Climate and COVID-19 transmission: a cross-sectional study in Africa.

The role of climate in the Coronavirus disease 2019 (COVID-19) transmission appears to be controversial, as reported in earlier studies. In Africa, the subject is poorly documented. In this study, over the period from January 1st, 2020 to September 31, 2022, the daily variations in cumulative confirmed cases of COVID-19 for each African country (54 countries) are modelled through time-series-based approaches and using meteorological factors as covariates. It is suggested from the findings that climate plays a role in COVID-19 transmission since at least one meteorological factor is found to be significant in 32 countries. In decreasing order, the most often occurring meteorological factors are dewpoint temperature, relative and absolute humidity, average temperature and solar radiation. Most of these factors show a lagged effect with confirmed cases (between 0 and 28 days). Also, some meteorological factors exhibit contrasting effects on COVID-19 transmission, resulting in both positive and negative association with cumulative cases, therefore highlighting the complex nature of the interplay between climate and COVID-19 transmission.

Understanding the COVID-19 pandemic prevalence in Africa through optimal feature selection and clustering: evidence from a statistical perspective.

The COVID-19 pandemic, which outbroke in Wuhan (China) in December 2019, severely hit almost all sectors of activity in the world as a consequence of the restrictive measures imposed. Two years later, Africa still emerges as the least affected continent by the pandemic. This study analyzed COVID-19 prevalence across African countries through country-level variables prior to clustering. Using Spearman-rank correlation, multicollinearity analysis and univariate filtering, 9 country-level variables were identified from an initial set of 34 variables. These variables relate to socioeconomic status, population structure, healthcare system and environment and the climatic setting. A clustering of the 54 African countries is further carried out through the use of agglomerative hierarchical clustering (AHC) method, which generated 3 distinctive clusters. Cluster 1 (11 countries) is the most affected by COVID-19 (median of 63,508.6 confirmed cases and 946.5 deaths per million) and is composed of countries with the highest socioeconomic status. Cluster 2 (27 countries) is the least affected (median of 4473.7 confirmed cases and 81.2 deaths per million), and mainly features countries with the least socioeconomic features and international exposure. Cluster 3 (16 countries) is intermediate in terms of COVID-19 prevalence (median of 2569.3 confirmed cases and 35.7 deaths per million) and features countries the least urbanized and geographically close to the equator, with intermediate international exposure and socioeconomic features. These findings shed light on the main features of COVID-19 prevalence in Africa and might help refine effectively coping management strategies of the ongoing pandemic. Supplementary Information: The online version contains supplementary material available at 10.1007/s10668-022-02646-3.

A dynamic land use/land cover input helps in picturing the Sahelian paradox: Assessing variability and attribution of changes in surface runoff in a Sahelian watershed.

In Sahelian landscapes, land use/land cover (LULC) dynamics and climate variability are already known to affect the water cycle. In its current practice however, hydrological modelling does not account for LULC changes. This issue pertains to rapidly evolving watersheds and might result in critical inaccuracies in the simulated processes. In this study, the Soil and Water Assessment Tool (SWAT) model was used to simulate surface runoff in the small Sahelian watershed of Tougou, which underwent significant LULC changes between 1952 and 2017. Based on rainfall/runoff data acquired from 2004 to 2018, the SWAT model was calibrated under two scenarios: a static land use scenario (SLU) using a single LULC map (in 1999) and a dynamic land use scenario (DLU) integrating 3 LULC maps (1999, 2009 and 2017). The DLU scenario estimated with higher accuracy surface runoff, deep aquifer infiltration and actual evapotranspiration processes. Based on the calibrated parameters, surface runoff was simulated during the historical period 1952-2003 under four scenarios with static LULC maps (in 1952, 1973, 1986 and 1999) opposed to a fifth scenario integrating these LULC maps dynamically. The DLU scenario was found to be more effective at picturing the so-called Sahelian paradox (i.e. the increase in surface runoff despite the decrease in rainfall), reported in the literature for small watersheds in the Sahel. The analysis of variability revealed that fluctuations in surface runoff were both influenced by rainfall and LULC changes. Furthermore, the isolated contributions of climate variability and LULC changes on surface runoff showed that LULC conditions played a dominant role (ηlulc = +393.1%) in the runoff increase over climate (ηcl = -297%) during the historical period. These results highlight the importance of accounting for LULC dynamics in hydrological modelling and advocate the development of integrated modelling frameworks for hydrologists and water resource managers.