Field investigation with real-time virus genetic characterisation support of a cluster of Ebola virus disease cases in Dubréka, Guinea, April to June 2015

On 11 May 2015, the Dubréka prefecture, Guinea, reported nine laboratory-confirmed cases of Ebola virus disease (EVD). None could be epidemiologically linked to cases previously reported in the prefecture. We describe the epidemiological and molecular investigations of this event. We used the Dubréka EVD registers and the Ebola treatment centre’s (ETC) records to characterise chains of transmission. Real-time field Ebola virus sequencing was employed to support epidemiological results. An epidemiological cluster of 32 cases was found, of which 27 were laboratory confirmed, 24 were isolated and 20 died. Real-time viral sequencing on 12 cases demonstrated SL3 lineage viruses with sequences differing by one to three nt inside a single phylogenetic cluster. For isolated cases, the average time between symptom onset and ETC referral was 2.8 days (interquartile range (IQR): 1–4). The average time between sample collection and molecular results’ availability was 3 days (IQR: 2–5). In an area with scarce resources, the genetic characterisation supported the outbreak investigations in real time, linking cases where epidemiological investigation was limited and reassuring that the responsible strain was already circulating in Guinea. We recommend coupling thorough epidemiological and genomic investigations to control EVD clusters.Peer Reviewe

Data from: Impact of the Ebola outbreak on Trypanosoma brucei gambiense infection medical activities in coastal Guinea, 2014-2015: a retrospective analysis from the Guinean national Human African Trypanosomiasis control program

Background: The 2014–2015 Ebola outbreak massively hit Guinea. The coastal districts of Boffa, Dubreka and Forecariah, three major foci of Human African Trypanosomiasis (HAT), were particularly affected. We aimed to assess the impact of this epidemic on sleeping sickness screening and caring activities. Methodology/Principal findings: We used preexisting data from the Guinean sleeping sickness control program, collected between 2012 and 2015. We described monthly: the number of persons (i) screened actively; (ii) or passively; (iii) treated for HAT; (iv) attending post-treatment follow-up visits. We compared clinical data, treatment characteristics and Disability Adjusted Life-Years (DALYs) before (February 2012 to December 2013) and during (January 2014 to October 2015) the Ebola outbreak period according to available data. Whereas 32,221 persons were actively screened from February 2012 to December 2013, before the official declaration of the first Ebola case in Guinea, no active screening campaigns could be performed during the Ebola outbreak. Following the reinforcement and extension of HAT passive surveillance system early in 2014, the number of persons tested passively by month increased from 7 to 286 between April and September 2014 and then abruptly decreased to 180 until January 2015 and to none after March 2015. 213 patients initiated HAT treatment, 154 (72%) before Ebola and 59 (28%) during the Ebola outbreak. Those initiating HAT therapy during Ebola outbreak were recruited through passive screening and diagnosed at a later stage 2 of the disease (96% vs. 55% before Ebola, p<0.0001). The proportion of patients attending the 3 months and 6 months post-treatment follow-up visits decreased from 44% to 10% (p <0.0001) and from 16% to 3% (p = 0.017) respectively. The DALYs generated before the Ebola outbreak were estimated to 48.7 (46.7–51.5) and increased up to 168.7 (162.7–174.7), 284.9 (277.1–292.8) and 466.3 (455.7–477.0) during Ebola assuming case fatality rates of 2%, 5% and 10% respectively among under-reported HAT cases. Conclusions/Significance: The 2014–2015 Ebola outbreak deeply impacted HAT screening activities in Guinea. Active screening campaigns were stopped. Passive screening dramatically decreased during the Ebola period, but trends could not be compared with pre-Ebola period (data not available). Few patients were diagnosed with more advanced HAT during the Ebola period and retention rates in follow-up were lowered. The drop in newly diagnosed HAT cases during Ebola epidemic is unlikely due to a fall in HAT incidence. Even if we were unable to demonstrate it directly, it is much more probably the consequence of hampered screening activities and of the fear of the population on subsequent confirmation and linkage to care. Reinforced program monitoring, alternative control strategies and sustainable financial and human resources allocation are mandatory during post Ebola period to reduce HAT burden in Guinea

Unravelling human trypanotolerance: IL8 is associated with infection control whereas IL10 and TNFα are associated with subsequent disease development

In West Africa, Trypanosoma brucei gambiense, causing human African trypanosomiasis (HAT), is associated with a great diversity of infection outcomes. In addition to patients who can be diagnosed in the early hemolymphatic phase (stage 1) or meningoencephalitic phase (stage 2), a number of individuals can mount long-lasting specific serological responses while the results of microscopic investigations are negative (SERO TL+). Evidence is now increasing to indicate that these are asymptomatic subjects with low-grade parasitemia. The goal of our study was to investigate the type of immune response occurring in these "trypanotolerant" subjects. Cytokines levels were measured in healthy endemic controls (n = 40), stage 1 (n = 10), early stage 2 (n = 19), and late stage 2 patients (n = 23) and in a cohort of SERO TL+ individuals (n = 60) who were followed up for two years to assess the evolution of their parasitological and serological status. In contrast to HAT patients which T-cell responses appeared to be activated with increased levels of IL2, IL4, and IL10, SERO TL+ exhibited high levels of proinflammatory cytokines (IL6, IL8 and TNFα) and an almost absence of IL12p70. In SERO TL+, high levels of IL10 and low levels of TNFα were associated with an increased risk of developing HAT whereas high levels of IL8 predicted that serology would become negative. Further studies using high throughput technologies, hopefully will provide a more detailed view of the critical molecules or pathways underlying the trypanotolerant phenotype

Reducing Human-Tsetse Contact Significantly Enhances the Efficacy of Sleeping Sickness Active Screening Campaigns: A Promising Result in the Context of Elimination

Background Control of gambiense sleeping sickness, a neglected tropical disease targeted for elimination by 2020, relies mainly on mass screening of populations at risk and treatment of cases. This strategy is however challenged by the existence of undetected reservoirs of parasites that contribute to the maintenance of transmission. In this study, performed in the Boffa disease focus of Guinea, we evaluated the value of adding vector control to medical surveys and measured its impact on disease burden. Methods The focus was divided into two parts (screen and treat in the western part; screen and treat plus vector control in the eastern part) separated by the Rio Pongo river. Population census and baseline entomological data were collected from the entire focus at the beginning of the study and insecticide impregnated targets were deployed on the eastern bank only. Medical surveys were performed in both areas in 2012 and 2013. Findings In the vector control area, there was an 80% decrease in tsetse density, resulting in a significant decrease of human tsetse contacts, and a decrease of disease prevalence (from 0.3% to 0.1%; p=0.01), and an almost nil incidence of new infections (1%, p<0.0001) with a disease prevalence increasing slightly (from 0.5 to 0.7%, p=0.34). Interpretation Combining medical and vector control was decisive in reducing T. b. gambiense transmission and in speeding up progress towards elimination. Similar strategies could be applied in other foci

Conducting active screening for human African trypanosomiasis with rapid diagnostic tests: The Guinean experience (2016–2021)

International audienceStrategies to detect Human African Trypanosomiasis (HAT) cases rely on serological screening of populations exposed to trypanosomes. In Guinea, mass medical screening surveys performed with the Card Agglutination Test for Trypanosomiasis have been progressively replaced by door-to-door approaches using Rapid Diagnostic Tests (RDTs) since 2016. However, RDTs availability represents a major concern and medical teams must often adapt, even in the absence of prior RDT performance evaluation. For the last 5 years, the Guinean HAT National Control Program had to combine three different RDTs according to their availability and price: the SD Bioline HAT (not available anymore), the HAT Sero-K-SeT (most expensive), and recently the Abbott Bioline HAT 2.0 (limited field evaluation). Here, we assess the performance of these RDTs, alone or in different combinations, through the analysis of both prospective and retrospective data. A parallel assessment showed a higher positivity rate of Abbott Bioline HAT 2.0 (6.0%, n = 2,250) as compared to HAT Sero-K-SeT (1.9%), with a combined positive predictive value (PPV) of 20.0%. However, an evaluation of Abbott Bioline HAT 2.0 alone revealed a low PPV of 3.9% (n = 6,930) which was surpassed when using Abbott Bioline HAT 2.0 in first line and HAT Sero-K-SeT as a secondary test before confirmation, with a combined PPV reaching 44.4%. A retrospective evaluation of all 3 RDTs was then conducted on 189 plasma samples from the HAT-NCP biobank, confirming the higher sensitivity (94.0% [85.6–97.7%]) and lower specificity (83.6% [76.0–89.1%]) of Abbott Bioline HAT 2.0 as compared to SD Bioline HAT (Se 64.2% [52.2–74.6%]—Sp 98.4% [94.2–99.5%]) and HAT Sero-K-SeT (Se 88.1% [78.2–93.8%]—Sp 98.4% [94.2–99.5%]). A comparison of Abbott Bioline HAT 2.0 and malaria-RDT positivity rates on 479 subjects living in HAT-free malaria-endemic areas further revealed that a significantly higher proportion of subjects positive in Abbott Bioline HAT 2.0 were also positive in malaria-RDT, suggesting a possible cross-reaction of Abbott Bioline HAT 2.0 with malaria-related biological factors in about 10% of malaria cases. This would explain, at least in part, the limited specificity of Abbott Bioline HAT 2.0. Overall, Abbott Bioline HAT 2.0 seems suitable as first line RDT in combination with a second HAT RDT to prevent confirmatory lab overload and loss of suspects during referral for confirmation. A state-of-the-art prospective comparative study is further required for comparing all current and future HAT RDTs to propose an optimal combination of RDTs for door-to-door active screening

Ebola virus disease and HAT treatment centers spatial distribution.

<p>This figure shows the spatial distribution of Ebola virus disease (EVD) incidence with both EVD and Human African Trypanosomiasis (HAT) treatment centers and newly diagnosed HAT cases during the study period (before and during Ebola outbreak) in coastal Guinea. *HAT: Human African Trypanosomiasis. ¥ Before Ebola: from Feb.2012 to Dec.2013. § During Ebola: from Jan.2014 to Oct.2015.</p

Cytokine profiles and infection outcomes in gambiense HAT.

<p>Cytokine profiles and infection outcomes in gambiense HAT.</p

Cytokine levels measured in the CSF and plasma of HAT patients according to the disease stage.

1<p>Interquartile range.</p>2<p>P values are given for the three class comparisons (stage1, early stage 2, and late stage 2), p-values<0.05 are in bold.</p><p>Cytokine levels measured in the CSF and plasma of HAT patients according to the disease stage.</p

Timeline of the Ebola epidemic and HAT control activities.

<p>This figure shows in parallel the main events of the Ebola epidemic and of Human African Trypanosomiasis (HAT) control activities between October 2013 and April 2016.</p

Impact of Ebola outbreak on HAT testing and caring activities in Guinea, January 2012 to October 2015.

<p>This panel figure displays: (A) Monthly evolution of the number of persons diagnosed during active campaigns by HAT treatment center; (B) Monthly evolution of the number of persons tested passively by HAT treatment center (and as from 2014 corresponding district); (C) Monthly evolution of the number of persons initiating therapy by HAT treatment center; (D) Monthly evolution of the number of persons initiating HAT therapy according to the type of screening; (E) Monthly evolution of the number of persons initiating HAT therapy by disease stage at diagnosis (F) Monthly evolution of the number of persons attending 3 months post-treatment follow-up visit. HAT: Human African Trypanosomiasis: *Passive routine testing data were available only between January 2014 and October 2015. **All post-treatment follow-up visits were centralized at the Dubreka center whatever the place where the patients received HAT therapy (Dubreka, Boffa or Forecariah HAT centers).</p