Mas e Deg ee P og am in
Da a Science and Ad anced Analy ics
P edic ing Dengue Fe e Incidence and Disease Dynamics unde
Clima e Change in Sou heas Asia
Josephine Lu e
Mas e Thesis
p esen ed as pa ial equi emen o ob aining a Mas e ’s Deg ee in Da a Science and Ad anced Analy ics
NOVA In o ma ion Managemen School
Ins i u o Supe io de Es a ís ica e Ges ão de In o mação
Uni e sidade No a de Lisboa
MDSAA
NOVA In o ma ion Managemen School
Ins i u o Supe io de Es a ís ica e Ges ão de In o mação
Uni e sidade No a de Lisboa
P edic ing Dengue Fe e Incidence and Disease Dynamics unde Clima e Change in
Sou heas Asia
by
Josephine Lu e
Mas e Thesis p esen ed as pa ial equi emen o ob aining he Mas e ’s deg ee in Da a
Science and Ad anced Analy ics, wi h a specializa ion in Business Analy ics
Supe ised by
Robe o Hen iques, PhD, No a In o ma ion Managemen School
July, 2024
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STATEMENT OF INTEGRITY
I he eby decla e ha ing conduc ed his academic wo k wi h in eg i y. I con i m ha I ha e no
used plagia ism o any o m o undue use o in o ma ion o alsi ica ion o esul s along he
p ocess leading o i s elabo a ion. I u he decla e ha I ha e ully acknowledged he Rules
o Conduc and Code o Hono om he NOVA In o ma ion Managemen School.
[Be lin, July 1s 2024]
ii
ABSTRACT
Dengue e e is a clima e-sensi i e ec o -bo ne disease p ima ily ansmi ed by Aedes
mosqui oes, A. aegyp i and A. albopic us. P e ious esea ch has analyzed he ela ionship
be ween clima e and disease, wi h a ying ou comes. Tempe a u e and p ecipi a ion ha e
been demons a ed as ele an p edic o s in mos s udies. The e ec s o clima e change on
dengue e e we e ound o be unce ain, highligh ing he need o u he s udy.
This s udy analyzed how en i onmen al a iables in e ac wi h disease ansmission, enabling
p edic i e modeling o o ecas dengue incidence. Fo deploymen , clima e change
simula ions we e used as a amewo k o assess he disease’s esponse o changing
en i onmen al ac o s. The incidence and en i onmen al da a o 17 Sou heas Asian loca ions
we e collec ed om he na ional Minis ies o Heal h and he Na ional Oceanic and
A mosphe ic Adminis a ion (NOAA) om 2016-2023. T adi ional machine lea ning and deep
lea ning models we e used o o ecas dengue incidence based on en inpu ea u es o
empe a u e, p ecipi a ion, and lagged obse a ions.
The p edic i e abili y was e alua ed using Mean Absolu e E o (MAE) and Roo Mean Squa ed
E o (RMSE). Deep and machine lea ning models showed simila esul s o p edic ing dengue
incidence. The Con olu ional Neu al Ne wo k (CNN) achie ed he lowes e o wi h an a e age
MAE o 10.10 and RMSE o 13.61 on he alida ion se . Models showed a ying p edic i e
abili ies ac oss loca ions. Despi e ex ensi e da a p epa a ion, some loca ions pe o med
wo se on all models, indica ing po en ial issues wi h ini ial da a quali y. E o s we e educed
o all models on he es se , wi h CNN demons a ing supe io wi h an a e age MAE o 5.06
and RMSE o 7.09. Al hough e o s dec eased wi h addi ional da a, model pe o mance could
bene i om addi ional a iables ha we e no included.
Las ly, CNN was deployed o assess he disease’s esponse o clima e change. The p edic ed
model was ound o be sensi i e o simula ed changes in o al p ecipi a ion and mean
empe a u e. Resul s show posi i e and nega i e changes in he annual incidence a es o
bo h emission scena ios, wi h a posi i e linea end obse ed o mean empe a u e.
KEYWORDS
Dengue Fe e ; Incidence Fo ecas ; Deep Lea ning, Machine Lea ning; Clima e Change
Sus ainable De elopmen Goals (SDG):
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TABLE OF CONTENTS
1. In oduc ion .................................................................................................................. 1
2. Li e a u e Re iew.......................................................................................................... 3
2.1. Dengue T ansmission and Vec o .......................................................................... 3
2.2. En i onmen al Va iables ....................................................................................... 5
2.3. Clima e Change and Ex eme Wea he E en s ..................................................... 6
2.4. Summa y ................................................................................................................ 8
3. Me hodology ..............................................................................................................10
3.1. In oduc ion .........................................................................................................10
3.2. Domain Unde s anding .......................................................................................11
3.3. Da a Unde s anding.............................................................................................12
3.4. Da a P epa a ion .................................................................................................14
3.4.1. Da a P ep ocessing .......................................................................................14
3.4.2. Fea u e Enginee ing .....................................................................................19
3.4.3. Fea u e Selec ion ..........................................................................................19
3.5. Modeling ..............................................................................................................20
3.5.1. Model De elopmen .....................................................................................20
3.5.2. Model E alua ion..........................................................................................23
3.6. E alua ion ............................................................................................................23
3.7. Deploymen .........................................................................................................24
4. Resul s and Discussion ................................................................................................25
4.1. En i onmen al D i e s .........................................................................................25
4.2. P edic i e Modeling.............................................................................................25
4.3. Clima e Change Impac Assessmen ...................................................................27
5. Conclusions .................................................................................................................29
6. Limi a ions and Recommenda ions o Fu u e Wo k .................................................30
Re e ences .......................................................................................................................32
Appendix A ......................................................................................................................36
Appendix B.......................................................................................................................37
Appendix C.......................................................................................................................38
Appendix D ......................................................................................................................39
Appendix E .......................................................................................................................40
Appendix F .......................................................................................................................41
Appendix G ......................................................................................................................42
Appendix H ......................................................................................................................43

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Appendix I ........................................................................................................................44
Appendix J .......................................................................................................................45
Appendix K .......................................................................................................................46
Appendix L .......................................................................................................................48
Appendix M .....................................................................................................................50
LIST OF FIGURES
Figu e 1 - CRISP-DM me hodology ........................................................................................... 10
Figu e 2 - De ailed o e iew o applied me hods .................................................................... 11
Figu e 3 - Domain objec i es .................................................................................................... 12
Figu e 4 - Da a mining goals ..................................................................................................... 12
Figu e 5 - Singapo e: MICE impu a ion o he empe a u e a iables ................................... 16
Figu e 6 - Singapo e: MICE impu a ion o he p ecipi a ion and empe a u e a iables ...... 17
Figu e 7 - Manila, Philippines: Po en ial missing alues .......................................................... 18
Figu e 8 - Holis ic model: Non-aligned ime-based spli .......................................................... 20
Figu e 9 – Holis ic model: Aligned ime-based spli ................................................................. 21
Figu e 10 - Loca ion-speci ic ime-based spli .......................................................................... 22
Figu e 11 - Simula ed changes in mean empe a u e and annual incidence a e ................... 27
Figu e 12 - Simula ed changes in o al p ecipi a ion and annual incidence a e .................... 28
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LIST OF TABLES
Table 1 - Da a collec ion sou ces ............................................................................................. 14
Table 2 - T ans o med en i onmen al a iables ...................................................................... 15
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LIST OF ABBREVIATIONS AND ACRONYMS
A. Aedes
AR5 Fi h Assessmen Repo
CNN Con olu ional Neu al Ne wo k
CRISP-DM C oss-Indus y S anda d P ocess o Da a Mining
DENV Dengue-Vi us
DT Decision T ee
EIP Ex insic incuba ion pe iod
EMA Exponen ial Mo ing A e age
FNN Feed o wa d Neu al Ne wo k
GRU Ga ed Recu en Uni
IPCC In e go e nmen al Panel on Clima e Change
IQR In e qua ile Range
KNN K-Nea es Neighbo
LOCF Las Obse a ion Ca ied Fo wa d
LSTM Long Sho -Te m Memo y
LSTM-ATT Long Sho -Te m Memo y wi h A en ion
MAE Mean Absolu e E o
MCAR Missing Comple ely a Random
MICE Mul i a ia e Impu a ion by Chained Equa ions
MLP Mul ilaye Pe cep on
NOAA Na ional Oceanic and A mosphe ic Adminis a ion
NOCB Nex Obse a ion Ca ied Backwa d
RCP Rep esen a i e Concen a ion Pa hways
RF Random Fo es
RFE Recu si e Fea u e Elimina ion
6
he b eeding p ocess (Benedum e al., 2018). Re iewed li e a u e showcased he disease’s
sensi i i y o clima e a iabili y and he complexi y o concluding disease associa ions.
Small en i onmen al changes a ec disease ansmission, while ising empe a u es expand
he ec o habi a bu cons ain i us i ali y, mosqui o ep oduc ion, and de elopmen in
al eady wa m egions (Li e al., 2018). Seah e al. (2021) examined he impac o maximum
ambien empe a u e and hea wa es in Singapo e. Acco dingly, he ela i e isk o dengue
in ec ions linea ly inc eases wi h ising maximum empe a u es bu exhibi s a non-linea
dec ease beyond a h eshold o 31 °C. Highe empe a u es may enhance ansmission due o
inc eased bi ing a es and a sho e i us EIP. Howe e , p olonged high empe a u es educe
mosqui o li espan and egg- o-adul su i al, leading o a decline in he Aedes popula ion and
lowe dengue ansmission isk o e ime. Simila esul s we e epo ed o hea wa es.
Hea wa es commonly las 2-3 days, wi h 1-2 hea days pe week ha ing no e ec , while 3-6
hea wa e days lead o a dec ease in epo ed dengue in ec ions. Du ing ex eme hea , people
may spend mo e ime indoo s, educing ec o -hos con ac and lowe ing ansmission isk.
Con a y o o he esea ch, hei s udy iden i ied ha he impac o cumula i e p ecipi a ion
on dengue incidence is cons ained due o he signi icance o non-na u al b eeding si es (Seah
e al., 2021). To summa ize, empe a u e has a di ec and indi ec in luence on disease
ansmission.
Findings emphasize he need o a ime se ies analysis wi h lagged obse a ions o unde s and
delayed disease esponse. Wang e al. (2022) explo ed sho - e m associa ions be ween
ex eme wea he e en s and dengue e e in ec ion isk in Sou h and Sou heas Asia.
Ex emely low empe a u es educed ansmission isk wi h a 1-3 week lag bu no o
ex ended lag obse a ions. Ex emely high empe a u es we e associa ed wi h an inc eased
in ec ion isk wi h a lag o 2-3 weeks, depending on he numbe o ex emely ho days in a
week. Ex eme ain all was associa ed wi h a signi ican dec ease in in ec ion isk wi h a lag o
0-4 weeks, wi h he lowes isk a a lag o 2 weeks and an inc easing isk deno ed a a lag o 7
weeks. Thei esea ch concluded he impo ance o unde s anding he sho - e m e ec s o
clima e a iabili y on dengue ansmission (Wang e al., 2022). In con as , long- e m o ecas s
we e associa ed wi h inaccu a e p edic ions due o dec eased accu acy (Nu aini e al., 2021),
deno ing he di e ence in he signi icance and eliabili y o sho - and long- e m analyses.
2.3. CLIMATE CHANGE AND EXTREME WEATHER EVENTS
The clima e-disease ela ionship has been ho oughly esea ched, pa icula ly in Sou heas
Asia, wi h ecommenda ions o u he assess he impac s o clima e change (Kulka ni e al.,
2022). Ha ing examined he geog aphical expansion o dengue associa ed wi h clima e
change, daily mean empe a u e and empe a u e a ia ions s and ou as he main ac o s
in luencing dengue dis ibu ion, ollowed by p ecipi a ion (Ebi & Nealon, 2016).
Unde s anding he signi icance o he changing clima e on dengue e e is ele an o
assessing he disease’s isk and adap ing mi iga ion s a egies (Da is e al., 2021). A ele an
impulse o his esea ch was es ablished by p ojec ing dengue epidemics in opical a eas in

7
Sou h and Sou heas Asia unde di e en emission scena ios. Resul s demons a ed ha
ansmission isk is expec ed o inc ease o all scena ios, showing a peak in dengue e e
epidemic size and ou b eak du a ion (Wang e al., 2023). Mos esea ch inco po a ing clima e
change e e s o he In e go e nmen al Panel on Clima e Change (IPCC). I s Fi h Assessmen
Repo (AR5) p ojec s clima e change, impac , and isk un il 2100 based on ou
Rep esen a i e Concen a ion Pa hways (RCP) scena ios. Each scena io desc ibes di e en
le el o g eenhouse gas emissions and adia i e o cing, wi h a low-emission scena io
(RCP2.6), wo in e media e scena ios (RCP4.5 and RCP6.0), and a high emission scena io
(RCP8.5) (IPCC, 2014).
Clima e change e e s o an al e a ion o ex eme wea he e en s, such as El Niño, yphoons,
loods, d ough s, and hea wa es, in equency and magni ude and a long- e m change in
en i onmen al a iables in luencing he su i al, eplica ion, de elopmen , and dis ibu ion
o he ec o and i us (Li e al., 2018). El Niño e e s o he pe iodic wa ming o sea su ace
empe a u es, a ec ing global wea he pa e ns (Sunda i & K ishnamoo hy, 2019). An
in e ac ion o en i onmen al a ia ions and long- e m clima e change migh os e
ansmission (Ebi & Nealon, 2016). Ex eme wea he e en s a e p edic ed o become mo e
equen (Wang e al., 2022), a ec ing Aedes mosqui oes and ul ima ely p omo ing disease
ansmission (Li e al., 2018). Howe e , ex eme wea he e en s do no exhibi a uni o m
de ini ion. Fo ins ance, Seah e al. (2021) es ed di e en hea wa e de ini ions by adjus ing
he pe cen iles and numbe o consecu i e hea wa e days.
Cheng e al. (2021) explo ed he ela ionship be ween dengue ou b eaks and clima e change-
induced ex eme wea he e en s in China, obse ing a posi i e associa ion and delayed
e ec s. Du ing o immedia ely a e ex eme wea he e en s, he isk o dengue ou b eaks
did no homogenously inc ease, wi h dec eases in some ins ances. A peak in dengue ou b eak
isk was obse ed app oxima ely 1.5 mon hs pos -hea wa es and 1.5-3 mon hs a e ex eme
p ecipi a ion and humidi y, wi h e ec s las ing 2-3 mon hs. The empo al delay showcases
he du a ion o nega i e and posi i e e ec s o en i onmen al condi ions on dengue
ansmission. In conclusion, he impo ance o u he explo ing ex eme wea he e en s in
dengue-p one Asia-Paci ic coun ies and hei ou b eak scale was emphasized (Cheng e al.,
2021).
Bonnin e al. (2022) in eg a ed u u e clima e scena ios in o de eloping a p ocess-based
dengue model o Sou heas Asia. Thei s udy examined seasonal co ela ions, e ealing ha
a u u e inc ease in empe a u e is he main d i e o inc easing seasonal densi ies o Aedes
mosqui oes. The op imal empe a u e o A. aegyp i was iden i ied as 33°C and o A.
albopic us as 29°C. Beyond hese empe a u es, ising empe a u es led o lowe densi ies o
adul emale mosqui oes. P ecipi a ion exhibi s lowe signi icance, ye i s inc ease co ela es
wi h highe adul emale densi ies o bo h mosqui o species. Despi e some a ea-speci ic
di e ences, bo h densi ies a e p ojec ed o inc ease in all u u e clima e scena ios. The s udy
concludes ha e en wi h a igo ous implemen a ion o g eenhouse gas emissions educ ion
8
s a egies, mosqui o densi ies will no decline (Bonnin e al., 2022). Howe e , o he esea ch
a gues ha educed g eenhouse gas emissions would dec ease he sui abili y o ec o
habi a s (K aeme e al., 2019), wi h dis ibu ion sui abili y emaining consis en o sligh ly
dec easing (Da is e al., 2021). The alidi y o hese con adic o y s a emen s is add essed in
he subsequen analysis.
Messina e al. (2019) le e aged IPCC p ojec ions o 2020, 2050, and 2080 o assess dengue
isk based on 2015 using a boos ed eg ession ee. Acco dingly, empe a u e and annual
cumula i e p ecipi a ion we e de e mined as he p ima y ac o s in luencing dengue
sui abili y. P ojec ions un il 2080 indica e a iable shi s in dengue sui abili y’s geog aphical
and empo al dis ibu ion, wi h minimal global changes bu signi ican subna ional a ia ions.
Asian ci ies in coas al eas e n China and Japan a e expec ed o become mo e sui able by 2050.
The model ou come o he isk assessmen depended on he in eg a ed clima e scena io. The
scena io RCP6.0 sugges ed a global inc ease in dengue isk, po en ially a ec ing an addi ional
2.25 billion people be ween 2015 and 2080, encompassing o e 60% o he wo ld's
popula ion. This inc ease would esul om popula ion g ow h in al eady endemic a eas
ins ead o he sugges ed sp ead o dengue in new popula ions. Con a ily, scena io RCP4.5
indica ed a po en ial educ ion in dengue isk be ween 2050 and 2080 (Messina e al., 2019).
Clima e change scena ios a e le e aged in he subsequen analysis o e alua e disease
dynamics in Sou heas Asia u he .
2.4. SUMMARY
The li e a u e e iew p o ides a s uc u ed explo a ion, highligh ing undamen al s udies,
disease cha ac e is ics, and con adic ions. By add essing he complexi y o dengue
ansmission, ec o ecology, and he impac o en i onmen al a iables, seasonali y, and
clima e change, he aim was o es ablish a p o ound unde s anding and add ess knowledge
gaps ela ed o he esea ch ques ion.
Due o he elabo a ed ele ance o disease cha ac e is ics and associa ions, quali a i e
epo ed di e ences, and non-compa able s udy se ings o di e en loca ions, s udy pe iods,
and a iables, he li e a u e e iew did no ocus on compa ing pe o mance me ics. The
e iewed li e a u e buil he ounda ion o he geog aphical ocus, da a collec ion, and
a iable selec ion, which is c ucial o model de elopmen .
De ining he esea ch scope p o ed challenging gi en he a ie y o spa ial co e age anging
om u ban se ings o c oss-bo de egions. The geog aphical ocus o his s udy has been
de e mined h ough a scoping e iew ha emphasized he impac o clima e change on
dengue incidence in Sou heas Asia (Kulka ni e al., 2022). The amewo k o Li e al. (2018)
was an inspi a ion o ollow a mul idimensional app oach by in eg a ing clima e change o
d i e he meaning ulness o he unde lying esea ch. Gi en he epea ed emphasis on he
signi icance o empe a u e and p ecipi a ion on disease ansmission (Bonnin e al., 2022; Ebi
9
& Nealon, 2016; Messina e al., 2019), hese ac o s a e used as p edic o s o he ime se ies
analysis.
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3. METHODOLOGY
3.1. INTRODUCTION
The esea ch me hodology aligns wi h he CRISP-DM (C oss-Indus y S anda d P ocess o
Da a Mining) amewo k es ablished by Chapman (2000). The schema ollows a s uc u ed
sequence ha o ms a cycle o six phases: (1) Business Unde s anding, (2) Da a Unde s anding,
(3) Da a P epa a ion, (4) Modeling, (5) E alua ion, and (6) Deploymen (Chapman e al., 2000).
The cyclical p ocess is illus a ed in Figu e 1.
Figu e 1 - CRISP-DM me hodology
In he ollowing, Business Unde s anding will be e e ed o as Domain Unde s anding o
be e align wi h he esea ch amewo k. In his phase, he cu en s a e o esea ch was
analyzed o de e mine he esea ch need, di ec ion, and scope o his s udy. Subsequen ly,
he p ojec was ini ialized by de ining he esea ch ques ion and objec i es.
Wi hin he Da a Unde s anding, aw da a was analyzed o e i y sui able o ma and quali y.
Following he da a ex ac ion, a p o ound explo a o y analysis was pe o med o iden i y da a
limi a ions and inconsis encies shaping he Da a P epa a ion.
The Da a P epa a ion phase includes da a p ep ocessing, ea u e enginee ing, and selec ion.
The aim was o clean he da a and add ess da a quali y issues obse ed du ing Da a
Unde s anding. Da a om he di e en loca ions was s anda dized, agg ega ed, and
combined in a o ma sui able o Modeling.
The Modeling encompasses epe i i e model aining and e alua ion. Models we e
de eloped, hype pa ame e uning was conduc ed, and a ious modeling and alida ion
app oaches we e es ed. In addi ion, he pe o mance o applied da a p epa a ion echniques
was assessed o de e mine he bes pe o mance.
Business
Unde s anding
Deploymen
Modeling
E alua ion
Da a P epa a ion
Da a
Unde s anding
Da a
11
Wi hin he E alua ion, he esea ch p ocess was e iewed o p e en i egula i ies, and he
success o he domain objec i es and da a mining goals we e con i med be o e Deploymen .
The Deploymen phase began wi h addi ional esea ch ha p o ided sui able clima e change
simula ions o Sou heas Asia. Finally, he o ecas was deployed in o he selec ed clima e
change amewo k. The p ojec has been examined in de ail o ensu e seamless moni o ing
and main enance in he u u e.
Va ious me hods ha e been applied wi hin hese s ages, p esen ed in Figu e 2 and u he
explained in he subsequen chap e s.
Figu e 2 - De ailed o e iew o applied me hods
3.2. DOMAIN UNDERSTANDING
Dengue e e ep esen s a signi ican heal h bu den in Sou heas Asia, wi h en i onmen al
ac o s such as empe a u e and p ecipi a ion a o ing i s sp ead. Unde s anding disease
associa ions and p edic ing ansmission is c ucial o de eloping p e en ion s a egies.
Building upon he esea ch backg ound and need discussed in he li e a u e e iew, his
p ojec aimed o combine open-sou ce da a om loca ions wi hin Sou heas Asia wi h da a
science ools o enhance he cu en s a e o esea ch. The p ojec used Py hon p og amming
language and suppo ing lib a ies, whose espec i e e sions a e linked in Appendix A.
This esea ch add essed he ques ion: "How can p edic i e modeling using en i onmen al
a iables assess dengue e e incidence and i s esponse o clima e change in Sou heas
Asia?". Th ee domain objec i es we e o mula ed (Figu e 3). The i s objec i e was o iden i y

12
signi ican en i onmen al d i e s o disease ansmission. The second was o de elop he
p edic i e model o dengue incidence using empe a u e and p ecipi a ion a iables. Finally,
he hi d objec i e was o assess how he disease esponds o clima e change.
Figu e 3 - Domain objec i es
F om a echnical pe spec i e, he da a mining goals we e de ined (Figu e 4). Fi s , collec da a
om mul iple Sou heas Asian loca ions. Second, analyze da a as pa o a p o ound
explo a o y analysis. Thi d, p epa e and ans o m aw en i onmen al and incidence da a o
enhance da a quali y and in e p e abili y. Fou h, de elop and op imize selec ed machine and
deep lea ning models in he mul i a ia e ime se ies analysis o c ea e a highly accu a e
dengue incidence o ecas ha gene alizes well on unseen da a. Las ly, e alua e model
pe o mance o ensu e deploymen eadiness.
Figu e 4 - Da a mining goals
3.3. DATA UNDERSTANDING
The geog aphical scope wi hin Sou heas Asia was de e mined by da a a ailabili y and
su iciency. Key insigh s o da a sea ch we e ga he ed h ough he li e a u e e iew.
En i onmen al da a was explo ed on he websi es o na ional me eo ological, en i onmen al,
and clima ological depa men s. Dengue e e incidence da a was collec ed om na ional
heal h minis ies, disease con ol di isions, and WHO coun y o ices. I da a was no publicly
a ailable, i was eques ed indi idually. Un o una ely, digi al eques s o nine o he
Sou heas Asian loca ions did no ecei e a posi i e esponse.
To es ablish a ounda ion o he model de elopmen , cohesi e da a poin s on a egional le el
in daily, weekly, o mon hly in e als we e sough o en i onmen al a iables and dengue
e e incidence. Yea ly da a was no conside ed, as he goal was o cap u e pa e ns and
Iden i ying
en i onmen al d i e s
o disease
ansmission
De eloping he
p edic i e model
Assessing he impac
o clima e change on
dengue incidence
Da a
Collec ion
Da a
Analysis
Da a
P epa a ion
P edic i e
Modeling
Model
E alua ion
13
seasonal ends o e ime o unde s and disease associa ions and e ec i ely add ess he
esea ch ques ion.
Chap e 2 discusses key en i onmen al ac o s in luencing disease sp ead, e ealing a ia ions
in he choice o a iables. Some s udies examined dengue co ela ions wi h en i onmen al
a iables, while o he s inco po a ed ex eme wea he e en s, conside ing hei excessi e
occu ence as a po en ial indica o o clima e change. Fu he mo e, as highligh ed in he
p e ious chap e , analyses yielded incohesi e esul s. While ce ain en i onmen al a iables
eme ged in luen ial in some ins ances, o he s ailed o con i m such asse ions, adding
complexi y o he a iable selec ion. Gi en hei demons a ed ele ance in e iewed
li e a u e, empe a u e and p ecipi a ion ha e been chosen o he model es ablishmen .
Wea he and clima e ex emes a e no di ec ly in eg a ed h ough ules o de ini ions.
Ex emes can be iden i ied h ough pa e ns and cha ac e is ics such as he du a ion and
in ensi y lea ned du ing he aining phase. Dengue e e was no di e en ia ed om mo e
se e e o ms o he i us in ec ion, including dengue hemo hagic e e and dengue shock
synd ome. Da a poin s o hese se e e o ms we e conside ed dengue e e and summed up.
Addi ionally, se o ypes o dengue i us (DENV) DENV 1, DENV 2, DENV 3, and DENV 4 we e
no dis inguished. The seconda y da a was acqui ed om mul iple sou ces a quali y and
accu acy ha canno be gua an eed o e i ied. The da a explo a ion e ealed some
inconsis encies, pa ially isualized in subsequen phases, acili a ing he Py hon lib a ies
Ma plo lib (Hun e , 2007) and Seabo n (Waskom, 2021). The en i e explo a o y analysis, along
wi h he co esponding Jupy e no ebooks, is p o ided in Appendix B.
In conclusion, comp ehensi e da a poin s we e es ablished o 17 loca ions wi hin Malaysia,
he Philippines, and Singapo e. Sequen ial weekly dengue incidence was acqui ed om
na ional depa men s. Daily en i onmen al da a, including p ecipi a ion, maximum
empe a u e, minimum empe a u e, and a e age empe a u e, was ob ained om he
Na ional Oceanic and A mosphe ic Adminis a ion (NOAA) o each coun y, enhancing da a
consis ency (Table 1).
14
Table 1 - Da a collec ion sou ces
Coun y
En i onmen al a iables
Dengue incidence
Time ame
Da a sou ce
Missing
alues
Da a sou ce
Missing
alues
Malaysia
NOAA
Yes
Minis y o
Heal h,
Malaysia
Yes
2017-2022
Philippines
NOAA
Yes
Depa men o
Heal h-
Epidemiology
Bu eau,
Philippines
No
2016-2020
Singapo e
NOAA
Yes
Minis y o
Heal h,
Singapo e
No
2016-2023
3.4. DATA PREPARATION
3.4.1. Da a P ep ocessing
A he beginning o he Da a P epa a ion phase, he da a was ans o med in o a o ma
sui able o he modeling. Fo each loca ion, he Excel ile was s anda dized as ollows:
▪ The i s shee , d _1, con ained daily en i onmen al da a.
▪ The second shee , d _2, con ained weekly incidence da a.
▪ The hi d shee , d _3, combined and agg ega ed he weekly incidence and daily
en i onmen al da a o a s anda d mon hly scale o enhance in e p e abili y.
Consequen ly, he da a quali y and p epa a ion o he i s wo da ase s di ec ly a ec ed he
hi d da ase . Las ly, he mon hly en i onmen al da a was ans o med in o en meaning ul
a iables. Table 2 displays he inal o ma o he hi d da ase , which a iables we e used o
he modeling.
15
Table 2 - T ans o med en i onmen al a iables
The ini ial p ep ocessing ocused on he i s wo da ase s holding aw da a using Pandas (The
pandas de elopmen eam, 2024) and NumPy (Ha is e al., 2020) lib a ies. The main goal o
dealing wi h ime se ies da a was o main ain cohesi e da a poin s conside ing he empo al
o de o obse a ions while cap u ing seasonali y and ends o explain disease in e ac ions.
A i s , missing alues we e impu ed o ensu e consis en da a. The ype o missing alues
was classi ied as Missing Comple ely a Random (MCAR) based on he absence o dis inc
pa e ns a speci ic loca ions, imes, o a iables. The p obabili y o missingness was assumed
o be he same o all samples. Each a iable exhibi ed dis inc pa e ns o missing alues, wi h
some showing con inuous gaps. Consequen ly, mul iple me hods we e explo ed and es ed
indi idually o all a iables. These me hods included Las Obse a ion Ca ied Fo wa d (LOCF)
and Nex Obse a ion Ca ied Backwa d (NOCB), Simple Mo ing A e age (SMA), Exponen ial
Mo ing A e age (EMA), Rolling Mean S a is ics, wi h Seasonal and T end Decomposi ion using
Loess (STL) Decomposi ion, and Mul i a ia e Impu a ion by Chained Equa ions (MICE)
Impu a ion using I e a i eImpu e om Sciki -lea n (Ped egosa e al., 2011).
Va iable
Uni
Desc ip ion
Min_Daily_P cp
Millime e s (mm)
Minimum daily p ecipi a ion
Max_Daily_P cp
Millime e s (mm)
Maximum daily p ecipi a ion
Mon hly_A g_P cp
Millime e s (mm)
Mon hly a e age p ecipi a ion based on daily
empe a u e epo ing
Mon hly_To al_P cp
Millime e s (mm)
Mon hly o al p ecipi a ion based on
cumula ed daily p ecipi a ion
Mon hly_A g_Temp
Deg ees Celsius (°C)
Mon hly a e age empe a u e based on daily
empe a u e epo ing
Min_Daily_Temp
Deg ees Celsius (°C)
Minimum daily p ecipi a ion o he espec i e
mon h
Max_Daily_Temp
Deg ees Celsius (°C)
Maximum daily p ecipi a ion o he espec i e
mon h
Min_A e age_Temp
Deg ees Celsius (°C)
Minimum alue o he a e age daily
empe a u e
Max_A e age_Temp
Deg ees Celsius (°C)
Maximum alue o he a e age daily
empe a u e
N_Raining_Days
Deg ees Celsius (°C)
Cumula i e numbe o ainy days in he
espec i e mon h
22
Figu e 10 - Loca ion-speci ic ime-based spli
The aining phase included a ime se ies c oss- alida ion using TimeSe iesSpli om he
Sciki -lea n lib a y (Ped egosa e al., 2011). He e, he model was i ed o he aining da a and
e alua ed on he alida ion da a in 5 spli s, using an expanding aining window o ensu e
obus gene aliza ion on unseen da a. The spli is displayed in Appendix G using he example
o Singapo e, Loca ion Code 11. The encoded a iable Loca ion Code was d opped o mi iga e
he bias o o dinal anking.
Fo ecas ing models we e de eloped wi h he aim o p ocessing ime se ies eg ession da a
wi h a non-linea ela ionship be ween a ge and independen a iables. Time se ies da a
was ans o med in o a supe ised lea ning p oblem h ough app op ia e da a p epa a ion
and spli ing, making i sui able o adi ional machine lea ning models. Suppo Vec o
Reg ession (SVR), K-Nea es Neighbo (KNN), AdaBoos Reg esso , Decision T ee (DT), and i s
ensemble, RF, we e implemen ed using Sciki -lea n (Ped egosa e al., 2011). XGBoos was
in eg a ed using he XGBoos lib a y (Chen & Gues in, 2016). These models we e ini ially
employed wi h de aul pa ame e s o assess di e en capping app oaches, ea u e selec ion
echniques, and scaling me hods based on he alida ion se . Subsequen ly, hype pa ame e
uning was conduc ed using G idSea chCV om he Sciki -lea n lib a y (Ped egosa e al., 2011)
o op imize model pe o mance by adjus ing pa ame e s while p e en ing o e i ing. Tuned
pa ame e s a e p esen ed in Appendix H.

23
The ini ial esea ch employing deep lea ning models using clima e da a o o ecas long- and
sho - e m dengue incidence and ou b eaks was conduc ed by T an e al. (2022) in Vie nam.
Thei ime se ies analysis e ealed ha Con olu ional Neu al Ne wo k (CNN), Long Sho -Te m
Memo y (LSTM), and Long Sho -Te m Memo y wi h A en ion (LSTM-ATT) we e he bes -
pe o ming models (T an e al., 2022), in o ming he decision o inco po a e deep lea ning
models in o he unde lying analysis o ensu e alignmen wi h he la es esea ch on dengue
e e p edic ion. Deep Lea ning models based on neu al ne wo ks we e implemen ed using
Ke as API wi h Tenso Flow (Abadi e al., 2015). Applied deep lea ning models we e di ided
in o Recu en Neu al Ne wo k (RNN), Feed o wa d Neu al Ne wo k (FNN), and CNN.
SimpleRNN, Ga ed Recu en Uni (GRU), and LSTM we e used o he RNN. A Mul i-Laye
Pe cep on (MLP) wi h wo hidden laye s was implemen ed o he FNN, wi h 1-dimensional
CNN being used o he CNN, passing in o ma ion o wa d. The models we e ained and
e alua ed using ime se ies c oss- alida ion wi h 5 spli s. Ea ly s opping c i e ia we e
implemen ed o a oid o e i ing, which would s op he aining p ocess i no imp o emen
was obse ed a e 5 consecu i e epochs. The con igu a ion o each model is p o ided in
Appendix I.
3.5.2. Model E alua ion
The examined alida ion me hods ha e been discussed in he model de elopmen . In
conclusion, he ain- es spli and ime se ies c oss- alida o wi h 5 spli s we e applied o
assess model pe o mance, ensu ing empo al o de o a oid da a leakage while spli ing
loca ion-speci ic da a. E alua ion me ics o eg ession mean absolu e e o (MAE) (Equa ion
2) and oo mean squa ed e o (RMSE) (Equa ion 3) om Sciki -lea n (Ped egosa e al., 2011)
we e employed o quan i y he models' p edic i e abili y on unseen da a. The RMSE penalizes
la ge e o s h ough he squa e oo , making i pa icula ly ele an o eg ession asks. MAE
was chosen due o i s easy in e p e abili y, di ec ly ep esen ing he a e age e o .
(2) 𝑀𝐴𝐸=1
𝑛∑ |𝑦𝑖−𝑦𝑖|
𝑛
𝑖=1 (3) 𝑅𝑀𝑆𝐸=√1
𝑛∑ (𝑦𝑖−𝑦𝑖)
𝑛
𝑖=1 2
▪ 𝑛 is he numbe o obse a ions.
▪ 𝑦𝑖 is he ac ual alue.
▪ 𝑦𝑖 is he p edic ed alue.
▪ |𝑦𝑖−𝑦𝑖| is he absolu e di e ence be ween he ac ual and p edic ed alues.
▪ (𝑦𝑖−𝑦𝑖)2 is he squa ed di e ence be ween he ac ual and he p edic ed alues.
3.6. EVALUATION
The CRISP-DM amewo k es ablished a sys ema ic and obus ounda ion o add ess he
domain objec i es. En i onmen al ac o s d i ing disease sp ead we e analyzed. The p ojec ’s
aim o de elop a o ecas using dengue incidence and en i onmen al a iables in Sou heas
Asia was achie ed. Subsequen ly, he p ojec p oceeds o he deploymen phase o add ess
he las objec i e.
24
The inal e iew e ealed ha all da a mining goals we e success ully ealized. The da a sea ch
yielded su icien and eliable da a poin s o 17 Sou heas Asian loca ions. Guided by he
ini ial explo a o y analysis, a ious da a p epa a ion echniques we e es ed. Mul iple
machine and deep lea ning models we e de eloped using di e en app oaches, ollowed by
model op imiza ion h ough hype pa ame e uning. Las ly, p edic i e capabili y was
e alua ed h ough epe i i e aining and alida ion using he e alua ion me ics MAE and
RMSE.
3.7. DEPLOYMENT
In he las phase, he o ecas was deployed o assess he disease's esponse o changing
clima e. The in eg a ed clima e change simula ions we e ob ained om Sen ian e al. (2022),
who analyzed clima e change in e ac ions wi h monsoon seasons o assess he impac o
empe a u e and ain all a iabili y in Sou heas Asia. Thei esea ch le e aged he p ojec ed
emission scena ios RCP4.5 and RCP8.5 om he IPCC AR5. Based on hese wo scena ios, hey
simula ed he pe cen age change o Sou heas Asia's mon hly mean o al p ecipi a ion and
mean su ace empe a u e in Janua y and July o 2030, 2050, 2070, and 2100 ela i e o 2013
(Sen ian e al., 2022).
The unde lying esea ch employed hese simula ed changes as a amewo k o p edic
espec i e changes in annual incidence a es. The a iables Mon hly_To al_P cp and
Mon hly_A g_Temp we e modi ied wi h he simula ed changes in he es se , ce e is pa ibus.
I he simula ed changes in o al p ecipi a ion esul ed in nega i e alues, hey we e se o 0.
A e he aining phase, he disease esponse assessmen was pe o med on he en i e es
se comp ising 12 da a poin s, ep esen ing a yea . Gi en he ele ance o lagged
obse a ions, hei simula ed changes we e cap u ed by p edic ing subsequen mon hs.
Finally, he p edic ed changes in annual incidence a es we e compa ed o he ac ual
p edic ions o he a ge a iable o assess disease dynamics unde clima e change gi en wo
scena ios. In conclusion, he inal objec i e was add essed.
25
4. RESULTS AND DISCUSSION
The esea ch ques ion led o h ee domain objec i es.
4.1. ENVIRONMENTAL DRIVERS
The i s objec i e was o explo e he en i onmen al d i e s o disease sp ead. Al hough he
li e a u e discusses he impo ance o en i onmen al a iables, emphasizing empe a u e and
p ecipi a ion, s a is ical echniques did no yield signi ican esul s. In he explo a o y analysis,
independen a iables demons a ed unsa is ac o y ou comes in he co ela ion analysis, wi h
Max_Daily_Temp_lag_4 showing he highes co ela ion o 0.11 wi h he a ge a iable
(Appendix J). Con a y o o he s udies conduc ed in Singapo e (Seah e al., 2021), maximum
daily empe a u e posi i ely co ela ed wi h dengue incidence ac oss all lags (Appendix J).
Howe e , esea ch indings a e consis en wi h Wang e al. (2022), showing a dec eased
co ela ion o minimum daily empe a u e a lag 2, ollowed by an inc ease in co ela ion a
subsequen lags. Simila o hei obse a ion o ex emely high empe a u es being associa ed
wi h inc eased isk a a 2-3 weeks lag (Wang e al., 2022), his esea ch yielded compa able
esul s, showing an inc easing co ela ion be ween maximum empe a u e and incidence un il
lag 4, ollowed by a dec ease in co ela ion (Appendix J). The ou ea u e selec ion me hods
epea edly highligh ed some a iables, indica ing hei signi icance (Appendix F). Las ly,
a ying incidence was obse ed ac oss di e en yea s and loca ions (Appendix D), leading o
no e idence o a causal ela ionship wi h he ainy season as ou lined in p e ious li e a u e (Li
e al., 2018; Wo ld Heal h O ganiza ion, 2024a).
4.2. PREDICTIVE MODELING
Fo he second objec i e, he ini ial app oach was es ablishing one holis ic model ac oss all
loca ions o c ea e a dengue incidence o ecas o Sou heas Asia. The p ima y e alua ion
was based on he alida ion se wi hou pa ame e op imiza ion o main ain a compa able
se ing o analyzing he pe o mance o di e en da a p epa a ion echniques and models. In
he p ep ocessing, wo ou lie capping logics we e applied o mi iga e bias and enhance da a
quali y. The combina ion o bo h app oaches e ealed he bes pe o mance. Consequen ly,
aside om da a agg ega ion and he capping o ela i e ou lie s, he model pe o mance
ad anced om add essing ex emes on a mul i-loca ion le el o he holis ic model.
T aining all a iables, he b oade esea ch amewo k esul ed in unde i ing, wi h low
p edic i e powe o all models. Wi hou hype pa ame e uning, KNN and RF eme ged as he
bes -pe o ming models, wi h an MAE o 17.62 and 18.90 and RMSE o 41.58 and 42.13 ac oss
loca ions, ep esen ing la ge e o s. Al hough ea u e impo ance analysis showed high
ele ance o he a iable Loca ion Code in bo h me hods (Appendix E), he la ge e o
sugges s ha he model could no di e en ia e be ween loca ions e ec i ely. Consequen ly,
he di icul y in lea ning loca ion-speci ic pa e ns a ose, suppo ed by dec eased and a ying
26
aining leng hs (Figu e 9), which migh ha e led o he unde ep esen a ion o some
loca ions.
The independen app oach encompassing mul iple loca ion-speci ic p edic ions showed
be e esul s. E alua ion me ics we e a e aged ac oss 5 spli s and loca ions o de e mine
supe io model pe o mance. Fo each ime se ies model, loca ion-speci ic ou lie capping was
pe o med. A e hype pa ame e uning, AdaBoos achie ed he bes esul on he alida ion
se o scale-in a ian models wi h an a e age MAE o 13.19 and an RMSE o 16.75 using RF
ea u e selec ion. Fo scale- a ian models, SVR eme ged as he bes model wi h an a e age
MAE o 13.03 and an RMSE o 16.35 using Robus Scale . The ea u e selec ion me hods
exhibi ed inc eased pe o mance o ce ain models and me hods. Howe e , all ou me hods
demons a ed in e io esul s compa ed o aining all a iables wi h SVR, which yielded he
bes pe o mance ac oss all machine lea ning models.
Deep lea ning models we e ained, wi h SimpleRNN showing an a e age MAE o 15.88 and
an RMSE o 19.31, ou pe o ming GRU and LSTM. Fo he FNN, MLP yielded an a e age MAE
o 13.25 and an RMSE o 16.83. CNN eme ged as he bes deep lea ning model o p edic ing
dengue incidence, wi h an a e age MAE o 10.10 and an RMSE o 13.61. The inal assessmen
e ealed ha deep lea ning models ou pe o med machine lea ning models. Appendix K
p esen s he loca ion-speci ic MAE and RMSE sco es on he alida ion se s o he ou supe io
models.
All p ojec phases ha e been hough ully pe o med o mi iga e bias, enhance da a quali y,
and yield an e ec i e p edic ion. Howe e , he pe o mance o he p edic i e models showed
consis en ly in e io esul s o ce ain loca ions on he alida ion se (Appendix K). Re iewing
he da a explo a ion did no explain a ying p edic i e abili ies, as nei he a common pa e n,
such as in he case o missing alues, no a coun y-speci ic co ela ion was obse ed.
Unde epo ing, as a gued by Bha e al. (2013), may ha e comp omised he in eg i y o he
aw da a. Consequen ly, p edic ions migh ha e been a ec ed by inaccu acies in he
seconda y open-sou ce da a.
Models showed an o e all dec ease in e o s on he es se , which is a good indica o o
a oiding o e i ing. AdaBoos and SVR showed an MAE o 10.22 and 9.31 and RMSE o 12.49
and 11.34 on he es se . MLP yielded an MAE o 6.82 and an RMSE o 8.87. CNN was chosen
o he deploymen , wi h an MAE o 5.06 and an RMSE o 7.09 on he es se . La ge e o s
we e educed in almos all loca ions, which could be explained by inc eased da a su iciency.
Howe e , a loca ion-speci ic shi in e o s was obse ed in some ins ances. This sugges s ha
eliable p edic ion was no ied o ce ain loca ions. Ins ead, he a ying e o s could ha e
been ela ed o o he ac o s ha we e no included. The loca ion-speci ic e alua ion me ics
on he es se a e de ailed in Appendix L.
27
4.3. CLIMATE CHANGE IMPACT ASSESSMENT
Fo he hi d objec i e, CNN was le e aged o p edic he changes in annual incidence a es
based on he simula ed changes in mean empe a u e and o al p ecipi a ion o Sen ian e al.
(2022). Kuching, Malaysia, was chosen o he deploymen wi h he lowes p edic i e e o
ac oss all loca ions. The pe cen age changes in he annual incidence a es in Kuching,
Malaysia, o bo h emission scena ios a e p esen ed in Appendix M.
Fo bo h scena ios, a posi i e linea end be ween he change in he annual dengue incidence
a e in Kuching, Malaysia, and he mean empe a u e was obse ed (Figu e 11). Howe e , no
clea end was ound be ween he changes in o al p ecipi a ion and he a ge a iable,
showing sca e ed da a poin s in Figu e 12. These obse a ions a e consis en wi h Ebi and
Nealon (2016), who epo ed ha daily mean empe a u e and empe a u e a ia ions a e he
main d i e s o dengue incidence.
In conclusion, assessing disease dynamics based on changes in o al p ecipi a ion, mean
empe a u e, and lagged obse a ions ep esen s a subs an ial achie emen . This success
unde sco es he model's sensi i i y in p edic ing dengue incidence and demons a es i s
abili y o assess he impac o clima e change on disease ansmission.
Figu e 11 - Simula ed changes in mean empe a u e and annual incidence a e

28
Figu e 12 - Simula ed changes in o al p ecipi a ion and annual incidence a e
29
5. CONCLUSIONS
This s udy aimed o align wi h he la es esea ch on dengue e e p edic ion, combining open-
sou ce da a wi h da a science ools. The CRISP-DM schema employed a sys ema ic app oach
and s anda dized s uc u e, enhancing he p ojec ’s obus ness and u u e main enance. The
a ie y o applied me hods con ibu es o success ully answe ing he esea ch ques ion.
CNN eme ged as he bes -pe o ming model wi h an MAE o 5.06 and an RMSE o 7.09 on he
es se , demons a ing ha p edic i e modeling using en i onmen al a iables can e ec i ely
assess dengue e e incidence. Deep lea ning models yielded a sligh dec ease in e o s
compa ed o adi ional machine lea ning models. The educ ion o e o s achie ed
h oughou he p ojec highligh s he necessi y o p o ound da a p epa a ion and he
e ec i eness o he chosen app oach.
Applied models consis en ly showed in e io esul s o ce ain loca ions on he alida ion se .
The e iew o he explo a o y analysis did no explain a ying p edic i e abili ies. Ex ensi e
da a p epa a ion and he mul i ude o applied models led o he assump ion ha high
p edic i e e o s o ce ain loca ions we e due o seconda y da a ob ained. Fo ins ance,
Bha e al. (2013) a gued ha he e is unde epo ing o dengue cap u e. In con as , a
loca ion-speci ic shi in e o s was obse ed on he es se wi h o e all dec eased e o s,
showing a ia ions wi h inc eased e o s in some ins ances. This esul indica ed ha he
models gene alized well wi h inc easing da a su iciency, sugges ing ha a ying e o s we e
ela ed o o he unconside ed ac o s.
The clima e change impac assessmen esul s showed ha o al p ecipi a ion and mean
empe a u e changes enabled a disease esponse, indica ing a sensi i e model. The simula ed
changes showed a ying e ec s on annual incidence a es o bo h scena ios, wi h a posi i e
linea end obse ed o mean empe a u e. In conclusion, he model e ec i ely assessed
disease dynamics unde clima e change.
30
6. LIMITATIONS AND RECOMMENDATIONS FOR FUTURE WORK
This wo k encoun e ed some no able limi a ions. Fi s ly, p e iously applied con ol measu es
ha in luenced egional condi ions and dengue dis ibu ion we e no conside ed, which migh
ha e a ec ed he p edic i e abili y. Addi ionally, limi ed da a a ailabili y could ha e educed
p edic i e powe . Dec eased aining se s due o mul iple independen ime se ies may ha e
limi ed he model's abili y o lea n sequen ial dynamics. As dec eased e o s we e obse ed
on he es se s, inc eased da a olume h ough con inuous main enance could imp o e
u u e loca ion-speci ic model pe o mance.
The independen app oach o loca ion-speci ic ime se ies makes he o ecas egionally
lexible. Howe e , his p ojec was based on en i onmen al a iables ha ha e demons a ed
ele ance o p edic ing dengue e e in Sou heas Asia. The e o e, i is no ecommended o
expand he o ecas globally. Ins ead, u u e wo k could enhance he Sou heas Asian analysis
and build on T an e al. (2022), who demons a ed supe io model pe o mance by adding an
a en ion laye a e he LSTM ne wo k. In addi ion, u he wo k may expand he analysis
wi h addi ional obse a ions. The li e a u e ou lines a posi i e associa ion be ween dengue
e e and socioeconomic ac o s, which migh enhance modeling e o s. Va ying incidences
ac oss loca ions may esul om unique geog aphical condi ions di ec ly a ec ing mosqui o
habi a s. Adding demog aphic and opog aphic a iables could imp o e he p edic ion o
dengue incidence. Mo eo e , imp o ed da a documen a ion migh allow he di e en ia ion
o dengue-incidence-induced Aedes species o enhance he in e p e abili y o disease
dynamics.
The clima e change simula ions and hei applica ion encompass limi a ions. While o al
p ecipi a ion and mean empe a u e changes enabled a disease esponse, hei e ec s migh
be in e ela ed. Fu u e wo k should analyze associa ions be ween independen a iables and
hei in e ela ed e ec s o enhance dengue isk assessmen . In his p ojec , he impac o
clima e change on he annual incidence a es was assessed on he es se and no empo ally
aligned wi h he p ojec ions un il 2100 due o he assump ion o con inuously inc easing e o
o he unde lying p edic ion and equi ed wea he o ecas s. A long- e m goal o he hi d
objec i e would be o in eg a e a egional clima e model a he han elying on seconda y
clima e change simula ions. This way, ime pe iods could be homogenized o inc ease
ep esen a i eness. Fu he mo e, he addi ional emission scena ios RCP2.6 and RCP6.0 could
be inco po a ed. In addi ion, clima e change simula ions we e limi ed o wo a iables a
speci ic imes amps. Since independen a iables in he unde lying analysis a e ela ed,
simul aneous change is expec ed. Simula ing changes o he emaining a iables would
enhance he model's p edic i e abili y, allowing a comp ehensi e assessmen o disease
dynamics. Las ly, clima e change was de ined as a long- e m change in en i onmen al
a iables (Li e al., 2018). The e o e, simula ed changes a he speci ied imes amps do no
ep esen a eal-wo ld scena io.
31
Las ly, unde s anding he disease cycle, ini ia ing ac o s, and iden i ying isk a eas is c ucial
o mi iga e u u e dengue isk. Da a documen a ion and s o age need o be enhanced globally
and made publicly a ailable o acili a e knowledge exchange and enable esea ch. Da a
collec ion e ealed ha na ional incidence epo ing demands mo e s anda diza ion.
Fu he mo e, c oss-na ional collabo a ion could imp o e disease cap u e and analysis. In his
ega d, da a science could unlock g ea po en ial o add ess he u u e disease bu den.
38
APPENDIX C

39
APPENDIX D
40
APPENDIX E
41
APPENDIX F
Fea u e
Me hod
To al
Coun
Selec F om
Model
using RF
Selec F om
Model
using XG
Boos
RFE using
RF
Fo wa d
Fea u e
Selec ion
using RF
Loca ion Code
1
1
1
1
4
Max_Daily_Temp_lag_3
1
1
1
0
3
Mon hly_A g_Temp_lag_5
0
1
1
1
3
Mon hly_A g_Temp_lag_2
1
0
1
1
3
Mon hly_A g_Temp
1
0
1
1
3
Mon hly_To al_P cp_lag_6
1
1
1
0
3
Mon hly_A g_Temp_lag_3
1
1
1
0
3
N_Raining_Days_lag_6
1
1
1
0
3
Max_Daily_Temp_lag_4
0
1
0
1
2
Mon hly_A g_Temp_lag_1
1
1
0
0
2
Mon hly_To al_P cp
0
0
0
1
1
Mon hly_To al_P cp_lag_1
0
0
1
0
1
Mon hly_A g_Temp_lag_6
0
0
0
1
1
Max_Daily_P cp_lag_1
0
1
0
0
1
Max_Daily_Temp_lag_6
1
0
0
0
1
N_Raining_Days_lag_1
0
0
0
1
1
Min_Daily_Temp_lag_2
0
0
0
1
1
N_Raining_Days
0
0
0
1
1
Max_Daily_P cp_lag_2
0
0
1
0
1
No e. 1: Fea u e was selec ed by he co esponding me hod, 0: Fea u e was no selec ed.
42
APPENDIX G
43
APPENDIX H
Model
Hype pa ame e s
DT
{}
RF
{max_dep h: 20, max_ ea u es: 'sq ', min_samples_lea : 4, min_samples_spli :
10}
XGBoos
{colsample_by ee: 0.7, lea ning_ a e: 0.014, max_dep h: 9, min_child_weigh :
2, n_es ima o s: 141, eg_lambda: 3, subsample: 0.956}
AdaBoos
{lea ning_ a e: 0.01, loss: 'linea '}
KNN
{me ic: 'euclidean', n_neighbo s: 10, weigh s: 'dis ance'}
SVR
{C: 100, ke nel: ' b '}

44
APPENDIX I
Model
Model Con igu a ions
SimpleRNN
{Model: Sequen ial, Laye 1: SimpleRNN, 50 uni s, Ou pu Laye : Dense, 1
uni , Loss Func ion: MAE, Op imize : Adam, Epochs: 50, Ba ch Size: 72}
GRU
{Model: Sequen ial, Laye 1: GRU, 50 uni s, Ou pu Laye : Dense, 1 uni , Loss
Func ion: MAE, Op imize : Adam, Epochs: 50, Ba ch Size: 72}
LSTM
{Model: Sequen ial, Laye 1: LSTM, 50 uni s, Ou pu Laye : Dense, 1 uni ,
Loss Func ion: MAE, Op imize : Adam, Epochs: 50, Ba ch Size: 72}
MLP
{Model: Sequen ial, Laye 1: Dense, 64 uni s, ReLU ac i a ion, Laye 2:
Dense, 32 uni s, ReLU ac i a ion, Ou pu Laye : Dense, 1 uni , Loss Func ion:
MAE, Op imize : Adam, Epochs: 50, Ba ch Size: 72}
CNN
{Model: Sequen ial, Laye 1: Dense, 64 uni s, ReLU ac i a ion, Laye 2:
Dense, 31 uni s, ReLU ac i a ion, Ou pu Laye : Dense, 1 uni , Loss Func ion:
MAE, Op imize : Adam, Epochs: 50, Ba ch Size: 72}
45
APPENDIX J
46
APPENDIX K
MAE on he alida ion se
Loca ion Code
Name
AdaBoos
SVR
CNN
MLP
0
Baguio, RP
10.99
11.06
8.56
16.93
1
Dagupan, RP
13.07
10.39
14.51
20.45
2
Ko a Kinabalu
In e na ional, MY
3.75
3.53
2.46
12.47
3
Kuala Lumpu
In e na ional, MY
27.11
29.14
22.09
13.60
4
Kuan an, MY
6.89
7.45
4.14
13.44
5
Kuching, MY
3.18
2.86
2.03
11.83
6
Labuan, MY
5.51
4.10
1.83
14.44
7
Malacca, MY
9.25
8.07
5.05
16.01
8
Manila, RP
8.10
8.36
9.04
18.91
9
Me sing, MY
10.17
9.86
4.66
13.25
10
Penang
In e na ional, MY
12.80
11.37
5.74
12.52
11
Singapo e Changi
In e na ional, SN
19.17
19.99
18.63
18.92
12
Si iawan, MY
6.77
5.57
1.96
11.68
13
Sul an Abdul Aziz
Shah In e na ional,
MY
20.71
22.84
15.65
13.00
14
Sul an Ismail Pe a,
MY
11.63
10.80
7.77
17.44
15
Su igao, RP
21.71
24.01
18.68
23.48
16
Zamboanga, RP
33.48
32.10
28.84
21.36
A e age MAE
13.19
13.03
10.10
13.25
47
RMSE on he alida ion se
Loca ion Code
Name
AdaBoos
SVR
CNN
MLP
0
Baguio, RP
14.56
13.13
11.40
21.62
1
Dagupan, RP
15.73
13.27
17.65
25.91
2
Ko a Kinabalu
In e na ional, MY
4.56
4.26
3.22
16.87
3
Kuala Lumpu
In e na ional, MY
31.21
33.07
26.07
17.32
4
Kuan an, MY
7.73
8.30
4.79
18.20
5
Kuching, MY
3.71
3.26
2.26
15.56
6
Labuan, MY
6.66
4.92
2.49
19.65
7
Malacca, MY
10.80
9.31
6.11
21.79
8
Manila, RP
9.79
10.27
11.43
26.00
9
Me sing, MY
11.62
11.34
5.92
16.83
10
Penang
In e na ional, MY
14.50
13.9
7.60
16.50
11
Singapo e Changi
In e na ional, SN
25.63
25.81
25.05
25.89
12
Si iawan, MY
7.50
6.59
2.69
15.78
13
Sul an Abdul Aziz
Shah In e na ional,
MY
25.34
26.93
21.27
17.14
14
Sul an Ismail Pe a,
MY
13.81
12.84
10.37
23.86
15
Su igao, RP
28.68
30.51
24.66
29.73
16
Zamboanga, RP
52.85
50.16
48.39
29.52
A e age MAE
16.75
16.35
13.61
16.83