring, and precision-based flood early warning designs before, during, and after flood events.

Geographical trend analysis of COVID-19 pandemic onset in Africa.

Little has been documented in literature concerning the manner of occurrence and spread of COVID-19 in Africa. Understanding the geographic nature of the corona virus pandemic may offer critical response signals for Africa. This paper employed analysis of variance (ANOVA) to show that significant variations exist among African countries', particularly total population as well as those using basic drinking water services, gross national income, expenditure on health, number of physicians and air transport passengers. Although we have only considered the number of confirmed corona virus infections noting that the fatality may be too early to discuss, we have relied on data from the European Centre for Disease Prevention and Control (ECDC) to establish a significant association between international mobility based on average annual air passenger carried (r â€‹= â€‹0.6) which also successfully predicted (R 2 â€‹= â€‹0.501) the number of COVID-19 cases reported in each country along with the population density (R 2 â€‹= â€‹0.418). We also detected that COVID-19 cases report y geometrically increased daily x (R 2 â€‹= â€‹0.860) with a 2nd order polynomial equation in the form of y â€‹= â€‹0.3993 â€‹× â€‹2-8.7569 x and a clustered spatial pattern with a nearest neighbour ratio of 0.025 significant at 0.05 α-level. African countries have responded to the pandemic in different ways including partial lockdown, closure of borders and airports as well as providing test centres. We concluded that 40% of Africa are categorized as emerging hot spots while responses differ significantly across regions.

Early warning systems development for agricultural drought assessment in Nigeria.

The existing drought monitoring mechanisms in the sub-Saharan Africa region mostly depend on the conventional methods of drought monitoring. These methods have limitations based on timeliness, objectivity, reliability, and adequacy. This study aims to identify the spread and frequency of drought in Nigeria using Remote Sensing/Geographic Information Systems techniques to determine the areas that are at risk of drought events within the country. The study further develops a web-GIS application platform that provides drought early warning signals. Monthly NOAA-AVHRR Pathfinder NDVI images of 1 km by 1 km spatial resolution and MODIS with a spatial resolution of 500 m by 500 m were used in this study together with rainfall data from 25 synoptic stations covering 32 years. The spatio-temporal variation of drought showed that drought occurred at different times of the year in all parts of the country with the highest drought risk in the north-eastern parts. The map view showed that the high drought risk covered 5.98% (55,312 km2) of the country's landmass, while low drought risk covered 42.4% (391,881 km2) and very low drought risk areas 51.5% (476,578 km2). Results revealed that a strong relationship exists between annual rainfall and season-integrated NDVI (r2 = 0.6). Based on the spatio-temporal distribution and frequency of droughts in Nigeria, drought monitoring using remote sensing techniques of VCI and NDVI could play an invaluable role in food security and drought preparedness. The map view from the web-based drought monitoring system, developed in this study, is accessible through localhost.

Mapping Seriphium plumosum encroachment and interaction with wildfire and environmental factors in a protected mountainous grassland.

Accurate information on the distribution of invasive native species could provide important and effective procedures for managing savannah environment, especially in sensitive mountainous grasslands. The study detected and mapped Seriphium plumosum within a mountainous landscape and linked the georeferenced occurrence data with the corresponding site-specific environmental factors to predict the locations of unknown populations using a MaxEnt niche model. We also explored the relative contribution in terms of species interaction with its surrounding biophysical environment. The AUC value of 0.876 estimated for the species distribution is an indication of a good model fit. Our findings indicated that Seriphium plumosum preferred areas with higher temperature associated with recurrence fire events and limited soil moisture. It was concluded that the projected conditions of increasing temperature and fire events could promote widespread gain of niche space for Seriphium plumosum while at the same time altering community structure and composition, hydrological properties, and other vital ecosystem services in the study area.

Dominant malaria vector species in Nigeria: Modelling potential distribution of Anopheles gambiae sensu lato and its siblings with MaxEnt.

Malaria is a major infectious disease that still affects nearly half of the world's population. Information on spatial distribution of malaria vector species is needed to improve malaria control efforts. In this study we used Maximum Entropy Model (MaxEnt) to estimate the potential distribution of Anopheles gambiae sensu lato and its siblings: Anopheles gambiae sensu stricto, and Anopheles arabiensis in Nigeria. Species occurrence data collected during the period 1900-2010 was used together with 19 bioclimatic, landuse and terrain variables. Results show that these species are currently widespread across all ecological zones. Temperature fluctuation from mean diurnal temperature range, extreme temperature and precipitation conditions, high humidity in dry season from precipitation during warm months, and land use and land cover dynamics have the greatest influence on the current seasonal distribution of the Anopheles species. MaxEnt performed statistically significantly better than random with AUC approximately 0.7 for estimation of the Anopheles species environmental suitability, distribution and variable importance. This model result can contribute to surveillance efforts and control strategies for malaria eradication.