=Paper=
{{Paper
|id=Vol-2030/HAICTA_2017_paper85
|storemode=property
|title=Information Mining from Health Protection Data Against Mosquitoes
|pdfUrl=https://ceur-ws.org/Vol-2030/HAICTA_2017_paper85.pdf
|volume=Vol-2030
|authors=Stavros Valsamidis,Lambros Tsourgiannis,Giannoula Florou,Athanasios Mandilas,Michael Nikolaidis
|dblpUrl=https://dblp.org/rec/conf/haicta/ValsamidisTFMN17
}}
==Information Mining from Health Protection Data Against Mosquitoes==
https://ceur-ws.org/Vol-2030/HAICTA_2017_paper85.pdf
Information mining from health protection data against
mosquitoes
Stavros Valsamidis1, Lambros Tsourgiannis2, Giannoula Florou1, Athanasios
Mandilas1, Michael Nikolaidis1
1
Department of Accounting and Finance, EMaTTech, Kavala, Greece
e-mail: svalsam@teikav.edu.gr, ltsourgiannis@gmail.com, gflorou@teikav.edu.gr,
smand@teiemt.gr, mnikol@teikav.edu.gr
2
Directorate of Public Health and Social Care of Regional District of Xanthi, Region of
Eastern Macedonia and Thrace, Xanthi, Greece
Abstract. West Nile Virus (WNV) first time appeared in 2012 in the Regional
District of Xanthi in Greece in Northern Eastern Greece and in total 63 cases
were reported between 2012-2014. The Region of Eastern Macedonia and
Thrace (EMTh) conducted a mosquito and vector control program. In this
study, we apply three different data mining techniques to data about 314
households and concerned 2014. They were analyzed using the classification
algorithm PART, the clustering algorithm k-means and the association rule
mining algorithm, Apriori from the WEKA data mining package. The results
indicate that the infected persons have some common characteristics.
Hopefully, this aims to generate detailed knowledge of household use of
insecticide consumer products to kill mosquitoes, the factors that affect the use
of these products. The findings indicate that there is room for improvement
towards the self protection methods against mosquitoes.
Keywords: Mosquitoes, Protection, Classification, Clustering, Association
Rule Mining.
1 Introduction
Mosquito borne diseases are major public health problems in many countries in
all over the world. Regions in countries such as India, Mexico, Thailand, USA
reported many different diseases caused by mosquito bites (Rosendaal, 1997;
Raghavendra et al., 2011; Pandit et al., 2010). In recent years, Greece suffered by
WNV which was responsible for many serious diseases and even deaths which
reported during the period 2012-2015.
West Nile Virus (WNV) is a widespread disease mainly transmitted by
mosquitoes. In particular, West Nile Virus (WNV) outbreak occurred in the Region
of Eastern Macedonia and Thrace in 2012-2015. WNV infection can be
asymptomatic or symptomatic in humans, with 4:1 ration (Center of Disease Control
734
and Prevention, 2015). This virus is transmitted by mosquitoes and can cause illness
which can me mild resulting in influenza – like symptoms or severe affecting the
central nervous system causing encephalitis (Lorono-Pino et al., 2014; Jones et al.,
2014). In many WNV outbreaks reported deaths (Jones et al. 2014; He et al., 2014).
While humans are considered dead-end hosts once infected, birds have been
documented to produce high enough levels of the virus to spread WNV to
mosquitoes. Thus, bird populations produce a significant impact upon the growth of
the disease. Approximately 80% of cases in humans show no noticeable symptoms,
and the infected recover on their own. Another 20% develop mild symptoms similar
to a flu. A serious neurological illness occurs in less than 1% of the infected
population. Currently, there is no cure or preventative shot for this disease.
Preventative measures, including killing off mosquitoes and minimizing personal
exposure to mosquitoes, are the most effective ways to combat WNV.
WNV not only posses risk to health but diseases in endemic areas place a burden
on households, on health services and the economic growth of the local communities
(Koenraadt et al., 2006; Tyagi et al., 2005). Therefore, citizens’ protection against
mosquito bites is very important for public health.
Whilst several studies demonstrated the efficacy of mosquito management in
response to WNV, Carney et al. (2005), Barber et. al. (2010) mainly suggested a
reduction in human WNV cases associated with the application of mosquito control
programs. Nowadays studies have revealed that citizens' knowledge, attitude, and
practice of various methods of personal and household protection against mosquito
bites vary in different endemic regions of tropical countries (Pandit et al., 2010).
Various methods for protection from mosquito bites are used globally including
repellent oils, smoldering coils, vaporizing mats, repellent creams, liquid vaporizer
(Raghavendra et al., 2011). The market in these products worldwide is worth about 2
billion USD per year (WHO, 1998). Effectiveness of these methods lasts between
five to seven hours with 60-80% protection (Curtis et. al., 1989; Ansari et al., 1990).
The motivation is the effective control of the infectious diseases transmitted by
the inset particularly mosquito (Pandit et al., 2010). The use of personal or household
protection methods are indicators of socioeconomic status, which has been reported
as an important factor associated with diseases transmitted by mosquitoes and more
particular with malaria (Tyagi et al., 2005). They also argued that the high usage of
mosquito repellents by urban respondents and the low usage in rural respondents is
explained the impact of socioeconomic conditions on the selection of protection
means in communities (Tyagi et al., 2005). Moreover, education and knowledge of
protection from mosquito bites, the promotion of health education and the positive
role of women and family members in community interventions must be emphasized,
is associated with less malaria infection (Tyagi et al., 2005; He et al., 2014). Another
study aimed to identify the association between demographic characteristics
(including age, sex, education, occupation, sub-district), knowledge of the population
on symptoms of dengue, vector and prevention against mosquitoes; and practices
such as container protection and mosquito reduction (Koenraadt et al., 2006).
The common household use of insecticide consumer products to kill mosquitoes:
aerosol spray cans with insecticide were used to kill mosquitoes in 70% of homes,
and insecticide emitters were used in 10–20% of homes (Lorono-Pinoet al., 2013).
This heavy use of insecticide consumer products is not surprising in light of our
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previous reports of large numbers of Ae. aegypti and another human-biting mosquito,
Culex quinquesfasciatus, being present in homes in Merida City (Garcia-Rejon et al.,
2008; Lorono-Pino et al., 2013). Other studies have reported use of insecticide
consumer products for 28–89% of households in dengue endemic settings in Asia
(van Benthem et al., 2002; Itrat et al., 2008; Syed et al., 2010; Naing et al., 2011; Al-
Dubai et al., 2013; Mayxay et al., 2001) or the Americas (Shuaib et al., 2010). This is
unfortunate because, as shown by a recent study from a malaria-endemic area in
Africa, much can be learned from in-depth assessments of household use of pest
control products (Nalwanga and Ssempebwa, 2011). Moreover, there are potential
negative health effects, particularly for asthma and respiratory diseases, from
inhalation of pesticide aerosols or vapors (Hernandez et al., 2011). Improved
knowledge of the extent of household use of insecticide consumer products is
important not only to determine the willingness of households to invest in the use of
domicile-targeted insecticide-based products – to kill mosquitoes, cockroaches, and
other indoor pests – but also to help assess the overall insecticide exposure in the
environment stemming from household use, vector control program applications to
suppress mosquitoes or other arthropods spreading pathogens to humans or domestic
animals, and agricultural applications to protect crops.
On the other hand, data mining is an iterative process of creating predictive and
descriptive models, by uncovering previously unknown trends and patterns in vast
amounts of data, in order to extract useful information and support decision making
(Kantardzic, 2003). Data mining methods are divided into three major categories
(Witten & Frank, 2005). The first category involves the classification methods,
whereas the second the clustering ones and the third the association rule mining
methods. Classification methods use a training dataset in order to estimate some
parameters of a mathematical model that could in theory optimally assign each case
from a new dataset into a specific class. In other words, the training set is used to
train the classification technique how to perform its classification. Clustering refers
to methods where a training set is not available. Thus, there is no previous
knowledge about the data to assign them to specific groups. In this case, clustering
techniques can be used to split a set of unknown cases into clusters. Association rule
mining discovers relationships, sometimes hidden, among attributes (variables) in a
dataset.
Data mining techniques have already been applied for analyzing swarms. Swarm
Intelligence is quite an emerging area of research (Timmis et al., 2010). A swarm is a
large number of homogenous, simple agents interacting locally among themselves,
and their environment, with no central control to allow a global interesting behaviour
to emerge. Example of swarm is considered the population of mosquitoes. An early
warning system for West Nile virus (WNV) outbreaks provides a basis for targeted
public education and surveillance activities as well as timelier larval and adult
mosquito control (Mostashari et al., 2003). They adapted the spatial scan statistic for
prospective detection of infectious disease outbreaks, applied the results to data on
dead birds reported from New York City in 2000, and reviewed its utility in
providing an early warning of WNV activity in 2001. Data mining techniques were
applied for dengue infection in order to correctly classify the patients since these
classes require different treatment (Thitiprayoonwongse et al., 2012). Dengue
infection is an epidemic disease typically found in tropical region. Decision trees was
736
the main technique for the prediction of day0 date which is the critical date of dengue
patients that some patients face the fatal condition..
The present study was conducted to assess the awareness and practices of
mosquito bite prevention methods among households of REMTH by using data
mining techniques. Total 314 households have participated in the study from
REMTH area. Telephonic interviews using a structured questionnaire performed to
all households. The study was conducted in the month of August 2014. The pilot pre-
tested structure questionnaire was used to collect the data. Study respondents were
57% male and 43% female. Almost 99% had knowledge about breeding places of
mosquito, but poor knowledge about biting time (20%). 71% of participants knew
that mosquito bite causes WNV. 39% of households were using mosquito net as
protection against the bite, but only 10% were using insecticide treated bed net.
There is need of increasing use of insecticide treated bed nets and continuous
updating of knowledge about various aspects of mosquito bite.
Section 2 describes the dataset and the methodology used in our research.
Sections 3 presents the results from the data mining methods and Section 4 discusses
the results and refers to the main conclusions of our research.
2 Data and Methodology
In this section the dataset we used in our methodology is described in detail.
Also, the data mining methods applied to the mosquitoes data are explained and
analyzed.
2.1 The Dataset
The dataset was collected from the Directorate of Public Health and Social
Welfare of Xanthi. The data were collected during 2014 and involve 340 households
from REMTh. The data are originally in ASCII form. Each household is described by
7 variables. The seven variables which are used in the analysis described in the
methodology section are Infected, Age, No_Children, Family_Members, Occupation,
Education, Exp_Selfprot_Program. Table 1 describes each variable in detail.
Table 1. The variables used in our analysis
Variable Name Description Type
Infected If there was an infected by WNV person (0: No, 1: Nominal
Yes)
Age The age of the respondent Numeric
No_Children The number of children of the respondent Numeric
Family_Members The number of family members of the respondent Numeric
Occupation The occupation of the respondent Nominal
Education The education level of the respondent Nominal
Exp_Selfprot_Program The expenses of the respondent for self-protection Numeric
measures
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2.2 Data mining techniques
The WEKA (Waikato Environment for Knowledge Analysis) (Witten & Frank,
2005) computer package was used in order to apply classification, clustering and
association rule mining methods to the dataset. WEKA is open source software that
provides a collection of machine learning and data mining algorithms. Fig. 1 shows
the basic Graphical User Interface (GUI) of WEKA. One of the main objectives of
WEKA is to mine information from existing agricultural datasets (Cunningham and
Holmes, 1999) and the main reason for choosing
Fig. 1. WEKA environment
There are various classification methods implemented in WEKA, like ZeroR,
OneR, PART etc. In the classification step, the algorithm PART (Witten & Frank,
2000) was applied to our data. It generates a decision list by using the separate-and-
conquer method and builds a partial C4.5 decision tree in each iteration and makes
the "best" leaf into a rule. PART can parsimoniously discover and represent simple
relationships between the real data (Cunningham and Holmes, 1999). In our case the
variable “Infected” is used as a class and shows whether a person was infected or not
by WNV.
The clustering step uses the k-means algorithm (MacQueen, 1967; Kaufmann &
Rousseeuw, 1990), called SimpleKMeans in WEKA. K-means is an efficient
partitioning algorithm that decomposes the data set into a set of k disjoint clusters. It
is a repetitive algorithm in which the items are moved among the various clusters
until they reach the desired set of clusters. With this algorithm a great degree of
similarity for the items of the same cluster and a large difference of items, which
belong to different clusters, are achieved. The variable “Infected” is used in order to
738
assess the accuracy of the clustering and investigate its impact on olive tree
cultivation.
Association rule mining is one of the most well studied data mining tasks. It
discovers relationships among attributes (variables) in datasets, producing if-then
statements concerning attribute-values (Agrawal et al., 1993). An association rule X
⇒ Y expresses a close correlation among items in a dataset, in which transactions in
the dataset where X occurs, there is a high probability of having Y as well. In an
association rule X and Y are called respectively the antecedent and consequent of the
rule. The strength of such a rule is measured by values of its support and confidence.
The confidence of the rule is the percentage of transactions with antecedent X in the
dataset that also contain the consequent Y. The support of the rule is the percentage
of transactions in the dataset that contain both the antecedent and the consequent Y in
all transactions in the dataset.
The WEKA system has several association rule-discovering algorithms available.
The Apriori algorithm (Agrawal & Srikant, 1994) is used for finding association
rules over the discretized LMS data table in Appendix 1. Apriori is the best-known
algorithm to mine association rules. It uses a breadth-first search strategy to counting
the support of item sets and uses a candidate generation function, which exploits the
downward closure property of support. Iteratively reduces the minimum support until
it finds the required number of rules with the given minimum confidence.
There are different techniques of categorization for association rule mining. Most
of the subjective approaches involve user participation in order to express, in
accordance with his/her previous knowledge, which rules are of interest. One
technique is based on unexpectedness and actionability (Liu et al, 1996; Liu et al,
2000). Unexpectedness expresses which rules are interesting if they are unknown to
the user or contradict the user’s knowledge. Actionability expresses that rules are
interesting if users can do something with them to their advantage. The number of
rules can be decreased to unexpected and actionable rules only (García et al., 2007).
Another technique proposes the division of the discovered rules into three categories
(Minaei-Bidgoli et al., 2004). (1) Expected and previously known: This type of rule
confirms user beliefs, and can be used to validate our approach. Though perhaps
already known, many of these rules are still useful for the user as a form of empirical
verification of expectations. (2) Unexpected: This type of rule contradicts user
beliefs. This group of unanticipated correlations can supply interesting rules, yet their
interestingness and possible actionability still requires further investigation. (3)
Unknown: This type of rule does not clearly belong to any category, and should be
categorized by domain specific experts.
3 Results
The first step before applying the data mining methods described in the previous
section is the pre-processing of the data in order to prepare them for data analysis.
739
3.1 Pre-processing
The filter NumericalToNominal was applied to the data in order to convert
numeric variables and their values to nominal. For example, number 0 and 1 in
variable Infected are converted to nominal, where 0 signifies not infected and 1
infected. Fig. 2 depicts all the variables used in our analysis. The two colours
correspond to Infected (Red) and not Infected (Blue).It is noteworthy that only 16
respondents were infected by WNV.
Fig. 2. Visualization of the attributes with class variable “Infected”
It is worth mentioning that all the sixteen infected persons belong to the same Age
group, have the same Education level and the same Occupation but they have spent
different amount of money for self protection. These findings are depicted on Fig3a,
Fig3b, Fig3c and Fig3d.
Fig. 3a. Visualization of the attribute Age with class variable “Infected” (the 16 infected
persons correspond to the older people)
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Fig. 3b. Visualization of the attribute Occupation with class variable “Infected” (the 16
infected persons correspond to the farmers)
Fig. 3c. Visualization of the attribute Education with class variable “Infected” (the 16 infected
persons correspond to the people of low education)
Fig. 3d. Visualization of the attribute Exp_SelfProt_Program with class variable “Infected”
(most of the 16 infected persons correspond to the people who do not expend money for self-
protection against mosquitoes)
3.2 Classification
In the classification step, the algorithm PART is applied which generates a
decision list. It uses separate-and-conquer method and builds a partial C4.5 decision
tree in each iteration and makes the "best" leaf into a rule. The attribute “Infected” is
used as a class.
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Table 3. Classification results using variable “Infected” as class.
PART decision list
Occupation = 7: 0 (90.29/1.0)
Age = 2: 0 (62.0)
Age = 3: 0 (56.71)
Age = 4: 0 (34.0)
Age = 1: 0 (30.0)
Age = 5: 0 (22.0/2.0)
Occupation = 6: 1 (16.0/3.0) : 0 (3.0)
Number of Rules : 8
The results indicate that the attributes, which describe the classification, are the
variables Age and Occupation. This means that variable Infected is more closely
related to the variables Age and Occupation than the other variables and therefore in
some Ages (Old people) and some Occupations (Farmers) there are more infected
persons than in other categories. A possible explanation for these results is that the
older people are more vulnerable and farmers have more probabilities to be bitten by
mosquitoes. Table 4 presents the overall accuracy of the model computed from the
training dataset and is equal to 97.13%.
Table 4. Stratified cross-validation.
Summary
Correctly Classified Instances 305 97.1338%
Incorrectly Classified Instances 9 2.8662%
Kappa statistic 0.7278
Mean absolute error 0.0421
Root mean squared error 0.1526
Relative absolute error 42.229%
Root relative squared error 69.3346%
Coverage of cases (0.95 level) 99.0446%
Mean rel. region size (0.95 level) 55.8917%
Total Number of Instances 314
Table 5 presents that the worst performance based on the F-measure that
combines precision and recall is for the class Infected and equals 74.3%, whereas the
best performance is for the class Not Infected and equals 98.5%.
Table 5. Detailed Accuracy By Class
FP ROC PRC
TP Rate Precision Recall F-Measure MCC Class
Rate Area Area
0,980 0,188 0,990 0,980 0,985 0,731 0,939 0,994 0
0,813 0,020 0,684 0,813 0,743 0,731 0,939 0,607 1
Weighted 0,971 0,179 0,974 0,971 0,972 0,731 0,939 0,974
Avg
Table 6 presents the confusion matrix for the two classes, a:infected and b: Not
infected.
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Table 6. Confusion Matrix
a b <-- classified as
292 6 a=0
3 13 b=1
3.3 Clustering
The clustering step was performed using the k-means algorithm (SimpleKmeans
in the context of WEKA). The number of clusters is set to 2, since the variable
“Infected” was used to compute the accuracy of the clustering and inspect the impact
of the Infected to the other variables.
Table 7. Clustering results. Variable “Infected” is used for assessing the clustering.
Final cluster centroids
Cluster#
Attribute Full Data 0 1
(314) (182) (132)
Age 2 2 6
No_Children 2 2 2
Family_Members 2 4 2
Occupation 7 5 7
Education 1 2 1
Exp_Selfprot_Program 1 1 1
Table 7 shows the results of the clustering. The incorrectly clustered instances
(farmers) are 36.94% based on variable “Infected”. It is also evident from the cluster
centroids that not infected, represented as cluster 0 in the results, contains households
that have not persons infected by WNV compared to cluster 1 (persons infected by
WNV).
3.4 Association rule mining
The Apriori algorithm (Agrawal et al., 1993) was used for finding association
rules for our dataset. The algorithm was executed using a minimum support of 0.1
and a minimum confidence of 0.9, as parameters. WEKA produced a list of 14 rules
(Table 8) with the support of the antecedent and the consequent (total number of
items) at 0.1 minimum, and the confidence of the rule at 0.9 minimum (percentage of
items in a 0 to 1 scale).
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Table 8. The Apriori algorithm results based on the confidence metric.
Best rules found
1. Education=2 105 => Infected=0 105 lift:(1.05) lev:(0.02) [5] conv:(5.35)
Education=2
2. Exp_Selfprot_Progra => Infected=0 100 lift:(1.05) lev:(0.02) [5] conv:(5.1)
m=1 100
3. Occupation=7 90 => Infected=0 89 lift:(1.04) lev:(0.01) [3] conv:(2.29)
Occupation=7
4. Exp_Selfprot_Progra => Infected=0 86 lift:(1.04) lev:(0.01) [3] conv:(2.22)
m=1 87
Infected=0 Exp_Selfprot_Program=1 79 lift:(1.05) lev:(0.01)
5. Family_Members=2 => [3] conv:(1.89)
81
Infected=0 Exp_Selfprot_Program=1 95 lift:(1.04) lev:(0.01)
6. => [3] conv:(1.72)
Education=1 98
Exp_Selfprot_Program=1 87 lift:(1.04) lev:(0.01)
7. Occupation=7 90 => [3] conv:(1.58)
Infected=0 Exp_Selfprot_Program=1 86 lift:(1.04) lev:(0.01)
8. => [3] conv:(1.56)
Occupation=7 89
Exp_Selfprot_Progra Infected=0 280 lift:(1.01) lev:(0.01) [2]
9. => conv:(1.14)
m=1 292
Family_Members=2 Exp_Selfprot_Program=1 86 lift:(1.03) lev:(0.01)
10. => [2] conv:(1.26)
90
Infected=0 Exp_Selfprot_Program=1 86 lift:(1.07)
11. Occupation=7 90 => lev:(0.02) [5] conv:(1.95)
Exp_Selfprot_Program=1 100 lift:(1.02)
12. Education=2 105 => lev:(0.01) [2] conv:(1.23)
Infected=0 Exp_Selfprot_Program=1 100 lift:(1.02) lev:(0.01)
13. => [2] conv:(1.23)
Education=2 105
Infected=0 Exp_Selfprot_Program=1 100
14. Education=2 105 => lift:(1.07) lev:(0.02) [6] conv:(1.89)
The application of the Apriori algorithm for association provided some
interesting outcomes for the attributes regarding the protection of households against
the mosquitoes. Table 8 shows the association rules that can be discovered. There are
of course some uninteresting rules, like rules 2 and 4. They present relatively known
information since it is an expected or conforming relationship between variables.
There are also a couple of symmetrical rules, since the antecedent element and the
consequent element are interchanged. There is also a similar triad of rules, rules with
the same element in antecedent and consequent but interchanged, such as rules 1, 3
and 7.
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Summarizing the results from the classification, the clustering and the association
rule mining methods we can conclude that:
(i) The attributes which best describe the classification are the variables Age and
occupation. The attribute “Infected” is used as a class.
(ii) Using “Infected” as class attribute in clustering, namely if there is a person
who was infected or nor, the results show that the two clusters have only two
attributes with the same value (No_Children and Exp_Selfprot_Program).
(iii) In association rule mining, although there are some trivial rules, namely
expected and previously known, like rules 2 and 4 that show that are mutually
dependent, there are also rules like 1, 3, 8, 9 and 10, which offer a lot of
actionability.
Overall, the infection by mosquitoes depends on Age, Occupation and Education
and surprisingly not on the expenses for the protection against them.
4 Conclusions
The application of three different data mining techniques towards self-protection
methods against mosquitoes is presented in this paper. The results show some
interesting outcomes.
This study investigated the main demographic characteristics that affect the
buying behavior of people towards the self – protection measures against
mosquitoes. It showed that the amount consumers’ spend for buying goods for self-
protection against mosquitoes is mainly affected by the existence of children, age,
number of family members, education and occupation. Hence this study supports the
arguments of other study that socioeconomic conditions are associated with self-
protection measures against mosquitoes. Furthermore, this study indicated that the
people expenses towards self-protection methods against mosquitoes are
significantly related to their infection from WNV. Besides, people who have been
infected from WNV have different demographic profile in comparison with people
who have not been infected.
According to the results of the current study, most of the people who spend less
than 20 euro per month per household for self-protection against mosquitoes are
mainly old (more than 61 years old, low educated, retiree, with two people in their
family) and these people mainly consist of the population who are mainly infected by
WNV.
Further investigation is still required since the results are based on only region. In
the future we plan to extend the study to other regions.
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