Spreading of SARS-CoV-2 in West Africa and assessment of risk factors.

Although the African continent is, for the moment, less impacted than the rest of the world, it still faces the risk of a spread of COVID-19. In this study, we have conducted a systematic review of the information available in the literature in order to provide an overview of the epidemiological and clinical features of COVID-19 pandemic in West Africa and of the impact of risk factors such as comorbidities, climatic conditions and demography on the pandemic. Burkina Faso is used as a case study to better describe the situation in West Africa. The epidemiological situation of COVID-19 in West Africa is marked by a continuous increase in the numbers of confirmed cases. This geographic area had on 29 July 2020, 131 049 confirmed cases by polymerase chain reaction, 88 305 recoveries and 2102 deaths. Several factors may influence the SARS-CoV-2 circulation in Africa: (i) comorbidities: diabetes mellitus and high blood pressure could lead to an increase in the number of severe cases of SARS-CoV-2; (ii) climatic factors: the high temperatures could be a factor contributing to slow the spread of the virus and (iii) demography: the West Africa population is very young and this could be a factor limiting the occurrence of severe forms of SARS-CoV-2 infection. Although the spread of the SARS-CoV-2 epidemic in West Africa is relatively slow compared to European countries, vigilance must remain. Difficulties in access to diagnostic tests, lack of hospital equipment, but also the large number of people working in the informal sector (such as trading, businesses, transport and restoration) makes it difficult to apply preventive measures, namely physical distancing and containment.

Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali.

BACKGROUND: The risk of Plasmodium falciparum infection is variable over space and time and this variability is related to environmental variability. Environmental factors affect the biological cycle of both vector and parasite. Despite this strong relationship, environmental effects have rarely been included in malaria transmission models.Remote sensing data on environment were incorporated into a temporal model of the transmission, to forecast the evolution of malaria epidemiology, in a locality of Sudanese savannah area. METHODS: A dynamic cohort was constituted in June 1996 and followed up until June 2001 in the locality of Bancoumana, Mali. The 15-day composite vegetation index (NDVI), issued from satellite imagery series (NOAA) from July 1981 to December 2006, was used as remote sensing data.The statistical relationship between NDVI and incidence of P. falciparum infection was assessed by ARIMA analysis. ROC analysis provided an NDVI value for the prediction of an increase in incidence of parasitaemia.Malaria transmission was modelled using an SIRS-type model, adapted to Bancoumana's data. Environmental factors influenced vector mortality and aggressiveness, as well as length of the gonotrophic cycle. NDVI observations from 1981 to 2001 were used for the simulation of the extrinsic variable of a hidden Markov chain model. Observations from 2002 to 2006 served as external validation. RESULTS: The seasonal pattern of P. falciparum incidence was significantly explained by NDVI, with a delay of 15 days (p = 0.001). An NDVI threshold of 0.361 (p = 0.007) provided a Diagnostic Odd Ratio (DOR) of 2.64 (CI95% [1.26;5.52]).The deterministic transmission model, with stochastic environmental factor, predicted an endemo-epidemic pattern of malaria infection. The incidences of parasitaemia were adequately modelled, using the observed NDVI as well as the NDVI simulations. Transmission pattern have been modelled and observed values were adequately predicted. The error parameters have shown the smallest values for a monthly model of environmental changes. CONCLUSION: Remote-sensed data were coupled with field study data in order to drive a malaria transmission model. Several studies have shown that the NDVI presents significant correlations with climate variables, such as precipitations particularly in Sudanese savannah environments. Non-linear model combining environmental variables, predisposition factors and transmission pattern can be used for community level risk evaluation.