https://orcid.org/0000-0001-9141-074X
Figures
Abstract
Introduction
Dengue continues to be a major public health concern in Malaysia, as evidenced by the significant surge in cumulative dengue case numbers and deaths in 2023 compared to the previous year. While previous studies have explored the interplay of abiotic and biotic factors of mosquito density and dengue cases on a local scale in Malaysia, there is a notable gap in the research focusing on adult Aedes mosquito populations.
Aims
This study aims to contribute to the existing knowledge by investigating the association and time lags (TLs) between daily microclimate (DM), mosquito indices (MIs), and dengue cases at the residence level.
Methods
In this longitudinal study, field data were collected over 26 weeks using data loggers, gravid oviposit sticky (GOS) traps, and non-structural 1 (NS1) test kits in both non-dengue hotspot (NDH) and dengue hotspots (DH). The collected data encompassed DM variables, vegetation cover (VC), MIs, and number of dengue cases. An autocorrelation analysis was conducted to determine the TLs between MIs and their preceding values, while a cross-correlation analysis revealed the TLs between MIs and DM variables.
Results
The study indicated there are positive correlations between the adult index (AI) of Ae. albopictus, their preceding values and rainfall at an NDH. Conversely, the AIs of total Aedes at the DH exhibited positive correlations with their preceding values, temperature, rainfall, and maximum relative humidity (RH), but negative correlations with the mean and maximum RH. The dengue-positive trap index (DPTI) of total Aedes at DHs demonstrated positive associations with their preceding values, mean temperature, minimum temperature, maximum RH, and rainfall, with negative correlations observed for the maximum temperature, mean RH, and minimum RH. Similar trends were identified for the Ae. aegypti and Ae. albopictus at DHs. The association between dengue cases, DM, and MIs was inconclusive due to underreported cases.
Conclusions
This study highlighted the DM and TLs of dengue virus-infected and non-infected adult female Aedes mosquitoes using onsite data collection. Furthermore, this study presents a replicable methodology that can be adopted by researchers worldwide for investigating the dynamics of dengue transmission in similar settings. The findings offer valuable insights for decision-makers, providing them with evidence-based information to implement targeted interventions and strategies aimed at controlling Aedes mosquito populations and mitigating the spread of dengue virus infections.
Citation: Mohd Hardy Abdullah NA, Dom NC, Pradhan B, Salleh SA, Dapari R (2025) Temporal associations between microclimate, adult Aedes mosquito indices, and dengue cases at the residence level in Malaysia: Implications for targeted interventions. PLoS ONE 20(2): e0316564. https://doi.org/10.1371/journal.pone.0316564
Editor: Rajib Chowdhury, World Health Organization, Regional Office for South-East Asia, INDIA
Received: April 17, 2024; Accepted: December 11, 2024; Published: February 3, 2025
Copyright: © 2025 Mohd Hardy Abdullah et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript.
Funding: This work was supported by the Fundamental Research Grant Scheme, Ministry of Higher Education Malaysia (FRGS/1/2021/SKK06/UITM/02/16). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Dengue is a viral disease transmitted by mosquitoes, is caused by four closely related serotypes of the dengue virus [1]. In Malaysia, the primary vector for this disease is the Ae. aegypti, while the Ae. albopictus serves as a secondary vector [2]. The dengue virus is transmitted vertically through the bite of an infected mosquito [3,4]. Both the Ae. aegypti and Ae. albopictus are known to coexist in urban and suburban areas in Malaysia, and they are considered to be sympatric species occupying similar ecological niches [5–9]. The surveillance and control of Aedes mosquitoes remain a high priority, given the high dengue case numbers and mortality rates in Malaysia. As of 2023, the cumulative dengue cases reported was 123133, with 100 deaths. These figures represent an alarming increase of 86.3% in cumulative cases and 78.6% in deaths compared to the previous year [10].
The five strategies implemented in Integrated Vector Management for dengue in Malaysia includes reprioritising Aedes surveillance areas, strengthening information system for effective disease surveillance and response, legislative changes, community participation and intersectoral collaboration, as well as changing fogging formulation to water-based pyrethroid and mass abating [11]. In the event of a new dengue case or controlled outbreak, the local authorities are required to conduct the destruction of mosquito breeding places and fogging within 200 metres of case in 24 hours [12]. Meanwhile, local authorities are required to conduct these activities within 400 metres of case in 24 hours for uncontrolled outbreak and hotspot area [12]. Most of the dengue prevention measures are passive, as they can only be conducted after dengue cases are registered in the eDengue database. The dengue carrier needs to seek treatment and meet the clinical case definition and laboratory confirmation of dengue fever before being registered in eDengue [13]. This delay may cause the effective time for prevention measures to elapse.
Prior studies have pointed out the importance of climate factors, especially temperature, humidity, and rainfall, on dengue transmission as they are crucial to the mosquito population, mosquito density, and mosquito survival rate [14–16]. For instance, ambient temperatures can alter the lifespan of the mosquito and the extrinsic incubation period of the dengue virus. In turn, these traits affect the rate of pathogen transmission [17,18]. Meanwhile, RH can cause mortality in adult mosquitoes at low humidity, whereas high RH is associated with heavy rainfall [19–21]. Rainfall increases the mosquito population by generating mosquito breeding sites and vegetation cover (VC) [22]. The cross-correlation between rainfall and dengue increases, especially in cities with adequate water sanitation, such as piped water and sewerage [23]. At the same time, heavy rainfall decreases the mosquito density by flushing away those mosquitoes in their immature life stages [23]. Therefore, total rainfall one month prior has the most significant association with both the number of dengue cases and mosquito density [24,25].
Although, on a large topographical scale, the spread of mosquito vectors is mainly affected by climate, the Aedes mosquito population is influenced by the availability of larval habitats, VC, and daily microclimate (DM) on a local scale [26–29]. Therefore, the association of abiotic and biotic factors to mosquito density, and at sites is well established in Malaysia [30–35]. However, there are very few studies on adult Aedes mosquito populations as most researchers prefer to use the ovitrap index and number of larvae as the mosquito density variables [32,33]. However, immature stage indicators do not directly correlate with the risk of dengue infection and do not always spatiotemporally correlate with adult indicators [36–38].
Additionally, many studies on adult Aedes mosquitoes in Malaysia are aimed at finding the most efficient adult mosquito traps and use the non-structural 1 (NS1) antigen test kit on pooled trapped adult mosquitoes as an early dengue surveillance or control [39–42]. According to their research, adult Aedes mosquitoes can be effectively captured using sticky ovitraps, and the NS1 test kits can successfully detect the dengue virus in the captured adult mosquitoes. Therefore, this study was aimed at expanding the knowledge on the correlation and time lags (TLs) between different DMs, adult female mosquito indices (MIs), and dengue cases at both a dengue hotspot (DH) and non-dengue hotspot (NDH). This study utilised gravid oviposit sticky (GOS) traps and NS1 test kits to detect mosquito density and the presence of the dengue virus in a sample of female adult Aedes mosquitoes.
Methods
Study sites
A longitudinal study was conducted at two hostels, namely, a NDH and a DH. The NDH was located in Jeram, a sub-district in Kuala Selangor. It was selected because it was not recorded as a DH for the period 2017–2021. The NDH had four 10-storey blocks that accommodated 3003 people. It was selected as the control site after a preliminary study was conducted using ovitraps and larvae surveys detect the presence of Ae. albopictus larvae in ovitraps, drains, and vases in the area. The preliminary study showed that this residential environment was suitable for the survival and development of Aedes mosquitoes. On the other hand, the DH, which was located in Bukit Raja, a sub-district in Petaling, had been identified as such in 2017, 2018, and 2020. The DH had five 4-storey blocks accommodating 2870 people. These sites were selected because the population in both study sites spends a significant amount of time within the area, reducing the risk of dengue transmission from neighbouring workplaces. Additionally, both sites have their own dengue preventive measures which include monthly fogging, and this preventive measure continues during data collection. The data collection was conducted from 6th February until 6th August 2023 (26 weeks) with permission from local authority.
Dengue case
The data on dengue cases at the study sites were taken from the Idengue website, the information in this system is obtained from Ministry of Health Malaysia and updated daily.
Field data collection
Daily microclimate (DM).
The DM parameters collected at both study sites were the RH, rainfall, and temperature. The RH and temperature parameters were collected hourly using Tinytag Plus 2 (Gemini Data Loggers). The data loggers were placed in a well-ventilated area and mounted 2 metres above ground. Meanwhile, the rainfall data were collected from 11 rain gauge stations in the Kuala Selangor district and 15 stations in the Petaling district (S1 Table), which were updated daily on the Info Banjir JPS Selangor website [43].
Aedes mosquito collection.
The Aedes mosquitoes were captured using GOS traps, comprising a black cylindrical high-density polyethylene (HDPE) plastic container (15 x 8 cm) and two double-sided sticky papers (5 x 3 cm). A mesh with a 4-cm hole at the centre was placed on the top of the entrance to the container to prevent larger insects from entering the trap. Then, the sticky papers were placed near the hole parallel to one another. The container was filled with 300 mL of 10% hay infusion water made from week-old hay as the attractant [44]. Furthermore, 20 GOS traps were placed 20 m apart from each other at each study site [45,46]. This spacing provided an even trap placement coverage, and Aedes mosquitoes were captured across the entire study site. The contents of the trap were collected weekly, and any missing or damaged traps were recorded and then replaced.
Vegetation cover (VC).
The onsite VC was visually assessed, as per the study by Walker et al. [47]. The values reflected the percentage of green VC area within a 3-m radius of the traps. The ‘per cent vegetation’ followed the protocols by Walker et al. [47], which involved an estimation by visual examination by a single viewer for consistency. The sites were classified into one of four VC levels: 1 = < 10% VC, 2 = 10–25% VC, 3 = 25–50% VC, and 4 = >50% VC [47].
Detection of dengue non-structural 1 (NS1) antigen.
The trapped adult Aedes mosquitoes were sorted and identified using a Dino-Lite handheld digital microscope (AnMo Electronics Corporation) and based on the morphological keys in the laboratory. Next, the female adult Ae. aegypti and Ae. albopictus mosquitoes were pooled per trap for the NS1 antigen test according to the procedures modified by Cheng et al. [48]. The mosquito sample was prepared by homogenising the Aedes mosquito pools in 1.5-mL tubes containing 200 μl of phosphate-buffered saline (PBS) using pellet pestles. The ProDetect® Dengue NS1 Ag Rapid Test was used for the NS1 antigen detection. The NS1 disposable pipette provided inside the kit was used to collect 75 μL of the sample, which was dropped into the sample well of the cassette. The result was recorded after 10 minutes, as stated in the manufacturer’s information.
Data analysis
Daily microclimate (DM) description and interpolation.
The hourly RH and temperature collected from the data logger were calculated into a daily and weekly format in Microsoft® Excel™ Open XML Spreadsheet (XLSX). Meanwhile, all the rain gauge stations were geocoded to produce a point shapefile using ArcGIS Desktop Version 10.8. This shapefile was used in the inverse distance weighting (IDW) interpolation to estimate the rainfall at the study sites based on the data at nearby rain gauge stations. This method was selected due to its high performance for univariate interpolations [49,50]. The IDW function in ArcGIS was used with default settings, with the power set to 2 to control the significance of known points upon the interpolated values. The IDW was computed as:
(1)
where: w(x, y) is the estimated results at points (x, y), N is the number of observational points surrounding (x, y), λi is the weight of the observed results (wi) at points (xi, yi), di is the distance between points (xi, yi) and (x, y). The exponent (p) affects the weighting of wi on w [46]. In this study, p = 2. The values on the maps that coincided with the study sites were then extracted and organised in Microsoft® Excel™ Open XLSX into daily and weekly formats.
Mosquito indices (MIs) and daily interpolation.
Meanwhile, the outcomes of the field data collection were measured in terms of weekly MIs. These MIs were further segregated into total Aedes, Ae. albopictus, and Ae. aegypti. The indices were the adult sticky trap index (ASTI), adult index (AI), and dengue-positive trap index (DPTI). The indices, which were adapted and modified from Liew et al. [44], were calculated as follows:
(2)
(3)
(4)
Only AI and DPTI were used in the correlation analysis. These values were converted into daily data using cubic spline interpolation in R software. The interpolation provided a means of estimating the value at the new data points within the range of parameters, and the cubic spline interpolation formed a smooth curve through a series of shape points [51]. Taking (n+1) nodes on the interval [a, b]:
(5)
At each interval [xi−1, xi], f(x) is a cubic polynomial function,
(6)
where, f(x) is continuous in the interval [a, b].
In this study, a cubic spline interpolation was used to interpolate at the starting point six path nodes and the target point to form a completely smooth spline by connecting all the interpolation points. The interpolated data were used for a correlation analysis.
Autocorrelation and cross-correlation.
An autocorrelation analysis was conducted in R software to determine if the daily AI and DPTI and dengue cases were affected by their own preceding values with TLs of 1 to 91 days using the autocorrelation coefficient and partial autocorrelation coefficient. {Xt} was a stationary time series with length T. A time series {Xt} with a mean function, μt = E[Xt] and autocovariance function [52]:
(9)
where, {Xt−h} is the lagged time series by h periods and μX is the expected value of {Xt}. The autocorrelation function (ACF) was:
(10)
The partial autocorrelation function (PACF) measures the degree of association between {Xt} and {Xt−h}, whereas the other TLs were not considered. The PACF was calculated as:
(11)
The cross-correlation analysis was conducted to determine the optimal TL between DM, AI, DPTI and dengue cases between 0 to 91 days. The cross-correlation function (CCF) between the variable {Xi} and {Xj} was defined by the ratio of covariance to root-mean variance [53]:
(12)
where, the sample covariance {γi,j} was found using:
(13)
The critical values for all the correlation analyses were set at the 5% significance level (p < 0.05). Lastly, the correlation analysis at the DH was further segregated into total Aedes, Ae. albopictus, and Ae. aegypti.
Results
Daily microclimate (DM)
The daily mean temperature at the NDH during the study period was 28.90°C (SD = 1.33). The lowest and highest daily mean temperatures recorded were 24.29 and 31.62°C, respectively. Meanwhile, the daily mean RH was 80.33% (SD = 6.79), with 53.59% and 96.78% being the lowest and highest daily mean humidity recorded, respectively. The daily mean rainfall was 5.37 mm (SD = 10.62) and the highest rainfall recorded was 64.66 mm. Meanwhile, the weekly mean temperature and RH at the NDH indicated that the parameters remained constant during the study period. The mean temperature ranged from 26.28–30.38°C, while the mean humidity ranged from 73.6–88.63%. In contrast, there were several weeks when the mean weekly rainfall was high in NDH. The highest weekly mean rainfall recorded was in Week 12 with 21.09 mm (SD = 10.95). Other weeks with a high weekly mean rainfall were Week 4 (M = 13.40 mm, SD = 10.86), Week 13 (M = 12.31 mm, SD = 20.26), and Week 17 (M = 12.04 mm, SD = 23.65). Fig 1A below summarises the weekly mean DM at the NDH.
Weekly mean daily microclimate at the (A) NDH and (B) DH.
At the DH, the daily mean temperature during the study period was 28.83°C (SD = 1.21). The lowest daily mean temperature recorded was 24.80°C and the highest was 31.38°C. The daily mean RH was 55.31% (SD = 15.95), with 30.36 and 90.54% being the lowest and highest daily RH recorded, respectively. The daily mean rainfall was 5.37 mm (SD = 9.64) and the highest daily mean rainfall recorded was 79.19 mm. The weekly mean temperature at DH shows that the parameter remained constant during the study period. The mean temperature ranged from 26.37–30.28°C. On the other hand, the weekly mean humidity remained constant within 72.06–81.18% from Weeks 1–7. The humidity significantly decreased from 81.18% to 36.10% between Weeks 7–11 before gradually increasing and remaining within the range of 38.55–58.92% for the remainder of the study period. There were several weeks when the weekly mean rainfall at the DH was high. The highest mean rainfall recorded was in Week 4 with 19.75 mm (SD = 16.05). The other weeks with a high mean rainfall were Week 5 (M = 11.35 mm, SD = 10.92), Week 12 (M = 14.29 mm, SD = 10.94), and Week 17 (M = 16.34 mm, SD = 29.10). Fig 1B summarises the weekly mean DM at the DH.
Vegetation cover (VC)
The GOS traps at the NDH had different VC levels. Most had < 10% of VC (70%), five had 10–25% of VC (25%), and only one had 25–50% of VC (5%). The NDH had different types of VC, which decreased as it got closer to the buildings at the site. In contrast, all the traps in the DH had < 10% of VC as the site was compacted with buildings and most of the area was asphalted, leaving only a small area for VC. Table 1 shows the VC within a 30-m radius of the ovitraps at each study site.
Mosquito Indices (MIs)
A total of 529 adult female Ae. albopictus mosquitoes were trapped at the NDH with a capture rate per week of 20.0 adult gravid female mosquitoes (SD = 6.44). No adult female Ae. aegypti were captured in NDH. There were no mixed captured containers and dengue cases at that site. The mean ASTI of Ae. albopictus in the NDH was 57.74% trap per week. Moreover, the mean AI of Ae. albopictus during the study period was 103.15%, which suggested that there was a high female adult population in the surveyed area even with continuous dengue interventions conducted at NDH. Meanwhile, Ae. albopictus (258 adult females) and Ae. aegypti (156 adult females) at the DH had a mean capture rate of 9.92 (SD = 5.20) and 6.00 (SD = 3.96) mosquitoes per week, respectively. Additionally, the rate for both species captured in the same container was 1.88 (SD = 1.68) traps per week, while the rate of dengue cases was 0.07 (SD = 0.09) case per week. The mean ASTI for the Ae. albopictus was 36.30%, while for the Ae. aegypti it was 19.87%. Meanwhile, the mean AI for Ae. albopictus and Ae. aegypti was 50.14% and 30.46%, respectively. This suggested that there was a moderate adult female population for both Aedes species at the DH, even though there were continuous dengue interventions during study period. Moreover, the mean DPTI was 1.93% for Ae. albopictus and 2.54% for Ae. aegypti. Table 2 summarises the weekly mean mosquito captured rate and MIs at the study sites.
A weekly MIs analysis was conducted at the NDH and DH. For the NDH, the range of weekly ASTI of Ae. albopictus was 40.00–84.21%. The highest weekly ASTI for Ae. albopictus was in Week 20 (%). The lowest weekly ASTI was in Weeks 3, 15, and 18. Other than that, AIs of Ae. albopictus greater than 100% were detected in Weeks 2 (110.0%), 4 (170.00%), 5 (140.00%), 17 (140.00%), 19 (100.50%), 20 (173.68%), 23 (130.00%), 24 (111.11%), and 25 (100.00%). Although there was a high AI at the NDH, the NS1 test indicated that there was no dengue virus detected in the captured Aedes mosquitoes. This was also reflected by the zero dengue case reported at the site throughout the study period. Fig 2 shows the weekly MIs at the NDH.
For the Ae. albopictus at DH, the highest ASTI was in Weeks 14 and 24 (60.00%). On the other hand, the lowest ASTI was in Week 4 (15.00%). There were several weeks when the AI was high for this mosquito species. These included Weeks 1(80.00%), 14 (100.00%), and 24 (100.00%). The DPTI of the Ae. albopictus was constant between 5 to 10% during the study period.
Meanwhile, the weekly ASTI for Ae. aegypti at the DH was 5.00–47.37%, with the highest ASTI being in Week 13 and the lowest in Week 8. Other weeks with a high ASTI were Weeks 17, 24 and 25, with 36.84%, 40.00% and 35.00% respectively. Moreover, a few high AIs for Ae. aegypti were identified in Weeks 13 (84.21%), 17 (52.63%), 23 (57.89%), and 25 (65.00%). Lastly, the weekly DPTI for Ae. aegypti was 5.0–10.5%. Fig 3 illustrates the weekly MIs for the Ae. albopictus and Ae. aegypti at the DH.
Correlation between daily microclimate (DM) and mosquito indices (MIs).
The autocorrelation analysis of the AI for Ae. albopictus at the NDH indicated that the optimal correlation of AI lags was on Day 21 (r = 0.70). On the other hand, the PACF result showed that the optimal correlation was on Day 8 (r = -0.70). The cross-correlation analysis between the daily AI and DM at the NDH showed that the optimum TL for the mean temperature was on Lag Day 45 (r = 0.24), while the optimum TL for the minimum (r = 0.27) and maximum temperature (r = 0.16) was on Day 44. Additionally, the optimum lag for the mean RH and rainfall was on Day 35 (r = -0.16) and 62 (r = 0.23), respectively. Lastly, the analysis indicated that there was no significant correlation between the AI and the minimum and maximum RH. The correlation plots for the AI and DM of the NDH are displayed in Fig 4.
Note: Blue areas indicate significant levels of correlation.
The analysis of the ACF and PACF of the AI for the total Aedes species and its preceding values at the DH revealed that the optimum TLs occurred on Lag Days 7 (r = 0.69) and 1 (r = 0.62). Additionally, the optimal lag for the mean temperature, minimum temperature, and maximum temperature was observed on Lag Day 52 (r = 0.32), 43 (r = 0.29), and 51 (r = 0.23), respectively. The optimum lag for the mean RH was on Day 26 (r = -0.37), while for the minimum and maximum RH, it was on Lag Day 28 (r = -0.42 and r = 0.26, respectively). Lastly, the optimal lag for rainfall was identified on Day 84 (r = 0.20).
Meanwhile, the ACF and PACF between the DPTI of the total Aedes species and its prior values identified the optimum TL as being on Lag Day 1 (r = 0.72). The cross-correlation analysis showed that the optimum lag for the mean, minimum, and maximum temperature was on Lag Day 51 (r = 0.30), 50 (r = 0.25), and 1 (r = 0.20), respectively. Moreover, the optimum lag for the mean, minimum, and maximum RH was on Day 80 (r = -0.29), 28 (r = -0.26), and 91 (r = 0.22), respectively. For the rainfall, the optimum lag was on Day 64 (r = 0.31). Fig 5 shows the correlation plots between the mosquito density and daily microclimate variables of the Aedes at the NDH.
Note: Blue areas indicate significant levels of correlation.
A further analysis was conducted to determine the correlation between the DM and MIs by species at the DH. The autocorrelation analysis detected the optimum lag of the AI of the Ae. Aegypti to its prior values was on Lag Day 1 (r = 0.64). The cross-correlation analysis between the AI and DM showed that the optimum lag for the mean, minimum, and maximum temperature was on Day 24 (r = 0.32), 23 (r = 0.33), and 38 (r = 0.25), respectively. The optimum lag for the mean, minimum, and maximum RH was on Day 26 (r = - 0.39), 28 (r = - 0.44), and 37 (r = 0.27), respectively. Lastly, the optimum lag for the rainfall was on Day 56 (r = 0.25).
The autocorrelation analysis between the DPTI of the Ae. aegypti with its earlier values implied that the optimum lag was on Lag Day 1 (r = 0.74). Furthermore, the cross-correlation between the DPTI and DM revealed that the optimum lag for the mean, minimum and maximum temperature was on Day 47 (r = 0.21), 71 (r = 0.17), and 1 (r = -0.24), respectively. The optimum lag for the mean RH was on Day 70 (r = -0.29), while for the minimum and maximum RH, it was on Day 91 (r = -0.27 and r = 0.25, respectively). For rainfall, the optimum lag was on Day 64 (r = 0.33). Fig 6 illustrates the correlation plot between the mosquito density variables and the daily microclimate of the Ae. aegypti at the DH.
Autocorrelation (I and II) and cross-correlation (III to IX) of Ae. aegypti based on (A) adult index (AI), and (B) dengue positive trap index (DPTI) on daily microclimate at DH over 91 lag days. Note: Blue areas indicate significant levels of correlation.
On the other hand, the ACF and PACF of the AI of the Ae. albopictus with prior values showed that the optimum lag was on Day 7 (r = 0.68) and 8 (r = -0.63), respectively. Meanwhile, the cross-correlation analysis between the AI and DM of the Ae. albopictus revealed that the optimum lag for the mean, minimum, and maximum temperature was on Day 45 (r = 0.26), 43 (r = 0.26), and 58 (r = 0.22), respectively. The optimum lag for the mean, minimum and maximum RH was on Lag Day 26 (r = -0.34), 28 (r = -0.38), and 19 (r = 0.24), respectively. Finally, the optimum lag for rainfall was on Lag Day 70 (r = 0.22).
Conversely, the autocorrelation analysis of the DPTI of the Ae. albopictus indicated that the optimum lag was on Day 1 (r = 0.75). The optimum lag for the mean, minimum, and maximum temperature was on Day 51 (r = 0.32), 50 (r = 0.28), and 17 (r = 0.19), respectively. Additionally, the optimum lag for the mean, minimum, and maximum RH was on Day 80 (r = - 0.26), 21 (r = - 0.24), and 23 (r = 0.20), respectively. The optimum TL for rainfall was achieved on Day 6 (r = 0.20). Fig 7 shows the correlation plots between the mosquito density and daily microclimate variables of the Ae. albopictus at the DH.
Autocorrelation (I and II) and cross-correlation (III to IX) of Ae. albopictus based on (A) adult index (AI), and (B) dengue positive trap index (DPTI) on daily microclimate at DH over 91 lag days. Note: Blue areas indicate significant levels of correlation.
Correlation between daily dengue cases, daily microclimate (DM), and Mosquito Indices (MIs)
The cross-correlation analysis between the daily dengue cases and daily microclimate in the DH revealed that the optimal TL for the minimum and maximum temperature was on Day 3 (r = -0.16) and 40 (r = -0.21), respectively. Additionally, the optimum lags for the mean (r = 0.16) and minimum RH (r = 0.15) were identified on Day 13, while the optimal lag for rainfall was on Day 39 (r = 0.37). Notably, no significant correlations were found between the AI and DPTI of the Aedes and Ae. albopictus and daily dengue cases (p > 0.05). However, the optimal lag between the daily dengue cases and the AI and DPTI of the Ae. aegypti was on Day 67 (r = 0.17) and 68 (r = 0.16), respectively. Fig 8 presents the correlation plots between the daily dengue cases, daily microclimate, and mosquito density at the DH.
Discussion
In this study, dengue vectors were present at both the study sites, with only the Ae. albopictus at the NDH, whereas the Ae. aegypti and Ae. albopictus were coexisting at the DH. Even though the adult index at the NDH was high, the Aedes mosquitoes were more of a nuisance as no dengue virus was found in any of the Ae. albopictus samples collected. This was supported by the fact that there were no daily dengue cases at the site during the study period. Conversely, the dengue virus was detected in both Aedes species at the DH, with a slightly higher detection of the dengue virus in the Ae. aegypti despite the lower capture rate of the latter species compared to the Ae. albopictus. The dengue cases in DH were aligned with the detection of Aedes mosquitoes infected with the dengue virus at the site.
Meanwhile, the presence of both Aedes species at the DH was also consistent with other studies in Malaysia that described the Ae. aegypti and Ae. albopictus as sympatric species occupying similar ecological niches [54]. The probability was low that the Ae. albopictus also shared the same ovitraps with the Ae. aegypti as their oviposition sites. This finding indicated that the Ae. albopictus was highly flexible in its choice of oviposition sites for laying its eggs in natural as well as artificial containers in urban green areas and highly urbanised habitats with less vegetation cover [55,56].
Moreover, the overall results of the autocorrelation analysis of the AI (%) and DPTI (%) at both sites indicated that there was a positive correlation with the prior AI and DPTI, respectively. This might be due to the tropical climate in Malaysia, with its warm and humid weather and average temperature of approximately 20–32°C throughout the year [57]. The warmer climate in tropical areas, with its hot and humid weather and moderate rainfall, is ideal for mosquitoes to be active all year round. The adult index of all the mosquito species at the NDH and DH are positively correlated with the mean, minimum, and maximum temperatures at different time-lags. On the other hand, only the dengue positive trap index of the Ae. albopictus at the dengue hotspot area positively correlated with all the temperature variables, whereas the dengue positive trap index for the Ae. aegypti positively correlated with the mean and minimum temperatures but negatively correlated with the maximum temperature. A study on the correlation between the mosquito density variables captured using a BG-mosquito trap and meteorological factors in Quzhou City, China showed that the mosquito density of the Ae. albopictus positively correlated to the mean temperature and mean air pressure at a lag of 0–4 weeks [58]. Other study also showed similar results, where the mosquito density of the blood-seeking female Ae. albopictus in Arco and Riva del Gard, Italy was positively affected by the accumulated temperature over 3–4 weeks before sampling [25].
The different time lags for temperature in each mosquito species might have been due to the impact of climate propagation through the life stages instead of appearing immediately. Furthermore, each species has a different development time and life expectancy. One study has conducted on the duration of the development and life expectancy of wild strains of the Ae. aegypti and Ae. albopictus from Penang Island. Their findings showed that the duration of the immature stage of development for the Ae. aegypti and Ae. albopictus was 8.76 and 9.47 days, respectively. Meanwhile, the life expectancy for the Ae. aegypti was 19.94 days and the Ae. albopictus was 19.01 days [5]. The systematic review and meta-analysis conducted showed that the Aedes mosquito can survive at temperatures of 18–35°C, while the optimal temperature for dengue transmission is 29.3°C [23,36]. Additionally, other study showed that temperature extremes of 16 and 36°C significantly reduce adult longevity and female fertility [59]. The DPTI (%) was negatively correlated with the maximum temperature, which might be explained by the fact that the maximum temperatures at the NDH and DH (31.62 and 31.38°C, respectively) were higher than the optimal temperature for dengue transmission.
The AI and DPTI of Ae. aegypti and DPTI of Ae. albopictus at DH were negatively correlated to mean and minimum RH but has positive corelation to maximum RH. A low relative humidity can be mortal to an adult mosquito as it can cause the fluids inside the mosquito to evaporate through its spiracle [19]. A laboratory study implied that the oviposition of the female Ae. aegypti was inhibited and there was a reduction in egg fertility at a temperature of 35°C and RH of 60%. On the other hand, none of these effects were found in the Ae. aegypti at temperatures of 25–30°C and RH of 80% [16]. This was in line with the RH found at the non-dengue hotspot and dengue hotspot area, where both sites had a minimum RH of 53.59 and 30.36%, respectively, which were below the limit for the survival of the Ae. aegypti.
There was a positive correlation between the adult index and dengue positive trap index at both the study sites with rainfall more than 56 days later. Several studies support strong associations between accumulated rainfall and higher vector density more than four weeks later [25,60,61]. For example, a study on the effects of extreme climate on the Ae. aegypti in Kenya showed that the mosquito eggs and adults were significantly more abundant one month following an abnormally wet month [60]. Meanwhile, other indicated that the accumulated precipitation over 1–4 weeks before sampling negatively correlated with the mosquito density of the female host-seeking Ae. albopictus as events such as flooding and excessive rainfall can flush out breeding sites, thus reducing the vector population [62,63].
Lastly, there was very low to no correlation between the daily dengue cases, daily microclimate, and mosquito density in this study. This might have been because the daily dengue cases were underreported. In Malaysia, from 2014 onwards, the daily dengue cases are registered in the eDengue database only after the cases meet both the clinical case definition and laboratory confirmation of dengue fever [13]. Moreover, most asymptomatic dengue patients might not seek medical attention [64,65].
Conclusion
The study conducted in both NDH and DH, where dengue interventions were implemented, revealed that the presence and high density of dengue vectors do not pose a threat when they are not carrying the dengue virus. Furthermore, the study identified the optimal lag times for dengue virus-infected and non-infected Aedes mosquitoes related to temperature, humidity, and rainfall. The different time-lags may be attributed to climate effects propagating through various life stages and different development times. However, the study is limited to two areas and may not fully capture the diversity of DH and NDH across the country. Moreover, there may be variability in environmental factors and mosquito abundance that are not accounted for in the analysis. Further study needs to be conducted include mosquito pesticide resistance and indoor capture rate before new interventions introduced to area with existing dengue interventions.
Supporting information
S1 Table. Rain gauge stations in Kuala Selangor and Petaling districts.
https://doi.org/10.1371/journal.pone.0316564.s001
(DOCX)
Acknowledgments
The authors wish to express their heartfelt gratitude to the organisations that were instrumental in the completion of this project. A special note of appreciation is extended to the Faculty of Health Sciences at Universiti Teknologi MARA (UiTM), Faculty of Medicine and Health Sciences, Universiti Putra Malaysia (UPM), University of Technology Sydney, Australia, and Universiti Kebangsaan Malaysia for their invaluable technical support and guidance during this research project.
References
- 1.
World Health Organization. Dengue and severe dengue [Internet]. World Health Organization. 2023 [cited 2024 Jan 10]. Available from: https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue
- 2. Selvarajoo S, Liew JWK, Tan W, Lim XY, Refai WF, Zaki RA, et al. Knowledge, attitude and practice on dengue prevention and dengue seroprevalence in a dengue hotspot in Malaysia: a cross-sectional study. Sci Rep [Internet]. 2020 Jun 12;10(1):1–13. Available from: https://www.nature.com/articles/s41598-020-66212-5#Sec9
- 3. Adnan RA, Ramli MF, Othman HF, Asha’ri ZH, Ismail SNS, Samsudin S. The impact of sociological and environmental factors for dengue infection in Kuala Lumpur, Malaysia. Acta Trop. 2021 Apr;216:105834. pmid:33485870
- 4. Sarfraz MS, Tripathi NK, Tipdecho T, Taravudh T, Thawisak T, Pornsuk K, et al. Analyzing the spatio-temporal relationship between dengue vector larval density and land-use using factor analysis and spatial ring mapping. BMC Public Health. 2012;12: 853. pmid:23043443
- 5. Ab Hamid N, Mohd Noor SN, Isa NR, Md Rodzay R, Bachtiar Effendi AM, Hafisool AA, et al. Vertical infestation profile of Aedes in selected urban high-rise residences in Malaysia. Trop Med Infect Dis. 2020 Jul 7;5(3):114. pmid:32646026
- 6. Hashim NA, Ahmad AH, Talib A, Athaillah F, Krishnan KT. Co-breeding association of Aedes albopictus (skuse) and Aedes aegypti (linnaeus) (diptera: culicidae) in relation to location and container size. Trop Life Sci Res. 2018 Mar;29(1):213–227. pmid:29644025
- 7. Rahim J, Ahmad AH, Maimusa AH, Irfan S. Updated abundance and distribution of Aedes albopictus (skuse) (diptera: culicidae) in Penang Island, Malaysia. Trop Biomed. 2018 Jun 1;35(2):308–320.
- 8. Alhaji HM, Abu HS, Nur Faeza AK, Hamdan A, Hamady D, Junaid R. Contribution of public places in proliferation of dengue vectors in Penang Island, Malaysia. Asian Pac J Trop Biomed. 2016;7.
- 9. Saifur RGM, Dieng H, Hassan AA, Salmah MRC, Satho T, Miake F, et al. Changing domesticity of Aedes aegypti in Northern Peninsular Malaysia: reproductive consequences and potential epidemiological implications. PLoS ONE. 2013; 72: e30919. pmid:22363516
- 10.
Ministry of Health Malaysia. Portal Rasmi Kementerian Kesihatan Malaysia [Internet]. www.moh.gov.my. [cited 2024 Jan 5]. Available from: https://www.moh.gov.my/index.php/database_stores/store_view_page/17/2472
- 11.
Malaysian Health Technology Assessment Section. Integrated vector management for Aedes control. [cited 2024 June 17]. Available from: https://www.moh.gov.my/moh/resources/Penerbitan/MAHTAS/HTA/Report%20HTA%20IVM%20for%20Aedes%20Control.pdf
- 12.
Ministry of Health Malaysia. Pelan stategik pencegahan dan kawalan denggi kebangsaan 2022–2026. 2022 [cited 2024 June 17]. Available from: https://www.moh.gov.my/moh/resources/Penerbitan/Pelan%20Strategik%20/Buku_Pelan_Strategik_Pencegahan_dan_Kawalan_Denggi_Kebangsaan_Final.pdf
- 13. Md Iderus NH, Singh S, Ghazali SM, Zulkifli AA, Ghazali NH, Lim MY, et al. The effects of the COVID-19 pandemic on dengue cases in Malaysia. Front Public Health. 2023 Aug 24;11. pmid:37693699
- 14. Cai X, Zhao J, Deng H, Xiao J, Liu T, Zeng W, et al. Effects of temperature, relative humidity, and illumination on the entomological parameters of Aedes albopictus: an experimental study. Int J Biometeorol. 2023 Apr;67(4):687–694. pmid:36884085
- 15. Rodrigues M, Marques GR, Serpa LL, Arduino B, Voltolini JC, Barbosa GL, et al. Density of Aedes aegypti and Aedes albopictus and its association with number of residents and meteorological variables in the home environment of dengue endemic area, São Paulo, Brazil. Parasit Vectors. 2015 Feb 19;8:115. pmid:25890384
- 16. Costa EAP, Santos EM, Correia JC, Albuquerque CMR. Impact of small variations in temperature and humidity on the reproductive activity and survival of Aedes aegypti (diptera, culicidae). Rev Bras Epidemiol. 2010;54(3):488–93.
- 17. Mordecai EA, Caldwell JM, Grossman MK, Lippi CA, Johnson LR, Neira M, et al. Thermal biology of mosquito-borne disease. Ecol Lett. 2019 Oct;22(10):1690–1708. pmid:31286630
- 18. Smith DL, Battle KE, Hay SI, Barker CM, Scott TW, et al. Ross, Macdonald, and a theory for the dynamics and control of mosquito-transmitted pathogens. PLOS Path. 2012;8(4): e1002588. pmid:22496640
- 19. Monintja TCN, Arsin AA, Amiruddin R, Syafar M. Analysis of temperature and humidity on dengue hemorrhagic fever in Manado municipality. Gac Sanit. 2021;35 Suppl 2:S330–S333. pmid:34929845
- 20. Mitovski T, Folkins I, Salzen K, Sigmond M. Temperature, relative humidity, and divergence response to high rainfall events in the tropics: observations and models. J Clim. 2010;23: 3613–3625.
- 21. Sallam MF, Fizer C, Pilant AN, Whung PY. systematic review: land cover, meteorological, and socioeconomic determinants of Aedes mosquito habitat for risk mapping. Int J Environ Res Public Health. 2017 Oct 16;14(10):1230. pmid:29035317
- 22. Oliveira JB, Murari TB, Nascimento Filho AS, Saba H, Moret MA, Cardoso CAL. Paradox between adequate sanitation and rainfall in dengue fever cases. SciTotal Environ. 2023;860:160491. pmid:36455745
- 23. Abdullah NAMH, Dom NC, Salleh SA, Salim H, Precha N. The association between dengue case and climate: a systematic review and meta-analysis. One Health. 2022 Oct 31;15:100452. pmid:36561711
- 24. Santos CAG, Guerra-Gomes IC, Gois BM, Peixoto RF, Keesen TSL, da Silva RM. Correlation of dengue incidence and rainfall occurrence using wavelet transform for João Pessoa city. Sci Total Environ. 2019;647:794–805. pmid:30096669
- 25. Roiz D, Rosà R, Arnoldi D, Rizzoli A. Effects of temperature and rainfall on the activity and dynamics of host-seeking Aedes albopictus females in Northern Italy. vector-borne and zoonotic diseases. 2010 Oct;10(8):811–6.
- 26. Johnson TL, Haque U, Monaghan AJ, Eisen L, Hahn MB, Hayden MH, et al. Modeling the environmental suitability for Aedes (stegomyia) aegypti and Aedes (stegomyia) albopictus (diptera: culicidae) in the contiguous United States. J Med Entomol. 2017 Sep 21;54(6):1605–14.
- 27. Evans MV, Hintz CW, Jones L, Shiau J, Solano N, Drake JM, et al. Microclimate and larval habitat density predict adult Aedes albopictus abundance in urban areas. Am J Trop Med Hyg. 2019 Aug 7;101(2):362–70.
- 28. Ferraguti M, Martínez-de la Puente J, Roiz D, Ruiz S, Soriguer R, Figuerola J. Effects of landscape anthropization on mosquito community composition and abundance. Sci Rep [Internet]. 2016 Jul 4;6:29002. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4931447/ pmid:27373794
- 29. Faraji A, Egizi A, Fonseca DM, Unlu I, Crepeau T, Healy SP, et al. Comparative host feeding patterns of the asian tiger mosquito, Aedes albopictus, in urban and suburban northeastern USA and implications for disease transmission. PLoS Negl Trop Dis [Internet]. 2014 Aug 7;8(8):e3037. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4125227/
- 30. Gopalsamy B, Yazan L, Nabila N, Razak A, Man M. Association of temperature and rainfall with Aedes mosquito population in 17th college of Universiti Putra Malaysia. Malaysian J Med Health Sci. 2021;17(2):2636–9346.
- 31. Yusof MM, Dom N, Ismail R, Zainuddin A. Assessing the temporal distribution of dengue vectors mosquitoes and its relationship with weather variables [Internet]. UKM J Article Repository. 2019 p. 112–25. Available from: https://journalarticle.ukm.my/13420/
- 32. Wee LK, Weng SN, Raduan N, Wah SK, Ming WH, Shi CH, et al. Relationship between rainfall and Aedes larval population at two insular sites in Pulau Ketam, Selangor, Malaysia. Southeast Asian J Trop Med Public Health. 2013;44(2).
- 33. Rohani A, Ismail S, Malinda M, Anuar AI Mazlan MZ, Maszaitun SM, et al. Aedes larval population dynamics and risk for dengue epidemics in Malaysia. Trop Biomed. 2011 Aug 1;28(2):237–48.
- 34. Muhammad N, Nik H, Halim A, Dom N, Megat Mokhtar A, Norashikin S, et al. Weather variability on mosquito-borne disease distribution in Terengganu, Malaysia: a retrospective study. Malaysian JMed Health Sci. 2021;17(SUPP8):2636–9346.
- 35. Mustafa AD, Azid A, Juahir H, Yunus K, Abidin IZ, Sulaiman NH, et al. Geographical information system (gis) for relationship between dengue disease and climatic factors at Cheras, Malaysia. Malaysian J AnalytSci. 2015 Dec 1;19(6):1318–26.
- 36. Liu-Helmersson J, Stenlund H, Wilder-Smith A, Rocklöv J. vectorial capacity of Aedes aegypti: effects of temperature and implications for global dengue epidemic potential. PLoS ONE. 2014 Mar 6;9(3):e89783.
- 37. Faridah L, Fauziah N, Agustian D, Gede I, Putra RE, Ekawardhani S, et al. Temporal correlation between urban microclimate, vector mosquito abundance, and dengue cases. J Med Entomol. 2022 Mar 19;59(3):1008–18. pmid:35305089
- 38. Getis A, Morrison AC, Gray K, Scott TW. characteristics of the spatial pattern of the dengue vector, Aedes aegypti, in Iquitos, Peru. PerspectivesSpatial Data Analys. 2008 Aug 25;203–25.
- 39. Roslan MA, Ngui R, Vythilingam I, Chan KF, Ong PS, Low CK, et al. Surveillance of Aedes aegypti and Aedes albopictus (diptera: culicidae) in high-rise apartment buildings in Selangor, Malaysia. Int J Trop Insect Sci. 2022 Feb 10;42(2):1959–69.
- 40. Selvarajoo S, Liew JWK, Chua TH, Tan W, Zaki RA, Ngui R, et al. Dengue surveillance using gravid oviposition sticky (GOS) trap and dengue non-structural 1 (NS1) antigen test in Malaysia: randomized controlled trial. Sci Rep. 2022 Jan 12;12(1). pmid:35022501
- 41. Liew JWK, Selvarajoo S, Phang WK, Mah Hassan M, Redzuan MS, Selva Kumar S, et al. Improved Aedes/dengue field surveillance using gravid oviposition sticky trap and dengue NS1 tests: epidemiological, entomological outcomes and community acceptance. Acta Trop. 2021 Apr;216:105829.
- 42. Bujang M, Sai L, Adnan T, Huck O, Ibrahim M, Husin D, et al. Adult Aedes mosquito and dengue virus surveillance in residential and public areas of Selangor, Malaysia. Southeast Asian J Trop Med Public Health. 2018;49(4).
- 43.
Department of Irrigation and Drainage Negeri Selangor. Info Banjir JPS Selangor. [cited 2023 February 6]. Available from: http://infobanjirjps.selangor.gov.my/index.html
- 44. Liew JWK, Selvarajoo S, Tan W, Ahmad Zaki R, Vythilingam I. Gravid oviposition sticky trap and dengue non-structural 1 antigen test for early surveillance of dengue in multi-storey dwellings: study protocol of a cluster randomized controlled trial. Infect Dis Poverty. 2019 Sep 3;8(1). pmid:31477185
- 45. James LD, Winter N, Stewart ATM, Feng RS, Nandram N, Mohammed A, et al. Field trials reveal the complexities of deploying and evaluating the impacts of yeast-baited ovitraps on Aedes mosquito densities in Trinidad, West Indies. Sci Rep. 2022 Mar 8;12(1). pmid:35260697
- 46. Surendran SN, Jayadas TTP, Thiruchenthooran V, Raveendran S, Tharsan A, Santhirasegaram S, et al. Aedes larval bionomics and implications for dengue control in the paradigmatic Jaffna peninsula, Northern Sri Lanka. Parasit Vectors. 2021 Mar 18;14(1).
- 47. Walker KR, Joy TK, Ellers-Kirk C, Ramberg FB. Human and environmental factors affecting Aedes aegypti distribution in an arid urban environment. J Am Mosq Control Asso. 2011 Jun;27(2):135–41.
- 48. Cheng L, Liu WL, Li HH, Su MP, Wu SC, Chen HW, et al. Releasing intracellular ns1 from mosquito cells for the detection of dengue virus-infected mosquitoes. Viruses. 2020 Sep 29;12(10):1105. pmid:33003584
- 49. Chutsagulprom N, Chaisee K, Wongsaijai B, Inkeaw P, Oonariya C. Spatial interpolation methods for estimating monthly rainfall distribution in Thailand. Theor Appl Climatol. 2022 Jan 27;148(1–2):317–28.
- 50. Fung KF, Chew KS, Huang YF, Ahmed AN, Teo FY, Ng JL, et al. Evaluation of spatial interpolation methods and spatiotemporal modeling of rainfall distribution in Peninsular Malaysia. Ain Shams Eng J [Internet]. 2022 Mar 1;13(2):101571. Available from: https://www.sciencedirect.com/science/article/pii/S209044792100335X
- 51. Lian J, Yu W, Xiao K, Liu W. Cubic spline interpolation-based robot path planning using a chaotic adaptive particle swarm optimization algorithm. Mathematical Problems Eng. 2020 Feb 20;2020:1–20.
- 52.
Shen L, Mcgree J, Zongyuan G, Yang X. Computational and statistical methods for analysing big data with applications. Amsterdam: Elsevier/Academic Press; 2016.
- 53. Boyd DW. Stochastic Analysis. Systems Analysis Modeling. 2001;211–27.
- 54. Maimusa HA, Ahmad AH, Kassim NF, Rahim J. Age-Stage, two-sex life table characteristics of Aedes albopictus and Aedes aegypti in Penang Island. J Am Mosq Control Assoc. 2016 Mar 1;32(1):1–11.
- 55. Sasmita HI, Neoh KB, Yusmalinar S, Anggraeni T, Chang NT, Bong LJ, et al. Ovitrap surveillance of dengue vector mosquitoes in Bandung city, West Java province, Indonesia. PLoS Negl Trop Dis. 2021 Oct 28;15(10):e0009896. pmid:34710083
- 56. Paupy C, Ollomo B, Kamgang B, Moutailler S, Rousset D, Demanou M, et al. Comparative role of Aedes albopictus and Aedes aegypti in the emergence of dengue and chikungunya in Central Africa. Vector Borne Zoonotic Dis. 2010 Apr;10(3):259–66. pmid:19725769
- 57. Tan KC. Trends of rainfall regime in Peninsular Malaysia during northeast and southwest monsoons. J Phys Conf Ser. 2018 Apr;995:012122.
- 58. Wang JN, Hou J, Zhong JY, Cao GP, Yu ZY, Wu Y, et al. Relationships between traditional larval indices and meteorological factors with the adult density of Aedes albopictus captured by BG-mosquito trap. PLoS ONE. 2020 Jun 11;15(6):e0234555–5.
- 59. Marinho RA, Beserra EB, Bezerra-Gusmão MA, Porto V de S, Olinda RA, dos Santos CAC. Effects of temperature on the life cycle, expansion, and dispersion of Aedes aegypti (diptera: culicidae) in three cities in Paraiba, Brazil. J Vector Ecol. 2016 May 27;41(1):1–10. pmid:27232118
- 60. Nosrat C, Altamirano J, Anyamba A, Caldwell JM, Damoah R, Mutuku F, et al. Impact of recent climate extremes on mosquito-borne disease transmission in Kenya. PLoS Negl Trop Dis. 2021 Mar 18;15(3):e0009182. pmid:33735293
- 61. Roiz D, Boussès P, Simard F, Paupy C, Fontenille D. Autochthonous Chikungunya Transmission and Extreme Climate Events in Southern France. PLoS Negl Trop Dis [Internet]. 2015 Jun 16;9(6):e0003854. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4469319/ pmid:26079620
- 62. Benedum CM, Seidahmed OME, Eltahir EAB, Markuzon N. Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore. PLoS Negl Trop Dis [Internet]. 2018 Dec 6;12(12):e0006935. Available from: https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0006935 pmid:30521523
- 63. Seidahmed OME, Eltahir EAB. A sequence of flushing and drying of breeding habitats of Aedes aegypti (l.) prior to the low dengue season in Singapore. PLoS Negl Trop Dis. 2016 Jul 26;10(7):e0004842.
- 64. Tan S, Wee J, Selvarajoo S, Lim XN, Foo CS, Refai WF, et al. Inapparent dengue in a community living among dengue-positive Aedes mosquitoes and in a hospital in Klang Valley, Malaysia. Acta Trop. 2020 Apr 1;204:105330–0.
- 65. Woon YL, Ng CW, Mudin RN, Suli Z. Health facility use by dengue patients in the Klang Valley, Malaysia: a secondary analysis of dengue surveillance data. Western Pac Surveill Response J. 2019 May 21;10(2):39–45. pmid:31720053