*Co esponding au ho : Elizabe h A. Adeola.
Copy igh © 2025 Au ho (s) e ain he copy igh o his a icle. This a icle is published unde he e ms o he C ea i e Commons A ibu ion Liscense 4.0.
T ans o ming esilience wi h p edic i e digi al win echnologies
I eoluwa Elemu e 1, Elizabe h A. Adeola 2, 3, *, Adeyinka G. Ologun 3, 4, Owoade O. Odesanya 3, 5, Pe e T.
Oluwasola 3, 6 and Olabisi D, Salau 7
1 School o Mechanical and Design Enginee ing, Uni e si y o Po smou h, Po smou h PO1 3DJ, UK.
2 Depa men o Cons uc ion P ojec Managemen – Bi mingham Ci y Uni e si y, Bi mingham, UK.
3 Facul y o Business and Media, Selinus Uni e si y o Sciences and Li e a u e, I aly.
4 Depa men o Business School, Uni e si y o Wol e hamp on, England, Uni ed Kingdom.
5 Depa men o Social Ca e, Heal h and Well-being, Uni e si y o Bol on, UK.
6 Depa men o Mic obiology, Fede al Uni e si y o Technology, Aku e, Nige ia.
7 School o Managemen Sciences and Accoun ing, Wazi i Uma u Fede al Poly echnic, Nige ia.
Wo ld Jou nal o Biology Pha macy and Heal h Sciences, 2025, 23(03), 450-458
Publica ion his o y: Recei ed on 09 Augus 2025; e ised on 18 Sep embe 2025; accep ed on 20 Sep embe 2025
A icle DOI: h ps://doi.o g/10.30574/wjbphs.2025.23.3.0850
Abs ac
This esea ch examines he ole o digi al win echnology in enhancing disas e p epa edness and esponse
amewo ks, wi h a ocus on scena ios in ol ing sunamis, ea hquakes, and loods. The p ima y objec i e was o
e alua e how digi al wins in eg a e eal- ime da a, p edic i e modelling, and s akeholde engagemen o enhance
esilience. A sys ema ic li e a u e e iew was conduc ed in acco dance wi h PRISMA guidelines, sc eening 342 s udies
and na owing he selec ion o 120 high-quali y sou ces ha me he inclusion c i e ia. The analysis e ealed ha digi al
win models imp o ed o ecas accu acy by an a e age o 28% compa ed o adi ional disas e models, pa icula ly in
sunami inunda ion mapping and u ban lood simula ions. Communi y engagemen h ough in e ac i e pla o ms was
epo ed in 62% o he e iewed cases, wi h di ec e idence o as e e acua ion and esou ce alloca ion. Pos -disas e
eco e y applica ions demons a ed measu able e iciency gains, educing in as uc u e es o a ion imes by
app oxima ely 15%. Howe e , da a gaps and in e ope abili y issues we e iden i ied as ecu ing limi a ions,
con ibu ing o an es ima ed e o ma gin o 8–12% in p edic i e ou pu s. O e all, he indings con i m ha digi al
wins o e a ans o ma i e pa hway o p oac i e disas e managemen . While challenges in da a quali y and
go e nance emain, hei in eg a ion in o na ional amewo ks could signi ican ly enhance bo h p epa edness and
esilience.
keywo ds: Digi al Twin Technology; Disas e P epa edness; Resilience Modeling; P edic i e Simula ion; Ea ly Wa ning
Sys ems; Risk Managemen
1. In oduc ion
Disas e s, whe he na u al o an h opogenic, ha e always posed a signi ican h ea o human li e, economic s abili y,
and socie al de elopmen . Among hese haza ds, sunamis emain one o he mos ca as ophic due o hei sudden
onse , immense des uc i e capaci y, and a - eaching socio-economic impac s. His o ical e en s such as he 2004
Indian Ocean sunami and he 2011 Tōhoku ea hquake and sunami in Japan highligh ed he de as a ing consequences
o inadequa e ea ly-wa ning sys ems and uncoo dina ed disas e esponse amewo ks [1], [2]. These e en s ha e
d i en a global emphasis on enhancing disas e p epa edness, le e aging echnological inno a ions, and s eng hening
o ecas ing and esponse s a egies [3], [4]. Recen ad ances in compu a ional science, emo e sensing, and da a-d i en
modelling ha e accele a ed he shi om eac i e disas e managemen o p oac i e p epa edness [5]. One o he mos
ans o ma i e echnologies in his domain is he Digi al Twin (DT)—a dynamic, i ual ep esen a ion o physical
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sys ems ha in eg a es eal- ime da a, simula ions, and p edic i e analy ics [6], [7]. Ini ially applied in indus ial
manu ac u ing and ae ospace [8], [9], digi al win echnology has ecen ly gained a en ion o i s po en ial in disas e
managemen and esilience planning [10], [11]. By con inuously synch onising wi h eal-wo ld da a, a digi al win
enables he simula ion o complex disas e scena ios, assessmen o ulne abili ies, and op imisa ion o esponse
measu es [12].
The applica ion o DTs o sunami o ecas ing and na ionwide disas e esponse is pa icula ly p omising. Con en ional
sunami o ecas ing sys ems ely hea ily on seismic senso s, ide gauges, and ocean buoys o eal- ime da a acquisi ion
[13], [14]. While e ec i e, hese sys ems o en ace limi a ions in p edic i e accu acy, da a in eg a ion, and apid
decision-making unde unce ain y [15], [16]. A digi al win amewo k enhances hese capabili ies by p o iding a
dynamic en i onmen whe e seismic da a, oceanog aphic models, and socio-economic a iables can be in eg a ed in o
p edic i e simula ions [17], [18]. These simula ions allow decision-make s o isualise po en ial disas e ou comes,
assess communi y ulne abili y, and es a ious esponse s a egies in eal- ime [19], [20].
Fu he mo e, he in eg a ion o In e ne o Things (IoT) senso s, a i icial in elligence (AI), and machine lea ning (ML)
wi h digi al win pla o ms enhances p edic i e analy ics and si ua ional awa eness [21], [22]. IoT-enabled senso s
deployed ac oss seismic zones and coas al egions p o ide con inuous s eams o high- esolu ion da a [23], [24]. AI and
ML algo i hms p ocess his da a o iden i y ea ly wa ning signals, op imise simula ion models, and gene a e p edic i e
insigh s wi h educed unce ain y [25], [26]. This usion o eal- ime moni o ing and p edic i e modelling enables
adap i e disas e p epa edness, mo ing beyond s a ic isk assessmen s o dynamic esilience planning [27], [28].
The sys em a chi ec u e o a na ionwide sunami o ecas and esponse model in eg a es seismic da a acquisi ion,
o ecas modelling, ea ly wa ning dissemina ion, and coo dina ed esponse [29]. Howe e , wi hou digi al win
in eg a ion, he sys em o en lacks adap i e eedback loops ha can e ine o ecas s and imp o e decision-making
du ing un olding disas e scena ios [30]. The inco po a ion o DT echnology p o ides a closed-loop sys em, whe e
i ual simula ions a e con inuously alida ed by eal-wo ld obse a ions, he eby enhancing accu acy and eliabili y
[31], [32]. This in e ac ion cycle os e s a mo e esilien and adap i e disas e esponse mechanism, ensu ing ha
esponse ac ions e ol e in pa allel wi h eal- ime condi ions [33].
Addi ionally, disas e p epa edness ex ends beyond immedia e esponse o long- e m communi y esilience and
eco e y [34]. A digi al win enables policymake s o simula e “wha -i ” scena ios, assess long- e m in as uc u al
ulne abili ies, and p io i ise in es men s in disas e mi iga ion [35]. Fo ins ance, p edic i e simula ions can e alua e
he e ec i eness o coas al de ences, e acua ion plans, and eme gency logis ics unde a ying haza d in ensi ies [36],
[37]. By doing so, he DT amewo k no only s eng hens eal- ime decision-making bu also in o ms sus ainable
disas e isk educ ion s a egies [38]. Despi e i s po en ial, he implemen a ion o digi al win amewo ks o disas e
p epa edness aces challenges, including da a in e ope abili y, compu a ional demands, and go e nance issues [39].
Ensu ing seamless in eg a ion ac oss di e se da a sou ces, secu ing sensi i e in o ma ion, and es ablishing mul i-
agency collabo a ion emain signi ican hu dles [40]. Add essing hese ba ie s equi es in e disciplina y e o s
in ol ing go e nmen agencies, scien i ic ins i u ions, and p i a e s akeholde s o build s anda dised, scalable, and
e hically go e ned digi al win sys ems.
This esea ch p oposes a comp ehensi e concep ual and sys em amewo k o digi al win-enabled disas e
p epa edness, wi h a ocus on sunami o ecas ing and na ionwide esponse mechanisms. By in eg a ing eal- ime
senso da a, ad anced simula ion models, and decision-suppo sys ems, he amewo k aims o enhance he accu acy
o sunami o ecas s, op imise eme gency esponses, and s eng hen na ional esilience. The s udy con ibu es no only
o he heo e ical ad ancemen o digi al win applica ions in disas e managemen bu also p o ides p ac ical insigh s
o policymake s, eme gency planne s, and coas al communi ies.
Figu e 1 p esen s he concep ual amewo k o a digi al win o disas e p epa edness, illus a ing he low om he
digi al win o simula ion, ollowed by analysis, and ul ima ely suppo ing e ec i e decision-making.
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Figu e 1 Concep ual amewo k o digi al wine o disas e p epa edness
2. Me hodology
The me hodological app oach adop ed in his esea ch was designed o ensu e a sys ema ic, anspa en , and
ep oducible p ocess o iden i ying, e alua ing, and syn hesising ele an li e a u e ela ed o digi al win echnologies
in disas e p epa edness. This me hodology is s uc u ed in o ou main s ages: esea ch design, li e a u e
iden i ica ion, es ablishmen o inclusion and exclusion c i e ia, and da a syn hesis. By ollowing his s uc u ed p ocess,
he s udy ensu es igou and minimises bias in he selec ion o wo ks, while cap u ing he mos ele an insigh s o
suppo i s esea ch objec i es.
2.1. Resea ch Design
This s udy employed a s uc u ed li e a u e e iew me hod, combining p inciples o sys ema ic e iew wi h na a i e
analysis o cap u e bo h he b ead h and dep h o he opic. While sys ema ic e iews o e eplicable and igo ous
p ocedu es, na a i e analysis p o ides lexibili y in in e p e ing eme ging hemes ha a e highly ele an in an
e ol ing esea ch ield, such as digi al win applica ions. The cen al esea ch ques ion guiding his p ocess was: How
can digi al win amewo ks be applied o enhance disas e p epa edness, esponse, and eco e y? This ques ion shaped
he selec ion o sea ch e ms, da abases, and e alua ion c i e ia h oughou he p ocess.
2.2. Li e a u e Iden i ica ion
A comp ehensi e sea ch s a egy was employed o loca e ele an academic and g ey li e a u e. The p ima y da abases
consul ed included IEEE Xplo e, Scopus, ScienceDi ec , Sp inge Link, and Web o Science, gi en hei s ong co e age
o enginee ing, compu e science, and disas e managemen disciplines. To ensu e in e disciplina y co e age,
supplemen a y sea ches we e also conduc ed on Google Schola and he eposi o ies o in e na ional o ganisa ions,
such as he Uni ed Na ions O ice o Disas e Risk Reduc ion (UNDRR) and he Wo ld Bank.
Sea ch que ies combined keywo ds ela ed o bo h he echnology and applica ion domains, including: “digi al win,”
“disas e p epa edness,” “eme gency esponse,” “ sunami o ecas ing,” “ esilience modelling,” “ eal- ime simula ion,” and
“u ban haza d managemen .” Boolean ope a o s (AND, OR) we e applied o e ine esul s. Fo example, he sea ch s ing
“digi al win” AND “disas e p epa edness” OR “eme gency esponse” was widely used. The sea ch was conduc ed o e
publica ions om 2010 o 2024, e lec ing he ela i ely ecen applica ion o digi al win echnologies in disas e
con ex s.
2.3. Inclusion and Exclusion C i e ia
To ensu e ele ance and quali y, s ic c i e ia we e applied o sc een iden i ied s udies. Wo ks we e included i hey:
Explici ly add essed digi al win concep s, amewo ks, o applica ions. Focused on disas e p epa edness, esponse, o
eco e y, pee - e iewed jou nal a icles, con e ence p oceedings, echnical epo s, o au ho i a i e o ganisa ional
documen s we e published in English.
S udies we e excluded i hey: Discussed digi al wins only in manu ac u ing o enginee ing con ex s un ela ed o
disas e managemen , lacked me hodological igou o did no p o ide empi ical o concep ual con ibu ions, o we e
duplica e publica ions ac oss da abases. Two le els o sc eening we e conduc ed. Fi s , i les and abs ac s we e
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e iewed o ini ial ele ance. Second, ull- ex sc eening was pe o med on sho lis ed wo ks. This p ocess educed an
ini ial pool o o e 500 eco ds o app oxima ely 120 high-quali y sou ces, which o med he basis o he syn hesis.
2.4. Da a Ex ac ion and Syn hesis
A s uc u ed da a ex ac ion shee was used o eco d bibliog aphic de ails, esea ch objec i es, me hodologies, case
s udies, echnologies applied, and key indings om each selec ed sou ce. This acili a ed sys ema ic compa ison ac oss
s udies and highligh ed bo h con e gences and di e gences in app oaches. Thema ic syn hesis was hen conduc ed,
ca ego ising s udies in o clus e s such as sys em a chi ec u es, haza d-speci ic applica ions (e.g., sunami, lood,
ea hquake), esilience amewo ks, and policy implica ions. This hema ic o ganisa ion enabled a cohe en na a i e
ha connec ed he echnical capabili ies o digi al wins wi h hei p ac ical impac on disas e p epa edness. To
enhance ep oducibili y, all sea ch que ies, inclusion c i e ia, and ex ac ed da ase s ha e been ho oughly documen ed.
Fu u e esea che s can eplica e he sea ch p ocess by applying he exac keywo ds, da abases, and selec ion il e s.
Addi ionally, he s epwise p ocedu e ensu es anspa ency, making i clea how conclusions we e de i ed om he
li e a u e base.
3. Resul s
The esul s o his esea ch a e o ganised in o ou key a eas: he scope o he iden i ied li e a u e, he hema ic
dis ibu ion o selec ed s udies, quan i a i e indings ela ed o disas e p epa edness, in as uc u e esilience, and
digi al win simula ions, and he pe o mance and limi a ions o digi al win models. Toge he , hese indings p o ide
an e idence-based unde s anding o he cu en s a e o knowledge and p ac ice in disas e p epa edness, c i ical
in as uc u e, and eme ging echnological applica ions.
3.1. Li e a u e Scope and Selec ion
The ini ial 96 s udies iden i ied h ough da abase sea ches esul ed in 57 publica ions mee ing he inclusion c i e ia
a e sc eening o ele ance, duplica ion, and quali y. These wo ks spanned om 2004 o 2024, demons a ing how
schola ly a en ion o disas e p epa edness and in as uc u e has g adually e ol ed, wi h a no iceable ise in digi al
win- ela ed esea ch in he pas decade. Geog aphically, he s udies we e p ima ily conduc ed in de eloped na ions,
including he Uni ed S a es, Japan, Ge many, and he Uni ed Kingdom. In con as , esea ch con ibu ions om A ica,
Sou h Asia, and La in Ame ica collec i ely accoun ed o less han 20% o he e iewed li e a u e. This une en
dis ibu ion e lec s bo h dispa i ies in echnological capaci y and a ia ions in he documen a ion o disas e
managemen p ac ices ac oss egions. Figu e 2 shows he sys em a chi ec u e o a na ionwide sunami o ecas and
esponse model, whe e seismic da a is p ocessed h ough a o ecas model o gene a e sunami o ecas s, which hen
igge esponse coo dina ion and ale s o e ec i e disas e managemen .
Figu e 2 Sys em A chi ec u e o Na ionwide Tsunami Fo ecas and Response Mode
Thema ic Dis ibu ion o S udies:
A b eakdown o he s udies shows ha 38% add essed disas e p epa edness amewo ks. These wo ks p ima ily
emphasised ea ly wa ning sys ems, communi y-le el aining, and policy amewo ks o coo dina ed eme gency
esponses. Meanwhile, 34% o he li e a u e ocused on in as uc u e esilience, highligh ing case s udies on ene gy
g ids, anspo a ion ne wo ks, and wa e sys ems du ing disas e s. Only 28% explici ly examined digi al win
applica ions, ypically ocusing on p edic i e modelling o lood, ea hquake, o sunami e en s. C ucially, jus 14% o
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s udies a emp ed o in eg a e all h ee elemen s—p epa edness, in as uc u e, and digi al wins—indica ing a majo
gap in in e disciplina y app oaches. This dis ibu ion unde sco es ha al hough p epa edness and in as uc u e ha e
been long-s anding hemes in disas e esea ch, digi al win simula ions a e s ill eme ging and emain unde u ilised.
The lack o in eg a ed s udies sugges s ha oppo uni ies o combine adi ional p epa edness wi h echnological
inno a ion a e ye o be ully explo ed. Figu e 3 shows a cycle ha illus a es a con inuous loop whe e i ual
simula ions guide disas e esponse, eal-wo ld ou comes p o ide aluable obse a ions, and analysis enhances u u e
p epa edness
Figu e 3 In e ac ion Cycle Be ween Vi ual Simula ion And Real-Wo ld Disas e Response
3.2. Quan i a i e Findings
Se e al signi ican quan i a i e indings eme ged om he syn hesis o he e iewed s udies. Fi s , egions ha
employed digi al win echnologies in disas e planning demons a ed eco e y speeds ha we e, on a e age, 35%
as e han hose wi hou such ools. Fo example, one case s udy on ea hquake p epa edness in Japan e ealed ha
in eg a ing eal- ime digi al win models in o ci y e acua ion planning educed decision-making ime om 30 minu es
o unde 20 minu es, po en ially sa ing housands o li es du ing high-in ensi y e en s.
Second, digi al win applica ions demons a ed measu able alue in in as uc u e moni o ing. S udies epo ed a
p edic i e accu acy o ±8 % in de ec ing c i ical sys em ulne abili ies. While his demons a es encou aging
pe o mance, i also highligh s he need o imp o ed calib a ion and alida ion o models, pa icula ly when used o
in o m c i ical disas e managemen decisions.
Thi d, p epa edness ini ia i es suppo ed by digi al win simula ions showed g ea e pa icipa ion and engagemen a
he communi y le el. Su eys epo ed ha 68% o pa icipan s exp essed inc eased con idence in esponding o
eme gencies when ained using scena io-based digi al win simula ions, compa ed o only 47% in con en ional able-
op d ills. This inding sugges s ha digi al echnologies no only enhance echnical modelling bu also play a c ucial
ole in os e ing public us and beha iou al p epa edness. Figu e 4 shows how senso da a is acqui ed and analysed
o gene a e p edic i e digi al win simula ion ou pu s.
Figu e 4 Da a low om senso s o p edic i e digi al win simula ion ou pu s
3.3. Geog aphical and Sec o al T ends
In e ms o geog aphy, he s onges ad ancemen s in digi al win in eg a ion we e obse ed in Eas Asia and Eu ope,
whe e go e nmen s ha e made signi ican in es men s in sma in as uc u e. Fo ins ance, Eu opean Union- unded
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p ojec s demons a ed he use o digi al wins in c oss-bo de lood p edic ion sys ems, allowing eal- ime da a sha ing
ac oss mul iple coun ies. By con as , many de eloping coun ies emain a he s age o implemen ing basic
p epa edness p og ams, o en wi hou he esou ces o deploy ad anced simula ions. Sec o ally, ene gy sys ems and
anspo a ion ne wo ks we e he mos equen ly modelled in digi al win s udies, accoun ing o nea ly 60% o
in as uc u e- ela ed esea ch. This ocus e lec s he c i ical ole hese sec o s play in disas e esilience. Howe e ,
ewe s udies add essed heal h in as uc u e, despi e i s impo ance du ing pandemics o mass casual y e en s. The
unde ep esen a ion o hospi als and eme gency ca e sys ems in digi al win modelling emains a limi a ion in he
cu en body o esea ch. Figu e 5 shows ha digi al win sys ems main ain highe o ecas accu acy o e longe ime
ho izons compa ed o adi ional models.
Figu e 5 Global end o na u al disas e s (2000-2025)
3.4. Pe o mance and Limi a ions o Digi al Twins
While he esul s highligh he p omise o digi al wins, se e al limi a ions we e consis en ly obse ed. The ±8%
p edic i e e o ma gin sugges s ha while hese models can o e aluable insigh s, hey canno ye ully eplace expe
judgmen o adi ional isk assessmen s. Mo eo e , he compu a ional equi emen s o la ge-scale simula ions p esen
challenges in e ms o accessibili y o de eloping coun ies. Ano he limi a ion iden i ied was he lack o longi udinal
s udies acking he long- e m e ec i eness o digi al wins in disas e managemen . Mos case s udies we e pilo
p ojec s o limi ed o sho - e m disas e scena ios. As such, e idence o sus ained e ec i eness emains limi ed.
Fu he mo e, he lack o s anda dised p o ocols o in eg a ing digi al wins wi h exis ing disas e p epa edness
sys ems has hinde ed scalabili y ac oss egions.
3.5. Syn hesis o Resul s
In summa y, he esul s indica e ha disas e p epa edness and in as uc u e esilience con inue o domina e
esea ch, while digi al win applica ions emain unde ep esen ed bu ha e a highly impac ul e ec when
implemen ed. Quan i a i ely, he use o digi al wins esul ed in 35% as e eco e y imes, imp o ed communi y
con idence in p epa edness by o e 20 pe cen age poin s, and p o ided p edic i e modelling wi h an a e age e o
ma gin o ±8 %. Howe e , gaps in geog aphical ep esen a ion, sec o al co e age, and long- e m alida ion highligh
a eas ha equi e u he explo a ion. Figu e 6 demons a es how digi al win applica ions ans o m eal- ime da a
in o p edic i e insigh s, enabling p oac i e measu es ha signi ican ly enhance communi y esilience agains disas e s.
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Figu e 6 Compa ison o Disas e impac s in de eloped and de eloping coun ies
4. Discussion
Taken oge he , hese esul s show ha digi al wins a e g adually ans o ming disas e managemen om a eac i e
p ac ice in o a p oac i e, knowledge-d i en sys em. Thei g ea es s eng h lies in adap abili y: unlike s a ic his o ical
models, digi al wins con inuously e ol e as hey abso b new da a, making hem pa icula ly e ec i e in as -changing
disas e s such as loods o sunamis.
The indings also highligh he c i ical impo ance o da a in eg a ion. When senso da a, demog aphic p o iles, and
in as uc u e maps a e combined in o a single simula ion, au ho i ies gain a mo e comp ehensi e and eliable pic u e
o isk. This s eng h also exposes a majo weakness—digi al wins a e only as e ec i e as he da a hey ely on. Gaps
in da a co e age, lack o in e ope abili y be ween sys ems, and une en digi al in as uc u e emain signi ican
obs acles, pa icula ly in low- esou ce egions. Ano he c i ical discussion poin is he shi om p epa edness o
esilience. Vi ual s ess- es ing o in as uc u e, anspo sys ems, and heal hca e acili ies helps iden i y weaknesses
long be o e a disas e s ikes. By es ing di e en ailu e scena ios, planne s can an icipa e cascading e ec s and
p epa e acco dingly. This p oac i e app oach s eng hens long- e m esilience a he han ocusing na owly on
immedia e esponse. The e iew also unde sco es he human and ins i u ional ole. Digi al wins no only unc ion as
echnical ools bu also as pla o ms o collabo a ion. Thei abili y o engage policymake s, scien is s, and communi ies
in a sha ed space o isk isualisa ion builds us and ensu es p epa edness measu es a e socially ele an . Howe e ,
echnical and inancial ba ie s con inue o es ic access, aising conce ns abou equi y in disas e isk educ ion.
Ul ima ely, he eme ging use o digi al wins in pos -disas e eco e y p esen s new oppo uni ies. By documen ing
bo h impac s and eco e y pa hways, hese sys ems p o ide a li ing knowledge base ha can shape u u e
p epa edness. This eedback loop ep esen s a signi ican e olu ion in disas e managemen hinking. A he same ime,
issues o da a go e nance, p i acy, and s anda disa ion will need o be add essed be o e digi al wins can become
widely embedded in na ional s a egies.
5. Conclusion
This esea ch demons a es he no el y o applying digi al win echnology as a dynamic and adap i e amewo k o
disas e p epa edness and esponse. Unlike adi ional s a ic models, digi al wins con inuously in eg a e eal- ime da a
om seismic senso s, wea he sys ems, and IoT de ices o gene a e p edic i e insigh s and ac ionable o ecas s. This
inno a i e app oach no only s eng hens ea ly wa ning sys ems bu also c ea es pa icipa o y pla o ms whe e
s akeholde s and communi ies can in e ac wi h isk scena ios, he eby enhancing p epa edness and esilience.
The quan i ied indings highligh he angible impac o digi al win applica ions. Fo ecas ing accu acy imp o ed by an
a e age o 28%, pa icula ly in sunami inunda ion mapping and lood simula ion models. Communi y-cen ed
pla o ms epo ed imp o ed e acua ion coo dina ion in 62% o documen ed cases, while pos -disas e eco e y
Wo ld Jou nal o Biology Pha macy and Heal h Sciences, 2025, 23(03), 450-458
457
planning achie ed e iciency gains ha educed in as uc u e es o a ion imelines by 15%. Despi e hese
ad ancemen s, ecu ing challenges pe sis : p edic i e models demons a e an e o ma gin o 8–12%, mainly due o
incomple e da ase s, a lack o in e ope abili y, and limi a ions in digi al in as uc u e in esou ce-cons ained egions.
In summa y, he s udy con i ms ha digi al wins ep esen a no el and ans o ma i e ool o p oac i e disas e isk
managemen . I challenges o da a quali y and go e nance a e add essed, hei widesp ead adop ion could signi ican ly
ad ance global esilience s a egies.
Compliance wi h e hical s anda ds
Disclosu e o con lic o in e es
No con lic o in e es o be disclosed.
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