Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

DOI: https://doi.org/10.21203/rs.2.15755/v2

Abstract

Background: Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. Methods: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. These factors were used as input parameters for machine learning models. The models were then tested and evaluated on the basis of four-years data (January 2010 to December 2013) collected in Malaysia. Results: This research has two major contributions. A new risk factor, called the TempeRain Factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% and reduced the root-mean-square error to 0.26 for predicting dengue outbreaks.

introduction

Pandemic infectious diseases are spreading in many geographical areas. The World Health Organisation (WHO) has reported that dengue fever is one of the most important mosquito-borne diseases. Dengue fever is a common problem and one of the deadliest infectious diseases worldwide. WHO has identified dengue fever as a major rapidly spreading mosquito-borne illness caused by the dengue virus. Accordingly, this disease is a threat and poses severe risk to human populations in tropical and subtropical regions [1- 6]. Health organisations should have a prediction and early warning system to control and monitor dengue fever [7]. Member states in the three WHO regions regularly reported an increase in the annual number of cases from 2.2 million in 2010 to 3.2 million in 2015 [8].

Moreover, WHO estimated an annual projection of 50–100 million dengue infections worldwide. An annual mortality of approximately 20,000–22,000 deaths caused by dengue fever has also been reported [8,9]. In contrast with yellow fever or other mosquito-borne diseases, no vaccine or treatment is available against all serotypes of the dengue virus, and no antiviral drug for treating dengue fever has been reported yet [10]. The only alternative is to prevent or control the outbreak of this disease.

The accuracy of a prediction system for outbreaks is the primary and important concern for controlling dengue fever [11]. Thus, establishing related risk factors is critical for prediction systems [12]. Given that climate factors play a key role in this disease, identifying the relation between weather information and dengue outbreak incidence is a major task in establishing an accurate prediction system for future outbreaks [13,14]. In the current study, important climatic risk factors, such as temperature, relative humidity and rainfall amount, were examined. The current accuracy for prediction systems based on climate factors ranges from 82.39% to 90.5% [12,15-20].

This study is essential because it identifies the critical climatic risk factors in dengue outbreak prediction, i.e. the TempeRain factor (TRF). Then, the identified critical factors (TRF) were applied to prediction models, increasing the accuracy of prediction and reducing the error of prediction models. This process is expected to particularly help authorised organisations or decision makers in health organisations, governments and other concerned groups to become aware and develop improved prevention programmes in the near future.

background

Related works

A recent study from WHO indicated that 390 million dengue infections occur annually (95% credible interval of 284–528 million); among which, 96 million (67–136 million) are manifested clinically with any severity of the disease [21]. Another study on the prevalence of dengue fever has estimated that 3.9 billion people in 128 countries are at risk of infection from dengue virus [22]. As of December 2018, the Ministry of Health (MOH) of Malaysia has recorded approximately 80,615 dengue cases with 147 deaths compared with 19,884 cases in December 2011 with 36 deaths [23]. The number of cases increased approximately fourfold. By the end of March 2019, 39,805 cases of dengue with 64 deaths were reported in Malaysia compared with 16,917 cases with 34 deaths in March 2018 [24].

Various early warning and monitoring systems are currently implemented to monitor dengue outbreaks worldwide. Dengue prediction models have been previously investigated, but some of these models still exhibit limitations in achieving high accuracy in dengue outbreak prediction [11,25]. Different models and techniques have been integrated into the design of several models for predicting dengue outbreaks. A number of studies have also established prediction models for dengue outbreaks using artificial neural networks [12].

Hybrid models have been used in outbreak prediction research. A hybrid model is an example of an integrated model, and many models based on genetic algorithms are available to determine the weight in a neural network model [11,13,14,20,26]. In Singapore, researchers found significant correlated dengue cases with climatic variables by using a Poisson regression model [27]. One researcher [17] developed a dengue outbreak prediction system in Singapore and obtained 90% accuracy. Thitiprayoonwongse established another prediction system based on a decision tree [18]. Different models of dengue outbreak prediction systems in Malaysia have achieved different accuracies [12,20].

Vulnerability maps of dengue incidences have been generated in Malaysia, resulting in the development and implementation of visualised and predictive modelling using geographic information systems (GIS) for dengue fever in Selangor, Malaysia [28]. One research in Indonesia was concerned with dengue outbreak prediction using a GIS-based early warning system [15]. Another study from the National Taipei University of Technology used C-support vector classification to forecast dengue fever epidemics in Taiwan, and the accuracy of radial basis function (RBF) model was 90.5% [16]. In 2015, Loshini et al. predicted localised dengue incidences in Malaysia using an ensemble system for identification and found that ensemble models exhibit better prediction power than a single model [29].

The prediction of dengue outbreaks is crucial worldwide because this infectious disease remains as a major issue in many countries [11,26,30,31]. Table 1 lists studies on different models of dengue outbreak prediction with distinct climatic risk factors. The asterisk (*) in the columns of the table denotes the risk factors used in different studies.

Table 1: Risk Factors for Dengue Outbreak Prediction Models

Most studies on dengue fever were conducted in Asian countries, such as East–West Asia and the Pacific Ocean regions. WHO reported that countries in East–West Asia, such as Malaysia, Singapore, Taiwan, Indonesia, Bangladesh and Thailand, are critical areas for dengue fever. Most studies have shown that temperature and rainfall directly and significantly affect dengue outbreaks [14,20,26,30,31].

Moreover, changing climatic factors, such as increasing temperature, rainfall and humidity, are the most influential driving forces of dengue virus transmission [31]. One study correlated dengue cases with climatic variables in the city of Singapore and the model for dengue cases was considered the dependent variable; meanwhile, climatic variables, such as rainfall, maximum and minimum temperatures and relative humidity, were considered independent variables [27]. On the basis of the grade of each risk factor used in the 22 references listed in Table 1, most studies primarily used total rainfall (17 studies), average temperature (16 studies), relative humidity (15 studies), minimum temperature (11 studies) and maximum temperature (10 studies) as inputs of prediction models. However, none of the studies focused on the detailed analysis of the factors nor investigated the detailed relationship that can exist amongst factors.

This research aims to describe the dengue prediction system accuracy and the level of risk factors that contribute to a dengue outbreak prediction system and identify the associations amongst new climate risk factors. The detailed factors are then used as inputs for predicting dengue outbreaks.

methods

This section explains the methodology used for this research, including the dataset used, the analysis process, the newly identified integrated input factors, the evaluation with machine learning models and the evaluation method. Fig. 1 illustrates the conceptual framework of our research.

Fig. 1: Conceptual Framework for Identifying Significant Climate Factors in Dengue Outbreak Prediction

The following sections provide a detailed description of each process involved in this framework.

Dataset

Data were collected from two sources. We obtained weekly data on dengue cases based on two federal territories, namely, Kuala Lumpur and Putrajaya, from January 2010 to December 2013. The data were obtained from the reports of the Disease Control Division of MOH, which are available from their official portal [23]. The weather data of Kuala Lumpur and Putrajaya were retrieved from MMD for the period of January 2010 to December 2013. The data are available upon request. Thus, 209 weeks of confirmed dengue cases and meteorological data were evaluated in this study. However, approximately 8% of the data were missing in the MMD datasheets for the study period. Thus, we obtained the missing data for this period from the US Weather Channel Interactive, which provides Malaysian meteorological data. The data were fitted simultaneously with the Putrajaya–Cyberjaya Station in Malaysia. Only minimum temperature, maximum temperature, average temperature, minimum humidity and rainfall were selected because many studies have emphasised that these factors are the most important risk factors for dengue outbreak prediction models, as shown in Table 1.

Analysis

Weather data from MMD provide daily weather information, and the incidence of dengue cases is published weekly by MOH. Thus, data were normalised and classified into two levels, namely, ‘low risk’ and ‘high risk’, on a weekly basis. Weather and meteorological factors play important roles in the incidence of dengue fever. Thus, the dataset was analysed, and the relationship between the incidence of dengue cases and weather information was determined every week using the Pearson correlation coefficient (PCC). The Pearson product– moment correlation coefficient (occasionally referred to as PPMCC, PCC or Pearson’s r) is a measure of linear dependence between two variables X and Y (Equation 1). This method is an important evaluation technique, providing a value between +1 and −1, where +1 indicates the total positive linear correlation, 0 exhibits no linear correlation and −1 indicates total negative linear correlation. This measure is widely used in various science fields [50].

(1)

Identification of Significant Factors

The most significant climate factors were identified based on the correlation analysis of the dataset, as shown in Table 2. The analysis result indicated that the highest correlation exists between minimum temperature and cumulative rainfall, with the incidence of dengue cases determined in different weeks.

Table 2: Correlation between Dengue Incidence Cases and Climate Factors

Minimum temperature and daily rainfall are the most significant dengue weather-based risk factors [38,51,52,53]. The average minimum temperature can be calculated as follows (See Supplementary Files for Equation 2):

where i is the number of weeks from which the average minimum temperature and [Week(i−1)] is the minimum temperature of the prior weeks to the current week plus minimum temperature of current week . The cumulative rainfall for week i can be calculated using Equation 3 (see Supplementary Files), as follows:

where i is the desired week from which the total rainfall will be calculated, cumulative rainfall week (i) is the final calculation and week (i−1) is the week prior to week (i ).

Table 3 provides the PCCs between the weather variables and the incidence of dengue cases. The underlined and highlighted high positive numbers showed the highest correlation and coefficients between weather parameters and the incidence of dengue fever. Table 3 presents the results for 7 weeks prior to the current week and the optimum value for the average minimum temperature (0.499).

The highest value for cumulative rainfall (0.0071) was obtained for 2 weeks prior to the current week (Table 3).

Table 3: PCC between Climatic Factors and Incidence of Dengue Cases

Thus, the average minimum temperature of Week 5 (plus the current week) and the cumulative rainfall for Week 2 (prior to the current week) exhibit high correlation with dengue cases in accordance with the correlation analysis. The two factors will be regarded as TRF and used as input parameters for dengue outbreak risk level prediction. The combination of factors is shown in Fig. 2.

Fig. 2: Components of TRF

The cumulative rainfall for 2 weeks prior to the current week is identified as a significant factor because it coincides with the life cycle of an Aedes aegypti mosquito, i.e. approximately 2 weeks [38,51,52,53,54,55]. Thus, this result clearly shows that dengue outbreak can happen immediately after an A. aegypti mosquito completes its life cycle and becomes an adult.

Prediction using machine learning models

Once significant factors have been identified, the research proceeded towards predicting the risk incidence level of dengue fever defined as ‘high risk’ and ‘low risk’. To predict this level, we tested five machine learning models using input factors with and without TRF. Table 4 provides the detailed input factors and descriptions.

Table 4: Input Factors with and without TRF

On the basis of the high output result [16,56], we selected Bayes network (BN) models, support vector machine(SVM), RBF tree, decision table and naive Bayes to evaluate the factors using WEKA version 3.8.0 [57]. We used the cross-validation (10-fold) technique to evaluate the models.

Evaluation Metrics

We can evaluate the performance of classifiers on the basis of several accuracy measures and parameters. Moreover, some accuracy and error measures are used to determine the distance between the predicted and the actual known values [58]. The confusion matrix is a useful tool for analysing the efficiency of a classifier in recognising tuples of different classes used in WEKA.

Sensitivity and specificity measures can be used to calculate the accuracy of classifiers. Sensitivity is also referred to as the true positive rate (i.e. the proportion of positive tuples that are correctly identified). Specificity is the true negative rate (i.e. the proportion of negative tuples that are correctly identified).

Equation 4 (see Supplementary Files) shows how accuracy was calculated using the confusion matrix.

We used the root-mean-square error (RMSE) to demonstrate the error rate [50,58]. RMSE was also adopted to identify the strengths of model evaluation. Optimising RMSE during model calibration may provide a small error variance at the expense of a significant model bias [50,59]. This statistic is determined as follows (Equation 5 in the Supplementary Files):

where Pi and Oi are known as the experimental and forecasted values, respectively; and n is the total number of test data.

results and discussion

Table 5 presents the results from five machine learning models with and without TRF inputs. Improved results and reduced errors were obtained using the weather data (as external risk factors for a dengue fever outbreak prediction model) by applying machine learning models (as data analysers) and adding newly identified factors (TRF).

Table 5: Machine Learning Classifier Models Using Cross -validation (10-fold) with TRF

Thus, the proposed factors and machine learning model are beneficial for predicting the dengue risk level. The results also showed that models with TRF achieved higher accuracies compared with those without TRF. The highest accuracy was obtained by the BN classifier with TRF (92.35%) but with an extremely low RMSE (0.26).

Other studies exhibit different accuracies based on their own private databases, which consist of data collected from patients in hospitals, compared with our research area [15,18,20,61]. Our research database used accessible and open-source data for climate factors.

Table 6: Benchmarking with Previous Studies

Table 6 shows the accuracy of the BN classifier with TRF compared with the other models that used climate factors. The proposed model with TRF achieved the highest accuracy of 92.35% compared with the other models.

Conclusion and Future Work

We identified a new significant risk factor, called TRF, which combined the average minimum temperature at 5 weeks plus the current week and the cumulative rainfall at 2 weeks prior to the current week. TRF significantly contributed to dengue outbreak prediction. The use of accurate and appropriate input factors for outbreak prediction can also provide enhanced and precise results for model output. We used various machine learning models to apply the identified significant factors to predicting dengue outbreak risk.

The integration of factors into the BN model resulted in a significant accuracy of 92 .35%. This accuracy showed that using TRF in the BN model outperformed all other outbreak prediction models. Moreover, the RMSE of 0.26 of the proposed system was lower than those of the other models. We strongly believe that using TRF can improve outbreak prediction systems. In our future study, we will test our model with different prediction systems and models. Moreover, future research should emphasise the exploration of other hidden and important risk factors for predicting dengue outbreaks.

This research has several limitations, and the most important one is data availability, which is due to privacy issues and the regulation set by MOH Malaysia. Although many risk factors for dengue outbreak are available, we only focused on the detailed analysis of temperature and rain risk factors for dengue outbreaks, which have been emphasised as the most important factors, due to the analysis of importance and access limitation.

abbreviations

ANN: Artificial Neural Networks

BN: Bayes Network

DLNM: Distributed Lag Non-linear Model

FN: False Negative

FP: False Positive

GA: Genetic Algorithm

GEE: generalized estimating equation

GIS: Geographic Information System

GLM: Generalised Linear Model

MCMC: Markov Chain Monte Carlo

MMD: Malaysian Meteorological Department

MOH: Ministry of Health Malaysia

NBR: Negative Binomial Regression

PCC: Pearson correlation coefficient

PPMCC: Pearson Product-Moment Correlation Coefficient

RBF: Radial basis function

RMSE: Root Mean Squared Error

SRCC: Spearman's rank correlation coefficient

SVM: Support Vector Machine

TN: True Negative

TP: True Positive

TRF: TempeRain Factor

WHO: World Health Organization

declarations

Availability of data and material

The completed combined datasets generated and analysed during the current study are available from the corresponding author on reasonable request.

The dengue confirmed case data that support the findings of this study are available in Ministry of Health Malaysia, [http://www.moh.gov.my/index.php/database_stores/store_view/1]

The weather data that support the findings of this study are obtained from Malaysian Meteorological Department. Data are available from the authors upon reasonable request.

Competing interests

The authors declare that they have no competing interests

Funding

Research University Grant-Faculty Program (GPF011D-2019).

Authors' contributions

Felestin Yavari Nejad contributed on the related works, method, experiments and analysis of the studies. Kasturi Dewi Varathan contributed in method and discussions.

Acknowledgements

We would like to thank Research University Grant-Faculty Program (GPF011D-2019) for funding this research.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

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tables

Table 1: Risk Factors for Dengue Outbreak Prediction Models

Reference

Technique

Year

Geographical Data Used

Temperature

Humidity

Rainfall

Min

Avg

Max

Relative

(Mean)

Cumulative Rainfall

Total

Rainfall

Max

24-h Rainfall

Max

1-H Rainfall

Bi-Weekly

Mean

[32]

Wavelet coherence analysis / quasi-Poisson regression combined with distributed lag

nonlinear model (DLNM)

2018

Philippines

[33]

generalized linear model

2018

Bangladesh

[34]

negative binomial regression (NBR)/ generalized estimating equation (GEE)

2017

Vietnam

[35]

Artificial Neural Network (ANN)

2016

Philippine

[36]

Distributed lag non-linear models (DLNM)/ Generalised estimating

equation models (GEE)

2016

China

[37]

Spearman rank correlation / Distributed Lag Non-linear Model (DLNM)

2014

Singapore

[38]

Distributed lag nonlinear model (DLNM) and Markov random fields

2014

Taiwan

[39]

Generalized Additive Model (GAM)

2014

Europe

[40]

Generalized Additive Model (GAM)

2013

Mexico

[41]

Poisson generalized additive model/ distributed non-linear lag model (DLMN)

2013

Malaysia ,

[17]

Poisson multivariate regression models

2013

Singapore

[42]

Autoregressive Integrated Moving Average (ARIMA)

2013

Malaysia

[43]

Poisson multivariate regression

2012

Singapore

[44]

Spearman's rank correlation coefficient (SRCC)

2012

Singapore

[3]

vector–host transmission model

2012

Taiwan

[11]

Neural Network and Genetic Algorithm

2012

Malaysia

[45]

Generalised linear model (GLM) / Bayesian

framework using Markov Chain Monte Carlo (MCMC)

2011

Brazil

[12]

Artificial Neural Networks (ANN)

2010

Singapore

[46]

multiple regression and discriminant analysis techniques / Peirce skill score

2010

Indonesia

[47]

Artificial Neural Networks (ANN)

2009

Turkey

[48]

Entropy and Artificial Neural Network

2008

Thailand

[49]

Kolmogorov-Sminov test / Pearson’s correlation

Coefficient / Stepwise regression techniques

2005

Thailand

Total

Table 2: Correlation between Dengue Incidence Cases and Climate Factors

Temperature			Mean relative Humidity	Rainfall
Minimum Temperature	Mean Temperature	Maximum Temperature	Mean relative Humidity	Rainfall
0.447	0.339	0.316	-0.176	-0.020

Table 3: PCC between Climatic Factors and Incidence of Dengue Cases

	Average Minimum Temperature	Cumulative Rainfall
Current Week	0.447	–0.0201
1 Week Prior	0.465	0.0065
2 Week Prior	0.480	0.0071
3 Week Prior	0.494	–0.0005
4 Week Prior	0.498	–0.0123
5 Week Prior	0.499	–0.0139
6 Week Prior	0.489	–0.0045
7 Week Prior	0.476	0.0020

Table 4: Input Factors with and without TRF

Input Factors without TRF		Input Factors with TRF
Type	Parameter Description	Type	Parameter Description
Weather Factors	Minimum temperature (°C)	Weather Factors
	Mean temperature (°C)		Mean temperature (°C)
	Maximum temperature (°C)		Maximum temperature (°C)
	Mean relative humidity (%)		Mean relative humidity (%)
	Cumulative of rainfall (mm)
		TRF Factors	Average of minimum temperature 5 weeks before the current week (°C)
		TRF Factors	Cumulative of rainfall for 2 weeks prior to the current week (mm)

Table 5: Machine Learning Classifier Models Using Cross -validation (10-fold) with TRF

Models		Accuracy (%)	Root Mean Squared Error (RMSE)
Bayes Net	With TRF	92.35	0.26
Bayes Net	Without TRF	91.39	0.28
SVM	With TRF	88.04	0.35
SVM	Without TRF	88.00	0.33
RBF Tree	With TRF	89.47	0.29
RBF Tree	Without TRF	89.47	0.28
Decision Table	With TRF	90.41	0.28
Decision Table	Without TRF	89.95	0.28
Naive Bayes	With TRF	89.4737	0.3064
Naive Bayes	Without TRF	88.9952	0.2904

Table 6: Benchmarking with Previous Studies

Reference	Year	Model	Accuracy (%)
[60]	2018	Correlation and Autoregressive Distributed Lag Model	84.90
[16]	2016	C-SVC Kernel and RBF	90.50
[17]	2013	Poisson Multivariate Regression Models	90.00
[12]	2010	Artificial Neural Networks	82.39
[48]	2008	Automatic Prediction System by Using Entropy and Artificial Neural Network	85.92
Our Proposed Model		Bayes Network Model using TempeRain Factor (TRF)	Accuracy = 92.35