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Abstract Background Head louse, Pediculus humanus capitis, is an obligatory blood-sucking ectoparasite, distributed worldwide. Phylogenetically, it occurs in five divergent mitochondrial clades (A–E); each exhibiting a particular geographical distribution. Recent studies suggest that, as in the case of body louse, head louse could be a disease vector. We aimed to study the genetic diversity of head lice collected in the Democratic Republic of the Congo (DR Congo) and to screen for louse-borne pathogens in these lice. Methods A total of 181 head lice were collected from 27 individuals at the Monkole Hospital Center located in Kinshasa. All head lice were genotyped and screened for the presence of louse-borne bacteria using molecular methods. We searched for Bartonella quintana, Borrelia recurrentis, Rickettsia prowazekii, Anaplasma spp., Yersinia pestis, Coxiella burnetii and Acinetobacter spp. Results Among these head lice, 67.4% (122/181) belonged to clade A and 24.3% (44/181) belonged to clade D. Additionally, for the first time in this area, we found clade E in 8.3% (15/181) of tested lice, from two infested individuals. Dual infestation with clades A and D was observed for 44.4% individuals. Thirty-three of the 181 head lice were infected only by different bacterial species of the genus Acinetobacter. Overall, 16 out of 27 individuals were infested (59.3%). Six Acinetobacter species were detected including Acinetobacter baumannii (8.3%), Acinetobacter johnsonii (1.7%), Acinetobacter soli (1.7%), Acinetobacter pittii (1.7%), Acinetobacter guillouiae (1.1%), as well as a new potential species named “Candidatus Acinetobacter pediculi”. Conclusions To our knowledge, this study reports for the first time, the presence of clade E head lice in DR Congo. This study is also the first to report the presence of Acinetobacter species DNAs in human head lice in DR Congo.

Objective: The outbreak of coronavirus disease 2019 (COVID-19) in South Kivu, Democratic Republic of Congo raised concerns regarding additional morbidity and mortality. Updating these indicators before a second wave is essential in order to prepare for additional help. Methods: From mid-May to mid-December 2020, weekly surveys were undertaken in sampled streets from 10 health areas to quantify the use of barrier measures, and interview pedestrians about sickness and deaths in their households. Crude death rates (CDRs) were estimated. Results: Minimal use or no use of face masks was observed in at least half of the streets. From May to December 2020, the number of suspected cases of COVID-19 increased six-fold (P < 0.05). Of deaths within 30 days preceding the interviews, 20% were considered to be related to COVID-19. The monthly CDRs at the beginning and end of the study were approximately 5 and 25 per 1000 population, respectively (P < 0.05); that is, annual CDRs of 60 and 260 per 1000 population, respectively. Thus, during the first wave, the estimated mortality rate increased by 50% compared with previous years, and increased at least four-fold by the end of 2020. Conclusion: Despite possible overestimations, the excess mortality in South Kivu is extremely concerning. This crisis calls for a rapid response and increased humanitarian assistance.

Abstract Background Malaria is one of the major causes of childhood death in sub-Saharan countries. A reliable estimation of malaria prevalence is important to guide and monitor progress toward control and elimination. The aim of the study was to estimate the true prevalence of malaria in children under five in the Democratic Republic of the Congo, Uganda and Kenya, using a Bayesian modelling framework that combined in a novel way malaria data from national household surveys with external information about the sensitivity and specificity of the malaria diagnostic methods used in those surveys—i.e., rapid diagnostic tests and light microscopy. Methods Data were used from the Demographic and Health Surveys (DHS) and Malaria Indicator Surveys (MIS) conducted in the Democratic Republic of the Congo (DHS 2013–2014), Uganda (MIS 2014–2015) and Kenya (MIS 2015), where information on infection status using rapid diagnostic tests and/or light microscopy was available for 13,573 children. True prevalence was estimated using a Bayesian model that accounted for the conditional dependence between the two diagnostic methods, and the uncertainty of their sensitivities and specificities obtained from expert opinion. Results The estimated true malaria prevalence was 20% (95% uncertainty interval [UI] 17%–23%) in the Democratic Republic of the Congo, 22% (95% UI 9–32%) in Uganda and 1% (95% UI 0–3%) in Kenya. According to the model estimations, rapid diagnostic tests had a satisfactory sensitivity and specificity, and light microscopy had a variable sensitivity, but a satisfactory specificity. Adding reported history of fever in the previous 14 days as a third diagnostic method to the model did not affect model estimates, highlighting the poor performance of this indicator as a malaria diagnostic. Conclusions In the absence of a gold standard test, Bayesian models can assist in the optimal estimation of the malaria burden, using individual results from several tests and expert opinion about the performance of those tests.