RESEARCH ARTICLE Open Access
Towa ds in eg a ed su eillance o
zoonoses: spa io empo al join modeling
o oden popula ion da a and human
ula emia cases in Finland
C. Ro ejanap ase
1*
, A. Lawson
2
, H. Rossow
3
, J. Sane
4
, O. Hui u
5
, H. Hen onen
5
and V. J. Del Rio Vilas
6
Abs ac
Backg ound: The e a e an inc easing numbe o geo-coded in o ma ion s eams a ailable which could imp o e public
heal h su eillance accu acy and e iciency when p ope ly in eg a ed. Speci ically, o zoono ic diseases, knowledge o
spa ial and empo al pa e ns o animal hos dis ibu ion can be used o aise awa eness o human isk and enhance ea ly
p edic ion accu acy o human incidence.
Me hods: To his end, we de elop a spa io empo al join modeling amewo k o in eg a e human case da a and animal
hos da a o o e a modeling al e na i e o combining mul iple su eillance da a s eams in a no el way. A case s udy is
p o ided o spa io empo al modeling o human ula emia incidence and oden popula ion da a om Finnish heal h ca e
dis ic s du ing yea s 1995–2012.
Resul s: Spa ial and empo al in o ma ion o oden abundance was shown o be use ul in p edic ing human
cases and in imp o ing ula emia isk es ima es in 40 and 75% o heal h ca e dis ic s, espec i ely. The human
ela i e isk es ima es’s anda d de ia ion wi h oden ’s in o ma ion inco po a ed a e smalle han hose om he
model ha has only human incidence.
Conclusions: These esul s suppo he in eg a ion o oden popula ion a iables o educe he unce ain y o
ula emia isk es ima es. Howe e , mo e in o ma ion on se e al co a ia es such as en i onmen al, beha io al, and
socio-economic ac o s can be in es iga ed u he o deepe unde s and he zoono ic ela ionship.
Keywo ds: Su eillance in eg a ion, Join diseases modeling, Zoonoses, Tula emia, Finland
Backg ound
Disease isk mapping is impo an o he unde s anding o
he spa ial epidemiology o in ec ious diseases. In mos cases
and e en o mul i-hos diseases such as zoonoses, isk es i-
ma ion has been conduc ed in a uni a ia e ashion based on
humancaseda aalone.Modelingo mul i a ia eheal h
da a, in o med by mul iple s eams o geo-coded in o ma-
ion, allows obse a ion o concu en pa e ns among da a
s eams and condi ioning on one ano he . As a esul , mul i-
a ia e me hods can deli e g ea e s a is ical powe and
lead o mo e p ecise isk es ima ion and enhanced e en
de ec ion. Speci ically, o zoono ic diseases, knowledge o
spa ial and empo al pa e ns o he animal hos could in-
o m incidence in humans.
In eg a ion o da a and analyses, whe he o popula ion o
heal h ela ed a iables, has been sugges ed o imp o e zoo-
noses su eillance accu acy and e iciency [1,2]. In eg a ion
appea s mo e easible o endemic zoonoses, and o hose
wi h domes ica ed animals as sou ce, gi en he likely g ea e
a ailabili y o animal heal h da a. Fo zoonoses wi h a
non-domes ica ed animal sou ce (e.g. syl a ic yellow e e , u-
la emia), a ailabili y o animal heal h da a is likely o be a lim-
i ing ac o owa ds in eg a ion and al e na i e animal da a
sou ces mus be sough .
Tula emia is an in ec ious disease caused by an in acellu-
la bac e ium, F ancisella ula ensis.Thediseaseisendemic
* Co espondence: [email p o ec ed]
1
Depa men o T opical Hygiene, Facul y o T opical Medicine, Mahidol
Uni e si y, Ra cha hewi, Bangkok 10400, Thailand
Full lis o au ho in o ma ion is a ailable a he end o he a icle
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Ro ejanap ase e al. BMC Medical Resea ch Me hodology (2018) 18:72
h ps://doi.o g/10.1186/s12874-018-0532-8
in No h Ame ica and pa s o Eu ope, wi h ecu en ou -
b eaks in Sweden and Finland [3,4]. F ancisella ula ensis
has a wide ange o hos s wi h ansmission mos commonly
ia a h opod ec o s [5]. Roden s could play a ole in he
zoono ic ansmission o he disease a e indings o a ela-
ionship be ween ole popula ion cycles and human ula -
emia incidence in Finland [6] and Sweden [7]. Speci ically in
Finland, oden popula ion dynamics displayed a spa io em-
po al ela ionship wi h human ula emia cases, such ha
human case numbe s peaked one yea a e peak oden
densi ies [6]. Simila indings, om s udies o ula emia ou -
b eaks, indica e ha high oden densi ies migh ela e o
occu ences in humans [8–11].
This wo k explo es he applica ion o spa io empo al join
models o concu en animal and human geo- e e enced
da a sou ces in an e o o explain possible pa e ns be-
ween he dis ibu ion and/o he abundance o he animal
hos and human disease.. The p oposed me hodology is
e alua ed on i s pe o mance in p edic ing human disease
isk and imp o ing isk es ima ion in a case s udy o ula -
emia human incidence and oden popula ion da a in
Finland. No only ou me hod con ains me hodological no -
el y wi h po en ial applica ions in spa ial epidemiology, i
also helps o e eal a disease pa e n in he case s udy which
was no conside ed in p e ious s udies.
Me hods
Da a sou ces
A comple e desc ip ion o he oden popula ion and human
ula emia incidence da a is a ailable om ea lie epo s [6].
B ie ly, da a on oden popula ion le els, p edominan ly bank
oles (Myodes gla eolus) and ield oles (Mic o us ag es is),
we e collec ed ac oss Finland by he Na u al Resou ces Ins i-
u e Finland and ca ego ized in o h ee popula ion le els: de-
cline, inc ease, and peak [12]. Human ula emia cases we e
epo ed as labo a o y-con i med o he Na ional In ec ious
Disease Regis e , kep by he Na ional Ins i u e o Heal h and
Wel a e o Finland. Bo h human cases and oden da a we e
agg ega ed in o 20 Finnish heal hca e dis ic s o e he pe iod
1995–2012 [6]. An indica o o quan i y he in luence o o-
den popula ion le els on human incidence is de eloped o
each heal h dis ic . Plo s o human cases and bina y oden
s a us o he 20 Finnish heal h dis ic s in he pe iod 1995–
2012 show a one-yea lagged inc ease in he numbe o hu-
man ula emia cases a e oden popula ion peaks o ce ain
dis ic s and yea s (Fig. 1). A simila pa e n was ound be-
ween human cases and he ca ego ical oden popula ion s a-
us (Fig. 2). These suppo he choice o spa io empo al
models which will be de eloped in he nex sec ion.
S a is ical me hodology
Wep oposeaBayesian amewo k ojoin lyanalyze oden
popula ion s a us and human case incidence. We assume
ha he human cases a e associa ed wi h oden s’s a us
h ough a la en s uc u e. Le human cases (coun s), h
i
,a
heal h dis ic iand ime ollow a Poisson dis ibu ion wi h
mean = e
i
θ
i
whe e e
i
is he expec ed numbe o human
cases in he i h heal h dis ic (p esumably cons an ac oss
he yea s) and θ
i
is he ela i e isk a he i h heal h dis ic
and yea The e a e a numbe o ways o calcula e he ex-
pec ed a e. In his pape , he expec ed a es, e
i
, a e calcu-
la ed as he a e age case coun a a ea io e he ime
pe iod as ei¼P hi
Twhe e Tis he leng h o s udy pe iod
Fig. 1 Human cases (solid line) and 2-le el oden s a us (do ) o he 20 Finnish heal h dis ic s o e yea s 1995–2012
Ro ejanap ase e al. BMC Medical Resea ch Me hodology (2018) 18:72 Page 2 o 8
(T= 8 yea s).Human popula ion da a was ob ained om
he Finnish Popula ion Regis e Cen e [13]. Howe e , he
popula ion a ia ion be ween egions was ound o be lim-
i ed and combined wi h he low o e all a e o he disease,
i was decided ha a ime a e aged a e would be app o-
p ia e in his case.
A simple app oach o join ly model human incidence
and oden popula ion da a is o conside a 2-le el o-
den popula ion indica o as in [6]. As a bina y a iable,
we deno e
i
= 0 i he numbe o oden s declined and
i
= 1 i he numbe o oden s is a peak o inc eased.
The 2-class oden s a us is hen assumed o ollow a
Be noulli dis ibu ion wi h pa ame e p
i
being he p ob-
abili y o oden o he i h heal h dis ic and yea .To
speci y he pa ame e s in he join likelihoods, θ
i
o
humans and p
i
o oden s, linea p edic o s a e decom-
posed addi i ely in o spa ial, and space- ime in e ac ion
andom e ec s as ollows
α
,α
h
a e he o e all mean le els o oden and hu-
man espec i ely, and assumed o ha e ze o-mean
Gaussian p io dis ibu ions. The la en a iables uh
i;u
i;
h
i;
ia e included o model non- empo al backg ound
a ia ion wi h spa ial and non-spa ial p io dis ibu ions.
The spa ial s uc u e o uh
i;u
i ollow an in insic condi-
ional au o eg essi e (ICAR) [14] model and he
non-spa ial dis ibu ion o h
i;
iis assumed o be
ze o-mean Gaussian p io dis ibu ion. A Gaussian dis-
ibu ion wi h ze o mean is assumed o λh
;λ
a =1
and an au o eg essi e p io dis ibu ion is assumed o
λh
;λ
a > 1 which allows o a ype o nonpa ame ic
empo al e ec . δh
i ;δ
i ep esen he empo al end o
each heal h dis ic a yea . We also assume ha he
space- ime andom e ec s o human and oden a e p o-
po ional wi h one-yea lag. This is suppo ed by he
inding sugges ed in [6] ha 1-yea empo al lag e ec
humani ¼hi Poisson eiθi
ðÞ
log θi
ðÞ¼αhþuh
iþ h
iþλh
þδh
i
δh
i ¼βiδ
i −1
βi¼βp
i i −1þβn
i1− i −1
ðÞ
oden i ¼ i Be noulli pi
ðÞ
logi pi
ðÞ¼α þu
iþ
iþus
kþ s
kþλ
þδ
i
αhN0;τ−1
αh

;α N0;τ−1
α

δh
i N0;τ−1
δh

;δ
i N0;τ−1
δ

λh
Nλh
−1;τ−1
λh

;λ
Nλ
−1;τ−1
λ

λh
1N0;τ−1
λh

;λ
1N0;τ−1
λ

uh
iICAR τ−1
uh

; h
iN0;τ−1
h

u
iICAR τ−1
u

;
iN0;τ−1

us
kICAR τ−1
us

; s
kN0;τ−1
s

βp
iN0;τ−1
βp

;βn
iN0;τ−1
βn

τ−1=2
Uni o m 0;10ðÞ
Fig. 2 Human cases (solid line) and 3-le el oden s a us (do ed line) o he 20 Finnish heal h dis ic s o e yea s 1995–2012
Ro ejanap ase e al. BMC Medical Resea ch Me hodology (2018) 18:72 Page 3 o 8
can be bene icial o p edic human ula emia ou b eaks.
To model unobse ed ecological e ec s associa ed wi h
ole cycles a he i e bo eal zones in Finland (Sou he n
Finland, Sou hwes e n and Inland Finland, Eas e n
Finland, No he n Finland, and Lapland) [13], wo add-
i ional pa ame e s us
k; s
ka e included as he spa ial and
non-spa ial con ex ual a iables o egional s a e le el k.
δ
i ;βp
i;βn
ia e assumed o ollow a ze o-mean Gaussian
p io dis ibu ion and he uni o m dis ibu ion on (0,10)
is used o model all s anda d de ia ion pa ame e s [15].
Al hough he e is some e idence ha a 2-le el oden
s a us could ha e a lagged p edic i e abili y on human
ula emia [6], we also wan o ex end ou conside a ion
o include he o iginal h ee oden le els in he join
modeling and assume a ca ego ical likelihood o oden
popula ion s a us. A speci ica ion o mul iple ca ego ies
o oden s a us can be de ined as
humani ¼hi Poisson eiθi
ðÞ
log θi
ðÞ¼αhþuh
iþ h
iþλh
þδh
i
δh
i ¼βi; i −1δ
i −1;i −1
oden i ¼ i Ca ego ical p1;i ;p2;i ;p3;i

pj;i ¼
exp μj;i

P3
j¼1exp μj;i

μj;i ¼α þu
iþ
iþus
kþ s
kþλ
þδ
j;i
whe e j= 1 indica es he declining oden popula ion
le el, j= 2 is he inc easing le el, and j= 3 is he oden ’s
popula ion a peak, and δ
j;i Nð0;τ−1
δ Þ. The o he p io
dis ibu ions o he andom e ec s e ms a e assumed
he same as he 2-le el model.
We u he assume ha he space- ime andom e ec s
o human and oden a e p opo ional wi h one-yea lag.
Tha is δh
i αδ
i −1;i −1, i.e. δh
i ¼βi; i −1δ
i −1;i −1whe e βi; i −1
is he p opo ional pa ame e . This speci ica ion is de-
eloped o examine he lagged e ec o oden s a us,
δ
i −1;i −1, a le el j=
i -1
in heal h dis ic ia ime -1on
he numbe o human cases h ough he space- ime
in e ac ion e m,δh
i .
Model e alua ion
To e alua e he models, we use wo goodness o i mea-
su es: he De iance In o ma ion C i e ion (DIC) [16,17]
whe e he e ec i e numbe o pa ame e s is es ima ed in
e ms o de iance’s a iance, and he Wa anabe-Akaike
in o ma ion c i e ion (WAIC) [18–20]. Bo h measu es
a e compu ed unde he likelihood o human da a o
compa e he models’ i wi h and wi hou he con ex ual
a iables. We also compa e he pos e io s anda d de ia-
ions o θ
i
om he wo models (bina y and ca ego ical)
wi h he oden da a, wi h hose om a model based
only human da a, o examine he bene i s o
inco po a ing animal da a in su eillance. Resul s a e ob-
ained om 10,000 pos e io samples using WinBUGS
so wa e a e a bu n-in pe iod o 10,000 d aws. To as-
sess he mixing o pos e io sample s, we adop
Gelman’sR
bs a is ics p oposed in [18,21] o mul iple
chain con e gence and con e ged chains should ha e
he alue o R
bapp oxima ely 1.
Resul s
The his og ams o R
bes ima es o θ
i
om pos e io sam-
ple unde he models a e displayed in Fig. 3. The R
bes i-
ma es unde all he models a e app oxima ely o less
han 1.005 which indica es he chains con e ge o a pos-
e io dis ibu ion. Table 1displays he DIC and he
measu es o model pe o mance. We compa ed he
models (bina y s. poly omous) on hei DIC and WAIC
alues unde he human likelihood. The bina y model
wi hou he con ex ual e ec shows he smalles DIC
and WAIC alues and hence i can be conside ed as ha -
ing he bes i o he da a. To p o ide e idence o he
bene i s om inco po a ing oden in o ma ion in o he
model, he pos e io es ima es o s anda d de ia ion
(SD) o θ
i
om bo h bina y and poly omous models a e
compa ed and p esen ed in Table 2 o each a ea o e
he ime pe iod. The SD es ima es o θ
i
wi h oden ’s in-
o ma ion inco po a ed a e smalle han hose om he
model ha has only human incidence. This esul sup-
po s he in eg a ion o oden popula ion a iables o
educe he unce ain y o ula emia isk es ima es.
To help assess he p edic i e pe o mance o oden
abundance da a on he occu ence o human cases, we
de elop a me ic on he 0 o 1 scale, namely he deg ee
o posi i e indica o (DP) o each heal h dis ic as
DPi¼expðβp
iÞ
expðβp
iÞþ1:The DP indica o is de i ed om he
bina y model wi hou he con ex ual ac o s as his
was he model wi h he lowes DIC and WAIC. High
alues o DP (close o 1) would indica e heal h dis ic s
wi h a high numbe o human ula emia cases in he
cu en yea , gi en inc easing o a peak oden popu-
la ions in he pas yea . DP can be in e p e ed in a
simila ashion o he sensi i i y o a diagnos ic es .
High alues o DP would sugges good p edic i e alue
o he oden da a on he occu ence o human ula -
emia cases. Yea -speci ic modeling was no conside ed
due o insu icien da a. Thus posi i e dependence
alues ep esen an a e aged e ec o oden popula-
ions on human incidence ac oss all he yea s (Table 3).
The e a e 8 (40%) heal h dis ic s, mos ly on he wes
and sou h o he coun y, wi h mean DP alues la ge
han 0.8 and he lowe 95% c edible in e al abo e
0.5 (Fig. 4).
Ro ejanap ase e al. BMC Medical Resea ch Me hodology (2018) 18:72 Page 4 o 8
Discussion
O he modi ica ions a e possible. Fo example, in join
modeling human and oden da a we could conside hu-
man cases being dependen on oden popula ion
h ough p
i
as a ca ego ical co a ia e. Fo spa ial uni ,
smalle o adminis a i e a eas di e en om heal h dis-
ic s may ha e led o mo e disc imina o y indings (o
no gi en inc eased noise om smalle uni s). Simila ly,
mo e in o ma i e da a on oden popula ion le els, e.g.
oden densi ies as in o he s udies [11] migh ha e e-
sul ed in mo e desc ip i e models. Roden popula ions
luc ua e wi h a highly a ying ampli ude, which means
ha abundances may a y subs an ially om one peak
o he nex , e en wi hin he same egions [12]. I ula -
emia ansmission o humans is a phase and densi y
dependen p ocess, as i mos likely is [6], a ia ion in
ole abundance du ing successi e peaks may educe he
p edic i e alue o models employed he e. Subsequen
s udies mus explo e he inco po a ion o da a on he
p ecise loca ion o he oden apping si es h ough he
use o some o m o in e pola ion [22]. In addi ion o
hos popula ion changes, en i onmen al ac o s also
seem o impac on he occu ence o ula emia ou -
b eaks [23,24]. Inco po a ion o e idence on mosqui o
dis ibu ion (no pu posely cap u ed a his momen in
Finland), ain all and wa e bodies in o ou models
would be s aigh o wa d.
Conclusions
Space- ime p oximi y and con ac pa e ns be ween
humans and animals play a cen al ole in in ec ion isk.
In his esea ch, we a emp ed o assess he su eillance
ele ance o egula ly collec ed oden popula ion da a
o i) imp o e ula emia isk es ima es and ii) in o m
Table 1 DIC (pD) and WAIC (pWAIC) co esponding o he human likelihood o he model compa ison
Roden s a us Con ex ual e ec DIC pD WAIC pWAIC
Bina y Wi h 4269.945 1115.127 3463.587 557.5636
Wi hou 4262.807 1110.623 3458.687 555.3114
Poly omous Wi h 4276.408 1123.747 3465.682 561.8733
Wi hou 4324.426 1170.232 3482.997 585.1162
Fig. 3 His og ams o ^
Rs a is ics o θ
i
om pos e io sample unde models
Ro ejanap ase e al. BMC Medical Resea ch Me hodology (2018) 18:72 Page 5 o 8

ea ly p edic ion o human ula emia cases in Finland. To
ha e ec , we de eloped bina y and poly omous a chi-
ec u es o join ly model human incidence and oden
s a us wi h one-yea lag. Ou esul s e u ned a he e o-
geneous pic u e bu o many heal h dis ic s oden
popula ion s a us was ele an o he occu ence o hu-
man ula emia cases. We ha e shown ha he inco po -
a ion o oden popula ion da a led o an imp o emen
in he accu acy o human isk es ima es in 15 (75%)
heal h dis ic s, compa ed o models only conside ing
human ula emia cases. Fu he mo e, ou pu posely
buil indica o (DP) showed ha in 8 (40%) o he heal h
dis ic s, inc easing and a -peak oden popula ions we e
obus p edic o s o human ula emia cases in he
ollowing yea . Howe e , o ew dis ic s (e.g. Länsi--
Pohja and Kainuu) whe e he model based only on hu-
man da a leads o mo e p ecise es ima es o incidence,
hese a eas also ha e he low alues o posi i e indica o
(DP) wi h he co esponding c edible in e als c ossing
0.5. This sugges s ha he dis ibu ion o zoono ic pa h-
ogens in animal and human popula ions spa ially a ies
as we assumed acco ding o local bio ic and abio ic de-
e minan s. To conduc u he in es iga ion, we need
mo e in o ma ion on se e al co a ia es such as en i on-
men al, beha io al, and socio-economic ac o s. How-
e e , he pla o m p oposed in his esea ch can acili a e
in iden i ica ion o geog aphical a eas ha a e po en ially
sui able o ansmission.
Ou objec i e was o de elop di e en models o
combine mul iple da a sou ces al eady a ailable, on
animals and humans, o be e in o m he occu ence
o zoonoses. The p esen wo k shows he u iliza ion
o animal popula ion da a (in he absence o animal
heal h- ela ed da a on oden s) o in o m human isk.
Al hough di e en model pa ame e iza ions and, in
pa icula , e idence on o he pu a i e p edic o s, as
epo ed elsewhe e [25], could con ibu e u he
Table 2 S anda d de ia ion o θ
i
calcula ed om pos e io
sample s o each a ea o e he ime pe iod o he models
Heal h dis ic Bina y Ca ego ical Only Human
Wi h
con ex ual
e ec
Wi hou
con ex ual
e ec
Wi h
con ex ual
e ec
Wi hou
con ex ual
e ec
Sou hwes
Finland
1.0216 1.0193 1.1402 1.1569 1.3604
Sa akun a 3.6330 3.6124 3.5493 3.5371 3.6365
Kan a-Häme 0.7336 0.7354 0.7632 0.7689 1.0398
Pi kanmaa 3.2779 3.2862 3.2114 3.2071 3.4309
Päijä -Häme 0.9032 0.9011 0.9682 1.0048 1.0475
Kymenlaakso 2.4761 2.4859 2.5092 2.4819 2.7240
Sou h Ka elia 0.5381 0.5268 0.5841 0.6060 0.6480
Sou he n
Sa onia
0.7490 0.7518 0.7448 0.7722 0.8808
Eas e n
Sa onia
0.7536 0.7551 0.7024 0.6833 0.7279
No h
Ka elia
0.5753 0.5695 0.5475 0.5732 0.5982
No he n
Sa onia
1.5263 1.5273 1.4859 1.5119 1.6492
Cen al
Finland
5.5499 5.5361 5.5562 5.6234 6.0488
Sou h
Bo hnia
4.7209 4.7507 4.7506 4.7505 4.7708
Vaasa 2.1997 2.2187 2.2057 2.1912 2.1572
Cen al
Bo hnia
3.2251 3.2411 3.2267 3.1927 3.1619
No h
Bo hnia
7.7036 7.6829 7.6786 7.6660 7.5354
Kainuu 0.1984 0.1957 0.2255 0.2468 0.2596
Länsi-Pohja 0.7437 0.7415 0.7377 0.7399 0.6897
Lapland 0.4929 0.4905 0.4979 0.5332 0.6323
Helsinki and
Uusimaa
3.3256 3.3243 3.3982 3.4146 3.8419
A e age
o e all a eas
2.2174 2.2176 2.2242 2.2331 2.3421
Table 3 The mean alues and 95% c edible in e als (C I) o DP
unde he bina y oden model wi hou he con ex ual e ec o
20 heal h dis ic s
Heal h dis ic Lowe 95% C I Mean Uppe 95% C I
Sou hwes Finland 0.0129 0.4974 0.9881
Sa akun a 0.8447 0.9686 1.0000
Kan a-Häme 0.0019 0.5099 0.9985
Pi kanmaa 0.7794 0.9428 0.9996
Päijä -Häme 0.0037 0.4921 0.9951
Kymenlaakso 0.0000 0.5057 1.0000
Sou h Ka elia 0.0004 0.5031 0.9996
Sou he n Sa onia 0.0001 0.5010 1.0000
Eas e n Sa onia 0.9407 0.9929 1.0000
No h Ka elia 0.0000 0.5005 1.0000
No he n Sa onia 0.0006 0.3543 0.9965
Cen al Finland 0.9485 0.9931 1.0000
Sou h Bo hnia 0.9276 0.9901 1.0000
Vaasa 0.9621 0.9953 1.0000
Cen al Bo hnia 0.9755 0.9973 1.0000
No h Bo hnia 0.8998 0.9832 1.0000
Kainuu 0.0004 0.5060 0.9992
Länsi-Pohja 0.0000 0.5789 1.0000
Lapland 0.0013 0.5064 0.9993
Helsinki and Uusimaa 0.0347 0.4788 0.9568
Ro ejanap ase e al. BMC Medical Resea ch Me hodology (2018) 18:72 Page 6 o 8
e idence o be e in o m human isk, ou p oposed
me hodology demons a es i s abili y o quan i y he
associa ion be ween he oden s a us and human
incidence. This is po en ially use ul in p edic ion o
human ou b eak when pu in he heal h-policy
pe spec i e.
Abb e ia ions
C I: C edible in e al; DIC: De iance In o ma ion C i e ion; DP: Deg ee o
posi i e indica o ; ICAR: In insic condi ional au o eg essi e model;
SD: S anda d de ia ion; WAIC: Wa anabe-Akaike in o ma ion c i e ion
Acknowledgemen s
We would like o hank he e iewe s o commen s ha g ea ly imp o ed
he manusc ip .
Funding
This esea ch is pa ially suppo ed by he Facul y o T opical Medicine,
Mahidol Uni e si y. The unding body had no ole in he design o analysis
o he s udy, in e p e a ion o esul s, o w i ing o he manusc ip .
A ailabili y o da a and ma e ials
The da a om he Na ional Ins i u e o Heal h and Wel a e o Finland is
a ailable on public domain and upon eques . The da a om he Na u al
Resou ces Ins i u e is a ailable o om he ep esen ing au ho s upon eques .
Au ho s’con ibu ions
Au ho s CR, AL and VJDRV designed he s udy wi h c i ical e iew om HR.
CR pe o med he s a is ical analyses in consul a ion wi h AL, VJDRV, HR, JS,
OH and HH. CR d a ed he pape wi h inpu om AL and VJDRV. HR, JS, OH
and HH we e esponsible o c i ical e ision and imp o emen s o he
manusc ip . All au ho s ead and app o ed he inal manusc ip .
E hics app o al and consen o pa icipa e
Su eillance da a om he Na ional In ec ious Disease Regis e is agg ega e,
de-iden i ed da a o public use and he seconda y analyses a e no
subjec ed o e hical app o al p ocess, acco ding o he Finnish In ec ious
Disease Ac (Sec ion 42).
Consen o publica ion
No Applicable.
Compe ing in e es s
The au ho s decla e ha hey ha e no compe ing in e es s.
Publishe ’sNo e
Sp inge Na u e emains neu al wi h ega d o ju isdic ional claims in
published maps and ins i u ional a ilia ions.
Au ho de ails
1
Depa men o T opical Hygiene, Facul y o T opical Medicine, Mahidol
Uni e si y, Ra cha hewi, Bangkok 10400, Thailand.
2
Depa men o Public
Heal h Sciences, Medical Uni e si y o Sou h Ca olina, Cha les on, SC 29425,
USA.
3
Depa men o Ve e ina y Biosciences, Facul y o Ve e ina y Medicine,
Uni e si y o Helsinki, Helsinki, Finland.
4
Na ional Ins i u e o Heal h and
Wel a e, Helsinki, Finland.
5
Na u al Resou ces Ins i u e Finland, Helsinki,
Finland.
6
School o Ve e ina y Medicine, Uni e si y o Su ey, Guild o d, UK.
Recei ed: 28 July 2017 Accep ed: 27 June 2018
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