Forecasting dengue incidence based on entomological indices, population density, and meteorological and environmental variables in the Gampaha District of Sri Lanka

The occurrence of infectious diseases and the high transmissibility of such infections is one of the unavoidable public issues in the world. The issue would be more severe if the transmission is multifactorial and there is no curative or prophylactic treatment for such infection. Dengue is an infectious mosquito-borne viral disease found in tropical and sub-tropical climates worldwide, and it mainly exists in urban and semi-urban environmental setups [1World Health Organization (2021). Dengue and severe dengue. Available at; https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue. (Accessed 5 December 2023)Google Scholar]. A virus causes dengue under the Flaviviridae family, which has four distinct serotypes [1World Health Organization (2021). Dengue and severe dengue. Available at; https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue. (Accessed 5 December 2023)Google Scholar, 2Murray N.E. Quam M.B. Wilder-Smith A. Epidemiology of dengue: past, present and future prospects.Clin Epidemiol. 2013; 5: 299-309Crossref PubMed Scopus (747) Google Scholar] transmitted by mosquitoes belonging to the genus Aedes. Dengue has been identified as a fast-spreading infection in the world, and about half of the world's population is now at risk of dengue, with an estimated 100–400 million infections occurring yearly [3World Health Organization. (2023). WHO Fact sheet; Dengue and severe dengue. Available at; https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue. (Accessed 5 December 2023).Google Scholar]. It has been evidenced that the number of dengue cases reported to WHO has increased over eight-fold over the last two decades [1World Health Organization (2021). Dengue and severe dengue. Available at; https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue. (Accessed 5 December 2023)Google Scholar]. Thus far, no successful treatment has been introduced for this infection. Therefore, reducing vector breeding habitats and suppressing disease vectors have been the main focus of disease control programmes worldwide. In control programmes, systematized vector surveillance programmes and interpretations by the entomological indices are the backbone of such efforts. Therefore, control approaches aided with scientific evidence would provide a better outcome in control interventions. In dengue vector surveillance and control programmes, larval-based indices such as House Index (HI), Breteau Index (BI), and Container Index (CI) are widely used in decision-making and disease prediction approaches [4Udayanga L. Gunathilaka N. Iqbal M.C.M. Najim M.M.M. Pahalagedara K. Abeyewickreme W. Empirical optimization of risk thresholds for dengue: an approach towards entomological management of Aedes mosquitoes based on larval indices in the Kandy District of Sri Lanka.Parasit Vectors. 2018; 11: 368Crossref PubMed Scopus (21) Google Scholar, 5Udayanga L. Aryaprema S. Gunathilaka N. Iqbal M.C.M. Fernando T. Abeyewickreme W. Larval Indices of Vector Mosquitoes as Predictors of Dengue Epidemics: An Approach to Manage Dengue Outbreaks Based on Entomological Parameters.Biomed Res Int. 2020; 20206386952Crossref Scopus (5) Google Scholar, 6Focks, DA. (2004). UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases. A review of entomological sampling methods and indicators for dengue vectors. World Health Organization. Available at; https://apps.who.int/iris/handle/10665/68575 (Accessed 12 November 2023).Google Scholar, 7Morales-Pérez A. Nava-Aguilera E. Hernández-Alvarez C. Alvarado-Castro V.M. Arosteguí J. Legorreta-Soberanis J. Flores-Moreno M. Morales-Nava L. Harris E. Ledogar R.J. Andersson N. Cockcroft A. Utility of entomological indices for predicting transmission of dengue virus: secondary analysis of data from the Camino Verde trial in Mexico and Nicaragua.PLoS Negl Trop Dis. 2020; 14e0008768Crossref PubMed Scopus (5) Google Scholar]. On the other hand, these indices can estimate the likelihood of disease transmission and ultimately set thresholds for initiating vector control interventions in a given area based on larval concentrations. The BI is primarily used as a decision-making parameter for vector management programmes in Sri Lanka. According to the current practice in Sri Lanka, chemical fogging is advised when the BI value is > 5 with reported dengue cases or BI > 20, even if there is no dengue case [8National Dengue Control Unit, Sri Lanka (2016). Intensive Inter-Sectoral Programme for the Prevention and Control of Dengue. National Dengue Control Unit, Sri Lanka: Colombo; 2016.Google Scholar]. Nonetheless, this national standard uses common threshold values to handle dengue epidemics, which is typically ineffective due to the dynamics of local vector populations that differ from place to place [7Morales-Pérez A. Nava-Aguilera E. Hernández-Alvarez C. Alvarado-Castro V.M. Arosteguí J. Legorreta-Soberanis J. Flores-Moreno M. Morales-Nava L. Harris E. Ledogar R.J. Andersson N. Cockcroft A. Utility of entomological indices for predicting transmission of dengue virus: secondary analysis of data from the Camino Verde trial in Mexico and Nicaragua.PLoS Negl Trop Dis. 2020; 14e0008768Crossref PubMed Scopus (5) Google Scholar]. Prediction of novel or re-emerging infectious diseases can effectively control and prevent large-scale outbreaks and epidemics. In predicting an infectious disease, the main work is to collect and analyze information using statistical/mathematical approaches and estimate temporal/spatial transmission/regular epidemic patterns [9Yang W. Deng M. Li C. Huang J. Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China.Int J Environ Res Public Health. 2020; 17: 2563Crossref PubMed Scopus (41) Google Scholar, 10Bai Z. Gong Y. Tian X. Cao Y. Liu W. Li J. The Rapid Assessment and Early Warning Models for COVID-19.Virol Sin. 2020; 35: 272-279Crossref PubMed Scopus (33) Google Scholar]. In Sri Lanka, there are several attempts have been made, which include mathematical modeling [11Withanage G.P. Viswakula S.D. Nilmini Silva Gunawardena Y.I. Hapugoda M.D. A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka.Parasites Vectors. 2018; 11: 262Crossref PubMed Scopus (31) Google Scholar, 12Kavinga H.W.B. Jayasundara D.D.M. Jayakody D.N.K. A New Dengue Outbreak Statistical Model using the Time Series Analysis.Eur J Eng Sci Tech. 2013; 2: 35-52Google Scholar, 13Gnanapragasam V.J. Lophatananon A. Wright K.A. Muir K.R. Gavin A. Greenberg D.C. Improving Clinical Risk Stratification at Diagnosis in Primary Prostate Cancer: A Prognostic Modelling Study.PLoS Med. 2016; 13e1002063Crossref PubMed Scopus (55) Google Scholar], ARIMA-based modeling based on the larval indices [4Udayanga L. Gunathilaka N. Iqbal M.C.M. Najim M.M.M. Pahalagedara K. Abeyewickreme W. Empirical optimization of risk thresholds for dengue: an approach towards entomological management of Aedes mosquitoes based on larval indices in the Kandy District of Sri Lanka.Parasit Vectors. 2018; 11: 368Crossref PubMed Scopus (21) Google Scholar, 14Attanayake A.M.C.H. Perera S.S.N. Forecasting COVID-19 Cases Using Alpha-Sutte Indicator: A Comparison with Autoregressive Integrated Moving Average (ARIMA) Method.Biomed Res. Int. 2020; 20208850199Crossref PubMed Scopus (7) Google Scholar], and meteorological variables [15Chandrakantha L. Risk Prediction Model for Dengue Transmission Based on Climate Data: Logistic Regression Approach.Stats. 2019; 2: 272-283Crossref Google Scholar]. Some studies have emphasized that dengue incidence is significantly related to meteorological variables such as maximum monthly temperature [16Wu P.C. Guo H.R. Lung S.C. Lin C.Y. Su H.J. Weather as an effective predictor for occurrence of dengue fever in Taiwan.Acta Trop. 2007; 103: 50-57Crossref PubMed Scopus (221) Google Scholar], minimum monthly temperature [16Wu P.C. Guo H.R. Lung S.C. Lin C.Y. Su H.J. Weather as an effective predictor for occurrence of dengue fever in Taiwan.Acta Trop. 2007; 103: 50-57Crossref PubMed Scopus (221) Google Scholar], weekly average maximum temperature [17Goto K. Kumarendran B. Mettananda S. Gunasekara D. Fujii Y. Kaneko S. Analysis of effects of meteorological factors on dengue incidence in Sri Lanka using time series data.PLoS One. 2013; 8e63717Crossref Scopus (60) Google Scholar], relative humidity [16Wu P.C. Guo H.R. Lung S.C. Lin C.Y. Su H.J. Weather as an effective predictor for occurrence of dengue fever in Taiwan.Acta Trop. 2007; 103: 50-57Crossref PubMed Scopus (221) Google Scholar], and rainfall with a two-month lag [11Withanage G.P. Viswakula S.D. Nilmini Silva Gunawardena Y.I. Hapugoda M.D. A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka.Parasites Vectors. 2018; 11: 262Crossref PubMed Scopus (31) Google Scholar, 15Chandrakantha L. Risk Prediction Model for Dengue Transmission Based on Climate Data: Logistic Regression Approach.Stats. 2019; 2: 272-283Crossref Google Scholar, 16Wu P.C. Guo H.R. Lung S.C. Lin C.Y. Su H.J. Weather as an effective predictor for occurrence of dengue fever in Taiwan.Acta Trop. 2007; 103: 50-57Crossref PubMed Scopus (221) Google Scholar]. Several other studies used meteorological and vector density variables to forecast dengue disease outbreaks [12Kavinga H.W.B. Jayasundara D.D.M. Jayakody D.N.K. A New Dengue Outbreak Statistical Model using the Time Series Analysis.Eur J Eng Sci Tech. 2013; 2: 35-52Google Scholar]. However, only a few studies have used solely vector indices correlated with dengue incidence [4Udayanga L. Gunathilaka N. Iqbal M.C.M. Najim M.M.M. Pahalagedara K. Abeyewickreme W. Empirical optimization of risk thresholds for dengue: an approach towards entomological management of Aedes mosquitoes based on larval indices in the Kandy District of Sri Lanka.Parasit Vectors. 2018; 11: 368Crossref PubMed Scopus (21) Google Scholar, 18Ong J. Aik J. Ng L.C. Short Report: Adult Aedes abundance and risk of dengue transmission.PLoS Negl Trop Dis. 2021; 15e0009475Crossref Scopus (16) Google Scholar]. The main objective of developing a prediction mechanism is to forecast distribution patterns and magnitude of dengue outbreaks to assess the risk of an epidemic. However, many of the attempts conducted in Sri Lanka have been accompanied by limited surveillance data collected from a few sampling locations for a short period. Further, all of these threshold values for disease prediction may be subjective to different places or localities depending on various elements such as environmental conditions, climate, geography, and socioeconomic/cultural behaviour of the community. Therefore, it is necessary to construct threshold values based on regional or city-level data to successfully perform vector control efforts based on entomological indices [6Focks, DA. (2004). UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases. A review of entomological sampling methods and indicators for dengue vectors. World Health Organization. Available at; https://apps.who.int/iris/handle/10665/68575 (Accessed 12 November 2023).Google Scholar, 7Morales-Pérez A. Nava-Aguilera E. Hernández-Alvarez C. Alvarado-Castro V.M. Arosteguí J. Legorreta-Soberanis J. Flores-Moreno M. Morales-Nava L. Harris E. Ledogar R.J. Andersson N. Cockcroft A. Utility of entomological indices for predicting transmission of dengue virus: secondary analysis of data from the Camino Verde trial in Mexico and Nicaragua.PLoS Negl Trop Dis. 2020; 14e0008768Crossref PubMed Scopus (5) Google Scholar, 19Bowman L.R. Runge-Ranzinger S. McCall P.J. Assessing the relationship between vector indices and dengue transmission: a systematic review of the evidence.PLoS Negl Trop Dis. 2014; 8e2848Crossref PubMed Scopus (218) Google Scholar]. Unfortunately, such crucial thresholds for the larval indices have rarely been found, which would allow for the effective adoption of preventative measures during a dengue outbreak. Meanwhile, selecting suitable prediction methods is the premise of implementing prevention and control of infectious diseases. It indirectly reduces the incidence rate and mortality and decreases economic/social losses, which lay the foundation for relevant follow-up policies. Therefore, the present study was conducted to establish localized threshold levels to reflect the frequency of dengue outbreaks and facilitate decision-making and implementation of effective, economic, and sustainable dengue vector control measures in the district of Gampaha using empirical-based modeling approaches. Gampaha District (7°05′22″ N, 80o 04'E) is located in the Western Province of Sri Lanka, covering an area of 1387 km2. It has a human population 2,574,324, recorded as the highest residential population in Sri Lanka. The annual rainfall is about 2500 mm, mainly received during two monsoonal periods from April to June and October to December [20Alahakoon N. Edirisinghe M. Spatial Variability of Rainfall Trends in Sri Lanka from 1989 to 2019 as an Indication of Climate Change.Int. J. Geogr. Inf. 2021; 10: 84Google Scholar]. The mean average temperature ranges from 22.7 to 34.3°C. In Sri Lanka, Gampaha District reports the second-highest dengue incidence in the country during the past two decades. Therefore, Gampaha District was selected for the present investigation. In selecting sites, areas with different environmental settings, such as urban, suburban, and rural, were selected randomly. "Urban areas" were defined as areas within towns and cities with a high population density, a low per capita land consumption, a low household land consumption, a low agricultural land consumption and increased reliance on piped-born water supplies. In contrast, the areas located outside towns and cities that are typically less developed, with a more significant proportion of land covered by agriculture and natural vegetation, a higher per capita/ household land consumption, and a greater reliance on well water were considered "Rural." Areas with mixed characteristics were considered "Suburban" [21Census and Statistics, Sri Lanka (2021). Available at http://www.statistics.gov.lk/ (Accessed 21 April 2023).Google Scholar, 22Ndenga B.A. Mutuku F.M. Ngugi H.N. Mbakaya J.O. Aswani P. Musunzaji P.S. Vulule J. Mukoko D. Kitron U. LaBeaud A.D. Characteristics of Aedes aegypti adult mosquitoes in rural and urban areas of western and coastal Kenya.PLoS One. 2017; 12e0189971Crossref PubMed Scopus (55) Google Scholar, 23Weeraratne B. Re-Defining Urban Areas in Sri Lanka.Working Paper Series No. 23, Institute of Policy Studies of Sri Lanka. 2016; Google Scholar]. The criteria used for the site classification is indicated in Table 1.Table 1Classification of Urban, Suburban, and, Rural areasEnvironmental settingLand area (km2)Household land consumption (Hectares)Per capita land consumption (Hectares)Household and agricultural land extend (Hectares)Population density (per km2)Pipe born water supply (no of households)Urban<100<0.1<0.01<1500>2500>5000Suburban100 -1500.1 - 0.30.01 - 0.11500 - 20001500-25003000 - 5000Rural>150>0.3>0.1>2000<1500<3000 Open table in a new tab Based on those criteria, three MOH areas in the Gampaha district were selected to represent Urban (Kelaniya, Negombo, and Wattala), Suburban (Attanagalla, Minuwangoda, and Gampaha) and Rural (Dompe, Mirigama, and Divulapitiya) environmental settings. In each selected MOH area, four sentinel sites (as Grama Niladhari [GN] divisions) were identified for entomological surveillance (Figure 1). The human population, population density, dengue incidence over the last five years, and feasibility of field operations to collect relevant data were considered factors in selecting the sentinel sites. Entomological surveys were conducted monthly from April 2016 to December 2019 using standard entomological techniques according to the guidelines specified by the World Health Organization [24World Health OrganizationTechnical handbook for dengue surveillance, dengue outbreak prediction/detection and outbreak response ("model contingency plan").World Health Organization. 2016; (978 92 4 154973 8)Google Scholar]. At each sampling attempt, a location was selected, considering a central point for entomological surveillance. The surveys were conducted, and random surveillance was continued within a 200 m to 300 m radius. The reported number of dengue cases, case density, and incidence data of selected MOH areas from 2014 to 2019 were collected in all the nine MOH areas chosen for the study from the Regional Director of Health Services office, Gampaha. The larval indices for 2014-2016 in the Gampaha district were also collected. Selected meteorological data (Monthly average rainfall, number of rainy days, monthly average temperature, and monthly average relative humidity) collected from the Katunayake monitoring station in the Gampaha District from January 2014 to December 2019 were obtained from the Department of Meteorology, Colombo 07, Sri Lanka. Since the traditional Stegomyia indices are considered important measures [24World Health OrganizationTechnical handbook for dengue surveillance, dengue outbreak prediction/detection and outbreak response ("model contingency plan").World Health Organization. 2016; (978 92 4 154973 8)Google Scholar] for monitoring dengue vector populations and forecasting dengue outbreaks, the Breteau Index (BI) was used as one of the explanatory variables of the dengue forecasting model. It was assumed that vector density and meteorological variables such as rainfall, temperature, and humidity relate directly and indirectly to the occurrence of dengue cases within the district. The temporal patterns of mosquito species presence were studied using time series models built for each species, and cross-correlation was employed separately to determine the lagging effect for each covariate. The meteorological variables such as monthly average rainfall, number of rainy days per month, monthly average temperature and monthly average relative humidity were used to capture the periodic pattern of dengue dynamics. Further, vector indices such as the Breteau Index Ae. aegypti (BIA) and Breteau Index Ae. albopictus (BIB) were also considered as explanatory variables. The case incidence (number of reported cases per 100,000 population) was used as a response variable to develop the model because it provides more reliable data than direct dengue patient counts. Pearson correlation analysis with cross-correlation (CCF) was performed to investigate the relationship between meteorological variables and vector indices on dengue case incidence. Only significant associations were further examined using regressions to determine the best predictor variables associated with the case incidence. The CCF analysis revealed that dengue case incidence had a medium significant positive correlation with average rainfall and average relative humidity (r= 0.511 to r=0.566) at a two-month lag. Therefore, all other meteorological variables (such as the number of rainy days and average monthly temperature) were ignored from further consideration of the model development due to multi-collinearity in the meteorological variables used in the study (Table 2).Table 2Cross-correlation function summary for case incidence with meteorological variables and vector indicesMonthly Lag periodExplanatory variablesAverage rainfall /mmNumber of rainy days/ monthsMonthly average Relative humidityMonthly average temperature / 0CBreteau Index for Aedes aegyptiBreteau Index for Aedes albopictus-30.1310.0050.017-0.053-0.170-0.383-2-0.224-0.412-0.400-0.095-0.532-0.457-1-0.367-0.430-0.390-0.194-0.388-0.0930-0.206-0.187-0.052-0.2320.2940.54310.3010.3840.471-0.2180.7770.80220.5660.4400.5010.0090.4330.31430.1980.1630.1440.249-0.163-0.202*The highest significant positive correlations were highlighted in red Open table in a new tab *The highest significant positive correlations were highlighted in red All meteorological and entomological variables were standardized before being used in the models. Based on the CCF data analysis, four models were developed under each environmental setup to forecast dengue outbreak situations in the district using multiple linear regression (MLR). This mathematical technique employs multiple explanatory variables to predict the result of a response variable. Case incidence without a lag period [DI (t)] was used as the response variable during the model development. The BIA with one-month lag [ BIA (t-1)], BIB with one-month lag [BIB (t-1)], monthly average rainfall at lag two [RFavg (t-2)], and monthly average humidity with two-month lag period [RHavg (t-2)] were used as the explanatory variables based on the findings according to the past literature [4Udayanga L. Gunathilaka N. Iqbal M.C.M. Najim M.M.M. Pahalagedara K. Abeyewickreme W. Empirical optimization of risk thresholds for dengue: an approach towards entomological management of Aedes mosquitoes based on larval indices in the Kandy District of Sri Lanka.Parasit Vectors. 2018; 11: 368Crossref PubMed Scopus (21) Google Scholar, 11Withanage G.P. Viswakula S.D. Nilmini Silva Gunawardena Y.I. Hapugoda M.D. A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka.Parasites Vectors. 2018; 11: 262Crossref PubMed Scopus (31) Google Scholar, 15Chandrakantha L. Risk Prediction Model for Dengue Transmission Based on Climate Data: Logistic Regression Approach.Stats. 2019; 2: 272-283Crossref Google Scholar, 16Wu P.C. Guo H.R. Lung S.C. Lin C.Y. Su H.J. Weather as an effective predictor for occurrence of dengue fever in Taiwan.Acta Trop. 2007; 103: 50-57Crossref PubMed Scopus (221) Google Scholar, 25Sun W. Xue L. Xie X. Spatial-temporal distribution of dengue and climate characteristics for two clusters in Sri Lanka from 2012 to 2016.Sci Rep. 2017; 712884Google Scholar] and Cross-Correlation Analysis performed. Four models were developed using stepwise multiple linear regression correlation with selected parameters (Table 3).Table 3Explanatory variables used in different prediction models in the studyModelVariables usedExplanatory variablesRegression equationsModel IBI for Ae. aegyptiBI for Ae. albopictusAll selected meteorological variablesBIA, BIB,RFavg, RHavgModel IIBI for Ae. aegyptiBI for Ae. albopictusMonthly average RFBIA,BIB, RFavgModel IIIBI for Ae. aegyptiBI for Ae. albopictusMonthly average RHBIA,BIB,RHavgModel IVBI for Ae. aegyptiBI for Ae. albopictusBIA, BIBDI (t): Case incidence without a lag period, BIA(t-1): Breteau Index Ae. aegypti at lag 1, BIB (t-1): Breteau Index Ae. albopictus at lag 1, RFavg (t-2): Monthly average rainfall at lag 2, RHavg (t-2): Monthly average relative humidity at lag 2. Open table in a new tab DI (t): Case incidence without a lag period, BIA(t-1): Breteau Index Ae. aegypti at lag 1, BIB (t-1): Breteau Index Ae. albopictus at lag 1, RFavg (t-2): Monthly average rainfall at lag 2, RHavg (t-2): Monthly average relative humidity at lag 2. At each time, Pearson's Coefficient of Correlation (r) was used to determine how well the data fit the regression model, and the value R-squared and adjusted R-squared were calculated. The value of r is always between -1 and +1. When the r value approaches zero, the correlation is said to be unrelated. If the value of r is +1, the variables are strongly correlated and if the value of r is -1, the variables are said to be negatively correlated. The P-value was also calculated to determine the significance of each explanatory variable. The SPSS version 26 was used to develop the model in MLR. Each time, heteroscedasticity and normality of residuals were tested to ascertain the influence of explanatory (independent) variables in the model. Heteroscedasticity was determined by mapping the residual value against the explanatory variables and normality was determined using the Kolmogorov- Smirnov and Shapiro- Wilk tests. The log-transformed case incidence (Lg DI) was employed for subsequent analysis. Leave-one-out cross-validation approach was used to determine the fitness of the model [26Stone M. Cross-validatory choice and assessment of statistical predictions.Journal of the Royal Statistical Society: Series. 1974; 36: 111-147Crossref Google Scholar]. In each iteration, one sample is left out (for a validation set), and the rest of the data (a training set) was used to fit the model. The procedure is repeated until every year has been used for a validation set. The distribution of residuals of the developed models was analyzed and normality plots were created to validate fitted models. The model was tested using three different MOH areas representing each environmental configuration from 2014 to 2018. The MOH areas of Jaela, Mahara, and Katana were chosen to represent urban, suburban, and rural settings, respectively. Spearman rank correlation test was performed to analyze the relationship between actual and predicted cases at each environmental setup. The model's good fit was further examined by plotting the model-fitted predicted values with actual reported dengue cases in the field. The epidemic level was visualized separately for each environmental setup using the epidemic channel concept when developing area-specific thresholds for predicting dengue outbreaks. Based on the quartile values (Q) taken from 2013-2016 data of the MOH area in the Gampaha district, the MOH areas were classified into three risk zones based on the case incidence. The BI for both the vectors indicated a seasonal pattern from May- June and October- November each year, with two distinct peaks in urban settings (Figure 2), with a significant peak in the latter part of the year by Ae. aegypti. However, in suburban (Figure 3) and rural areas (Figure 4), a major peak was observed mainly during the May – June period, followed by the first inter monsoonal rain in the April -May season.Figure 3Temporal distribution of Ae. aegypti and Ae. albopictus in suburban settings of the District of Gampaha from 2014-2019.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure 4Temporal distribution of Ae. aegypti and Ae. albopictus in rural settings of the District of Gampaha for the period of 2014-2019.View Large Image Figure ViewerDownload Hi-res image Download (PPT) The climatic variables, except temperature, demonstrated statistically significant, weak (0.20- 0.30) to moderate (0.40 – 0.59) positive correlation with Ae. aegypti, Ae. albopictus and Cumulative BI in urban and suburban areas at a 95% confidence level. On most occasions, the positive association was strongest with relative humidity and weakest with maximum monthly rainfall. It was also noted that the correlation of climatic variables with BI was statistically insignificant in rural areas except for relative humidity and cumulative BI, which demonstrated a weak correlation (r= 0.236; P= 0.050). A more systematic Cross-Association Function analysis revealed that Ae. aegypti and Ae. albopictus had a medium to high correlation with rainfall parameters and relative humidity at a one-month lag time and the monthly average temperature at a two-month lag time in urban settings (Figures 5 and 6). Both Ae. aegypti (r= 0.679) and Ae. albopictus (r= 0.669) demonstrated a higher cross-correlation with the number of rainy days than other climatic variables with a one-month lagging effect. During the study period, Ae. aegypti (r= 0.372) and Ae. albopictus (r= 0.584) showed a stronger positive correlation with the number of rainy days in suburban regions comparable to urban settings.Figure 6Cross-correlation analysis (CCF) between climatic variables [(a) monthly average rainfall / mm, (b) Monthly total rainfall / mm, (c) monthly maximum rainfall / mm, (d) monthly average relative humidity, (e) number of rainy days per month, (f) Monthly average temperature] with Ae. albopictus Breteau Index (urban). * The highest significant correlation was marked in greenView Large Image Figure ViewerDownload Hi-res image Download (PPT) The residual normal probability plot depicts a realistically straight line, whereas the residual sequence plot depicts a consistent distribution of errors around zero within ± 1. These findings demonstrate that residuals followed a normal distribution throughout the research period, confirming all approved models for each environmental configuration (Figure 7). Seven dengue outbreaks (Case incidence > 44) were observed in the Jaela MOH region throughout the model development period. The suggested model for urban environmental setup accurately predicted all outbreaks occurring over the 58-month testing period (Figure 8). When applied to suburban regions of Gampaha district; Biyagama MOH area, the model indicated seven out of eight epidemics occurred in (case incidence >26) with an accuracy of 88% (Figure 9), whereas in rural areas, Katana MOH area, the generated model predicted all seven outbreaks (case incidence > 18) with an accuracy of 100% preciseness (Figure 10).Figure 8The goodness of fitness of the model was developed for urban settings in the Gampaha district. Fitted and forecasted dengue case incidence from the developed model illustrated for a period of