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Molecular characterization of chikungunya virus during the 2019 outbreak in the Democratic Republic of the Congo.

ABSTRACT: Early 2019, a chikungunya virus (CHIKV) outbreak hit the Democratic Republic of the Congo (DRC). Though seldomly deadly, this mosquito-borne disease presents as an acute febrile (poly)arthralgia often followed by long-term sequelae. Although Aedes aegypti is the primary vector, an amino acid substitution in the viral envelope gene E1 (A226V) is causing concern as it results in increased transmission by Aedes albopictus, a mosquito with a much wider geographical distribution. Between January and March 2019, we collected human and mosquito samples in Kinshasa and Kongo Central province (Kasangulu and Matadi). Of the patients that were tested within 7 days of symptom onset, 49.7% (87/175) were RT-qPCR positive, while in the mosquito samples CHIKV was found in 1/2 pools in Kinshasa, 5/6 pools in Kasangulu, and 8/26 pools in Matadi. Phylogenetic analysis on whole-genome sequences showed that the circulating strain formed a monophyletic group within the ECSA2 lineage and harboured the A226V mutation. Our sequences did not cluster with sequences from previously reported outbreaks in the DRC nor with other known A226V-containing ECSA2 strains. This indicates a scenario of convergent evolution where A226V was acquired independently in response to a similar selection pressure for transmission by Ae. albopictus. This is in line with our entomological data where we detected Ae. albopictus more frequently than Ae. aegypti in two out of three affected areas. In conclusion, our findings suggest that CHIKV is adapting to the increased presence of Aedes albopictus in DRC.

SUBMITTER: Selhorst P

PROVIDER: S-EPMC8284967 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe 2018-2019 Ebola virus disease (EVD) outbreak in North Kivu and Ituri provinces in the Democratic Republic of the Congo (DRC) is the largest ever recorded in the DRC. It has been declared a Public Health Emergency of International Concern. The outbreak emerged in a region of chronic conflict and insecurity, and directed attacks against health care workers may have interfered with disease response activities. Our study characterizes and quantifies the broader conflict dynamics over the course of the outbreak by pairing epidemiological and all available spatial conflict data.MethodsWe build a set of conflict variables by mapping the spatial locations of all conflict events and their associated deaths in each of the affected health zones in North Kivu and Ituri, eastern DRC, before and during the outbreak. Using these data, we compare patterns of conflict before and during the outbreak in affected health zones and those not affected. We then test whether conflict is correlated with increased EVD transmission at the health zone level.FindingsThe incidence of conflict events per capita is ~?600 times more likely in Ituri and North Kivu than for the rest of the DRC. We identified 15 time periods of substantial uninterrupted transmission across 11 health zones and a total of 120 bi-weeks. We do not find significant short-term associations between the bi-week reproduction numbers and the number of conflicts. However, we do find that the incidence of conflict per capita was correlated with the incidence of EVD per capita at the health zone level for the entire outbreak (Pearson's r?=?0.33, 95% CI 0.05-0.57). In the two provinces, the monthly number of conflict events also increased by a factor of 2.7 in Ebola-affected health zones (p value <?0.05) compared to 2.0 where no transmission was reported and 1.3 in the rest of the DRC, in the period between February 2019 and July 2019.ConclusionWe characterized the association between variables documenting broad conflict levels and EVD transmission. Such assessment is important to understand if and how such conflict variables could be used to inform the outbreak response. We found that while these variables can help characterize long-term challenges and susceptibilities of the different regions they provide little insight on the short-term dynamics of EVD transmission.

Project description:BackgroundAs of February 25, 2019, 875 cases of Ebola virus disease (EVD) were reported in North Kivu and Ituri Provinces, Democratic Republic of Congo. Since the beginning of October 2018, the outbreak has largely shifted into regions in which active armed conflict has occurred, and in which EVD cases and their contacts have been difficult for health workers to reach. We used available data on the current outbreak, with case-count time series from prior outbreaks, to project the short-term and long-term course of the outbreak.MethodsFor short- and long-term projections, we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission rates estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used two regression models to estimate similar projection periods. Short- and long-term projections were estimated using negative binomial autoregression and Theil-Sen regression, respectively. We also used Gott's rule to estimate a baseline minimum-information projection. We then constructed an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20, 2018 to February 25, 2019, short-term model projections were validated against known case counts.ResultsDuring validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of February 25, the stochastic model projected a median case count of 933 cases by February 18 (95% prediction interval: 872-1054) and 955 cases by March 4 (95% prediction interval: 874-1105), while the auto-regression model projects median case counts of 889 (95% prediction interval: 876-933) and 898 (95% prediction interval: 877-983) cases for those dates, respectively. Projected median final counts range from 953 to 1,749. Although the outbreak is already larger than all past Ebola outbreaks other than the 2013-2016 outbreak of over 26,000 cases, our models do not project that it is likely to grow to that scale. The stochastic model estimates that vaccination coverage in this outbreak is lower than reported in its trial setting in Sierra Leone.ConclusionsOur projections are concentrated in a range up to about 300 cases beyond those already reported. While a catastrophic outbreak is not projected, it is not ruled out, and prevention and vigilance are warranted. Prospective validation of our models in real time allowed us to generate more accurate short-term forecasts, and this process may prove useful for future real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response challenges.

Project description:BackgroundChikungunya is an Aedes -borne disease characterised by febrile arthralgia and responsible for massive outbreaks. We present a prospective clinical cohort study and a retrospective serological study relating to a CHIK outbreak, in the Republic of Congo in 2011.Methodology and findingsWe analysed 317 suspected cases, of which 308 (97.2%) lived in the city of Brazzaville (66.6% in the South area). Amongst them, 37 (11.7%) were CHIKV+ve patients (i.e., biologically confirmed by a real-time RT-PCR assay), of whom 36 (97.3%) had fever, 22 (66.7%) myalgia and 32 (86.5%) arthralgia. All tested negative for dengue. The distribution of incident cases within Brazzaville districts was compared with CHIKV seroprevalence before the outbreak (34.4% in 517 blood donors), providing evidence for previous circulation of CHIKV. We applied a CHIK clinical score to 126 patients recruited within the two first day of illness (including 28 CHIKV+ves (22.2%)) with sensitivity (78.6%) and specificity (72.4%) values comparing with those of the referent study in Reunion Island. The negative predictive value was high (92%), but the positive predictive value (45%) indicate poor potential contribution to medical practice to identify CHIKV+ve patients in low prevalence outbreaks. However, the score allowed a slightly more accurate follow-up of the evolution of the outbreak than the criterion "fever+arthralgia". The complete sequencing of a Congolase isolate (Brazza_MRS1) demonstrated belonging to the East/Central/South African lineage and was further used for producing a robust genome-scale CHIKV phylogenetic analysis.Conclusions/significanceWe describe the first Chikungunya outbreak declared in the Republic of Congo. The seroprevalence study conducted amongst blood donors before outbreak provided evidence for previous CHIKV circulation. We suggest that a more systematic survey of the entomological situation and of arbovirus circulation is necessary in Central Africa for better understanding the environmental, microbiological and sociological determinants of emergence.

Project description:BackgroundThe Democratic Republic of the Congo has had 15 Ebola virus disease (EVD) outbreaks, from 1976 to 2023. On June 1, 2020, the Democratic Republic of the Congo declared an outbreak of EVD in the western Équateur Province (11th outbreak), proximal to the 2018 Tumba and Bikoro outbreak and concurrent with an outbreak in the eastern Nord Kivu Province. In this Article, we assessed whether the 11th outbreak was genetically related to previous or concurrent EVD outbreaks and connected available epidemiological and genetic data to identify sources of possible zoonotic spillover, uncover additional unreported cases of nosocomial transmission, and provide a deeper investigation into the 11th outbreak.MethodsWe analysed epidemiological factors from the 11th EVD outbreak to identify patient characteristics, epidemiological links, and transmission modes to explore virus spread through space, time, and age groups in the Équateur Province, Democratic Republic of the Congo. Trained field investigators and health professionals recorded data on suspected, probable, and confirmed cases, including demographic characteristics, possible exposures, symptom onset and signs and symptoms, and potentially exposed contacts. We used blood samples from individuals who were live suspected cases and oral swabs from individuals who were deceased to diagnose EVD. We applied whole-genome sequencing of 87 available Ebola virus genomes (from 130 individuals with EVD between May 19 and Sept 16, 2020), phylogenetic divergence versus time, and Bayesian reconstruction of phylogenetic trees to calculate viral substitution rates and study viral evolution. We linked the available epidemiological and genetic datasets to conduct a genomic and epidemiological study of the 11th EVD outbreak.FindingsBetween May 19 and Sept 16, 2020, 130 EVD (119 confirmed and 11 probable) cases were reported across 13 Équateur Province health zones. The individual identified as the index case reported frequent consumption of bat meat, suggesting the outbreak started due to zoonotic spillover. Sequencing revealed two circulating Ebola virus variants associated with this outbreak-a Mbandaka variant associated with the majority (97%) of cases and a Tumba-like variant with similarity to the ninth EVD outbreak in 2018. The Tumba-like variant exhibited a reduced substitution rate, suggesting transmission from a previous survivor of EVD.InterpretationIntegrating genetic and epidemiological data allowed for investigative fact-checking and verified patient-reported sources of possible zoonotic spillover. These results demonstrate that rapid genetic sequencing combined with epidemiological data can inform responders of the mechanisms of viral spread, uncover novel transmission modes, and provide a deeper understanding of the outbreak, which is ultimately needed for infection prevention and control during outbreaks.FundingWHO and US Centers for Disease Control and Prevention.

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Molecular characterization of chikungunya virus during the 2019 outbreak in the Democratic Republic of the Congo.

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