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Rift Valley fever (RVF) is a re-emerging vector-borne zoonosis with a high public health and veterinary impact. In West Africa, many lineages were previously detected, but since 2020, lineage H from South Africa has been the main cause of the outbreaks. In this study, clinical samples collected through national surveillance were screened for RVF virus (RVFV) acute infection by RT-PCR and IgM ELISA tests. Sequencing, genome mapping and in vitro phenotypic characterization in mammal cells were performed on RT-PCR positive samples in comparison with other epidemic lineages (G and C). Four RVFV human cases were detected in Senegal and the sequence analyses revealed that the strains belonged to lineage H. The in vitro kinetics and genome mapping showed different replication efficiency profiles for the tested RVFV lineages and non-conservative mutations, which were more common to lineage G or specific to lineage H. Our findings showed the re-emergence of lineage H in Senegal in 2022, its high viral replication efficiency in vitro and support the findings that genetic diversity affects viral replication. This study gives new insights into the biological properties of lineage H and calls for deeper studies to better assess its potential to cause a future threat in Senegal.

Crimean-Congo hemorrhagic fever (CCHF), the most widespread tick-borne viral human infection, poses a threat to global health. In this study, clinical samples collected through national surveillance systems were screened for acute CCHF virus (CCHFV) infection using RT-PCR and for exposure using ELISA. For any CCHF-positive sample, livestock and tick samples were also collected in the neighborhood of the confirmed case and tested using ELISA and RT-PCR, respectively. Genome sequencing and phylogenetic analyses were also performed on samples with positive RT-PCR results. In Eastern Senegal, two human cases and one Hyalomma tick positive for CCHF were identified and a seroprevalence in livestock ranging from 9.33% to 45.26% was detected. Phylogenetic analyses revealed that the human strain belonged to genotype I based on the available L segment. However, the tick strain showed a reassortant profile, with the L and M segments belonging to genotype I and the S segment belonging to genotype III. Our data also showed that our strains clustered with strains isolated in different countries, including Mauritania. Therefore, our findings confirmed the high genetic variability inside the CCHF genotypes and their introduction to Senegal from other countries. They also indicate an increasing CCHF threat in Senegal and emphasize the need to reinforce surveillance using a one-health approach.

In recent decades, computer vision has proven remarkably effective in addressing diverse issues in public health, from determining the diagnosis, prognosis, and treatment of diseases in humans to predicting infectious disease outbreaks. Here, we investigate whether convolutional neural networks (CNNs) can also demonstrate effectiveness in classifying the environmental stages of parasites of public health importance and their invertebrate hosts. We used schistosomiasis as a reference model. Schistosomiasis is a debilitating parasitic disease transmitted to humans via snail intermediate hosts. The parasite affects more than 200 million people in tropical and subtropical regions. We trained our CNN, a feed-forward neural network, on a limited dataset of 5,500 images of snails and 5,100 images of cercariae obtained from schistosomiasis transmission sites in the Senegal River Basin, a region in western Africa that is hyper-endemic for the disease. The image set included both images of two snail genera that are relevant to schistosomiasis transmission - that is, Bulinus spp. and Biomphalaria pfeifferi - as well as snail images that are non-component hosts for human schistosomiasis. Cercariae shed from Bi. pfeifferi and Bulinus spp. snails were classified into 11 categories, of which only two, S. haematobium and S. mansoni , are major etiological agents of human schistosomiasis. The algorithms, trained on 80% of the snail and parasite dataset, achieved 99% and 91% accuracy for snail and parasite classification, respectively, when used on the hold-out validation dataset - a performance comparable to that of experienced parasitologists. The promising results of this proof-of-concept study suggests that this CNN model, and potentially similar replicable models, have the potential to support the classification of snails and parasite of medical importance. In remote field settings where machine learning algorithms can be deployed on cost-effective and widely used mobile devices, such as smartphones, these models can be a valuable complement to laboratory identification by trained technicians. Future efforts must be dedicated to increasing dataset sizes for model training and validation, as well as testing these algorithms in diverse transmission settings and geographies., Competing Interests: PE and TN were employed by the company IBM Silicon Valley Lab, San Jose, CA 95141, USA. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Tallam, Liu, Chamberlin, Jones, Shome, Riveau, Ndione, Bandagny, Jouanard, Eck, Ngo, Sokolow and De Leo.)

We introduce an approach based on functional data analysis to identify patterns of malaria incidence to guide effective targeting of malaria control in a seasonal transmission area. Using functional data method, a smooth function (functional data or curve) was fitted from the time series of observed malaria incidence for each of 575 villages in west-central Senegal from 2008 to 2012. These 575 smooth functions were classified using hierarchical clustering (Ward's method), and several different dissimilarity measures. Validity indices were used to determine the number of distinct temporal patterns of malaria incidence. Epidemiological indicators characterizing the resulting malaria incidence patterns were determined from the velocity and acceleration of their incidences over time. We identified three distinct patterns of malaria incidence: high-, intermediate-, and low-incidence patterns in respectively 2% (12/575), 17% (97/575), and 81% (466/575) of villages. Epidemiological indicators characterizing the fluctuations in malaria incidence showed that seasonal outbreaks started later, and ended earlier, in the low-incidence pattern. Functional data analysis can be used to identify patterns of malaria incidence, by considering their temporal dynamics. Epidemiological indicators derived from their velocities and accelerations, may guide to target control measures according to patterns.

The implementation of long-lasting insecticidal-treated bed nets (LLINs) has contributed to halving the mortality rate due to malaria since 2000 in sub-Saharan Africa. These tools are highly effective against indoor-feeding malaria vectors. Thus, to achieve the World Health Assembly's new target to reduce the burden of malaria over the next 15 years by 90%, it is necessary to understand how the spatiotemporal dynamics of malaria vectors and human exposure to bites is modified in the context of scaling up global efforts to control malaria transmission. This study was conducted in Dielmo, a Senegalese village, after the introduction of LLINs and two rounds of LLINs renewals. Data analysis showed that implementation of LLINs correlated with a significant decrease in the biting densities of the main malaria vectors, Anopheles gambiae s.l. and Anopheles funestus , reducing malaria transmission. Other environment factors likely contributed to the decrease in An. funestus , but this trend was enhanced with the introduction of LLINs. The bulk of bites occurred during sleeping hours, but the residual vector populations of An. gambiae s.l. and An. funestus had an increased propensity to bite outdoors, so a risk of infectious bites remained for LLINs users. These results highlight the need to increase the level and correct use of LLINs and to combine this intervention with complementary control measures against residual exposure, such as spatial repellents and larval source management, to achieve the goal of eliminating malaria transmission.

Trench fever is poorly known by the staff of health facilities that manage febrile patients in Senegal. Bartonella quintana DNA was identified in 5 of 274 (2%) febrile patients from two rural dispensaries and 2 of 71 (3%) head lice specimens collected from the same villages., (© The American Society of Tropical Medicine and Hygiene.)

To evaluate immunity to vaccine-preventable diseases according to nutritional status, a longitudinal study was conducted in Senegalese children ages 1-9 years old. A linear regression analysis predicted that weight for age was positively associated with immunoglobulin G (IgG) response to tetanus toxoid in children born during the rainy season or at the beginning of the dry season. A relationship between village, time of visits, and levels of antibodies to tetanus showed that environmental factors played a role in modulating humoral immunity to tetanus vaccine over time. Moreover, a whole-blood stimulation assay highlighted that the production of interferon-γ (IFN-γ) in response to tetanus toxoid was compromised in stunted children. However, the absence of cytokine modulation in response to Mycobacterium tuberculosis-purified protein derivatives and phytohemagglutinin suggests that the overall ability to produce IFN-γ was preserved in stunted children. Therefore, these results show that nutritional status can specifically alter the efficacy of long-lasting immunity to tetanus.