
==== Front
BMC Infect Dis
BMC Infect Dis
BMC Infectious Diseases
1471-2334
BioMed Central London

39251932
9878
10.1186/s12879-024-09878-w
Research
Determining dengue infection risk in the Colombo district of Sri Lanka by inferencing the genetic parameters of Aedes mosquitoes
Chathurangika Piyumi 1
Premadasa Lakmini S. 2
Perera S. S. N. 1
De Silva Kushani kdesilva@maths.cmb.ac.lk

1
1 https://ror.org/02phn5242 grid.8065.b 0000 0001 2182 8067 Research & Development Centre for Mathematical Modeling, Department of Mathematics, Faculty of Science, University of Colombo, 00030 Colombo, Sri Lanka
2 https://ror.org/00wbskb04 grid.250889.e 0000 0001 2215 0219 International Center for the Advancement of Research and Education (I·CARE), Texas Biomedical Research Institute, San Antonio, 78227 TX USA
9 9 2024
9 9 2024
2024
24 94410 5 2024
4 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

For decades, dengue has posed a significant threat as a viral infectious disease, affecting numerous human lives globally, particularly in tropical regions, yet no cure has been discovered. The genetic trait of vector competence in Aedes mosquitoes, which facilitates dengue transmission, is difficult to measure and highly sensitive to environmental changes.

Methods

In this study we attempt, for the first time in a non-laboratory setting, to quantify the vector competence of Aedes mosquitoes assuming its homogeneity across both species; aegypti and albopictus and across the four Dengue serotypes. Estimating vector competence in relation to varying rainfall patterns was focused in this study to showcase the changes in this vector trait with respect to environmental variables. We quantify it using an existing mathematical model originally developed for malaria in a Bayesian inferencing setup. We conducted this study in the Colombo district of Sri Lanka where the highest number of human populations are threatened with dengue. Colombo district experiences continuous favorable temperature and humidity levels throughout the year creating ideal conditions for Aedes mosquitoes to thrive and transmit the Dengue disease. Therefore we only used the highly variable and seasonal rainfall as the primary environmental variable as it significantly influences the number of breeding sites and thereby impacting the population dynamics of Aedes.

Results

Our research successfully deduced vector competence values for the four identified seasons based on Monsoon rainfalls experienced in Colombo within a year. We used dengue data from 2009 - 2022 to infer the estimates. These estimated values have been corroborated through experimental studies documented in the literature, thereby validating the malaria model to estimate vector competence for dengue disease.

Conclusion

Our research findings conclude that environmental conditions can amplify vector competence within specific seasons, categorized by their environmental attributes. Additionally, the deduced vector competence offers compelling evidence that it impacts disease transmission, irrespective of geographical location, climate, or environmental factors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-024-09878-w.

Keywords

Vector competence
Vectorial capacity
Rainfall
Bayesian
Parameter estimation
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmcIntroduction

Dengue is a viral disease transmitted by Aedes mosquitoes, primarily Aedes aegypti and secondarily Aedes albopictus, reported predominantly in tropical and subtropical regions [1]. The tropical regions initially vulnerable to dengue fever are expected to expand rapidly due to climate change, accelerating the spread of the disease across new geographical areas and increasing its intensity [2]. As a result, this escalation increases the uncertainty of disease risk, leading to unprecedented outbreaks. For instance, Peru witnessed its largest outbreak in history coinciding with the Yaku Cyclone, while Pakistan experienced its outbreak in 2022 coinciding with record-breaking monsoon rainfall [3]. Located within the tropical zone where temperatures favor dengue transmission, Sri Lanka has been affected with the dengue viral disease since early 1960s, confirmed through serological testing [4]. After experiencing a notable surge in dengue instances, almost three times the figures recorded in 2021 and 2022, Sri Lanka received an outbreak alert, synchronized with the intense Southwest monsoon precipitation in 2024. The likelihood of dengue risk rapidly intensifying and extending the area of high-risk is expected to escalate exponentially, driven by erratic rainfall patterns and increasing temperatures attributed to climate change [5]. This could potentially overwhelm the healthcare system in a low-middle-income country like Sri Lanka.

Understanding disease transmission is a key component in moving forward with vector control, a crucial strategy to eradicate the disease. Mathematical models of disease transmission utilizing differential equations are frequently used to study how dengue spreads by taking into account the interactions between humans (hosts) and mosquitoes (vectors). Some models broaden their focus to incorporate external factors such as environmental conditions aiming to depict the transmission dynamics with greater accuracy. However, many studies overlooked internal factors of vectors, often simplifying complex nature of mosquito biology by assuming any mosquito encountering the dengue virus will inevitably become infected if the external factors are met, and, in turn, transmit the virus to another host [6–8]. i.e. for the events of A (Aedes), B (infection) and C (successful bite),1 pA,B|C=pA|B,CpB|C=pA|B,CpB,pinfectedAedes|successful bite=pinfection|successful bite fromAedespAedes,

with pinfection|successful bite fromAedes is 100% while assuming B is independent of C. In other words, we define event C (a successful bite) as the condition where all external factors are satisfied. Evidently, vector genetic studies have indicated not all vectors possess equal capability of transmitting the virus violating the hypothesis made in Eq. (1) [9–11]. Therefore, relying on the hypothesis of definite infection post-exposure may yield only partially accurate results within the mathematical framework.

Dengue virus (DENV) is transmitted to vectors during a viremic blood meal from an infected host, and the virus then replicates inside the vector, enabling transmission to a new host via saliva. The genetic factors within both the mosquito and the virus determine this physiological capability, known as vector competence (vc). vc is determined by a complex interplay between genetic factors within the vector, characteristics of the pathogen (virus), and environmental conditions [12–14]. Upon acquisition, the virus must overcome several internal barriers within the mosquito, which significantly reduce its population size, diversity, and complexity. These barriers, known as bottlenecks, impact the mosquito’s vc by affecting the virus’s ability to transmit through saliva [15–18]. The strength of these barriers varies among mosquito populations, leading to different susceptibilities to virus transmission [15]. In a laboratory setting, researchers have attempted to assess vc by infecting mosquitoes with the DENV and subsequently measuring the virus concentration in the mosquito body parts [15, 19, 20]. Despite limited mathematical analysis outside laboratory settings, this study aims to estimate vc in relation to external factors by employing Vectorial Capacity (Vs) as a novel approach. Vs quantifies potential secondary infections from a single infected host, similar to the basic reproduction number (R0), but includes vector, host, and pathogen interactions [21, 22].

The advent of genome sequencing technologies has significantly advanced our understanding of transcriptomics, enabling detailed studies on messenger RNA expression across various organisms. This progression from analyzing mRNA expressions has revolutionized the field through methods like expressed sequence tags (ESTs) and quantitative RT–PCR to comprehensively analyze full transciptomes via bulk RNA-Seq [23]. A pivotal moment for vc research was the detailed publication of the Aedes aegypti transcriptome, offering high-resolution gene expression data at various developmental stages and conditions, which facilitated groundbreaking work in gene drive strategies, functional genomic studies, and the identification of genetic markers associated with vc [24]. Transcriptomic profiling has also shed light on genetic bases for differential susceptibility to the DENV among Aedes aegypti strains, marking a significant advance in understanding mosquito immune responses and vc [25]. Moreover, tissue-specific and single-cell transcriptomic analyses have deepened our knowledge of vc, highlighting the genetic underpinnings that distinguish susceptible and refractory mosquito strains and suggesting new avenues for disease control and surveillance [26, 27]. Furthermore, metatranscriptomic profiling of mosquitoes offers a comprehensive view of their microbial ecosystems, providing valuable data for biosurveillance and understanding the genetic factors influencing vc [28–30].

The influence of climate factors, particularly the temperature, on vc have been experimented [14, 19]. Climate change significantly impacts mosquito genetics, with various factors driving evolutionary changes and adaptations. Furthermore, warmer temperatures and extended transmission seasons may favor genetic traits that enhance vc and survival over longer periods [14]. Urban heat islands present unique adaptation challenges, potentially leading to genetic changes facilitating survival in urban environments [12]. Lastly, the increasing use of insecticides in response to changing mosquito distributions encourages the spread of resistance genes, showcasing a direct genetic impact of climate change [31]. Together, these factors underscore the complex interplay between climate change and mosquito genetics, highlighting the need for integrated approaches to vector control and disease prevention in a changing world. Studies show that Vs varies with environmental factors, such as temperature and vector genetics, and is influenced by climate change [32, 33]. Its variability depends on specific vector species and viral strains [34–36]. Studies have used metrics like Infection Rate (IR), Dissemination Efficiency (DE), and Transmission Efficiency (TE) to assess vc, typically ranging from 0 to 1 [11, 15, 17].

All in all, we present in this paper on inferencing a genetic parameter of Aedes in order to gain insights on disease transmission. The rest of the paper is organized as follows. Model setup for parameter estimation section presents detailed discussion on the model and parameter estimation setup. Results are given in Results section followed by detailed discussion in Discussion section. Finally the paper concludes with Conclusions section stating future directions.

Model setup for parameter estimation

The literature predominantly presents two linear equations for Vs, of which, one is based on the vector’s daily survival rate, while the other is based on vector mortality rate [22, 34]. Due to the unavailability of accurate vector survival rate data, we used the equation with the vector mortality rate,2 Vs(t)=z(t)b2vce-μvEIPμv.

The descriptions of parameters in Eq. (2) are given in Table 1. In this study, we aim to quantify for the first time a quantitative measure for the genetic factor of Aedes mosquito, vc. The study is carried out based on Colombo district’s Colombo Municipal Council (CMC) area and we assume CMC area is representable of Colombo district. The Colombo district, with its highest population density, standing at the greatest risk, especially as it is heavily affected by the Southwest monsoon. As highlighted in the introduction, climate change impact on monsoon specifically can overwhelm the district with the disease. Understanding the risk in this area can help greatly to reduce the burden on limited healthcare system of Sri Lanka. In the study area, we assumed homogeneity of vc in Aedes aegypti and Aedes albopictus. Additionally, the results in this study majorly account for DENV–2 and DENV–3 serotypes as they are the most prevalent in Sri Lanka [37]. We verify the applicability of Vs formula in (2) for the dengue cases observed in Colombo. Vs, quantifies the risk of getting secondary humans infected by a single infected human. Therefore, the infected human population density, It+h_ can be considered proportionate to Vst,3 It+h_∝Vst,

where I(t+h_) is the infected human density at time t+h_ with intrinsic incubation period h_. Since Vs is defined for a unit time, we consider the unit time as a week in our estimation procedure. Since the estimation of vc is performed based on infected human population densities generated from successfully infected Aedes, the vc estimate gives an account of the TE [15]. Moreover, we demonstrate here how seasonal variations in environmental factors can modulate vc, leading to differing outcomes across seasons, warranting further investigations. In the Colombo district, environmental variables such as temperature and humidity consistently stay within ranges favorable for dengue transmission throughout the year [38]. Furthermore, studies have shown immediate temperature variations do not significantly impact dengue cases [39]. For these reasons and rainfall being the only significant variable impacting seasonality in Colombo district, we have chosen rainfall as the primary factor influencing dengue transmission in Colombo, as it directly affects mosquito breeding habitats and the subsequent spread of the virus. Additionally, both commercial and administrative cities in Sri Lanka are located in the Colombo district, resulting in excessive human mobility. Consequently, Colombo is a significant hotspot for disease transmission. The dengue infected population density in Colombo shows a consistent pattern aligning with monsoons and therefore we carried out this study with respect to four such identified seasons [40]. To that end we have the following relationship to find seasonal per-capita vector density for four identified seasons,4 z(t)=asR(t-τ),

where as is the rainfall coefficient for season s and time lag denoted by τ. By combining the Eqs. (3) and (4), we can write the linear relation of infected dengue density and vc,5 It+h_=KasR(t-τ)b2vce-μvEIPμv.

Table 1 Description of parameters & variables in Eq. (2)

Parameter	Description	
z	Per-capita vector density	
b	Vector biting rate	
μv	Vector mortality rate	
EIP	Extrinsic incubation period	
vc	Vector competence	
Vs	Vectorial capacity	
R	Rainfall	

In Eq. (5), the two parameters vc and K are not known. As the relationship between these two parameters is in a product form, accurately estimating unique values for vc becomes challenging when attempting to estimate both simultaneously. Therefore we first estimated lower bounds K for each season while fixing vc at its maximum value (i.e. vc=1). Subsequently, the accurate value of vc is estimated by setting K at its lower bound, thereby enabling the determination of a suitable estimate for vc up to 1.

Data of the study

All the identified variables and data of Eq. (5) are given in Table 2. Parameters common to Aedes vector and the dengue disease were extracted from the literature (see Table 2). We further assume there is no significant change observed in vc during the span of a single season i.e. vc is not a time dependent parameter within a season. For the parameter estimation we used annual average dengue data across the years from 2009 to 2022. We obtained the data from The National Dengue Control Unit (NDCU), Ministry of Health, Sri Lanka, with their permission to conduct the research, accompanied by a signed privacy policy, allowing us to use it for analysis and publication [41]. During this period, Colombo district experienced two outbreaks in 2017 and 2019. Outbreak years driven by unusually heavy rainfall that exceeds normal levels, can significantly alter the usual seasonal dengue incidence pattern. To avoid distorting this pattern, our study focuses on estimating vc under standard environmental conditions, excluding outbreak years. The rainfall coefficient of the four seasons were recalculated from [40] by omitting the outbreak years as well and are presented in Table 3. The rainfall data were obtained from the NASA power data access viewer [42]. The infected population density is calculated with respect to the total population in the Colombo district [43]. Table 2 The status of parameters and variables in the model (5). The definitions of the acronyms are given in Table 1

Description	Variables	
Unknown parameter	vc	
Known data (Observed)	I, R, K	
Known data ([40, 44])	b,μv,EIP,as	
I is the reported infected human cases, K is the proportionality constant, and as is the rainfall coefficient for season s

Table 3 Seasonal rainfall coefficients in Eq. (4) calculated excluding the outbreak years of 2017 and 2019, deeming them as outlier points

Season (s)	Month	Seasonal rainfall coefficient (as)	
1	April-August	7.495	
2	August-October	1.782	
3	October-January	5.596	
4	January-April	2.189	

Full Bayesian setup

In this section we setup the full Bayesian version for this model in Eq. (5). The Bayes’ theorem yields,6 pθ|y=pθpy|θpy,

where θ represents the vector of parameters and y denotes the vector of data/observables. The denominator in Eq. (6) is called the normalization constant and for the problem of parameter estimation, the denominator stays a constant. Therefore Eq. (6) reduces to the following after including all variables,7 pvc|It+h_,K,R(t-τ),P→,σ=k1pvc|RpRpIt+h_|vc,K,R,σ,

where P→=as,b,μv,EIP is the vector of other available literature data and 1/k1 is the normalization constant. The time stamp of the variable R(t-τ) on the right hand side of Eq. (7) is ignored since time stamp is not relevant to a probability distribution, i.e., prior distribution of R does not depend on a time stamp. A probability distribution was fitted to rainfall data from 2009 to 2022 to use as prior information of the rainfall distribution in the study area. The AIC suggested rainfall follows best with gamma distribution (from among normal, log normal, and gamma) and our result agrees with standard distribution for rainfall [45] (Fig. 1a). Here, α=0.9461 (shape parameter) and β=0.0219 (rate parameter) in the gamma distribution,8 p(R|α,β)=βαΓ(α)Rα-1exp-βR,R>0.

Fig. 1 Prior distributions: a gamma probability distribution for rainfall based on historical data in Colombo district, with α=0.9461,β=0.0219 (histogram is blue and gamma distribution is in black) (b) anticipated right skewed beta distribution for vc (supportive) enforcing small values highly probable. c anticipated left skewed beta distribution for vc (unsupportive) enforcing large values highly probable

Based on the literature, the range for vc in terms of TE ranges from 0 to 1 [10]. Consequently, in this study, we proposed two choices of beta distribution as prior distributions in the Bayesian setup to support both lower TE and higher TE. The choice of prior distribution was designed to encompass the range of vc, including both low and high extremes providing a more meaningful framework to use prior knowledge. We were allowed to explore how powerful the observed data is to influence the initial prior assumptions of vc to the final estimated values. The data reveal the extent to which these estimates are revised from their prior distributions. Although a gamma distribution can also accommodate to design a prior distribution, the range of vc fits with the domain of a beta distribution - thus employing beta is most suitable. The two prior distributions are shown in Fig. 1b and c. Accordingly, a beta distribution with γ=1.2 and δ=10 was chosen for a right skewed distribution (supportive prior) anticipating vc ranges close to zero (low TE) while a left skewed distribution (unsupportive prior) with γ=10 and δ=1.2 was chosen anticipating vc close to 1 (high TE) (Eq. (13)),9 pvc|γ,δ=Γ(γ+δ)Γ(γ)Γ(δ)vcγ-1(1-vc)δ-1,vc>0.

The observed data are the infected dengue densities (dengue incidence densities) in Colombo district during 2009 – 2022 [41]. Let us assume the errors in dengue data against Vs are normally distributed, since there is no evidence to suggest otherwise. With the assumption that errors are linear, we can write the likelihood distribution for n observed data as,10 pIt+h_|vc,K,R,σ=∏i=1n12πσ2exp-12σ2Iobsi-Iti+h_2

assuming σi=σ ∀ i. To avoid computational overflows, the log of the probability densities are taken.11 logpIt+h_|vc,K,R,σ=-n2log2πσ2-12σ2Iobsi-Iti+h_2

By putting together the prior distributions and the likelihood distribution, we get the full log posterior distribution (Eq. (12)). The prior distributions chosen are conjugate to the likelihood and thus no special treatment is needed in simulation step.12 pvc|It+h_,,K,R(t-τ),P→,σ=k1pvc|γ,δpR|α,βpIt+h_|vc,K,R,σ

13 logpvc|It+h_,,K,R(t-τ),P→,σ=logk1+logΓ(γ+δ)Γ(γ)Γ(δ)+(γ-1)logvc+(δ-1)log(1-vc)+logβαΓ(α)+α-1logR-βR-n2log2πσ2-12σ2Iobsi-Iti+h_2

With the built posterior distribution we estimated vc for 4 seasons. After parameter estimation we also quantified the uncertainty of the parameters by calculating the 95% credible intervals of the simulated marginal distributions of vc for each season. With these values of vc the uncertainty of the dengue risk was calculated for each season and are presented in the next section.

Results

We simulated the unnormalized posterior density for each season established in Eq. (13). These simulations were carried out using MCMC toolbox of Delayed Rejection Adaptive Metropolis in MATLAB [46]. For this estimation, the per-capita vector density was extracted from a previous study in which the rainfall data were used to estimate the seasonal rainfall coefficients (as) [40]. These values for the four seasons are given in Table 3.

In our model, the sensitivity analysis was carried out by sequentially estimating the two parameters. In particular we allowed the value of vc to have its upper bound in order to estimate the lowest possible K the model can handle. Afterward the actual value of vc was estimated which was supported by the model. We further this sensitivity via prior distributions of vc by allowing the model to locate its accurate value from the data. Although we did not use independent datasets to validate our model, we accomplished it within the parameter estimation mechanism. This procedure allowed us to find reasonable boundaries of vc ensuring a comprehensive examination of the model thereby validating its reliability and performance.

Estimating lower bound for K

 14 logpK|It+h_,,vc,R(t-τ),P→,σ=logk1+logβαΓ(α)+α-1logR-βR-n2log2πσ2-12σ2‖Iobsi-I(ti+h_)‖2

The first stage of parameter estimation aimed at determining the lower limit of K while setting the value of vc to its upper limit. The point estimate of K was derived by conducting m simulations using 20 random initial conditions.15 K∗=∑j=1m∑i=1wKijwm

where m=100000,w=20.

Estimating vc with optimum lower bound of K

The seasonal vc was estimated with the two beta prior distributions anticipated as mentioned in Model setup for parameter estimation section. For every season, MCMC was run 20 times starting from random initial value for vc. Each of these simulations, MCMC was set to run 100000 sample generations, which was a sufficient amount of samples to observe the convergence of the chains. With the obtained convergent chains, 30% of samples were burnt to obtain the correct marginal distribution. The mean marginal distribution obtained from the 20 runs was then utilized to find vc for each season. The point estimate for vc is calculated from the mean of the marginal distribution similar to Eq. 15 (see Table 4). Additionally, the DRAM toolbox samples error variance with inverse gamma distribution based on an adaptation mechanism [46],16 p(σ|r,q)=q-rΓ(r)σ(r-1)exp(-σ/q),

where r=(1+N)/2,q=2/(sd+SSE) with sd, N and SSE respectively represent the standard deviation of dengue data, sample size and Iobsi-Iti+h_2. For the four seasons, the sampled marginal distributions are indicated in Fig. 2a–d. From these marginal distributions, 95% credible intervals were calculated and are shown in Table 4. This uncertainty in the estimates considers both the uncertainties in the model structure and the quality of the data. Note that when the supportive beta prior is used, i.e. when we suggest vc possibly be lower in Colombo district, vc was estimated between 63% and 75% for seasons 1 and 2 while vc was estimated between 73% and 81% for seasons 3 and 4. In contrast, when the unsupportive beta prior is used, i.e. when a higher vc is suggested, vc was estimated between 90% and 96% for all the four seasons – which could be resulted from amplified influence for higher values of vc from the unsupportive prior. Overall, it can be noted that vc in the Colombo disrict is over 63% throughout the year. Table 4 Estimated parameter values for proportionality constant, K and vector competence, vc with supportive and unsupportive priors

Season	K lower bound	vc from Supportive Prior	vc from Unsupportive Prior	
Evc	Credible Interval vcL∗,vcU∗	Evc	Credible Interval vcL,vcU	
1	1.37×10-06	0.7077	(0.6676,0.7455)	0.9329	(0.9091,0.9540)	
2	4.22×10-06	0.6850	(0.6348,0.7310)	0.9286	(0.9029,0.9511)	
3	1.61×10-06	0.7609	(0.7280,0.7920)	0.9404	(0.9196,0.9588)	
4	2.98×10-06	0.7772	(0.7455,0.8067)	0.9425	(0.9228,0.9602)	
E(.) represents the expected value. vc∗ and vc∗ represent the lower and upper bounds of the estimated vector competence for supportive beta prior. vcL and vcU represent the lower and upper bounds of the estimated vector competence for unsupportive beta prior

Fig. 2 The posterior distribution of vector competence against its chosen supportive and unsupportive beta prior distributions are showcased in (a), (b), (c), and (d) respectively for seasons 1, 2, 3, and 4. The estimates of vector competence (vc) are shown in (e) with the error bars representing 95% credible intervals. In all the panels, results from the supportive and unsupportive priors are respectively shown in blue and orange colors

Using these estimated parameter values for vc, the estimated dengue density curves were obtained for the two cases of applying supportive and unsupportive beta priors (Figs. 3 and 4). Using the 95% credible intervals obtained for vc for each season, the uncertainties of dengue cases were obtained at e, 2e, 3e levels using the model in Eq. 2, where e=I(Evc)-IvcU when unsupportive prior is used and e=I(Evc)-Ivc∗ when supportive prior is used. Here the symbol E represents the expected value. These confidence bands are shown in gray color in Figs. 3 and 4 with the respective error bars. The model presented in Eq. 2 reliably captures the underlying trend of the dengue data. However, it should be noted the underestimation of predictions from model to the observed data. This is because the nonlinearity of dengue incidences cannot be predicted within a linear framework. The predicted curves shows nonlinearity generated from rainfall only. Thus, It is advisable to explore additional external factors influencing vectorial capacity in Eq. 2, or opt for a different modeling approach if the primary aim is to estimate dengue infection counts accurately. Nevertheless, the vectorial capacity equation has proven sufficient for estimating the levels of intrinsic mosquito factors, given that it is the available formula available that integrates host, pathogen, and vector elements.Fig. 3 Estimated dengue densities for the four seasons obtained using the supportive beta prior (right skewed) are shown where blue dots indicate the observed average dengue infected human population densities (Iobs) and gray dots indicate the estimated seasonal dengue risk (It+h_). The red error bars (e) represent the uncertainties of the respective estimated results. The gray color bands represent the uncertainties of the estimated results at e, 2e, 3e levels. a, b, c, and d represent the seasons 1, 2, 3, and 4 respectively

Fig. 4 Estimated dengue densities for the four seasons obtained using the unsupportive beta prior (left skewed) are shown where blue dots indicate the observed average dengue infected human population densities (Iobs) and gray dots indicate the estimated seasonal dengue risk (It+h_). The red error bars (e) represent the uncertainties of the respective estimated results. The gray color bands represent the uncertainties of the estimated results at e, 2e, 3e levels. a, b, c, and d represent the seasons 1, 2, 3, and 4 respectively

Discussion

Dengue, a vector-borne illness, has been spreading across tropical and subtropical regions worldwide for numerous decades [1]. However, these regions are anticipated to expand due to the effects of climate change, particularly based on rainfall patterns and temperatures [2]. The uncertainty of outbreaks has increased due to the uncertain complex nature of climate change influence on disease transmission, uncertainty in model frameworks and modeling assumptions, and data limitations leading to more uncertain situations in the future with respect to dengue spread [5, 47]. The Colombo district of Sri Lanka stands out as a densely populated area and has been under significant threat from dengue for an extended period. Specifically, the Colombo district offers favorable conditions for dengue transmission, including high population density, monsoon rains facilitating breeding grounds, suitable temperatures and humidity for mosquito proliferation. With the impact of climate change, alterations in rainfall patterns and rising temperatures are expected to render the Colombo district even more vulnerable and prone to unpredictable outbreaks, potentially overwhelming healthcare facilities. Dengue is influenced by both external and internal factors related to mosquitoes. However, the internal aspects of mosquito biology have often been overlooked, oversimplifying the intricate nature of the disease. Disease transmission compartmental models often disregard these internal factors, hypothesizing that mosquitoes have a 100% capability of becoming infected each time they bite an infected human [48–51]. In our study, we challenge this hypothesis by integrating the internal factors of mosquitoes into the model, aiming to estimate these factors based on observed dengue incidence data. Thus, we introduce a novel analysis to estimate the genetic trait known as vector competence (vc), which determines the mosquitoes’ susceptibility to infection. Notably, this marks the first attempt to estimate vc outside of laboratory conditions using a mathematical framework. This estimation of vc helps better model the climate change impact on vector genetics in determining risk of future disease transmission. It further benefits for studying vector adaptation in the midst of climate change and vector control methods.

This mathematical model not only accounts for the combined influence of both external and internal factors but also captures the interplay between these factors. Dengue outbreaks in Sri Lanka are closely synchronized with rainfall seasons, prompting us to integrate rainfall data into our mathematical framework, given that temperature and humidity consistently remain at favorable levels. Moreover, due to the patterns observed in rainfall, we carried out the study for an average year broken down to four seasons [40]. Our developed model establishes a concurrent relationship between vc and rainfall, offering a holistic understanding of disease emergence from dual perspectives. The estimation process involved employing the formula of vectorial capacity (Vs) within a Bayesian framework. In this model (5), two unknowns were identified: (1) the parameter of interest, vc, and (2) the proportionality constant, K. The parameter K serves solely as a scaling parameter, and therefore, its significance is negligible for the objectives of this study. Consequently, we determined a lower bound for K by setting vc at its upper bound, i.e., vc=1. Subsequently, these lower bounds of K were utilized in estimating vc by allowing the estimates of vc towards its upper bound as much as possible. Additionally, we endeavored to explore the lower and upper extremes of the estimates by employing supportive and unsupportive priors for vc (see the left skewed and right skewed beta distributions in Fig. 1). These dual mechanisms, (a) allowing to estimate vc near its upper bound and (b) subsequently attempting to push it towards both ends of the interval [0, 1], ensured the region of accurate estimates for vc.

One of the limitations of our study is the assumption of homogeneity in vc between Aedes aegypti and Aedes albopictus, which was necessary due to the lack of detailed data and information. One other limitation of this study is using mortality rate in Vs formula instead of survival rate data. This may oversimplify vector population dynamics overlooking potential variations in the transmission capabilities of these mosquito species. Moreover, while we included monsoon rainfall as the primary environmental variable, other factors such as urbanization, vector control measures, and socio-economic conditions could also significantly influence dengue transmission and may not be adequately represented in our model. Despite these limitations, our findings provide valuable insights into the influence of environmental factors on dengue transmission and underscore the importance of continuous model validation and improvement.

The estimates of vc from the supportive beta prior are all between 63%,80% for all four seasons across both species albopictus and aegypti. Although these results are obtained anticipating vc to be near its lower bound, the estimation gives higher values suggesting high vc in Colombo. Agreeing with these results, for the unsupportive beta prior, as anticipated, vc values are near its upper bound ranging between 90%,96% (see Fig. 2). When considered both supportive and unsupportive priors, the estimates for vc can lie in the bounds 63%,75% and 90%,95% respectively, for first two seasons where Colombo benefits from Southwest monsoon, known for its heavy rainfall. Similarly the range of vc estimates, respectively for supportive and unsupportive priors, can lie in the intervals 73%,81% and 92%,96% for last two seasons where Colombo benefit from Northeast monsoon characterized by comparatively lower rainfall. The intervals for seasons 1 and 2 (spanning 24 weeks) are comparatively narrower than those of seasons 3 and 4 (extending over 28 weeks). This difference could be linked to the decreased rainfall and prolonged warmer temperatures observed during the Northwest monsoon during seasons 3 and 4 (see Table A1 in Appendix A). This finding confirms that environmental conditions can enhance vc throughout a season, a period exceeding the lifespan of a mosquito. Obtaining different boundaries by different prior options justify the sensitivity of vc estimates to prior distributions. Further, these boundaries can provide insights into the stability of the vc estimates in the light of chosen prior as well as the accuracy of the values if a different prior is chosen.

The value of vc is often measured through experimental studies via three components, IR, DE, and TE. Since in this study, we estimate vc with respect to the reported infected human cases, our results reflect the TE. Several literature studies on vc based on experimental work are showcased in Table A2 in Appendix A. Studies conducted in Europe, Argentina and Uruguay present measurements of vc in subtropical and non-tropical environments [14, 15, 52]. These studies reveal lower values of vc (5-10% for serotype DENV–1, 20% and 42% for serotype DENV–2) and do not reflect the tropical settings in which vc is estimated in our study. Thus values from these regions do not relate and diverge from the estimates in our study. The experimental studies conducted in Brazil, Mexico, and Australia fall into the tropical setting. However, the Australian study was based on an Australian vector which is a different species than the vectors found in Sri Lanka [11]. Among the studies conducted in tropical regions, those in Mexico and Brazil demonstrate a range of moderate values for vc, varying between 11% and 62% for serotypes 1 and 4. Furthermore, the experiments quantify the interplay between mosquito genetics and pathogen characteristics through vc across different serotypes. For instance, DENV–2 has comparatively higher favorability, resulting in high vc values in facilitating increased disease transmission in Brazil. In Sri Lanka, where serotype 2 is most prevalent, our estimates of vc align with those from the Brazilian study, particularly for serotype 2, indicating higher values [20, 37]. Additionally, considering DENV–2’s greater advantage against mosquito immunity while the tropical setting provides highly favorable external conditions, it becomes evident why Sri Lanka, particularly Colombo, has remained consistently threatened by dengue for many decades. On a different aspect, despite Brazil and Mexico sharing similar environmental conditions, the measurement of vc for serotype DENV–1 varies significantly. This suggests when favorable climate conditions are provided, disease transmission from the same serotype can be influenced by vector genetics alone. In summary, the literature studies in Table A2 in Appendix A provide clear evidence of vc affecting disease transmission, regardless of geography, climate setting, or environmental conditions. However, our findings in this study cannot be generalized to any vector species (e.g. Aedes, Anopheles, Culex, etc.) because the value of vc depends on characteristics of the virus as well as the intrinsic characteristics of a vector. Thus we limit our findings in this study to Dengue, i.e., Aedes aegypti and Aedes albopictus.

Conclusions

This study has demonstrated vc plays a significant role in the transmission dynamics of dengue, influenced by both internal and external factors. Our mathematical model, which incorporates the effects of environmental conditions such as rainfall, provides a comprehensive understanding of how these factors interact with mosquito genetics to affect vc. The findings highlight favorable environmental conditions can enhance vc throughout a season, extending beyond the lifespan of a mosquito. Notably, our results suggest even within the same serotype, disease transmission can be significantly influenced by vector genetics alone, emphasizing the importance of considering both genetic and environmental variables in disease prediction models. These insights underscore the need for continuous monitoring and adaptation of vector control strategies, especially in light of climate change and its potential to alter the patterns of dengue outbreaks. As vector-borne diseases continue to change, it will be essential to conduct further research on the variations in vc across different contexts to develop effective control and prevention strategies.

Our study opens new avenues for future research to delve deeper into understanding the variations of vc across different geographical areas, virus strains, vector species, and vector genetics. Additionally, further investigations could explore the potential impacts of vc, offering insights into more effective strategies for dengue control and prevention in the face of evolving climate change and changing disease dynamics. The experimental data in Table A2 in Appendix A suggests mosquito genetics play a vital role in determining vc for various serotypes. For instance, serotype DENV–2 interacts favorably with the mosquito immune system, resulting in high vc values and facilitating increased disease transmission. Further, the model can be refined by incorporating additional environmental and socio-economic variables, using more specific data, and exploring non-homogeneous vector competence among different mosquito populations and dengue serotypes. Amidst these prospective avenues, vc evolves in response to its influencing factors, underscoring the importance of comprehending its evolution - especially in the context of climate change. By continuing to refine our understanding of vc and its implications for disease transmission, we can better prepare for and mitigate the impacts of dengue outbreaks in vulnerable regions.

Supplementary Information

Supplementary Material 1.

Acknowledgements

Dengue data for this study were kindly provided by Dr. Sudath Samaraweera, the Director of the National Dengue Control Unit, Ministry of Health of Sri Lanka. The authors would like to acknowledge the meaningful discussions had with Dr. Adom Giffin in improving the methodology of this work.

Authors’ contributions

P. C. - Methodology, Investigation, Simulations, Data curation, Writing the original draft, and editing. L. S. P. - Investigation, Literature Survey and writing, Interpretation of results, Review, and proofreading. S. S. N. P. - Review and discussions. K. D. S.- Conceptualization, Investigation, Methodology, Data curation, Writing the original draft, and editing, Supervising. All authors read and approved the final manuscript.

Funding

Not applicable.

Availability of data and materials

The data that support the findings of this study are available from The National Dengue Control Unit, Ministry of Health, Sri Lanka but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of The national Dengue Control Unit, Ministry of Health, Sri Lanka.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Simmons CP Farrar JJ van Vinh Chau N Wills B Dengue N Engl J Med. 2012 366 15 1423 1432 10.1056/NEJMra1110265 22494122
Simmons CP, Farrar JJ, van Vinh Chau N, Wills B. Dengue. N Engl J Med. 2012;366(15):1423–32.22494122 10.1056/NEJMra1110265
2. Harvell CD Mitchell CE Ward JR Altizer S Dobson AP Ostfeld RS Climate warming and disease risks for terrestrial and marine biota Science. 2002 296 5576 2158 2162 10.1126/science.1063699 12077394
Harvell CD, Mitchell CE, Ward JR, Altizer S, Dobson AP, Ostfeld RS, et al. Climate warming and disease risks for terrestrial and marine biota. Science. 2002;296(5576):2158–62.12077394 10.1126/science.1063699
3. Mordecai EA Tackling climate change and deforestation to protect against vector-borne diseases Nat Microbiol. 2023 8 12 2220 2222 10.1038/s41564-023-01533-5 38030900
Mordecai EA. Tackling climate change and deforestation to protect against vector-borne diseases. Nat Microbiol. 2023;8(12):2220–2.38030900 10.1038/s41564-023-01533-5
4. Vitarana T Jayakuru WS Historical account of dengue haemorrhagic fever in Sri Lanka WHO/SEARO Dengue Bull. 1997 21 117 118
Vitarana T, Jayakuru WS. Historical account of dengue haemorrhagic fever in Sri Lanka. WHO/SEARO Dengue Bull. 1997;21:117–8.
5. Messina JP Brady OJ Golding N Kraemer MU Wint GW Ray SE The current and future global distribution and population at risk of dengue Nat Microbiol. 2019 4 9 1508 1515 10.1038/s41564-019-0476-8 31182801
Messina JP, Brady OJ, Golding N, Kraemer MU, Wint GW, Ray SE, et al. The current and future global distribution and population at risk of dengue. Nat Microbiol. 2019;4(9):1508–15.31182801 10.1038/s41564-019-0476-8
6. Souza-Neto JA Powell JR Bonizzoni M Aedes aegypti vector competence studies: A review Infect Genet Evol. 2019 67 191 209 10.1016/j.meegid.2018.11.009 30465912
Souza-Neto JA, Powell JR, Bonizzoni M. Aedes aegypti vector competence studies: A review. Infect Genet Evol. 2019;67:191–209.30465912 10.1016/j.meegid.2018.11.009
7. Chung YK Pang FY Dengue virus infection rate in field populations of female Aedes aegypti and Aedes albopictus in Singapore Tropical Med Int Health. 2002 7 4 322 330 10.1046/j.1365-3156.2002.00873.x
Chung YK, Pang FY. Dengue virus infection rate in field populations of female Aedes aegypti and Aedes albopictus in Singapore. Tropical Med Int Health. 2002;7(4):322–30.10.1046/j.1365-3156.2002.00873.x
8. Gloria-Soria A Armstrong P Powell J Turner P Infection rate of Aedes aegypti mosquitoes with dengue virus depends on the interaction between temperature and mosquito genotype Proc R Soc B Biol Sci. 1864 2017 284 20171506
Gloria-Soria A, Armstrong P, Powell J, Turner P. Infection rate of Aedes aegypti mosquitoes with dengue virus depends on the interaction between temperature and mosquito genotype. Proc R Soc B Biol Sci. 1864;2017(284):20171506.
9. Tabachnick WJ. Genetics of Insect Vector Competence for Arboviruses. In: Advances in Disease Vector Research. vol. 10. New York: Springer; 1994. pp. 93–108.
10. Zhu C Jiang Y Zhang Q Gao J Li C Li C Vector competence of Aedes aegypti and screening for differentially expressed microRNAs exposed to Zika virus Parasites Vectors. 2021 14 504 10.1186/s13071-021-05007-7 34579782
Zhu C, Jiang Y, Zhang Q, Gao J, Li C, Li C, et al. Vector competence of Aedes aegypti and screening for differentially expressed microRNAs exposed to Zika virus. Parasites Vectors. 2021;14:504.34579782 10.1186/s13071-021-05007-7
11. Kain MP Skinner EB Athni TS Ramirez AL Mordecai EA van den Hurk AF Not all mosquitoes are created equal: A synthesis of vector competence experiments reinforces virus associations of Australian mosquitoes PLoS Negl Trop Dis. 2022 16 10 e0010768 10.1371/journal.pntd.0010768 36194577
Kain MP, Skinner EB, Athni TS, Ramirez AL, Mordecai EA, van den Hurk AF. Not all mosquitoes are created equal: A synthesis of vector competence experiments reinforces virus associations of Australian mosquitoes. PLoS Negl Trop Dis. 2022;16(10):e0010768.36194577 10.1371/journal.pntd.0010768
12. Lambrechts L Chevillon C Albright RG Thaisomboonsuk B Richardson JH Jarman RG Genetic specificity and potential for local adaptation between dengue viruses and mosquito vectors BMC Evol Biol. 2009 9 160 10.1186/1471-2148-9-160 19589156
Lambrechts L, Chevillon C, Albright RG, Thaisomboonsuk B, Richardson JH, Jarman RG, et al. Genetic specificity and potential for local adaptation between dengue viruses and mosquito vectors. BMC Evol Biol. 2009;9:160.19589156 10.1186/1471-2148-9-160
13. Viglietta M Bellone R Blisnick AA Failloux AB Vector specificity of arbovirus transmission Front Microbiol. 2021 12 773211 10.3389/fmicb.2021.773211 34956136
Viglietta M, Bellone R, Blisnick AA, Failloux AB. Vector specificity of arbovirus transmission. Front Microbiol. 2021;12:773211.34956136 10.3389/fmicb.2021.773211
14. Ciota AT Chin PA Ehrbar DJ Micieli MV Fonseca DM Kramer LD Differential effects of temperature and mosquito genetics determine transmissibility of arboviruses by Aedes aegypti in Argentina Am J Trop Med Hyg. 2018 99 2 417 424 10.4269/ajtmh.18-0097 29869610
Ciota AT, Chin PA, Ehrbar DJ, Micieli MV, Fonseca DM, Kramer LD. Differential effects of temperature and mosquito genetics determine transmissibility of arboviruses by Aedes aegypti in Argentina. Am J Trop Med Hyg. 2018;99(2):417–24.29869610 10.4269/ajtmh.18-0097
15. Mariconti M Obadia T Mousson L Malacrida A Gasperi G Failloux AB Estimating the risk of arbovirus transmission in Southern Europe using vector competence data Sci Rep. 2019 9 1 17852 10.1038/s41598-019-54395-5 31780744
Mariconti M, Obadia T, Mousson L, Malacrida A, Gasperi G, Failloux AB, et al. Estimating the risk of arbovirus transmission in Southern Europe using vector competence data. Sci Rep. 2019;9(1):17852.31780744 10.1038/s41598-019-54395-5
16. Beerntsen BT James AA Christensen BM Genetics of mosquito vector competence Microbiol Mol Biol Rev. 2000 64 1 115 137 10.1128/MMBR.64.1.115-137.2000 10704476
Beerntsen BT, James AA, Christensen BM. Genetics of mosquito vector competence. Microbiol Mol Biol Rev. 2000;64(1):115–37.10704476 10.1128/MMBR.64.1.115-137.2000
17. Hardy JL Houk EJ Kramer LD Reeves WC Intrinsic factors affecting vector competence of mosquitoes for arboviruses Ann Rev Entomol. 1983 28 1 229 262 10.1146/annurev.en.28.010183.001305 6131642
Hardy JL, Houk EJ, Kramer LD, Reeves WC. Intrinsic factors affecting vector competence of mosquitoes for arboviruses. Ann Rev Entomol. 1983;28(1):229–62.6131642 10.1146/annurev.en.28.010183.001305
18. Weaver SC Forrester NL Liu J Vasilakis N Population bottlenecks and founder effects: implications for mosquito-borne arboviral emergence Nat Rev Microbiol. 2021 19 3 184 195 10.1038/s41579-020-00482-8 33432235
Weaver SC, Forrester NL, Liu J, Vasilakis N. Population bottlenecks and founder effects: implications for mosquito-borne arboviral emergence. Nat Rev Microbiol. 2021;19(3):184–95.33432235 10.1038/s41579-020-00482-8
19. Obadia T Gutierrez-Bugallo G Duong V Nunez AI Fernandes RS Kamgang B Zika vector competence data reveals risks of outbreaks: the contribution of the European ZIKAlliance project Nat Commun. 2022 13 1 4490 10.1038/s41467-022-32234-y 35918360
Obadia T, Gutierrez-Bugallo G, Duong V, Nunez AI, Fernandes RS, Kamgang B, et al. Zika vector competence data reveals risks of outbreaks: the contribution of the European ZIKAlliance project. Nat Commun. 2022;13(1):4490.35918360 10.1038/s41467-022-32234-y
20. Chaves BA Godoy RSM Campolina TB Junior ABV Paz ADC Vaz EBDC Dengue infection susceptibility of five aedes aegypti populations from Manaus (Brazil) after challenge with virus serotypes 1–4 Viruses. 2021 14 1 20 10.3390/v14010020 35062224
Chaves BA, Godoy RSM, Campolina TB, Junior ABV, Paz ADC, Vaz EBDC, et al. Dengue infection susceptibility of five aedes aegypti populations from Manaus (Brazil) after challenge with virus serotypes 1–4. Viruses. 2021;14(1):20.35062224 10.3390/v14010020
21. Garrett-Jones C The prognosis for interruption of malaria transmission through assessment of the mosquito’s vectorial capacity Nature. 1964 204 1173 1175 10.1038/2041173a0 14268587
Garrett-Jones C. The prognosis for interruption of malaria transmission through assessment of the mosquito’s vectorial capacity. Nature. 1964;204:1173–5.14268587 10.1038/2041173a0
22. Macdonald G Epidemiologic models in studies of vetor-borne diseases: The re dyer lecture Public Health Rep. 1961 76 9 753 10.2307/4591271 13764730
Macdonald G. Epidemiologic models in studies of vetor-borne diseases: The re dyer lecture. Public Health Rep. 1961;76(9):753.13764730 10.2307/4591271
23. Lowe R Shirley N Bleackley M Dolan S Shafee T Transcriptomics technologies PLoS Comput Biol. 2017 13 e1005457 10.1371/journal.pcbi.1005457 28545146
Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T. Transcriptomics technologies. PLoS Comput Biol. 2017;13:e1005457.28545146 10.1371/journal.pcbi.1005457
24. Matthews BJ Dudchenko O Kingan SB Koren S Antoshechkin I Crawford JE Improved reference genome of Aedes aegypti informs arbovirus vector control Nature. 2018 563 501 507 10.1038/s41586-018-0692-z 30429615
Matthews BJ, Dudchenko O, Kingan SB, Koren S, Antoshechkin I, Crawford JE, et al. Improved reference genome of Aedes aegypti informs arbovirus vector control. Nature. 2018;563:501–7.30429615 10.1038/s41586-018-0692-z
25. Sim S Jupatanakul N Ramirez JL Kang S Romero-Vivas CM Mohammed H Transcriptomic profiling of diverse Aedes aegypti strains reveals increased basal-level immune activation in dengue virus-refractory populations and identifies novel virus-vector molecular interactions PLoS Negl Trop Dis. 2013 7 e2295 10.1371/journal.pntd.0002295 23861987
Sim S, Jupatanakul N, Ramirez JL, Kang S, Romero-Vivas CM, Mohammed H, et al. Transcriptomic profiling of diverse Aedes aegypti strains reveals increased basal-level immune activation in dengue virus-refractory populations and identifies novel virus-vector molecular interactions. PLoS Negl Trop Dis. 2013;7:e2295.23861987 10.1371/journal.pntd.0002295
26. Raddi G Barletta ABF Efremova M Ramirez JL Cantera R Teichmann SA Mosquito cellular immunity at single-cell resolution Science. 2020 369 1128 1132 10.1126/science.abc0322 32855340
Raddi G, Barletta ABF, Efremova M, Ramirez JL, Cantera R, Teichmann SA, et al. Mosquito cellular immunity at single-cell resolution. Science. 2020;369:1128–32.32855340 10.1126/science.abc0322
27. Behura SK Gomez-Machorro C Debruyn B Lovin DD Harker BW Romero-Severson J Influence of mosquito genotype on transcriptional response to dengue virus infection Funct Integr Genomics. 2014 14 581 589 10.1007/s10142-014-0376-1 24798794
Behura SK, Gomez-Machorro C, Debruyn B, Lovin DD, Harker BW, Romero-Severson J, et al. Influence of mosquito genotype on transcriptional response to dengue virus infection. Funct Integr Genomics. 2014;14:581–9.24798794 10.1007/s10142-014-0376-1
28. Shi C Zhao L Atoni E Zeng W Hu X Matthijnssens J Stability of the virome in lab- and field-collected Aedes albopictus mosquitoes across different developmental stages and possible core viruses in the publicly available virome data of Aedes mosquitoes mSystems. 2020 5 e00640 e00620 10.1128/mSystems.00640-20 32994288
Shi C, Zhao L, Atoni E, Zeng W, Hu X, Matthijnssens J, et al. Stability of the virome in lab- and field-collected Aedes albopictus mosquitoes across different developmental stages and possible core viruses in the publicly available virome data of Aedes mosquitoes. mSystems. 2020;5:e00640–e00620.32994288 10.1128/mSystems.00640-20
29. Chandler JA Liu RM Bennett SN RNA shotgun metagenomic sequencing of northern California (USA) mosquitoes uncovers viruses, bacteria, and fungi Front Microbiol. 2015 6 129043 10.3389/fmicb.2015.00185
Chandler JA, Liu RM, Bennett SN. RNA shotgun metagenomic sequencing of northern California (USA) mosquitoes uncovers viruses, bacteria, and fungi. Front Microbiol. 2015;6:129043.10.3389/fmicb.2015.00185
30. Batson J Dudas G Haas-Stapleton E Kistler AL Li LM Logan P Single mosquito metatranscriptomics identifies vectors, emerging pathogens and reservoirs in one assay eLife. 2021 10 e68353 10.7554/eLife.68353 33904402
Batson J, Dudas G, Haas-Stapleton E, Kistler AL, Li LM, Logan P, et al. Single mosquito metatranscriptomics identifies vectors, emerging pathogens and reservoirs in one assay. eLife. 2021;10:e68353.33904402 10.7554/eLife.68353
31. Agboka KM Wamalwa M Mutunga JM Tonnang HE A mathematical model for mapping the insecticide resistance trend in the Anopheles gambiae mosquito population under climate variability in Africa Sci Rep. 2024 14 1 9850 10.1038/s41598-024-60555-z 38684842
Agboka KM, Wamalwa M, Mutunga JM, Tonnang HE. A mathematical model for mapping the insecticide resistance trend in the Anopheles gambiae mosquito population under climate variability in Africa. Sci Rep. 2024;14(1):9850.38684842 10.1038/s41598-024-60555-z
32. Onyango MG Bialosuknia SM Payne AF Mathias N Kuo L Vigneron A Increased temperatures reduce the vectorial capacity of Aedes mosquitoes for Zika virus Emerg Microbes Infect. 2020 9 1 67 77 10.1080/22221751.2019.1707125 31894724
Onyango MG, Bialosuknia SM, Payne AF, Mathias N, Kuo L, Vigneron A, et al. Increased temperatures reduce the vectorial capacity of Aedes mosquitoes for Zika virus. Emerg Microbes Infect. 2020;9(1):67–77.31894724 10.1080/22221751.2019.1707125
33. Lambrechts L Paaijmans KP Fansiri T Carrington LB Kramer LD Thomas MB Impact of daily temperature fluctuations on dengue virus transmission by Aedes aegypti Proc Natl Acad Sci. 2011 108 18 7460 7465 10.1073/pnas.1101377108 21502510
Lambrechts L, Paaijmans KP, Fansiri T, Carrington LB, Kramer LD, Thomas MB, et al. Impact of daily temperature fluctuations on dengue virus transmission by Aedes aegypti. Proc Natl Acad Sci. 2011;108(18):7460–5.21502510 10.1073/pnas.1101377108
34. Catano-Lopez A Rojas-Diaz D Laniado H Arboleda-Sánchez S Puerta-Yepes ME Lizarralde-Bejarano DP An alternative model to explain the vectorial capacity using as example Aedes aegypti case in dengue transmission Heliyon. 2019 5 10 e02577 10.1016/j.heliyon.2019.e02577 31687486
Catano-Lopez A, Rojas-Diaz D, Laniado H, Arboleda-Sánchez S, Puerta-Yepes ME, Lizarralde-Bejarano DP. An alternative model to explain the vectorial capacity using as example Aedes aegypti case in dengue transmission. Heliyon. 2019;5(10):e02577.31687486 10.1016/j.heliyon.2019.e02577
35. Kramer LD Ciota AT Dissecting vectorial capacity for mosquito-borne viruses Curr Opin Virol. 2015 15 112 118 10.1016/j.coviro.2015.10.003 26569343
Kramer LD, Ciota AT. Dissecting vectorial capacity for mosquito-borne viruses. Curr Opin Virol. 2015;15:112–8.26569343 10.1016/j.coviro.2015.10.003
36. Anderson JR Rico-Hesse R Aedes aegypti vectorial capacity is determined by the infecting genotype of dengue virus Am J Trop Med Hyg. 2006 75 5 886 10.4269/ajtmh.2006.75.886 17123982
Anderson JR, Rico-Hesse R. Aedes aegypti vectorial capacity is determined by the infecting genotype of dengue virus. Am J Trop Med Hyg. 2006;75(5):886.17123982 10.4269/ajtmh.2006.75.886
37. Sirisena PDNN Noordeen F Evolution of dengue in Sri Lanka changes in the virus, vector, and climate Int J Infect Dis. 2014 19 6 12 10.1016/j.ijid.2013.10.012 24334026
Sirisena PDNN, Noordeen F. Evolution of dengue in Sri Lanka changes in the virus, vector, and climate. Int J Infect Dis. 2014;19:6–12.24334026 10.1016/j.ijid.2013.10.012
38. Udayanga L Gunathilaka N Iqbal M Abeyewickreme W Climate change induced vulnerability and adaption for dengue incidence in Colombo and Kandy districts: the detailed investigation in Sri Lanka Infect Dis Poverty. 2020 9 1 17 10.1186/s40249-020-00717-z 31996251
Udayanga L, Gunathilaka N, Iqbal M, Abeyewickreme W. Climate change induced vulnerability and adaption for dengue incidence in Colombo and Kandy districts: the detailed investigation in Sri Lanka. Infect Dis Poverty. 2020;9:1–17.31996251 10.1186/s40249-020-00717-z
39. Sirisena P Noordeen F Kurukulasuriya H Romesh TA Fernando L Effect of climatic factors and population density on the distribution of dengue in Sri Lanka: a GIS based evaluation for prediction of outbreaks PLoS ONE. 2017 12 1 e0166806 10.1371/journal.pone.0166806 28068339
Sirisena P, Noordeen F, Kurukulasuriya H, Romesh TA, Fernando L. Effect of climatic factors and population density on the distribution of dengue in Sri Lanka: a GIS based evaluation for prediction of outbreaks. PLoS ONE. 2017;12(1):e0166806.28068339 10.1371/journal.pone.0166806
40. Chathurangika P, Perera SSN, De Silva K. Estimating dynamics of dengue disease with environmental impact by quantifying the per-capita vector density. PREPRINT (Version 1) available at Research Square. 2024. 10.21203/rs.3.rs-4158187/v1.
41. National Dengue Control Unit. National Dengue Control Unit - Ministry of Health. 2024. https://www.dengue.health.gov.lk/?. Accessed 03 Aug 2024.
42. NASA. NASA POWER Data Access Viewer. 2024. Available from: https://power.larc.nasa.gov/data-access-viewer/. Accessed 07 Aug 2024.
43. Colombo Municipal Council. City of Colombo. 2024. https://www.colombo.mc.gov.lk/colombo.php. Accessed April 2024.
44. Derouich M Boutayeb A Twizell E A model of dengue fever Biomed Eng Online. 2003 2 1 10 10.1186/1475-925X-2-4 12605721
Derouich M, Boutayeb A, Twizell E. A model of dengue fever. Biomed Eng Online. 2003;2:1–10.12605721 10.1186/1475-925X-2-4
45. Lee T Modarres R Ouarda TB Data-based analysis of bivariate copula tail dependence for drought duration and severity Hydrol Process. 2013 27 10 1454 1463 10.1002/hyp.9233
Lee T, Modarres R, Ouarda TB. Data-based analysis of bivariate copula tail dependence for drought duration and severity. Hydrol Process. 2013;27(10):1454–63.10.1002/hyp.9233
46. Haario H Laine M Mira A Saksman E Dram: Efficient adaptive mcmc Stat Comput. 2006 16 339 354 10.1007/s11222-006-9438-0
Haario H, Laine M, Mira A, Saksman E. Dram: Efficient adaptive mcmc. Stat Comput. 2006;16:339–54.10.1007/s11222-006-9438-0
47. Franklinos LH Jones KE Redding DW Abubakar I The effect of global change on mosquito-borne disease Lancet Infect Dis. 2019 19 9 e302 e312 10.1016/S1473-3099(19)30161-6 31227327
Franklinos LH, Jones KE, Redding DW, Abubakar I. The effect of global change on mosquito-borne disease. Lancet Infect Dis. 2019;19(9):e302–12.31227327 10.1016/S1473-3099(19)30161-6
48. Pandey A Mubayi A Medlock J Comparing vector-host and SIR models for dengue transmission Math Biosci. 2013 246 2 252 259 10.1016/j.mbs.2013.10.007 24427785
Pandey A, Mubayi A, Medlock J. Comparing vector-host and SIR models for dengue transmission. Math Biosci. 2013;246(2):252–9.24427785 10.1016/j.mbs.2013.10.007
49. Zeng Q Yu X Ni H Xiao L Xu T Wu H Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm PLOS Negl Trop Dis. 2023 17 6 e0011418 10.1371/journal.pntd.0011418 37285385
Zeng Q, Yu X, Ni H, Xiao L, Xu T, Wu H, et al. Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm. PLOS Negl Trop Dis. 2023;17(6):e0011418.37285385 10.1371/journal.pntd.0011418
50. Kong L Wang J Li Z Lai S Liu Q Wu H Modeling the heterogeneity of dengue transmission in a city Int J Environ Res Public Health. 2018 15 6 1128 10.3390/ijerph15061128 29857503
Kong L, Wang J, Li Z, Lai S, Liu Q, Wu H, et al. Modeling the heterogeneity of dengue transmission in a city. Int J Environ Res Public Health. 2018;15(6):1128.29857503 10.3390/ijerph15061128
51. Rashkov P Venturino E Aguiar M Stollenwerk N Kooi BW On the role of vector modeling in a minimalistic epidemic model Math Biosci Eng. 2019 16 5 4314 4338 10.3934/mbe.2019215 31499664
Rashkov P, Venturino E, Aguiar M, Stollenwerk N, Kooi BW, et al. On the role of vector modeling in a minimalistic epidemic model. Math Biosci Eng. 2019;16(5):4314–38.31499664 10.3934/mbe.2019215
52. Lourenco-de Oliveira R Rua AV Vezzani D Willat G Vazeille M Mousson L Aedes aegypti from temperate regions of South America are highly competent to transmit dengue virus BMC Infect Dis. 2013 13 1 8 10.1186/1471-2334-13-610 23280237
Lourenco-de Oliveira R, Rua AV, Vezzani D, Willat G, Vazeille M, Mousson L, et al. Aedes aegypti from temperate regions of South America are highly competent to transmit dengue virus. BMC Infect Dis. 2013;13:1–8.23280237 10.1186/1471-2334-13-610
