Skip to main content
PMC home page
Frontiers in Public Health logoLink to Frontiers in Public Health
. 2020 Jul 24;8:328. doi: 10.3389/fpubh.2020.00328

Stegomyia Indices and Risk of Dengue Transmission: A Lack of Correlation

Triwibowo Ambar Garjito 1,2,3,*, Muhammad Choirul Hidajat 1, Revi Rosavika Kinansi 1, Riyani Setyaningsih 1, Yusnita Mirna Anggraeni 1, Mujiyanto 1, Wiwik Trapsilowati 1, Jastal 4, Ristiyanto 1, Tri Baskoro Tunggul Satoto 5, Laurent Gavotte 6, Sylvie Manguin 2,3, Roger Frutos 2,7,8
PMCID: PMC7393615  PMID: 32793541

Abstract

Dengue is present in 128 countries worldwide and is still expanding. There is currently no treatment or universally approved vaccine available. Therefore, prevention and control of mosquito vectors remain the most efficient ways of managing the risk of dengue outbreaks. The Stegomyia indices have been developed as quantitative indicators of the risk of dengue outbreaks. However, conflictual data are circulating about their reliability. We report in this article the first extensive study on Stegomyia indices, covering 78 locations of differing environmental and socio-economic conditions, climate, and population density across Indonesia, from West Sumatra to Papua. A total of 65,876 mosquito larvae and pupae were collected for the study. A correlation was found between incidence and human population density. No correlation was found between the incidence of dengue and the Stegomyia indices.

Keywords: Stegomyia indices, dengue, incidence, mosquito-borne disease, Aedes aegypti, Aedes albopictus

Introduction

Dengue is one of the most widespread mosquito-borne arbovirus disease worldwide. Dengue viruses are present in 128 countries worldwide with major public health, social and economic consequences (1–7). Dengue is a complex disease with a wide spectrum of clinical symptoms, ranging from asymptomatic to fatal, which is often unrecognized or misdiagnosed and confused with other fever-causing tropical diseases (8). The World Health Organization (WHO) estimates that about 390 million dengue infections occur annually, with 96 million clinical manifestation and 500,000 hospitalization (9). At least 2.5% of these hospitalizations result in death and almost half of the global world population is at risk of dengue infection (9). Southeast Asia is the most impacted region and displays the highest incidence of dengue worldwide with all four dengue serotypes circulating in most countries (1, 10).

Indonesia displays the highest dengue burden in Southeast Asia (11). First described in Jakarta and Surabaya in 1968, dengue expanded in all provinces and has become a major national health priority. The incidence of dengue has increased significantly over the past 47 years from 0.05/100,000 in 1968 to 50.75/100,000 in 2015 (12, 13). Indonesia is a hyperendemic country with all four dengue virus serotypes (DENV1 to DENV4) circulating. In 2015, the dengue endemic areas included 412 districts/municipalities out of a total of 497 (82.9%). Dengue is spreading in all human dwellings from large urban areas to small rural villages (11–15).

Dengue viruses (DENV) are mainly transmitted to humans by two species of Aedes mosquitoes, i.e., Aedes aegypti and Aedes albopictus. Ae. aegypti is the main dengue vector, highly anthropophilic, and well-adapted to urban life. It feeds mostly at daytime with a multiple host blood meal-seeking behavior, but can also bite at night depending on light conditions. Ae. aegypti breeds in a variety of artificial habitats with clear stagnant water (16). The secondary vector, Ae. albopictus, also known as Tiger mosquito, bites at daytime too but hosts also include animals such as amphibians, reptiles, birds and mammals. Ae. albopictus breeds in a wide variety of artificial and natural habitats such as tires, bamboo stumps, tree holes, etc. (17). In Indonesia, large-scale migrations from rural to urban areas over the past three decades have created slum settlements with inadequate water and sanitation facilities and poor waste management, leading to the emergence of many new breeding sites for both Ae. aegypti and Ae. albopictus (13, 14). The Indonesian climate with favorable tropical rainfall, temperature and humidity also facilitates the development of additional Aedes breeding sites (16). This situation has strongly increased the risk of dengue transmission in suburban areas.

The risk of dengue transmission is influenced by various factors, including trade of goods and human mobility, population density, urbanization, climate, presence of invasive populations of Aedes vectors and pathogens, virus evolution, density of competent vectors, and ineffective vector control strategies (18, 19). While an efficient vaccine is still under research, entomological surveillance and vector control remain the only ways to prevent and control dengue transmission (19–21). Therefore, WHO recommends a routine vector surveillance to provide a quantifiable measurement of dengue vector fluctuations and their geographical distribution for assessing the risk of outbreaks and to determine vector control interventions (2, 22). These indicators have been based on the traditional Stegomyia indices (HI, House Index; CI, Container Index; BI, Breteau Index) (23) to which a national Free Larva Index (FLI) was added in Indonesia. These larval and pupal indices remain the most used parameters to measure vector infestation since the capture of adult mosquitoes is labor-intensive and requires access to private premises (19, 24).

Initially, the Stegomyia indices were proposed to prevent and predict the risk of yellow fever transmission and critical thresholds have never been determined for dengue transmission (22, 25). A House Index (HI) threshold of 1% or less, or a Breteau Index (BI) threshold of five or less have been considered to prevent dengue transmission because of similarities in the epidemiology of dengue and yellow fever viruses (18, 26, 27). Furthermore, the Pan American Health Organization (PAHO) has divided the risk factors for dengue transmission into three levels: low (HI<0.1%), medium (0.1%<HI<5%), and high (HI>5%) (28). However, the reliability and sensitivity of the Stegomyia indices have been questioned (2, 19, 25, 29–31).

Until now, although several studies have been published on the reliability of the Stegomyia indices, no comprehensive analyses have yet been conducted. Articles were either reviews covering a broad range of regions and cases or technical articles providing quantitative data but limited to specific areas (2, 19, 25, 27, 28, 32–46). We therefore developed this study to analyze the relationship between Stegomyia indices and actual dengue situations over a very large zone covering 78 sampling sites throughout Indonesia from Sumatra to Papua corresponding to different locations (urban/rural) and ecosystems (coastal/non-coastal). We report here a complete analysis on the two main vectors, Ae. aegypti and Ae. albopictus.

Materials and Methods

Study Area

The study was conducted in 78 locations corresponding to 78 districts/municipalities in 26 dengue-endemic provinces in Indonesia (Figure 1, Table 2). These provinces were: Aceh, Riau, Riau Islands, West Sumatra, Jambi, Bangka Belitung, Lampung, Banten, West Java, Yogyakarta, Central Java, East Java, West Kalimantan, South Kalimantan, Central Kalimantan, East Kalimantan, Southeast Sulawesi, South Sulawesi, North Sulawesi, Central Sulawesi, Bali, West Nusa Tenggara, East Nusa Tenggara, Maluku, North Maluku, and West Papua. The mosquito collection was implemented as part of the “Rikhus Vektora” project in July–August 2016 in 48 districts/cities, the WHO project SEINO 1611945 in September–October 2016 in 12 additional city locations, and finally in 18 locations in May–July 2017 as part of the Rikhus Vektora project (Figure 1).

Figure 1.

Figure 1

Open in a new tab

Map of the sampling sites in Indonesia. (A) Locations of urban and rural sampling sites. (B) Locations of coastal and inland sampling sites. These maps are original artworks created by the authors from a blank map background of the Republic of Indonesia displaying the district limits. This map background was provided by the Indonesian Geospatial Information Agency under agreement to use it in publication signed with IVRCRD-NIHRD.

Table 2.

Entomological indices from Aedes larvae and pupae survey at 78 sampling sites in Indonesia.

Province Village Health centera Location Ecosystem Incidence Aedes aegypti Aedes albopictus Aedes aegypti + Aedes albopictus
Number of Ae. aegypti HI BI CI FLI Number of Ae. albopictus HI B I CI FLI Number of Ae. aegypti + Ae. albopictus HI BI CI FLI
Aceh Ujong Baroh Johan Pahlawan Urban Coastal 0 402 37 51 20.82 63 254 22 24 9.79 78 656 50 75 30.61 50
Aceh Blok Benke Kota Sigli Urban Coastal 67 882 31 36 15.93 69 0 0 0 0 100 882 31 36 15.93 69
Aceh Keude Aceh Idi Rayeuk Urban Coastal 0 1,315 57 74 35.41 43 74 1 1 0.48 99 1,389 58 75 35.88 42
Riau Selat Panjang Selatan Alah Air Urban Coastal 122 157 18 28 6.39 82 187 32 46 10.5 68 344 49 74 16.89 51
Riau Boncah Mahang Sebangar Urban Inland 54 74 10 11 2.56 90 311 19 27 6.29 81 385 29 38 8.86 71
Riau Bukit Kayu Kapur Bukit Kayu Kapur Rural Inland 62 146 32 42 13.13 68 475 22 26 8.13 78 621 54 68 21.25 46
Riau Islands Buliang Batuaji Urban Inland 41 1,275 15 15 2 85 0 0 0 0 100 1,275 15 15 2 85
Riau Islands Tiban indah Sekupang Urban Coastal 45 750 11 11 4.49 89 0 0 0 0 100 750 11 11 4.49 89
West Sumatra Pakandangan Enam Lingkung Urban Inland 100 909 18 21 5.66 82 1,045 38 51 13.75 62 1,954 49 72 19.41 51
West Sumatra Aua Kuniang Lembah Binuang Rural Inland 12 171 2 158 1.89 98 74 8 10 6.33 92 245 10 13 9.23 90
West Sumatra Salido Salido Urban Coastal 0 2,419 34 42 18.5 66 78 3 4 1.76 97 2,497 35 46 15.42 65
Jambi Kenali Besar Kenali Besar Urban Inland 91 900 34 51 16.45 66 0 0 0 0 100 900 34 51 16.45 66
Jambi Pinang Merah Kenali Besar Urban Inland 91 275 34 51 13.18 66 0 0 0 0 100 275 34 51 13.18 66
Jambi Lubuk Kepayang Air Hitam Rural Inland 0 80 25 34 11.15 75 185 13 19 6.23 87 265 38 53 17.38 62
Jambi Jaya Setia Muaro Bungo Urban Inland 210 234 44 68 15.77 56 0 0 0 0 100 234 44 68 15.77 56
Jambi Tungkal Harapan Tungkal II Urban Coastal 142 862 90 315 41.39 10 0 0 0 0 100 862 90 315 41.39 10
Bangka Belitung Kuto Panji Belinyu Urban Coastal 6 1,270 31 36 13.23 69 212 10 11 4.04 90 1,482 37 47 17.28 63
Bangka Belitung Mangkol Benteng Rural Inland 24 1,291 33 39 11.75 67 1,357 32 42 12.65 68 2,648 59 81 24.39 41
Bangka Belitung Air Saga Air Saga Urban Coastal 29 122 30 32 10.45 70 214 14 7.52 23 86 336 42 55 17.97 58
Lampung Jati Baru Tanjung Bintang Urban Coastal 5 20 4 4 1.98 96 134 10 12 5.94 90 154 14 16 7.92 86
Lampung Teluk Pandan Hanura Urban Coastal 23 490 46 53 22.94 54 68 3 3 1.29 97 558 47 56 24.24 53
Lampung Pasar Madang Kota Agung Urban Coastal 60 619 16 16 6.75 84 272 21 21 8.86 79 891 30 37 15.61 70
Banten Cipeucang Binuangeun Rural Coastal 0 541 39 50 25.64 61 18 4 4 2.05 96 559 42 54 27.69 58
Banten Cigondang Labuan Urban Inland 0 122 47 58 23.02 53 45 3 3 1.19 97 167 48 61 24.21 52
Banten Ciomas Padarincang Rural Inland 5 80 40 50 20.41 60 0 0 0 0 100 80 40 50 20.41 60
West Java Tambak Dahan Tambak Dahan Rural Inland 13 595 18 18 8.65 82 27 2 2 0.96 98 622 20 20 9.62 80
West Java Mekargalih Tarogong Urban Coastal 0 1,041 29 35 14.34 71 0 0 0 0 100 1,041 29 35 14.34 71
West Java Ciliang Parigi Rural Inland 0 28 4 4 1.78 96 175 10 10 4.44 90 203 12 14 6.22 88
Yogyakarta Kedungpoh Nglipar II Rural Inland 152 5 5 8 2.15 95 349 36 49 13.17 64 354 41 57 15.32 59
Yogyakarta Bugel Panjatan II Rural Inland 151 0 0 0 0 100 82 23 27 9.82 77 82 23 27 9.82 77
Yogyakarta Bangunharjo Sewon II Urban Inland 360 160 26 52 14.36 74 0 0 0 0 100 160 26 52 14.36 74
Central Java Sendang Mulyo Kedung Mundu Urban Inland 64 482 18 19 7.53 82 0 0 0 0 100 482 18 19 7.53 82
Central Java Sendang Guwo Kedung Mundu Urban Inland 64 402 16 21 10.24 84 0 0 0 0 100 402 16 21 10.24 84
East Java Seneporejo Silir Agung Rural Inland 35 284 14 15 10.27 86 135 6 6 4.11 94 419 21 21 14.38 79
East Java Sumber Dawesari Grati Urban Inland 24 1,530 33 37 21.51 67 0 0 0 0 100 1,530 33 37 21.51 67
East Java Jero Tumpang Urban Inland 217 33 23 23 9.91 77 448 4 4 1.72 96 481 26 27 11.64 74
West Kalimantan Tengah Kedondong Urban Inland 0 2,212 84 158 38.35 16 4 1 1 0.24 99 2,216 85 159 38.59 15
West Kalimantan Pangkalan Buton Sukadana Rural Inland 6 229 20 25 7.69 80 260 17 21 6.46 83 489 37 46 14.15 63
West Kalimantan Twi Mentibar Selakau Rural Coastal 0 387 28 34 13.18 72 80 5 5 1.94 95 467 33 39 15.17 67
South Kalimantan Pabahanan Pabahanan Rural Inland 31 1,192 43 54 17.65 57 8 4 4 1.31 96 1,200 47 58 18.95 53
South Kalimantan Sungai Kupang Sungai Kupang Rural Inland 14 1,147 59 93 22.14 41 170 3 4 0.95 97 1,317 62 97 23.09 38
South Kalimantan Sumber Rahayu Wanaraya Rural Inland 124 3,226 51 69 20.97 49 315 6 7 2.13 94 3,541 57 76 23.71 43
Central Kalimantan Tampang Tumbang Anjir Anjir Rural Inland 0 175 15 25 4.66 85 77 36 71 13.22 64 252 51 96 17.88 49
Central Kalimantan Tumbang Masao Tumbang Kunyi Rural Inland 0 48 27 36 14.29 73 103 5 7 2.778 95 151 32 43 17.06 68
Central Kalimantan Kantan Muara Pangkoh Rural Inland 0 146 32 44 12.02 68 28 6 9 2.46 94 174 37 53 14.48 63
East Kalimantan Sepinggan Baru 31 Sepinggan Baru Urban Coastal 562 900 61 104 35 39 0 0 0 0 100 900 61 104 35 39
East Kalimantan Sepinggan Baru 59 Sepinggan Baru Urban Coastal 562 1,075 53 124 26 47 0 0 0 0 100 1,075 53 124 26 47
South East Sulawesi Bajo Indah Soropia Rural Inland 0 123 45 63 22.91 55 0 0 0 0 100 123 45 63 22.91 55
South East Sulawesi Laea Poleyang Selatan Rural Coastal 431 758 25 38 12.26 75 25 1 1 0.32 99 783 26 39 12.58 74
South East Sulawesi Raha 3 Katobu Urban Inland 0 1,243 70 106 30.73 30 67 23 25 7.24 77 1,310 93 131 37.97 7
South Sulawesi Lestari Tomoni Rural Inland 458 103 27 30 6.61 73 53 15 18 3.96 85 156 30 48 10.57 70
South Sulawesi Palambarae Bontonyeleng Rural Inland 72 240 32 70 14.99 68 239 6 11 2.36 94 479 48 81 17.34 52
South Sulawesi Bawasalo Segeri Rural Coastal 722 281 87 141 21.33 13 138 46 51 7.72 54 419 87 192 29.05 13
North Sulawesi Bahu Bahu Urban Inland 170 407 13 13 7.1 87 0 0 0 0 100 407 13 13 7.1 87
North Sulawesi Manembo Nembo Atas Sagerat Urban Inland 35 224 23 28 10.18 77 30 25 29 10.18 75 254 44 57 20.73 56
North Sulawesi Leilem Sonder Urban Coastal 0 423 26 40 13.65 74 152 7 10 3.41 93 575 32 50 17.06 68
Central Sulawesi Balaroa Sangurara Urban Inland 200 950 32 52 10.55 68 0 0 0 0 100 950 32 52 10.55 68
Central Sulawesi Ujuna Kamonji Urban Inland 191 1,025 26 30 7.73 74 0 0 0 0 100 1,025 26 30 7.73 74
Bali Kaliakah Negara Urban Inland 325 68 12 17 6.29 88 37 6 8 2.96 94 105 19 25 9.26 81
Bali Padang Kerta Karangasem Urban Inland 1,087 37 15 18 8.05 85 44 20 22 9.32 80 81 27 41 17.37 73
Bali Buduk Mengwi Urban Inland 1,036 98 25 42 16.54 75 80 20 20 7.87 80 178 45 62 24.41 55
Bali Sesetan Denpasar Selatan I Urban Coastal 924 825 23 30 11.81 77 0 0 0 0 100 825 23 30 11.81 77
Bali Panjer Denpasar Selatan I Urban Inland 924 625 30 36 11.8 70 0 0 0 0 100 625 30 36 11.8 70
West Nusa Tenggara Kramajaya Narmada Urban Inland 17 126 9 9 5.59 91 55 2 2 1.24 98 181 11 11 6.83 89
West Nusa Tenggara Pela Monta Rural Coastal 0 534 26 29 11.79 74 0 0 0 0 100 534 26 29 11.79 74
West Nusa Tenggara Medana Tanjung Rural Inland 0 55 20 20 10.26 80 0 0 0 0 100 55 20 20 10.26 80
East Nusa Tenggara Bairafu Umanen Urban Inland 4 174 41 45 26.47 59 0 0 0 0 100 174 41 45 26.47 59
East Nusa Tenggara Nanganesa Ngalupolo Urban Inland 0 2,352 52 66 33.33 48 5 2 2 1.01 98 2,357 52 68 34.34 48
East Nusa Tenggara Wendewa Utara Mamboro Rural Coastal 0 2,882 63 88 45.59 37 10 1 1 0.52 99 2,892 64 89 46.11 36
Maluku Sifnana Saumlaki Urban Coastal 0 333 72 72 26.28 28 0 0 0 0 100 333 72 72 26.28 28
Maluku Siwalima Siwalima Urban Coastal 0 2,078 60 83 36.24 40 66 3 3 1.31 97 2,144 60 86 37.55 40
Maluku Faan Watdek Rural Coastal 0 5,650 81 157 35.84 19 1,095 18 31 7.08 82 6,745 91 188 42.92 9
North Maluku Labuha Labuha Urban Coastal 0 2,160 30 44 15.02 70 859 10 28 9.56 90 3,019 33 72 24.57 67
North Maluku Norweda Weda Rural Inland 0 140 4 4 1.92 96 52 1 1 0.48 99 192 5 5 2.4 95
North Maluku Nakamura Daruba Urban Coastal 0 19 2 2 1.05 98 188 24 28 14.66 76 207 26 30 15.71 74
West Papua Wagom Utara Sekban Rural Inland 0 583 77 187 33.33 23 28 20 22 3.92 80 611 77 209 37.25 23
West Papua Prafi Mulia Prafi Rural Inland 6 170 54 80 15.59 46 0 0 0 0 100 170 54 80 15.59 46
West Papua Warsadim Warsadim Rural Coastal 0 0b 0 0 0 100 0b 0 0 0 100 0b 0 0 0 100
Open in a new tab

HI, House Index; CI, Container Index; BI, Breteau Index; FLI, Free Larva Index.

a

Health Centers are Community Health Centers (CHC) or Puskesmas in Indonesian. They are government-mandated community health clinics providing healthcare for population on sub-district. These clinics are present in every sub-districts.

b

All mosquitoes collected were Aedes malayanensis.

Study Design

The sampling plan was built using entomological data, dengue cases, socio-demographic and spatial data. Collections were undertaken at three time periods, July-August 2016 in 48 locations, September-October 2016 in 12 additional locations, and in May-July 2017 in 18 locations. These sampling periods correspond to rainy seasons in the respective locations. Each sampling periods was determined after the actual start of the rainy season and was initiated at least 1 month after the beginning of the rainy season. At least 100 households were taken at random in each sampling location to assess the presence of Aedes breeding sites. Three separate assessments were conducted at the same time. Ae. aegypti larvae and pupae, Ae. albopictus larvae and pupae, and Ae. aegypti + Ae. albopictus larvae and pupae were separately recorded in each sampling location. The Stegomyia indices were calculated for each sampling location for the three categories using the following formulas (23, 47, 48):

Container Index (CI): number of infected containers × 100/total number of containers

House Index (HI): number of infected houses × 100/total number of houses

Breteau Index (BI): number of positive containers/number of houses explored × 100

These indices were completed by a legal Indonesian index, the Free Larva Index (FLI) calculated according to the following formula:

FLI: number of houses without larva × 100/total number houses

The Free Larva Index (FLI) is the reverse of the House Index (HI) making these two indices strongly negatively correlated.

Entomological Data Collection

Artificial and natural water-holding containers, which were potential Aedes breeding sites, were sampled using standardized sampling methods (23, 47–49). All pupae and larvae from positive containers were collected in separate small ziplock plastic bags. Afterwards, all samples were transported to field laboratories and counted. Due to difficulties to identify species at the larval and pupal stages, all larvae and pupae from each container were transferred to separate individual adult cages. Collected Aedes larvae and pupae were placed in rearing jars filled with 150 mL of freshwater and were covered with fine gauze. All larvae were fed with fish food (TetraBits, Germany). Larvae and pupae were reared until the emergence of adults for species identification.

Sociodemographic Data Collection

The incidence, number of new dengue cases per total population for the time of the study, was considered for each community health center. Sampling locations were discriminated according to their status; i.e., urban or rural, as defined by the Ministry of Health, Republic of Indonesia, and according to the ecosystem, i.e., coastal or inland. Urban areas were defined as areas without major agricultural activity and displaying concentrations of centralized government services, social services, and economic activities. Rural areas were defined as areas having major agricultural activity, including the management of natural resources and displaying local government services, social services, and economic activities. The official discrimination between urban and rural areas is based on facilities, services, and equipment offered and not on a population density threshold. Coastal areas were terrestrial environments under marine influence whereas inland areas were far enough from the seashore to no longer be under marine influence. The number of dengue cases was taken from the national health data profile for district/city level in the time of study. The density of population (Table 1) in the zone of action of the health centers at the time of study were taken from the centralized database of health centers from the Ministry of Health, Republic of Indonesia.

Table 1.

Population density in the sampling sites.

Province Village Health center Location Ecosystem Incidence Population density (number of persons/km2)
Aceh Ujong Baroh Johan Pahlawan Urban Coastal 0 1028.52
Aceh Blok Benke Kota Sigli Urban Coastal 67 2148.97
Aceh Keude Aceh Idi Rayeuk Urban Coastal 0 479.71
Riau Selat Panjang Selatan Alah Air Urban Coastal 122 669.52
Riau Boncah Mahang Sebangar Urban Inland 54 501.34
Riau Bukit Kayu Kapur Bukit Kayu Kapur Rural Inland 62 247.66
Riau Islands Buliang Batuaji Urban Inland 41 2917.02
Riau Islands Tiban indah Sekupang Urban Coastal 45 744.95
West Sumatra Pakandangan Enam Lingkung Urban Inland 100 485.43
West Sumatra Aua Kuniang Lembah Binuang Rural Inland 12 105.70
West Sumatra Salido Salido Urban Coastal 0 103.13
Jambi Kenali Besar Kenali Besar Urban Inland 91 1711.55
Jambi Pinang Merah Kenali Besar Urban Inland 91 1711.55
Jambi Lubuk Kepayang Air Hitam Rural Inland 0 24.12
Jambi Jaya Setia Muaro Bungo Urban Inland 210 1141.70
Jambi Tungkal Harapan Tungkal II Urban Coastal 142 1172.59
Bangka Belitung Kuto Panji Belinyu Urban Coastal 6 82.26
Bangka Belitung Mangkol Benteng Rural Inland 24 436.76
Bangka Belitung Air Saga Air Saga Urban Coastal 29 1033.30
Lampung Jati Baru Tanjung Bintang Urban Coastal 5 648.43
Lampung Teluk Pandan Hanura Urban Coastal 23 448.82
Lampung Pasar Madang Kota Agung Urban Coastal 60 545.46
Banten Cipeucang Binuangeun Rural Coastal 0 401.94
Banten Cigondang Labuan Urban Inland 0 3585.31
Banten Ciomas Padarincang Rural Inland 5 642.19
West Java Tambak Dahan Tambak Dahan Rural Inland 13 827.33
West Java Mekargalih Tarogong Urban Coastal 0 1630.22
West Java Ciliang Parigi Rural Inland 0 454.20
Yogyakarta Kedungpoh Nglipar II Rural Inland 152 401.93
Yogyakarta Bugel Panjatan II Rural Inland 151 727.26
Yogyakarta Bangunharjo Sewon II Urban Inland 360 1953.24
Central Java Sendang Mulyo Kedung Mundu Urban Inland 64 9272.12
Central Java Sendang Guwo Kedung Mundu Urban Inland 64 9272.12
East Java Seneporejo Silir Agung Rural Inland 35 920.06
East Java Sumber Dawesari Grati Urban Inland 24 1523.13
East Java Jero Tumpang Urban Inland 217 1101.93
West Kalimantan Tengah Kedondong Urban Inland 0 223.53
West Kalimantan Pangkalan Buton Sukadana Rural Inland 6 183.96
West Kalimantan Twi Mentibar Selakau Rural Coastal 0 90.74
South Kalimantan Pabahanan Pabahanan Rural Inland 31 101.27
South Kalimantan Sungai Kupang Sungai Kupang Rural Inland 14 834.65
South Kalimantan Sumber Rahayu Wanaraya Rural Inland 124 70.56
Central Kalimantan Tampang Tumbang Anjir Anjir Rural Inland 0 32.28
Central Kalimantan Tumbang Masao Tumbang Kunyi Rural Inland 0 2.87
Central Kalimantan Kantan Muara Pangkoh Rural Inland 0 39.71
East Kalimantan Sepinggan Baru 31 Sepinggan Baru Urban Coastal 562 2699.96
East Kalimantan Sepinggan Baru 59 Sepinggan Baru Urban Coastal 562 2699.96
South East Sulawesi Bajo Indah Soropia Rural Inland 0 1355.43
South East Sulawesi Laea Poleyang Selatan Rural Coastal 431 77.51
South East Sulawesi Raha 3 Katobu Urban Inland 0 2245.73
South Sulawesi Lestari Tomoni Rural Inland 458 101.93
South Sulawesi Palambarae Bontonyeleng Rural Inland 72 536.27
South Sulawesi Bawasalo Segeri Rural Coastal 722 560.74
North Sulawesi Bahu Bahu Urban Inland 170 1576.64
North Sulawesi Manembo Nembo Atas Sagerat Urban Inland 35 905.92
North Sulawesi Leilem Sonder Urban Coastal 0 318.76
Central Sulawesi Balaroa Sangurara Urban Inland 200 3935.79
Central Sulawesi Ujuna Kamonji Urban Inland 191 5131.52
Bali Kaliakah Negara Urban Inland 325 518.09
Bali Padang Kerta Karangasem Urban Inland 1,087 1116.93
Bali Buduk Mengwi Urban Inland 1,036 2111.19
Bali Sesetan Denpasar Selatan I Urban Coastal 924 5265.03
Bali Panjer Denpasar Selatan I Urban Inland 924 5265.03
West Nusa Tenggara Kramajaya Narmada Urban Inland 17 817.78
West Nusa Tenggara Pela Monta Rural Coastal 0 149.72
West Nusa Tenggara Medana Tanjung Rural Inland 0 416.65
East Nusa Tenggara Bairafu Umanen Urban Inland 4 1486.56
East Nusa Tenggara Nanganesa Ngalupolo Urban Inland 0 140.72
East Nusa Tenggara Wendewa Utara Mamboro Rural Coastal 0 43.34
Maluku Sifnana Saumlaki Urban Coastal 0 262.43
Maluku Siwalima Siwalima Urban Coastal 0 173.50
Maluku Faan Watdek Rural Coastal 0 79.37
North Maluku Labuha Labuha Urban Coastal 0 143.68
North Maluku Norweda Weda Rural Inland 0 39.25
North Maluku Nakamura Daruba Urban Coastal 0 66.44
West Papua Wagom Utara Sekban Rural Inland 0 163.39
West Papua Prafi Mulia Prafi Rural Inland 6 50.90
West Papua Warsadim Warsadim Rural Coastal 0 3.55
Open in a new tab

Data Analysis

A principal component analysis (PCA) was conducted using the incidence, the human population density and the four Stegomyia indices (HI, BI, CI, and FLI). The PCA analysis was performed on the totality of the 50 sampling locations where dengue cases have been reported by health centers. Three sets of analyses were performed separately for Ae. aegypti, Ae. albopictus and for the sum of Ae. aegypti and Ae. albopictus mosquitoes. The normality of the data distribution was assessed using the Kolmogorov-Smirnov normality test (50). Potential correlations between incidence and each index, and between incidence and average human densities were assessed using the Kendall τ (tau) coefficient test for rank correlation (51). This statistical test determines whether there is an ordinal association between two measured parameters. Under the null hypothesis of independence of the two datasets tested, the Kendall tau (τ) coefficient is expected to be equal to 0. Thus, a p > 0.05 indicates an acceptance of the null hypothesis and therefore an absence of correlation between the two datasets. The Kendall τ (tau) coefficient test for rank correlation was performed for all sites (78 sites), and only for sites were dengue cases have been recorded (50 sites). The influence of locations and ecosystems on incidence and mosquito densities was tested by Kruskal-Wallis test followed by a Siegel and Castellan post-hoc test for the datasets not displaying a normal distribution, and by ANOVA followed by a Bonferroni post-hoc test for datasets characterized by a normal distribution. All analyses were performed using Statistica v10.

Results

Sampling and Data Collection

Mosquitoes were collected in a total of 78 locations out of which 46 were classified as urban and 32 as rural (Figure 1, Table 2). A total of 65,876 mosquito larvae (including 55,389 Ae. aegypti and 10,487 Ae. albopictus), were collected in the 78 sampling sites (Table 2). With the exception of Warsadim in West Papua where only Ae. malayanensis was found, either Ae. aegypti or Ae. albopictus or both were found in all other sampling sites. Apart from Warsadim, only one site, did not host any Ae. aegypti, i.e., Bugel in the Province of Yogyakarta, whereas 26 sites were free of Ae. albopictus. The combination of Ae. aegypti and Ae. albopictus was found in 50 sampling sites (Table 2). Out of the 78 health centers analyzed, 28 did not display any case of dengue during the time of the study (Table 2). For the 50 locations displaying dengue cases, the incidence ranged from 4 in Bairafu (East Nusa Tenggara) to 1,087 in Padang Kerta (Bali) (Table 2).

Data Normality

The D-statistic from Kolmogorov-Smirnov normality test for dengue incidence indicates that the data do not follow a normal distribution (p = 0.002; Figure 2). Similarly, the number of mosquito larvae caught does not follow a normal distribution for Ae. aegypti (p = 0.0492), as well as for Ae. albopictus (p = 0.0023). The sum of all Ae. aegypti and Ae. albopictus larvae was the only dataset following a normal distribution (p = 0.0751).

Figure 2.

Figure 2

Open in a new tab

Non-normal distribution of dengue incidence.

Correlation Between Dengue Infection Rates and Human Density

The PCA analysis indicated a clear correlation between dengue incidence and the human population density registered for each location (Figure 3). This correlation was confirmed by the Kendall rank correlation coefficients test (τ = 0.242; p = 0.0125), indicating that the dengue incidence increased along with the human population density.

Figure 3.

Figure 3

Open in a new tab

Principal Component Analysis (PCA) of indices, number of mosquitoes, human population density, and incidence of dengue. (A) PCA for Ae. aegypti. (B) PCA for Ae. albopictus. (C) PCA for Ae. aegypti and Ae. albopictus together.

Correlation Between Dengue Infection Rates and Larvae Indices

Tests on the value of the coefficient τ (Kendall rank correlation coefficients test) for the incidence of each sampling location vs. each of the indices at the same location were systematically higher than the limit p-value of 0.05 indicating that the test was significant. Only places clinical dengue cases have been recorded were considered in the analysis. The null hypothesis of independence of the data was therefore accepted indicating that there was no correlation between the incidences, any of the indices (CI, HI, BI and FLI) and the number of mosquitoes in all of the 50 epidemic locations analyzed (Table 3). This lack of correlation was observed for Ae. aegypti alone, for Ae. albopictus alone and for the sum of Ae. aegypti and Ae. albopictus (Table 3). The Principal Component Analysis (PCA) displayed a very high level of explanation for the datasets tested (Figure 3). For Ae. aegypti alone, the PCA explained 69.82% of the data spread (axis 1: 52.28% and axis 2: 17.54%) (Figure 3A). For Ae. albopictus alone, the PCA explained 79.38% of the data spread (axis 1: 61.91% and axis 2: 17.47%) (Figure 3B). For both species, i.e., Ae. aegypti and Ae. albopictus considered together, the level of explanation of the data spread given by the PCA analysis was 73.22% (axis 1: 55.08% and axis 2: 18.14%) (Figure 3C). For each PCA, the same observations can be made, namely: (i) a strong autocorrelation of the different indices with each other, (ii) a correlation between the indices and the total number of mosquitoes, (iii) a correlation between dengue incidence and average human density, and finally (iv) a complete lack of correlation between dengue incidence in a study site and the Stegomyia indices shown by the orthogonal position observed in all PCA analyses between indices and incidence.

Table 3.

Tau (τ) and p-values obtained for incidence and entomological indices by Kendall rank correlation coefficients test.

Species House Index Breteau Index Container Index Free Larva Index
ALL LOCATIONS CONSIDERED
Ae. aegypti τ = −0.101 τ = −0.062 τ = −0.134 τ = 0.101
p = 0.1926 p = 0.4248 p = 0.0821 p = 0.1926
Ae. albopictus τ = −0.039 τ = −0.056 τ = −0.057 τ = 0.039
p = 0.6107 p = 0.4659 p = 0.4633 p = 0.6107
Ae. aegypti and Ae. albopictus τ = −0.085 τ = −0.039 τ = −0.144 τ = 0.085
p = 0.2731 p = 0.6107 p = 0.0506 p = 0.2731
LOCATIONS WITH NO DENGUE CASES EXCLUDED
Ae. aegypti τ = 0.037 τ = 0.065 τ = 0.043 τ = −0.037
p = 0.7066 p = 0.5034 p = 0.6575 p = 0.7066
Ae. albopictus τ = −0.014 τ = −0.023 τ = −0.043 τ = 0.014
p = 0.8869 p = 0.8184 p = 0.6575 p = 0.8869
Ae. aegypti and Ae. albopictus τ = 0.043 τ = 0.131 τ = 0.016 τ = −0.043
p = 0.6575 p = 0.1808 p = 0.8737 p = 0.6575
Open in a new tab

Influence of Locations and Ecosystems

The incidence was not significantly correlated with the different environments considered: urban vs. rural (Figure 1A) and coastal vs. inland (Figure 1B) (Kruskal-Wallis: H = 7.72; p = 0.0523). Mosquito distributions were significantly different (tested by Kruskal-Wallis non-parametric statistical test) for each type of environment for both Ae. aegypti (H = 8.43; p = 0.038) and Ae. albopictus (H = 7.96; p = 0.0468). Differences (Siegel and Castellan post-hoc test) were marginal and only appeared between urban/inland and urban/coastal for Ae. aegypti (p = 0.037) and between rural/inland and rural/coastal for Ae. albopictus (p = 0.0404). For the combination of both species, which is the only dataset in this work following a normal distribution, the ANOVA test indicated no difference between environments (F = 2.045; p = 0.1149).

Discussion

Following to the use of Stegomyia indices to predict the risk of dengue outbreaks several articles in the literature questioned their efficiency (19, 28, 45, 46). A systematic review on the application of the Stegomyia indices to predict dengue outbreaks was conducted (2). Out of all the articles reviewed 15 were ranked as “weak studies” and no clear conclusion could be reached (2). Out of 13 articles directly dealing with the relationship between Stegomyia indices and dengue infection, 4 concluded on a correlation, 4 concluded on a lack of correlation, and 5 reported inconclusive discussions (2). More recent articles published on the subject also provided various conclusions. One article concluded on the lack of correlation (45), the second concluded on a correlation (46), and the last two were inconclusive, depending on the type of analysis performed (19, 29).

The work reported here brings explanations on the diverging conclusions reached by the previous studies. The first point to consider is that all the works previously reported on this topic were focused on a single place or a limited area. No studies were performed over a very large geographic area encompassing different local climates and environmental conditions. Therefore, each study was strongly influenced by local geographic and climatic conditions but also specific urbanization and socio-economic conditions, which could have biased the data. Furthermore, these previous studies were all independent investigations with variations in sampling schemes and methodologies, making difficult a comparative analysis. Our study is based on a very large cross-section of locations of various sizes, with different urban environments throughout all of Indonesia. The geographic coverage of this work and the integration of a large set of data into a single analysis made data smoothing possible as well as elimination of variations due to specific environments or socio-economic conditions.

Data analysis in all previous studies utilized parametric statistics. However, as reported in this work, the data considered do not follow a normal, Gaussian distribution. Since parametric statistics are not well-suited for non-normal datasets, this could well-explain the contradictory conclusions previously reported. Consequently, we applied non-parametric methods to correct for bias. The dengue vectors are anthropophilic mosquitoes (52) and therefore the distribution of breeding sites is influenced by human societal aspects (53). The real drivers behind the distribution of Aedes breeding sites are demography, urbanization, and socio-economic level. This is supported by the correlation observed between the density of human populations and the incidence of dengue. These societal, sociological, and economical aspects do not follow a normal distribution and therefore the distribution of mosquitoes, thus the entomological indices, as well as the incidence of dengue do not either. Consequently, our application of non-parametric statistical analysis of the data, which to our knowledge was not done in any previous studies (2, 19, 25, 28, 30, 32–46), provides a very robust statistical conclusion strengthened by the size of the study and the multiplicity of sites and conditions.

We conclude that there is no correlation between the incidence of dengue and any of the Stegomyia indices. The very high level of explanation provided by the PCAs is a consequence of both the nature of the data studied and the absence of correlation between incidence and indices. Indeed, the first axis (abscissa on the graphs) explains the dispersion of the indices, which are necessarily correlated since they represent different elements of the mosquito population density in a study area. The second axis (ordered on the graphs) explains the dispersion of the incidence data. The lack of correlation between the two types of data is clearly represented by the orthogonality of the vectors of the various indices with respect to dengue incidence. None of the datasets influences the position of the other. Therefore, the data dispersion occurs in each set only, which considerably increases the explanation of the axes. This total lack of correlation is observed for both Ae. aegypti and Ae. albopictus, which eliminates any possibility of species-related interaction. This is also expected since the main drivers are linked to societal aspects and both species are anthropophilic (53).

The Stegomyia indices are not relevant descriptors for assessing the risk of dengue outbreak. They are not related to the vector competence. These indices are simply demographic descriptors. The higher the population, the higher the value of the descriptor. However, the main reason for this discrepancy is that they are targeting the wrong level of biological significance. The Stegomyia indices are targeting the species level, which is a good compromise between a reasonable work investment for collecting data and a systematic level accurate enough to avoid dispersion of data. Furthermore, the species is the widely recognized level of classification for the identification of living organisms. However, a species is an intellectual construction and is not biologically relevant. The relevant level of discrimination with respect to biological functions, and therefore vector competence, is the population or subspecies (54–56). A species should be regarded as a metapopulation or the combination of crossfertile genetically distinct populations displaying differing phenotypic traits (57). The vector competence of Aedes and other mosquitoes was shown to be related to specific populations (16, 56, 58–60) and not to the species per se. Targeting the species level with demographic descriptors can thus be misleading, hence the contradictory results obtained when assessing the efficiency of Stegomyia indices for predicting dengue outbreaks. A very high demography of a poorly vectoring population will lead to actions of prevention in the absence of risk of outbreak, whereas a low demography of a very good vectoring population would lead to a lack of action in the presence of a high risk of outbreak.

If not related to the Stegomyia indices, the dengue incidence is instead statistically related to the human population density. This is not really surprising since Aedes mosquitoes fly an average of 250 meters around their breeding site. Considering this short distance of flight, there is more chance for an infected mosquito to find a blood meal within flying distance in densely populated area than in a dispersed habitat. Other approaches than the Stegomyia indices, based on societal and urbanistic parameters should then be considered. The “One house/One inspector” approach recently implemented in Indonesia by the Ministry of Health is an interesting and sound alternative to the Stegomyia indices based on the monitoring and elimination of breeding sites at the household level (61). The philosophy of intervention developed in Indonesia is the prevention of dengue transmission through community participation. The approach implemented is the 3M approach, i.e., covering water containers (Menutup), cleaning water containers (Menguras), and burying discarded containers (Mengubur). The implementation is under the responsibility of families in each household. At least one person in each household is in charge of monitoring Aedes larvae in all water storage. However, to efficiently implement surveillance and risk analysis, people must be given reliable indices. It would therefore be important to communicate on the lack of reliability of the Stegomyia indices and to support the development of novel, more reliable, sociology-related markers, and actions taking into account the correlation between human population density and dengue incidence such as urbanism, type of housing, or socioeconomic level.

Data Availability Statement

All datasets generated for this study are included in the article.

Author Contributions

TG participated in all part of the work. MH, R, RS, YA, and TS contributed to the conception and design of the study, and participated to the field work. J organized the field work. R, M, and WT did the field work and built the database. LG did the statistical analyses. SM did the supervision and corrections. RF did the analyses, supervision, and writing. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

Funding. This study was conducted by using a large dataset established as part of the Rikhus Vektora project on disease vectors and reservoirs in 2016–2017 and as part of the WHO project SEINO 1611945 in 2016. Both projects were conducted under the supervision and coordination of the Institute for Vector and Reservoir Control Research and Development, National Institute of Health Research and Development, Ministry of Health, Republic of Indonesia. TG, RF, SM, and TS were supported in part by the Nusantara projects Zika & Co. and SOCIAL.

References

  • 1.WHO Global Strategy for Dengue Prevention and Control 2012–2020. Geneva: World Health Organization; (2012). Available online at: https://www.who.int/denguecontrol/9789241504034/en/ [Google Scholar]
  • 2.Bowman LR, Runge-Ranzinger S, McCall PJ. Assessing the relationship between vector indices and dengue transmission: a systematic review of the evidence. PLoS Negl Trop Dis. (2014) 8:e2848. 10.1371/journal.pntd.0002848 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Shepard DS, Undurraga E, Halasa Y. Economic and disease burden of dengue in Southeast Asia. PLoS Negl Trop Dis. (2013) 7:e2055 10.1371/journal.pntd.0002055 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Ausvetplan Disease Strategy Japanese Encephalitis. Agriculture and Resource Management Council of Australia and New Zealand; (1998). p. 1–41. Available online at: http://www.international-food-safety.com/pdf/ausvet-japanese.pdf [Google Scholar]
  • 5.Castro MC, Wilson ME, Bloom DE. Disease and economic burdens of dengue. Lancet Infect Dis. (2017) 17:e70–8. 10.1016/S1473-3099(16)30545-X [DOI] [PubMed] [Google Scholar]
  • 6.de Almeida Silva Teixeira L, dos Santos Nogueira FP, Nascentes GAN. Prospective study of patients with persistent symptoms of dengue in Brazil. Rev Inst Med Trop São Paulo. (2017) 59:1–9. 10.1590/s1678-9946201759065 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Martelli CMT, Nascimento NE, Suaya JA, Siqueira JB, Souza WV, Turchi MD, et al. Quality of life among adults with confirmed dengue in Brazil. Am J Trop Med Hyg. (2011) 85:732–8. 10.4269/ajtmh.2011.11-0067 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Murray NE, Quam MB, Wilder-Smith A. Epidemiology of dengue: past, present and future prospects. Clin Epidemiol. (2013) 5:299–309. 10.2147/CLEP.S34440 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. Nature. (2013) 496:504–7. 10.1038/nature12060 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Undurraga EA, Halasa YA, Shepard DS. Use of expansion factors to estimate the burden of dengue in Southeast Asia: a systematic analysis. PLoS Negl Trop Dis. (2013) 7:e2056 10.1371/journal.pntd.0002056 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Prayitno A, Taurel AF, Nealon J, Satari HI, Karyanti MR, Sekartini R, et al. Dengue seroprevalence and force of primary infection in a representative population of urban dwelling Indonesian children. PLoS Negl Trop Dis. (2017) 11:e0005621 10.1371/journal.pntd.0005621 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Harapan H, Michie A, Mudatsir M, Sasmono RT, Imrie A. Epidemiology of dengue hemorrhagic fever in Indonesia: analysis of five decades data from the national disease surveillance. BMC Res Notes. (2019) 12:350. 10.1186/s13104-019-4379-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.MoH Indonesia. Dengue Haemorrhagic Fever in Indonesia. Jakarta: Data and information center, Ministry of Health Indonesia; (2016). [Google Scholar]
  • 14.Kustriastuti R, Sutomo S. Evolution of dengue prevention and control programme in Indonesia DF/DHF disease burden. Dengue Bull. (2005) 29:1–7. [Google Scholar]
  • 15.Tam CC, O'Driscoll M, Taurel AF, Nealon J, Hadinegoro SR. Geographic variation in dengue seroprevalence and force of infection in the urban paediatric population of Indonesia. PLoS Negl Trop Dis. (2018) 12:e0006932. 10.1371/journal.pntd.0006932 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Powell JR, Tabachnick WJ. History of domestication and spread of Aedes aegypti-a review. Mem Inst Oswaldo Cruz. (2013) 108:11–7. 10.1590/0074-0276130395 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Higa Y. Dengue vectors and their spatial distribution. Trop Med Health. (2011) 39:17–27. 10.2149/tmh.2011-S04 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Kuno G. Review of the factors modulating dengue transmission. Epidemiol Rev. (1995) 17:321–35. 10.1093/oxfordjournals.epirev.a036196 [DOI] [PubMed] [Google Scholar]
  • 19.Chang FS, Tseng YT, Hsu PS, Chen CD, Lian I Bin, Chao DY. Re-assess vector indices threshold as an early warning tool for predicting dengue epidemic in a dengue non-endemic country. PLoS Negl Trop Dis. (2015) 9:1–20. 10.1371/journal.pntd.0004043 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Wilder-Smith A, Ooi EE, Vasudevan SG, Gubler DJ. Update on dengue: epidemiology, virus evolution, antiviral drugs, and vaccine development. Curr Infect Dis Rep. (2010) 12:157–64. 10.1007/s11908-010-0102-7 [DOI] [PubMed] [Google Scholar]
  • 21.Parra MCP, Fávaro EA, Dibo MR, Mondini A, Eiras ÁE, Kroon EG, et al. Using adult Aedes aegypti females to predict areas at risk for dengue transmission: a spatial case-control study. Acta Trop. (2018) 182:43–53. 10.1016/j.actatropica.2018.02.018 [DOI] [PubMed] [Google Scholar]
  • 22.World Health Organization Dengue: Guidelines for Diagnosis, Treatment, Prevention and Control (2009). Available online at: https://apps.who.int/iris/handle/10665/44188 [PubMed]
  • 23.MoH Indonesia. Guidelines for Dengue Haemorrhagic Fever Control Program in Indonesia (in Bahasa Indonesia). Directorate general disease prevention and control, MoH Indonesia: (2015). [Google Scholar]
  • 24.Focks D. A review of entomological sampling methods and indicators for dengue vectors. Dengue Bull. (2003) 28:208389. [Google Scholar]
  • 25.Barbosa GL, Donalísio MR, Stephan C, Lourenço RW, Andrade VR, Arduino M de B, et al. Spatial distribution of the risk of dengue and the entomological indicators in Sumaré, State of São Paulo, Brazil. PLoS Negl Trop Dis. (2014) 8:e287 10.1371/journal.pntd.0002873 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Sanchez L, Vanlerberghe V, Alfonso L, Marquetti MDC, Guzman MG, Bisset J, et al. Aedes aegypti larval indices and risk for dengue epidemics. Emerg Infect Dis. (2006) 12:800–806. 10.3201/eid1205.050866 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Tun-Lin W, Kay BH, Barnes A, Forsyth S. Critical examination of Aedes aegypti indices: correlations with abundance. Am J Trop Med Hyg. (1996) 540:543–7. 10.4269/ajtmh.1996.54.543 [DOI] [PubMed] [Google Scholar]
  • 28.Pan American Health Organization Dengue and Dengue Hemorrhagic Fever in the Americas: Guidelines for Prevention and Control. Washington, DC: PAHO Scientific Publication No; 548. (1994). [Google Scholar]
  • 29.Cromwell EA, Stoddard ST, Barker CM, Van Rie A, Messer WB, Meshnick SR, et al. The relationship between entomological indicators of Aedes aegypti abundance and dengue virus infection. PLoS Negl Trop Dis. (2017) 11:1–22. 10.1371/journal.pntd.0005429 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Pontes RJ, Freeman J, Oliveira-Lima JW, Hodgson JC, Spielman A. Vector densities that potentiate dengue outbreaks in a Brazilian city. Am J Trop Med Hyg. (2000) 62:378–83. 10.4269/ajtmh.2000.62.378 [DOI] [PubMed] [Google Scholar]
  • 31.Chadee DD. Dengue cases and Aedes aegypti indices in Trinidad, West Indies. Acta Trop. (2009) 112:174–80. 10.1016/j.actatropica.2009.07.017 [DOI] [PubMed] [Google Scholar]
  • 32.Basso C, Caffera RM, Da Rosa EG, Lairihoy R, González C, Norbis W, et al. Mosquito-producing containers, spatia distribution, and relationship between Aedes aegypti population indices on the southern boundary of its distribution in South America (Salto, Uruguay). Am J Trop Med Hyg. (2012) 87:1083–8. 10.4269/ajtmh.2012.12-0328 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Sulaiman S, Pawanchee ZA, Arifin Z, Wahab A. Relationship between Breteau and house indices and cases of dengue/dengue hemorrhagic fever in Kuala Lumpur, Malaysia. J Am Mosq Control Assoc. (1996) 12:494–6. [PubMed] [Google Scholar]
  • 34.Katyal R, Kumar K, Gill KS, Sharma RS. Impact of intervention measures on DF/DHF cases and Aedes aegypti indices in Delhi, India: an update, 2001. Dengue Bull. (2003) 27:163–7. [Google Scholar]
  • 35.Chadee DD, Williams FL, Kitron UD. Impact of vector control on a dengue fever outbreak in Trinidad, West Indies, in 1998. Trop Med Int Health. (2005) 10:748–54. 10.1111/j.1365-3156.2005.01449.x [DOI] [PubMed] [Google Scholar]
  • 36.Romero-Vivas CM, Falconar AK. Investigation of relationships between Aedes aegypti egg, larvae, pupae, and adult density indices where their main breeding sites were located indoors. J Am Mosq Control Assoc. (2005) 21:15–22. 10.2987/8756-971X(2005)21[15:IORBAA]2.0.CO;2 [DOI] [PubMed] [Google Scholar]
  • 37.Corrêa PRL, França E, Bogutchi TF. Aedes aegypti infestation and occurrence of dengue in the city of Belo Horizonte, Brazil. Revista de Saude Publica. (2005) 39:33–40. 10.1590/S0034-89102005000100005 [DOI] [PubMed] [Google Scholar]
  • 38.Sanchez L, Cortinas J, Pelaez O, Gutierrez H, Concepción D, Van Der Stuyft, et al. Breteau index threshold levels indicating risk for dengue transmission in areas with low Aedes infestation. Trop Med Int Health. (2010) 15:173–5. 10.1111/j.1365-3156.2009.02437.x [DOI] [PubMed] [Google Scholar]
  • 39.Chaikoolvatana A, Singhasivanon P, Haddawy P. Utilization of a geographical information system for surveillance of Aedes aegypti and dengue haemorrhagic fever in north-eastern Thailand. Dengue Bull. (2007) 31:75–82. [Google Scholar]
  • 40.Gürtler RE, Garelli FM, Coto HD. Effects of a five-year citywide intervention program to control Aedes aegypti and prevent dengue outbreaks in northern Argentina. PLoS Negl Trop Dis. (2009) 3:e427. 10.1371/journal.pntd.0000427 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Honório NA, Nogueira RMR, Codeço CT, Carvalho MS, Cruz OG, Magalhães Mde A, et al. Spatial evaluation and modeling of dengue seroprevalence and vector density in Rio de Janeiro, Brazil. PLoS Negl Trop Dis. (2009) 3:e545. 10.1371/journal.pntd.0000545 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Lin CH, Wen TH. Using geographically weighted regression (GWR) to explore spatial varying relationships of immature mosquitoes and human densities with the incidence of dengue. Int J Environ Res Public Health. (2011) 8:2798–815. 10.3390/ijerph8072798 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Arboleda S, Jaramillo ON, Peterson AT. Spatial and temporal dynamics of Aedes aegypti larval sites in Bello, Colombia. J Vector Ecol. (2012) 37:37–48. 10.1111/j.1948-7134.2012.00198.x [DOI] [PubMed] [Google Scholar]
  • 44.Pham HV, Doan HT, Phan TT, Minh NN. Ecological factors associated with dengue fever in a central highlands province, Vietnam. BMC Infect Dis. (2011) 11:172. 10.1186/1471-2334-11-172 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Wijayanti SP, Sunaryo S, Suprihatin S, McFarlane M, Rainey SM, Dietrich I, et al. Dengue in Java, Indonesia: relevance of mosquito indices as risk predictors. PLoS Negl Trop Dis. (2016) 10:e0004500 10.1371/journal.pntd.0004500 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Agha SB, Tchouassi DP, Bastos AD, Sang R. Assessment of risk of dengue and yellow fever virus transmission in three major Kenyan cities based on Stegomyia indices. PLoS Negl Trop Dis. (2017) 11:e0005858. 10.1371/journal.pntd.0005858 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.World Health Organization Comprehensive Guidelines for Prevention and Control of Dengue and Dengue Haemorrhagic Fever. Revised an. World Health Organization, Regional Office for South-East Asia; (2011). Available online at: http://origin.searo.who.int/entity/vector_borne_tropical_diseases/documents/SEAROTPS60/en/ [Google Scholar]
  • 48.World Health Organization . Entomological Surveillance for Aedes spp. in the Context of Zika virus. (2016). 1–10. Available online at: https://apps.who.int/iris/handle/10665/204894
  • 49.IVRCRD Manual on mosquitoes field sampling methods. In: Bahasa Indonesia. 3rd ed Salatiga: National Institute of Helath Research and Development MoH Indonesia; (2017). [Google Scholar]
  • 50.Razali NM, Wah YB. Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests. J Stat Model Analyt. (2011) 2:21–33. [Google Scholar]
  • 51.Sen PK. Estimates of the regression coefficient based on Kendall's tau. J Am Stat Assoc. (1968) 63:1379–89. 10.1080/01621459.1968.10480934 [DOI] [Google Scholar]
  • 52.Scott TW, Takken W. Feeding strategies of anthropophilic mosquitoes result in increased risk of pathogen transmission. Trends Parasitol. (2012) 28:114–21. 10.1016/j.pt.2012.01.001 [DOI] [PubMed] [Google Scholar]
  • 53.Brown JE, Evans BR, Zheng W, Obas V, Barrera-Martinez L, Egizi A, et al. Human impacts have shaped historical and recent evolution in Aedes aegypti, the dengue and yellow fever mosquito. Evolution. (2014) 68:514–25. 10.1111/evo.12281 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Tabachnick WJ, Powell JR. A world-wide survey of genetic variation in the yellow fever mosquito, Aedes aegypti. Genet Res. (1979) 34:215–29. 10.1017/S0016672300019467 [DOI] [PubMed] [Google Scholar]
  • 55.Tabachnick WJ, Wallis G, Aitken TH, Miller BR, Amato GT, Lorenz L, et al. Oral infection of Aedes aegypti with yellow fever virus: geographic variation and genetic considerations. Am J Trop Med Hyg. (1985) 34:1219–24. 10.4269/ajtmh.1985.34.1219 [DOI] [PubMed] [Google Scholar]
  • 56.Black WC, IV, Bennett KE, Gorrochótegui-Escalante N, Barillas-Mury CV, Fernández-Salas I, de Lourdes Muñoz, et al. Flavivirus susceptibility in Aedes aegypti. Arch Med Res. (2002) 33:379–88. 10.1016/S0188-4409(02)00373-9 [DOI] [PubMed] [Google Scholar]
  • 57.Hanski I, Gilpin M. Metapopulation dynamics: brief history and conceptual domain. Biol J Linnean Soc. (1991) 42:3–16. 10.1111/j.1095-8312.1991.tb00548.x [DOI] [Google Scholar]
  • 58.Bennett KE, Olson KE, de Lourdes Muñoz M, Fernandez-Salas I, Farfan-Ale JA, Higgs S, et al. Variation in vector competence for dengue 2 virus among 24 collections of Aedes aegypti from Mexico and the United States. Am J Trop Med Hyg. (2002) 67:85–92. 10.4269/ajtmh.2002.67.85 [DOI] [PubMed] [Google Scholar]
  • 59.Ocampo CB, Wesson DM. Population dynamics of Aedes aegypti from a dengue hyperendemic urban setting in Colombia. Am J Trop Med Hyg. (2004) 71:506–13. 10.4269/ajtmh.2004.71.506 [DOI] [PubMed] [Google Scholar]
  • 60.Pham TKL, Briant L, Gavotte L, Labbe P, Perriat-Sanguinet M, Cornillot E, et al. Incidence of dengue and chikungunya viruses in mosquitoes and human patients in border provinces of Vietnam. Parasit Vect. (2017) 10:556. 10.1186/s13071-017-2422-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.MoH Indonesia. Technical Guidelines of One House One Inspectors for Dengue Vector Control Program. Jakarta: Directorate general of Disease Prevention and Control, Ministry of Health Indonesia; (2016). p. 68. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All datasets generated for this study are included in the article.

Articles from Frontiers in Public Health are provided here courtesy of Frontiers Media SA

RESOURCES