The surveys took place in Kinshasa, capital city of Democratic Republic of Congo (DRC), located in the Central-African region. Kinshasa lies at 279 m above sea level and is characterized by a tropical climate with a rainy season between October and May, and a dry season from June to September. The average temperature varies between 18°C and 32°C and the average monthly rainfall varies between 2 and 222 mm, in dry and rainy season respectively. Kinshasa encompasses 9965 km² and has an estimated population of almost 12 million people. The city is administratively subdivided into 24 communes, which are grouped in four districts: Tshangu in the East, Lukunga in the North, Mont Amba in the South-East and Funa in the Center-West. In this study, four communes were purposively selected to capture diverse ecological, urbanization, water supply systems and epidemiological conditions (i.e. history of arbovirus outbreaks) (figure 1).
N’Djili is a peri-urban commune in the east of the city, pertaining to the Tshangu district, where many informal economic activities, specifically vehicle repair shops, are located. Urban infrastructure, such as waste water infrastructure and garbage collection, is deficient. Almost all (97%) of the houses have a water supply system in their compound, but an important proportion of them has water quality, volume and availability problems. The population density of this area is estimated at 39 000 persons/km².
Kalamu II is a commune in the center of town, belonging to the Funa district, and is highly residential. The main economic activity is technical service provision. It has an estimated population density of 47 000 persons/km².
Mont Ngafula I is situated in the south of the city, bordering Mont Amba district, and is an example of a semi-urban area with an estimated population density of 730 persons/km². It is geographically characterized by the presence of hills (and erosions) and small valleys. The main economic activity is agriculture and the selling of agriculture products to Kinshasa city. Mont Ngafula I is emblematic of unplanned urbanization with deficient water supply system – in terms of both supply (i.e. as low as two times/week) and waste water disposal.
Lingwala is a commune in the center of the town, pertaining to the Lukunga district, with a lot of informal markets. It is a more urbanized area with fairly good water supply. Population density is estimated at 33 000 persons/km².
Study design and data collection
Two cross-sectional surveys were done, one in the rainy season (18 January – 16 February, 2018) and one in the dry season (2 – 27 July, 2018). To detect 10% of the houses being positive for Aedes spp. mosquitoes with 80% power, 3% precision and allowing for a 5% alfa-error, 400 houses needed to be surveyed in each survey site. In each of the four selected communes, one neighborhood has been randomly chosen (all neighborhoods per commune listed, followed by random number selection procedure) as study site. Each day, 80 houses were inspected, using a systematic sampling approach: on a landmark (roundabout or main road) random points were identified for each team as their starting point to enter the (smaller) avenues. With a sampling interval of three houses, starting on the right side of the avenue, each of the 4 teams inspected the selected houses up to reaching a maximum of 20 houses/day. When the avenue came to an end and the quota of 20 was not yet reached, the team turned back, approaching the houses on the other side of the street until the sample quota was reached. In each selected house, the entire house was inspected inside and outside. If there was more than one house per compound, a random house was chosen to inspect, but the entire outside part of the compound was inspected. The next day, the next avenue (going left from the one of the previous day) was sampled. By this procedure, representative sampling was achieved. When one commune was finalized, the four entomological teams went to another commune and followed the same methodology. All communes were covered in four weeks’ time. Each entomological survey team consisted of three persons, pre-trained by the entomology department of the ‘Institut National de Recherche Biomédicale’ (INRB), one entomologist of the INRB (supervisor) and one community health worker.
In each compound, all water holding containers were inspected and if immature stages of mosquitoes (i.e. larvae or pupae) were observed, they were collected in plastic bottles (one bottle per larval habitat) and transported to the laboratory at INRB for genus identification (Anopheles, Aedes, Culex). The place, category and positivity/negativity of each container were recorded. For larvae, only positivity and negativity was recorded; for pupae, the number of pupae was counted per positive habitat. Surveys were implemented identically, but as samples were randomly selected, houses had equal probability of inclusion in one, both, or neither survey. Both surveys were largely realized by the same field team members.
Species identification: morphology and DNA-based
Each day, a random sample of 50 Aedes genus larvae/pupae were reared to adults in the insectarium to allow species identification using morphological keys [33,34]. F0 adults were stored at -20°C for DNA barcoding to validate the morphological identification of Ae. aegypti and Ae. albopictus and confirm the presence of the identified species in Kinshasa. Therefor five specimens of each species were randomly selected per survey site. DNA barcoding is a technique based on the amplification of a standard barcode - the partial mitochondrial cytochrome c oxidase subunit I gene for animals. Sanger sequencing of the 658 bp COI standard barcode was performed using the LCO1490 and HCO2198 universal primers [35,36]. Amplifications were carried out in a 20 µl reaction mixture containing 2 µl of DNA template, 2 µl of 10X buffer, 1.5 mM MgCl2, 0.2 mM dNTP, 0.4 µM of each primer, and 0.03 units/µl of PlatinumTM Taq DNA Polymerase (InvitrogenTM). PCR products and negative controls were checked on a 1.5% agarose gel, using a UV transilluminator and the MidoriGreenTM Direct (NIPPON Genetics Europe) method. Positive amplicons were purified using the ExoSAP-ITTM protocol and sequenced in both directions on an ABI 3230xl capillary DNA sequencer using BigDye Terminator v3.1 chemistry (ThermoFisher Scientific). Subsequently, the generated sequences were compared to a library of reference sequences. A specimen was identified by analyzing its percentage sequence similarity with these reference sequences under the assumption that genetic diversity is lower within than between species. A rooted Neighbour-Joining tree was constructed including a sub-selection of the Ae. albopictus and Ae. aegypti barcodes available from online repositories, together with the newly generated haplotypes (full details of the protocol can be found in Additional File 2).
Data were entered in an Access database and 5% of the data were manually validated to detect errors. Data were cleaned and types of recipients regrouped into categories, adapted from guidelines used in dengue-endemic regions : water storage tanks or cistern(> 15 L); small water deposits used for daily kitchen and cleaning activities (< 15 L); rubbish and discards ; natural tree and bamboo holes ; artificials that are used in the households and cannot be destroyed (for example animal drinking pots); used tires; natural ground pools. Data were analyzed using IBM SPSS Statistics, version 25. We calculated per round and per commune House Index (number of houses positive for at least one container with immature stages of Aedes spp. per 100 inspected houses), Breteau Index (number of containers positive for immature stages of Aedes spp. per 100 inspected houses), Container Index (number of containers positive for immature stages of Aedes spp. per 100 inspected containers), and Pupal Index (number of Aedes spp. pupae per 100 inspected houses). The relative contribution to pupal productivity was calculated and defined as the total number of pupae of Aedes spp. per category of larval habitat divided by the total number of pupae of Aedes spp. collected per commune and per survey round. A descriptive analysis was done. In order to evaluate the factors determining Aedes spp. immature stage positivity, a logistic regression model was conducted and associated variables were identified based on a backwards conditional model, taking into account the clustering at household-level by inserting the household identification variable as a random factor in the model.
The number of larval habitats with at least one immature stage of Anopheles spp. was enumerated and its proportional importance calculated for each season and respective commune.