Heterogeneity in influenza seasonality and vaccine effectiveness in

1www.eurosurveillance.org

R 

Heterogeneity in inuenza seasonality and vaccine

eectiveness in Australia, Chile, New Zealand and

South Africa: early estimates of the 2019 inuenza

season

Sheena G Sullivan

, Carmen S Arriola², Judy Bocacao³, Pamela Burgos, Patricia Bustos, Kylie S Carville, Allen C Cheng

7,8

Monique BM Chilver

, Cheryl Cohen

, Yi-Mo Deng

, Nathalie El Omeiri

, Rodrigo A Fasce

, Orienka Hellferscee

, Q Sue Huang

Cecilia Gonzalez

, Lauren Jelley

, Vivian KY Leung¹, Liza Lopez

, Johanna M McAnerney

, Andrea McNeill

, Maria F Olivares

Heidi Peck

, Viviana Sotomayor

, Stefano Tempia

2,10,16,17

, Natalia Vergara

, Anne von Gottberg

, Sibongile Walaza

, Timothy

Wood

1. World Health Organization (WHO) Collaborating Centre for Reference and Research on Inﬂuenza, Royal Melbourne Hospital,

and Doherty Department, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne,

Australia

2. Inﬂuenza Division, Centers for Disease Control and Prevention, Atlanta, United States

3. National Inﬂuenza Centre, Institute of Environmental Science and Research, Wellington, New Zealand

4. Programa Nacional de Inmunizaciones, Ministerio de Salud, Santiago, Chile

5. Sección de Virus Respiratorios y Exantematicos, Instituto de Salud Publica de Chile, Santiago, Chile

6. Victorian Infectious Diseases Reference Laboratory, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and

Immunity, Melbourne, Australia

7. School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia

8. Department of Infectious Diseases, Alfred Health, and Central Clinical School, Monash University, Melbourne, Australia

9. Discipline of General Practice, University of Adelaide, Adelaide, Australia

10. National Institute for Communicable Diseases, Johannesburg, South Africa

11. WHO Collaborating Centre for Reference and Research on Inﬂuenza, Royal Melbourne Hospital, at the Peter Doherty Institute

for Reference and Research on Inﬂuenza, Melbourne, Australia

12. Pan American Health Organization(PAHO)/WHO Regional Oce for the Americas, Washington, United States

13. Subdepartamento de Enfermedades Virales, Instituto de Salud Publica de Chile, Santiago, Chile

14. Health Intelligence Team, Institute of Environmental Science and Research, Wellington, New Zealand

15. Departamento de Epidemiologia, Ministerio de Salud, Santiago, Chile

16. Inﬂuenza Program, Centers for Disease Control and Prevention, Pretoria, South Africa

17. MassGenics, Duluth, United States

Correspondence:

Sheena G Sullivan (Sheena.Sullivan@inﬂuenzacentre.org)

Citation style for this article:

Sullivan Sheena G, Arriola Carmen S, Bocacao Judy, Burgos Pamela, Bustos Patricia, Carville Kylie S, Cheng Allen C, Chilver Monique BM, Cohen Cheryl, Deng

Yi-Mo, El Omeiri Nathalie, Fasce Rodrigo A, Hellferscee Orienka, Huang Q Sue, Gonzalez Cecilia, Jelley Lauren, Leung Vivian KY, Lopez Liza, McAnerney Johanna

M, McNeill Andrea, Olivares Maria F, Peck Heidi, Sotomayor Viviana, Tempia Stefano, Vergara Natalia, von Gottberg Anne, Walaza Sibongile, Wood Timothy.

Heterogeneity in inﬂuenza seasonality and vaccine eectiveness in Australia, Chile, New Zealand and South Africa: early estimates of the 2019 inﬂuenza season.

Euro Surveill. 2019;24(45):pii=1900645. https://doi.org/10.2807/1560-7917.ES.2019.24.45.1900645

Article submitted on 23 Oct 2019 / accepted on 06 Nov 2019 / published on 07 Nov 2019

We compared 2019 influenza seasonality and vac-

cine effectiveness (VE) in four southern hemisphere

countries: Australia, Chile, New Zealand and South

Africa. Influenza seasons differed in timing, duration,

intensity and predominant circulating viruses. VE esti-

mates were also heterogeneous, with all-ages point

estimates ranging from 7–70% (I

: 33%) for A(H1N1)

pdm09, 4–57% (I

: 49%) for A(H3N2) and 29–66% (I

0%) for B. Caution should be applied when attempting

to use southern hemisphere data to predict the north-

ern hemisphere influenza season.

In Australia, Chile, New Zealand and South Africa, sen-

tinel surveillance is conducted in primary care and/or

hospitals to monitor the timing, intensity and impact

of influenza seasons, and to estimate influenza vac-

cine effectiveness (VE). While the influenza epidem-

ics of these four southern hemisphere countries often

coincide, the type of epidemic experienced can vary.

Nevertheless, the influenza season experienced in

southern hemisphere countries has sometimes been

interpreted as a forewarning to the northern hemi-

sphere [1]. Here, we describe the heterogeneity expe-

rienced during the 2019 influenza season in these four

countries and provide early VE estimates.

Influenza surveillance systems

The sentinel surveillance systems used in this analy-

sis are described in detail in the Table. For Australia,

influenza-like illness (ILI) surveillance data came from

the Australian Sentinel Practices Research Network

(ASPREN), supplemented by the Victorian Sentinel

Practice Influenza Network (VicSPIN) [2]. Hospital

surveillance data were obtained from the Influenza

Complications Alert Network (FluCAN) [3]. In Chile,

severe acute respiratory infection (SARI) sentinel sur-

veillance included seven sentinel hospitals distrib-

uted across six of 16 administrative regions [4]. In New

Zealand, ILI surveillance leverages general practice-

registered patients in all 20 district health boards,

2 www.eurosurveillance.org

T

Summary of key differences in case and exposure ascertainment for syndromic and virological surveillance and vaccine

effectiveness estimation, four southern hemisphere countries, 2019 influenza season

Characteristic Australia Chile New Zealand South Africa

Source populations

ILI: 394 GPs at sentinel general

practices nationwide participate

in syndromic ILI surveillance; 222

GPs participate in swab testing; 21

sentinel hospitals nation-wide

Seven sentinel hospitals in 6/16

regions

86 sentinel practices (ILI

patients) in 20 district health

boards and four hospitals

(SARI patients)

Syndromic: a healthcare

provider network

Virological and VE: Sentinel

general practices (ILI

patients) in 6/9 regions

Period used for

weekly rates

ILI: weeks 1–52

2019: weeks 1–39

Hospitals: weeks 14–44

2019: weeks 14–39

Weeks 1–52

2019: weeks 10–33

Weeks 18–39

2019: weeks 18–39

Weeks 1–52

2019: weeks 1–38

Clinical case

definition

ILI: fever or history of

fever AND cough, fatigue/malaise

Hospitals: suspected influenza (not

SARI)

SARI: history of fever, or

measured fever of ≥ 38 C° AND

cough AND onset within the last

10 days AND hospitalisation

ILI: acute respiratory illness

with a history of fever or

measured fever of ≥ 38 °C, AND

cough, AND onset within the

past 10 days

SARI: as above, but requiring

hospitalisation

ILI: measured fever (≥ 38 °C)

or history of fever, cough,

onset ≤ 10 days

Virological testing

ILI: Around 50% of patients are

swabbed for testing by RT-PCR at

SA Pathology, Adelaide or the NIC,

Melbourne.

Hospitals: RT-PCR testing done at

each hospital.

Sequencing performed by

WHOCCRRI, Melbourne.

RT-PCR or direct

immunofluorescence followed

by RT-PCR-positive for pan-

negative and influenza-positive

specimens for subtyping.

Testing and sequencing

performed at NIC, Santiago.

RT-PCR testing at NIC,

Wellington.

Sequencing performed by

WHOCCRRI, Melbourne.

RT-PCR testing by NIC,

Johannesburg.

Sequencing performed by

WHOCCRRI, Melbourne or

Worldwide Influenza Centre,

Crick Institute, London.

Study period for VE

estimation

ILI: 28 Apr 2019–9 Oct 2019

Hospitals: 1 Apr 2019–16 Aug 2019

SARI: 4 Mar 2019–18 Aug 2019 ILI and SARI: 29 Apr 2019–29

Sep 2019

ILI: 15 Apr 2019–18 Aug 2019

Cases/controls for VE

estimates

ILI: test-positive cases vs test-

negative controls

Hospitals: test-positive cases;

control are the next admitted test-

negative patient (≤ 2 weeks)

Test-positive cases vs test-

negative controls

Test-positive cases vs test-

negative controls

Test-positive cases vs test-

negative controls

Vaccination status

ascertainment

Medical record, self-report or

vaccination registry

Medical record or vaccination

registries (no verbal reports)

Vaccination registry and

self-report

Medical record or

self-reported

Vaccination coverage

among influenza-

negative controls

included in VE

estimates

Overall: 49% ILI; 47% hospitals

Adults: 46% ILI; 41% hospitals

Children: 26% ILI; 33% hospitals

Elderly: 78% ILI; 73% hospitals

Overall: 61% SARI

Adults: 41% SARI

Children: 72% SARI

Elderly: 64% SARI

Overall: 26% ILI; 33% SARI

Adults: 26% ILI; 36% SARI

Children: 9% ILI

Elderly: 70% ILI; 66% SARI

Overall: 11% ILI

Adult: 11% ILI

Children: 9% ILI

Elderly: 35% ILI

Vaccines licensed

< 5 years: Flu Quadri Junior (Sanofi)

< 65 years: Afluria Quad (Seqirus),

FluQuadri (Sanofi) and Fluarix Tetra

(GSK)

≥ 65 years: Fluad (Seqiris; trivalent

with B/Yamagata component)

Influvac (Abbott)

(inactivated subunit vaccine)

TIV included a B/Victoria-lineage

component

6–35 months: Fluarix Tetra

(GSK)

≥ 3 years: FluQuadri (Sanofi),

Influvac (Abbott)

≥ 5 years only: Afluria Quad

(Seqiris)

Vaxigrip (Sanofi Pasteur)

(inactivated split-virion

vaccine) and Influvac

(Abbott) (inactivated subunit

vaccine)

All TIV

Target groups for

vaccination

Recommended for all.

Free for pregnant women; people

aged < 5 years or ≥ 65 years;

Aboriginal and Torres Strait Islander

peoples; people aged 5–64 years

with chronic conditions.

Pregnant women from 13 weeks

gestation; children aged 6–59

months, adults aged ≥ 65 years;

poultry and pig farm workers;

patients with chronic conditions

aged 5– 64 years; carriers of

some risk conditions; healthcare

workers.

Pregnant women; people

aged ≥ 65 years; people

aged < 65 years with a medical

condition that increases

their risk of developing

complications from influenza

and the condition is specified

in the Influenza Immunisation

Programme eligibility criteria;

children aged ≤ 4 years with

previous hospitalisation

for respiratory illness or

with a history of significant

respiratory illness.

Pregnant women at all

stages of pregnancy,

including the post-partum

period; HIV-infected

individuals; adults or

children who are at high risk

for influenza complications

because of underlying

medical conditions or

who are receiving regular

medical care for conditions

such as chronic pulmonary

disease; persons aged ≥ 65

years.

GP: general practice; GSK: Glaxo Smith Kline; ILI: influenza-like illness; NIC: National Influenza Centre; QIV: quadrivalent inactivated vaccine; SARI: severe acute respiratory

illness; TIV: trivalent inactivated vaccine; VE: vaccine effectiveness; WHOCCRRI: World Health Organization Collaborating Centre for Reference and Research on Influenza.

Numbers are provided for 2019.

Children: 6 months–17 years of age; Adults: 18–64 years of age; Elderly: ≥ 65 years of age.

Only patients in a target group for vaccination are included in SARI surveillance in Chile so these numbers do not necessarily reflect coverage in the whole population.

3www.eurosurveillance.org

ca 540,000, while SARI surveillance includes four

public hospitals in Auckland and Counties Manukau

District Health Boards [5]. Syndromic surveillance

data from South Africa came from outpatient presen-

tations to a large private healthcare provider network,

based on International Classification of Diseases (ICD-

10) codes for pneumonia and influenza (J9-J11) [6,7].

Virological surveillance in South Africa was conducted

through the Viral Watch network [8].

Seasonality

Weekly 2019 influenza activity rates, e.g. ILI consulta-

tions per week, were plotted against the mean weekly

rate for influenza seasons from 2013 to 2018. All rates

were smoothed using a 3-week moving average. The

moving epidemic method (MEM) package [9] in R soft-

ware version 3.6.1 (R Foundation, Vienna, Austria) was

used for calculating means and seasonal thresholds

using default values to show the onset and intensity of

the season (Figure 1A). The specifications used for the

MEM may differ from published national surveillance

reports. The onset and peak of the influenza season

was at least 5 weeks early in Australia and 1 to 2 weeks

early in Chile, New Zealand and South Africa. Activity

was well above expected levels in South Africa and

very high in Chile, but only reached moderate levels in

Australia or New Zealand. The seasons experienced in

Chile and South Africa were also much shorter in dura-

tion than in Australia and New Zealand.

Virological data

Virological data are shown in Figure 1B and highlight

the variation in predominant viruses circulating among

countries. For example, while influenza A(H3N2) virus

clearly predominated in South Africa and was detected

at very high levels with the positivity reaching 80%

during the peak period, the predominant virus in Chile

was A(H1N1)pdm09. In New Zealand, both influenza A

and B viruses were detected; however, their relative

frequency differed between ILI and SARI surveillance,

with B viruses detected among roughly half (51%;

604/1,179) of ILI patients but only a quarter (27%;

104/385) of SARI patients.

Genetic characterisation of selected viruses showed

further differences among countries, although the num-

ber of samples characterised was small. Circulating

A(H1N1)pdm09 viruses were similar, with most falling

into subclade 6B.1A-P5 in Australia, New Zealand and

Chile. Differences in the predominant circulating clade

were observed for A(H3N2). Of 192 viruses sequenced

in Australia, 186 were 3C.2a1b (3C.2a1b + 131K: n =

182; 3C.2a1b + 135K: n = 4), with just six 3C.3a. The

majority of A(H3N2) viruses sequenced in New Zealand

also clustered in clade 3C.2a1b. In Chile, of 31 viruses

sequenced, 13 fell into the clade 3C.2a1b and 18 to

3C.3a. A limited selection of only 10 viruses from South

Africa suggested co-circulation of 3C.2a1b + 131K,

3C.2a1b + 135K and 3C.3a viruses. For influenza B,

nearly all viruses characterised in Australian pri-

mary care surveillance (107/108) and in New Zealand

(167/169) were B/Victoria lineage viruses, while all

11 influenza B viruses characterised in Chile were B/

Yamagata.

Vaccine effectiveness estimation

The virological data depicted in Figure 1B formed the

basis for VE estimation. All systems followed a test-

negative design, where the odds ratio (OR) comparing

the odds of vaccination among test-positive cases

vs test-negative controls was used to derive VE, i.e.

VE = (1−OR

adj

)×100% [10]. Estimates were made sepa-

rately for each country, virus and age group, incorpo-

rating covariates considered important by each site

(Figure 2). The heterogeneity among estimates within

each virus/age group combination was measured by

and τ

[11]. All networks were able to provide data for

the A(H3N2) VE. Too few A(H1N1)pdm09 and B cases

were detected in South Africa to enable VE estimation.

For A(H1N1)pdm09, heterogeneity was low overall

: 22%). For adults, although heterogeneity was not

high (I

: 58%), VE estimates ranged from −6% (95%

compatibility interval (CI): −96 to 42) in New Zealand

to 72% (95% CI: 51–84) among people in a target

group for vaccination in Chile. Only Chile was able to

provide VE estimates for children (65%; 95% CI: 49–76)

and elderly, i.e. adults aged ≥ 65 years (74%; 95% CI:

51–86).

For A(H3N2), heterogeneity was moderate overall

: 49%), but higher for adults (I

: 59%). In Australia,

South Africa and New Zealand hospitals, VE point esti-

mates ranged from 34% to 57% across age groups;

however, in Chile and New Zealand primary care, esti-

mates were often close to or beyond the null.

For influenza B, heterogeneity was low overall (I

: 0%),

despite differences in the predominant lineage and

the use of trivalent vaccine in Chile but quadrivalent

in New Zealand and Australia. Overall VE was lowest

in Chile (29%; 95% CI: −23 to 59). Here, the B compo-

nent for trivalent vaccines included a B/Victoria-like

virus, but most viruses circulating were B/Yamagata

thereby suggesting this low VE may be attributable to

lineage mismatch. Only one VE estimate was available

for elderly adults (Chile: 44%; 95% CI: −10 to 72) and

children (Australia: 55%; 95% CI: 20–76).

Discussion

We have shown that within countries of the southern

hemisphere, the timing, duration and intensity of the

influenza seasons, the predominant circulating viruses,

and VE all varied in the 2019 influenza season, even

between neighbouring countries such as Australia and

New Zealand. Similar observations have been reported

from Europe [9]. Thus, it appears that activity in one

country is not indicative of activity in another country,

even when influenza seasons are contemporaneous.

The early VE estimates for the 2019 influenza sea-

son in the southern hemisphere presented here were

highest for influenza A(H1N1)pdm09 and lowest for

4 www.eurosurveillance.org

F 1

Influenza activity (A) and influenza detections (B) for Australia, Chile, New Zealand and South Africa, 2019 influenza

season

A(H1N1)pdm09

A(H3N2)

A(Unsubtyped)

Australia:

A. Inﬂuenza activity plots B. Inﬂuenza detections by type and subtype

Primary care

ILI rate

(per 1000 consultations)

1 5 9 13 19 25 31 37 43 49

Medium (40%)

High (90%)

1 5 9 13 18 23 28 33 38 43 48

Number of cases

Australia:

Hospitals

Hospitalisations

(per 1000 beds)

1 5 9 13 19 25 31 37 43 49

Medium (40%)

High (90%)

1 5 9 13 18 23 28 33 38 43 48

Number of cases

100

150

200

250

Chile:

Hospitals

SARI rate

(per 100 discharges)

1 5 9 13 19 25 31 37 43 49

Medium (40%)

High (90%)

Ver

y high (97.5%)

1 5 9 13 18 23 28 33 38 43 48

Number of cases

New Zealand:

Primary care

ILI rate

(per 100,000 population)

1 5 9 13 19 25 31 37 43 49

100

150

Medium (40%)

High (90%)

1 5 9 13 18 23 28 33 38 43 48

Number of cases

100

120

New Zealand:

Hospitals

SARI rate

(per 100,000 population)

1 5 9 13 19 25 31 37 43 49

Medium (40%)

High (90%)

1 5 9 13 18 23 28 33 38 43 48

Number of cases

South Africa:

Primary care

P&I presentations

(per 100,000 population)

1 5 9 13 19 25 31 37 43 49

Medium (40%)

High (90%)

Ver

y high (97.5%)

1 5 9 13 18 23 28 33 38 43 48

Number of cases

100

120

140

Week

Pre-season threshold Post-season threshold

2013−2018 inﬂuenza seasons,

mean weekly rate

2019 inﬂuenza season

Week

ILI: influenza-like illness; P&I: pneumonia and influenza; SARI: severe acute respiratory infection.

Influenza activity plots (A) show the intensity of the 2019 influenza season compared with the average for 2013 to 2018. The point at which

2019 activity crossed baseline thresholds set by the prior 6 years’ data are marked with crosses. No post-season thresholds were estimated

for New Zealand.

Influenza detections by type and subtype (B) for patients enrolled in hospital and primary care surveillance for VE estimation. The data used

in vaccine effectiveness estimation are a subset restricted to those patients with complete information and recruited within the weeks used

for estimation (Table).

5www.eurosurveillance.org

F 2

Early vaccine effectiveness estimates against influenza A(H1N1)pdm09, A(H3N2) and B by age group and setting, Australia,

Chile, New Zealand and South Africa, 2019 influenza season

−20 0 50 100

Vaccine effectiveness

Australia

New Zealand

Australia

New Zealand

Chile

Australia

New Zealand

Chile

Primary care

Hospital

Primary care

Hospital

108

120

244

112

1,065

225

685

185

756

647

138

107

397

252

1,055

592

776

373

475

721

367

153

226

62 (39 to 78)

7 (−60 to 47)

70 ( 49 to 82)

54 (−8 to 80)

70 (60 to 77)

62 (34 to 80)

−6 (−96 to 42)

72 (51 to 84)

65 (49 to 76)

74 (51 to 86)

Children (< 18 years)

Elderly (

≥ 65 years)

Adults (18−64

years)

All patients

Setting

Positive

Negative

Network VE% (95% CI)

= 58.4 τ

= 0.026

= 33.3 τ

= 0.0048

Inﬂuenza A(H1N1)pdm09

−20 0 50 100

Vaccine effectiveness

Australia

New Zealand

South Africa

Australia

New Zealand

Chile

Australia

New Zealand

South Africa

Australia

New Zealand

Chile

Australia

New Zealand

Chile

Primary care

Hospital

Primary care

Hospital

Primary care

Hospital

274

108

325

139

434

309

665

303

246

168

374

148

130

1,065

225

685

185

649

647

138

308

397

252

1,055

592

320

776

373

322

721

367

199

226

242

200

37 (24 to 49)

4 (−29 to 29)

53 (23 to 72)

43 (22 to 59)

57 (21 to 76)

6 (−75 to 49)

39 (23 to 53)

0 (−41 to 30)

47 (−1 to 72)

50 (16 to 70)

−28 (−190 to 44)

39 (−27 to 71)

−24 (−167 to 43)

34 (−2 to 58)

40 (−34 to 73)

54 (−40 to 85)

Children (< 18 years)

Elderly (≥ 65 years)

Adults (18−64 years)

All patients

Setting

Positive

Negative

Network VE% (95% CI)

= 0 τ

= 0

= 7.43 τ

= 0.007

= 54.8 τ

= 0.025

= 49.3 τ

= 0.013

Inﬂuenza A(H3N2)

−20 0 50 100

Vaccine effectiveness

Australia

New Zealand

Australia

New Zealand

Chile

Australia

New Zealand

Chile

Australia

Primary care

Hospital

Primary care

Hospital

Primary care

140

188

367

104

1,065

225

685

185

756

647

138

397

1,055

592

776

373

475

721

367

101

226

242

63 (46 to 74)

56 (38 to 69)

52 (34 to 65)

66 (23 to 85)

29 (−23 to 59)

73 (57 to 84)

−2 (−192 to 64)

60 (−23 to 87)

44 (−10 to 72)

58 (29 to 77)

Children (< 18 years)

Elderly (≥ 65 years)

Adults (18−64 years)

All patients

Setting

Positive

Negative

Network VE% (95% CI)

= 0 τ

= 0

= 0 τ

= 0

Inﬂuenza B

CI: compatibility interval; V: vaccinated; UV: unvaccinated; VE: vaccine effectiveness.

and τ

are shown for measures of heterogeneity.

Estimates for Chile only include patients in a target group for vaccination; Australia used adjuvanted TIV for individuals ≥ 65 years of age.

Covariate adjustment: Australia primary care estimates adjusted for week (restricted cubic spline) and age group (where appropriate);

Australia hospital estimates adjusted for age group, comorbidities, indigenous ethnicity and pregnancy; Chile estimates adjusted for age,

month of symptom onset and pre-existing conditions; New Zealand estimates adjusted for age group; South Africa estimates adjusted for

seasonality and age.

6 www.eurosurveillance.org

A(H3N2). Early estimates often approximate final esti-

mates [12]. However, the utility of these estimates for

the northern hemisphere may be limited because the

2019 southern hemisphere vaccine differed from the

2019/20 northern hemisphere formulation in three

of four components, A(H1N1)pdm09, A(H3N2) and B/

Victoria. Nevertheless, these estimates or earlier ver-

sions of them were included with other data reviewed

at the WHO Consultation and Information Meeting on

the Composition of Influenza Virus Vaccines for Use in

the 2020 Southern Hemisphere Influenza Season dur-

ing 23–26 September 2019 in Geneva and provided

a general impression of the performance of the 2019

vaccine.

While heterogeneity in our VE estimates did not exceed

an I

of 60%, with so few studies, the sensitivity of sta-

tistical tests to detect heterogeneity is probably lim-

ited. This is exemplified by the I

of 0% for influenza B

estimates among adults despite differences in VE point

estimates of 75 percentage points (Figure 2). Thus,

low heterogeneity statistics do not alleviate concerns

about how to interpret discrepant VE point estimates.

There are many potential sources for this heterogeneity

that affect not only the VE estimates, but interpretation

of weekly activity rates. First, with random sampling,

we should not expect estimates to be the same [13].

Second, when samples are small they may be vulner-

able to statistical biases, such as sparse data bias,

and bias due to measurement errors may be more pro-

found [14]. Third, there were many differences in study

design (Table). Case ascertainment differed; for exam-

ple, a SARI case definition was used in New Zealand

and Chile, but not in Australian hospital surveillance.

Exposure ascertainment also differed, with varying

availability of registries to verify vaccination status and

the use of different vaccines. In particular, the adju-

vanted vaccines used among Australians ≥ 65 years of

age might be expected to yield higher VE than standard

vaccines [15]. Fourth, vaccine coverage varied (Table).

Low vaccination coverage, as observed in South Africa,

affects power and precision and can exacerbate the

bias induced by measurement errors. Higher cover-

age, as seen in Chile and among elderly patients in

New Zealand and Australia, may mean that many more

people in the sample are repeat vaccinees. Repeat vac-

cination may negatively impact VE and could result in

lower VE estimates in highly vaccinated populations

[16]. Finally, although only limited virological data

were available, we observed differences in circulating

A(H3N2) virus clades and B lineages. This may impact

both seasonality and VE, particularly as most A(H3N2)

viruses sequenced appeared to be in different clades

from the vaccine virus (3C.2a2). Notably, most A(H3N2)

viruses were also in different genetic groups from the

2019/20 northern hemisphere vaccine (3C.3a).

In conclusion, we have attempted to briefly summarise

and interpret the 2019 influenza season in four south-

ern hemisphere countries and have presented early VE

estimates. We observed substantial variation in avail-

able data on influenza seasonality and VE within the

southern hemisphere in 2019, which is unsurprising

given the many differences in surveillance among these

countries. Caution should be applied when attempting

to infer the impending northern hemisphere influenza

season based on these observations.

Ethical statements

Australia: Data were collected, used and reported

under the legislative authorisation of the Australian

state and territory legislation, and thus did not require

Human Research Ethics Committee approval.

Chile: The institutional review boards at the Pan

American Health Organization and United States CDC

reviewed the protocol and considered it a vaccination

effectiveness evaluation (non-intervention study).

Monitoring vaccine effectiveness in Chile is an objec-

tive of severe acute respiratory surveillance; thus,

ethics committee approval was not needed for data

collection and analysis. We did not collect personal

identifiers.

New Zealand: Influenza surveillance in New Zealand

is conducted in accordance with the Public Health Act

and thus ethics committee approval was not needed

for collection or use of these data.

South Africa: Influenza surveillance is conducted in

accordance with the Public Health Act and thus ethics

committee approval was not needed for collection or

use of these data.

Acknowledgements

Australia: We thank Daniel Blakely and Nigel Stocks at

University of Adelaide; Violeta Spirotoska at the University

of Melbourne; and staff at the Victorian Infectious

Diseases Reference Laboratory, SA Pathology and the

WHO Collaborating Centre for Reference and Research

on Influenza. The Australian Sentinel Practices Research

Network, the Influenza Complications Alert NEtwork and

the WHO Collaborating Centre for Reference and Research

on Influenza are supported by the Australian Government

Department of Health.

The Victorian Sentinel Practices Research Network is sup-

ported by the Victorian Government Department of Health

and Human Services.

Chile: We thank Patricia Bustos, Winston Andrade and

Alejandra Acevedo, Sección de Virus Respiratorios y

Exantematicos, Instituto de Salud Publica de Chile; Olga

López, Hospital Ernesto Torres Galdames, Iquique; Miriam

Blanco, Hospital Gustavo Fricke, Viña del Mar; Alejandra

Céspedes, Hospital San Juan de Dios, Santiago; Marta

Werner, Hospital Guillermo Grant Benavente, Concepción;

Tania Campos, Hospital Regional de Temuco; Camila Bolados,

Hospital Regional de Puerto Montt; Miguel Angel Descalzo,

WHO/PAHO consultant.

New Zealand: We thank Jacqui Ralston, Wendy Gunn and

Jessica Danielecz at the Institute of Environmental Science

and Research, Wellington.

7www.eurosurveillance.org

South Africa: We would like to acknowledge clinicians who

participate in the Viral Watch influenza surveillance net-

work and the Netcare Hospital Group for data on patient

consultations.

Conflict of interest

CC received grant funding to the institute from Sanofi

Pasteur, Programme for Applied Technologies in Health and

United States Centers for Disease Control and Prevention,

and travel funding from Parexel.

AvG has received research funding to the institute from

Sanofi and Pfizer, and reimbursement of travel expenses

from Sanofi and Pfizer.

Authors’ contributions

All authors contributed text or data to the draft, interpreted

results, and approved the final version of the manuscript.

SGS coordinated the study, prepared the first draft and man-

aged all revisions; KSC manages VicSPIN, MBMC manages

ASPREN and both contributed data; SGS performed VE es-

timation for ILI surveillance; ACC conceived the study, con-

tributed data, information and VE estimates for FluCAN, and

contributed to development of the manuscript; SGS, VKYL,

HP and YMD coordinated receipt of viruses for characterisa-

tion at the WHOCCRRI; YMD managed sequencing of viruses

in Melbourne.

VS is coordinator for VE estimation in Chile and provided

information about SARI surveillance and VE estimates; MFO

manages SARI surveillance and with NV provided validated

data from SARI surveillance; CG manages the national immu-

nisation program and with PBur, provided information about

vaccination; RAF and PBus managed virological surveillance

activities; NEO and CSA provided support to the VE network

in Chile and aided in drafting the manuscript.

QSH directs the National Influenza Centre in New Zealand,

TW provided epidemiological data and VE estimates and

both contributed to descriptions about influenza surveil-

lance in New Zealand; AM, LL and TW managed surveillance

data; JB and LJ were responsible for influenza testing and

laboratory analyses of samples sent to ESR as part of the

influenza surveillance program.

JMM, CC, OH, AvG and SW contributed surveillance data and

VE estimates for South Africa, and provided relevant text and

information during development of the draft. ST provided ex-

pert advice on surveillance VE estimation and contributed to

developing the draft.

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