This entomological survey was conducted in late monsoon in 2019 between August-November, the time when dengue incidence was high in the study area as well as throughout the Bangladesh [6]. The study location was the Chattogram city of Bangladesh. Chattogram is the second largest city of Bangladesh and an area poorly studied in terms of mosquito survey. A total of 12 sub-districts (Thana) under the Chattogram City Corporation (CCC) area were chosen purposively for this survey. A Thana is defined as the small administrative boundaries within the metropolitan area.
Entomological Survey:
An entomological survey for DENV was designed to detect immature stages of the Aedes aegypti (L.) (Diptera: Culicidae). Our survey targeted all groups of juveniles (1st − 4th instar larvae and pupae) by inspecting of all accessible water-holding containers in public and private areas, to identify the most productive and efficient container types for these species. Samples were taken by pipetting, dipping, or netting (WHO 1997) in small plastic jars with water. Each sample jar was labeled with the unique identification number, date, location, number of collected larvae/pupae. Breeding habitats of the collected mosquito species were recorded in a pre-defined survey data-sheet during the sample collection. Different indices were calculated to document the primary breeding source and density of the Aedes mosquito. A container was recorded as positive for Aedes if one or more juvenile Ae. aegypti or Ae. albopictus was found in the given type of container and were distinguished from those with no juveniles (negative)
Selection Of Properties
18 different locations were surveyed, including government-owned properties. The properties were purposively selected from each Thana and were categorized into nine classes, depending on the possession of living properties, working stations, and other public gathering places, as shown in Table 1.
Category
|
Number
|
Definition
|
Independent House
|
4
|
These were brick-built single-family homes, either single floor or duplex with surrounding garden.
|
Multistoried House
|
4
|
These were brick-built apartment houses having two or more floors. More than one family lives in these houses.
|
Slum
|
4
|
Slum houses are usually made of bamboo and tin, or even by mud walls. These are inadequate infrastructure with congested tenements.
|
Construction site
|
1
|
independent or multi-storied buildings in the construction phase
|
Police station
|
1
|
The local police station area in each Thana. This is usually an area of single building with open place and garden.
|
Educational institution
|
1
|
Any properties like schools, colleges, universities, or any others used for educational purposes, comprising small or big areas.
|
Hospital
|
1
|
Government or private hospitals, clinics diagnostic centers
|
Open place/park
|
1
|
Any sort of open spaces like parks, roadside places, or playgrounds without any establishment
|
Bus stand/garage
|
1
|
Small and large bus stands, garages, or fuel station
|
Classification Of Containers Found As Larval Development Sites
The containers were divided into two broad groups based on their purposes: controllable and disposable ones. Controllable containers were household containers that could be manipulated by man to avoid mosquito larval breeding which included concrete tanks, metal drums, flower pots, aluminum tanks, small buckets, and other plastic containers used to carry or store water. Disposable containers were those that are not used in households, are abandoned or stored in backyards having the potential as breeding sites in the rainy season. Examples of disposable containers include tires, cans, tubs, etc.
Identification Of Mosquitoes
After collection, the larvae and/or pupae were brought to the pathology and parasitology laboratory. Larvae were identified under microscopes in the laboratory. Pupae and the rest of the larvae were reared in rearing trays for the identification of adult mosquito. Species identification was completed using standard identification keys as described by [16]. The laboratory findings were recorded in the corresponding survey sheet.
Determine The Key Containers
All wet containers were divided into 6 categories based on the materials they were composed of, or the purposes they served in, namely, plastic receptacles, tin & metal receptacles, cement & clay receptacles, natural receptacles, vehicles & machinery parts, and other receptacles. The role of these groups of containers regarding the production of Aedes mosquito was estimated by the value of container productivity and container efficiency mentioned by [17]
PC = Positive containers
CP = Container productivity (no. of immature x 100/all immature)
CE = Container efficiency (productivity/prevalence of container)
Prevalence of container = no. of wet containers x 100/all containers
Estimate Different Indices
The number of different wet containers, the percentage of positive containers category, and finally, the percentage of Aedes larvae in each container category were calculated to determine the different Stegomyia indices as follows [18]
Container index: the percentage of water holding containers infested with larvae and/or pupae
House Index: the percentage of house infested with larvae and/or pupae
Pupae index: number of pupae per 100 households
Breteau index: the number of positive containers per 100 houses inspected.
Statistical analysis
We performed two different statistical analyses in this research i) zero-inflated negative binomial regression to find the factors associated with the number of immature Aedes positive mosquito per container, ii) binary logistic regression to find the odds of the background characteristics causing the containers to be Aedes positive. The exponential of the parameter of the logistic regression was the odds of the category of the background characteristics being Aedes positive of water-holding containers. The exponential of zero-inflated negative binomial regression was the ratio of the average number of immature Aedes positive mosquito among the groups of background characteristics. Data analysis was conducted in R version 3.5.2 and the binary logistic regression model was performed in SPSS version 25.