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Manuscript Number:

Title: Comparison of epidemical features of seasonal influenza across

different climatic zones in Australia

Article Type: Article (Original Research)

Keywords: Seasonal influenza epidemics, transmission rate, climatic zone,

epidemic duration, epidemic peak timing

Corresponding Author: Professor Wenbiao Hu,

Corresponding Author's Institution: Queensland University of Technology,

Brisbane, Australia

First Author: Wenbiao Hu

Order of Authors: Wenbiao Hu; Xiaodong Huang; Gabriel Milinovich; Ian

Barr; Hilary Bambrick

Manuscript Region of Origin: AUSTRALIA

Abstract: Background: Seasonal influenza epidemic patterns have been

highly influenced by weather and usually vary by tropical, subtropical

and temperate climates. Few studies investigate the features of seasonal

influenza in different age groups among geo-climatic regions according to

a specific climatic condition.

Objective: This study aimed to assess the differences in the epidemical

features of influenza A and B among six climatic zones in three age

groups (<15, 15-64 and 65+ years) in Australia.

Methods: National Notifiable Diseases Surveillance System (NNDSS) data on

weekly laboratory-confirmed cases of influenza A and B at a postcode

level were collected from the Australian Government Department of Health

between 1st January 2011 and 31st December 2013. Spatial and temporal

descriptive analyses and Dunnett- Tukey-Kramer (DTK) pairwise multiple

comparison tests were used to investigate the differences in seasonal

patterns, durations, peak timings and epidemic magnitude for influenza A

and B, stratified by the six climatic zones and age group. Bayesian

space-time models based on a spatial conditional autoregressive (CAR)

model combined with a susceptible, infectious and removed (SIR) model was

used to estimate transmission rates to explore differences in evolution

of influenza A and B epidemics.

Results: There were significant differences in mean weekly notification

rates of influenza A and B among the six climatic zones in the 0-14 and

15-64 age groups. Mean weekly notification rates were more likely to be

higher in the areas with a warm winter or a mild winter than in the area

with relatively colder winter. The ≥65 age group showed less spatial

variation in mean weekly notification rates of influenza A and B among

the six climatic zones. Mean duration, peak timing and transmission rates

of influenza A and B epidemics did not display synchronicity between

either the three age groups or the six climatic zones. The magnitude of

the linear growth and decay rates of mean weekly transmission rates

varied by different climatic zones and age groups.

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3307714

Conclusion: This study suggests that the epidemic features of influenza A

and B vary between geo-climatic regions and age groups. Our findings

provide valuable insight for public health authorities to adjust

prevention and control strategies of seasonal influenza for specific age

groups in specific climatic regions in Australia.


1

Comparison of epidemical features of seasonal influenza across different

climatic zones in Australia

Wenbiao Hu1*

, Xiaodong Huang2, Gabriel Milinovich

1, Ian Barr

3 and Hilary Bambrick

1

1School of Public Health and Social Work, Institute of Health and Biomedical Innovation,

Queensland University of Technology, Brisbane, Queensland, Australia.

2School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland

University of Technology, Brisbane, Queensland, Australia.

3World Health Organization (WHO) Collaborating Centre for Reference and Research on

Influenza, Melbourne, Victoria, Australia

*Corresponding author: Dr. Wenbiao Hu, School of Public Health and Social Work, Institute

of Health and Biomedical Innovation, Queensland University of Technology. Telephone:

+61-7-3138 5742; Email: [email protected]

Running title: Epidemical features of seasonal influenza across different climatic zones

Word count: 367 for abstract, 5,370 for text, 4 tables, 5 figures, 31 references

Manuscript


mailto:[email protected]

2

Abstract

Background: Seasonal influenza epidemic patterns have been highly influenced by weather

and usually vary by tropical, subtropical and temperate climates. Few studies investigate the

features of seasonal influenza in different age groups among geo-climatic regions according

to a specific climatic condition.

Objective: This study aimed to assess the differences in the epidemical features of influenza

A and B among six climatic zones in three age groups (<15, 15-64 and 65+ years) in

Australia.

Methods: National Notifiable Diseases Surveillance System (NNDSS) data on weekly

laboratory-confirmed cases of influenza A and B at a postcode level were collected from the

Australian Government Department of Health between 1st January 2011 and 31

st December

2013. Spatial and temporal descriptive analyses and Dunnett- Tukey-Kramer (DTK) pairwise

multiple comparison tests were used to investigate the differences in seasonal patterns,

durations, peak timings and epidemic magnitude for influenza A and B, stratified by the six

climatic zones and age group. Bayesian space-time models based on a spatial conditional

autoregressive (CAR) model combined with a susceptible, infectious and removed (SIR)

model was used to estimate transmission rates to explore differences in evolution of influenza

A and B epidemics.

Results: There were significant differences in mean weekly notification rates of influenza A

and B among the six climatic zones in the 0-14 and 15-64 age groups. Mean weekly

notification rates were more likely to be higher in the areas with a warm winter or a mild

winter than in the area with relatively colder winter. The ≥65 age group showed less spatial

variation in mean weekly notification rates of influenza A and B among the six climatic

zones. Mean duration, peak timing and transmission rates of influenza A and B epidemics did

not display synchronicity between either the three age groups or the six climatic zones. The

magnitude of the linear growth and decay rates of mean weekly transmission rates varied by

different climatic zones and age groups.

Conclusion: This study suggests that the epidemic features of influenza A and B vary

between geo-climatic regions and age groups. Our findings provide valuable insight for

public health authorities to adjust prevention and control strategies of seasonal influenza for

specific age groups in specific climatic regions in Australia.


3

Keywords: Seasonal influenza epidemics, transmission rate, climatic zone, epidemic

duration, epidemic peak timing.

Introduction

Seasonal influenza is an acute viral infection that causes substantial morbidity and mortality.

Annually, it is estimated that seasonal influenza results in 250,000–500,000 deaths globally,

with a particularly heavy burden on very young children and elderly people (WHO, 2016).

The influenza viruse is classified into 4 types, denoted A, B, C and D. Influenza viruses A

and B commonly circulate and outbreaks of these types occur throughout the world; the virus

has a propensity for antigenic drift and shift and as such, is considered a serious and constant

global public health problem (WHO, 2016), while influenza C and D cause little human

disease.

The annual temporal patterns of seasonal influenza are strongly associated with weather

fluctuation in temperate regions (J. Tamerius et al., 2011; J. D. Tamerius et al., 2013),

whereas the temporal patterns in tropical and subtropical regions are less well defined

(Bloom-Feshbach et al., 2013; Deyle, Maher, Hernandez, Basu, & Sugihara, 2016; Moura,

2010; J. D. Tamerius et al., 2013; WHO, 2016). “Flu season”, the period of most epidemic

activity of seasonal influenza, typically occurs from November to March in the northern

hemisphere and from May to September in the southern hemisphere (Lone Simonsen, 1999).

Within temperate regions, research has demonstrated cold temperature and low humidity

facilitate influenza transmission (Bloom-Feshbach et al., 2013; A. C. Lowen & Steel, 2014;

Shaman, Pitzer, Viboud, Grenfell, & Lipsitch, 2010; J. D. Tamerius et al., 2013). Within

tropical and subtropical regions, increased transmission is associated with humid-rainy

conditions (Shaman, Goldstein, & Lipsitch, 2010; J. Tamerius et al., 2011). Significant

relationships have also been demonstrated between influenza epidemics and latitudinal

variations (Bloom-Feshbach et al., 2013; Yu et al., 2013). However, climatic conditions may


4

vary significantly across a single latitude, even in same traditional climatic zone. For example,

small-scale local climate conditions are affected by the local ecosystem factors including

altitude, vegetation (desert/grassland/forest) and locality (coastal/inland). Hence, the

associations between influenza epidemics and sole latitude gradients may not fully mirror the

characteristics of seasonal influenza epidemics.

Most studies to date have focused on analysis of the epidemic patterns of seasonal influenza

among tropical, subtropical and temperate areas only (Deyle et al., 2016; Moura, Perdigão, &

Siqueira, 2009; Saha et al., 2014; J. Tamerius et al., 2011). Moreover, the spatial variation in

seasonality of influenza A and B (Yu et al., 2013) and the effects of weather conditions on

different age groups (Huang, Mengersen, Milinovich, & Hu, 2017) have been reported. To

our knowledge, few studies have explored the characteristics of the epidemics of seasonal

influenza A and B among different age groups in different climatic zones, classified

according to temperature and humidity. As such, uncertainties and limitations still exist in

fully understanding of seasonal influenza characteristics in different climatic zones and age

groups. The aim of this study was to characterize influenza A and B epidemics across a

diverse set of geo-climatic conditions and age groups (0-14, 15-64 and 65+ years of age). The

establishment of such features of influenza outbreaks will valuable information to assist in

future vaccination programs and to guide influenza prevention and control in people of

different age groups in specific geo-climatic zones.

Materials and Methods

Study site and data collection

Australia is in the southern hemisphere and surrounded by the Indian and Pacific oceans.

Climates significantly vary across the continent due to its large land size and wide variety of

landscapes. The data on weekly laboratory-confirmed cases of influenza A and B at a


5

postcode level, reported to the NNDSS, were collected from the Australian Government

Department of Health between 1st

January 2011 and 31st December 2013. The population size

of age groups at each postal area was based on the 2011 Census data in Australia (Australian

Bureau of Statistics, 2013).

The six climatic zones were classified according temperature and humidity data collected

over the period 1961 to 1990 by the Bureau of Meteorology (Australian Government Bureau

of Meteorology, 2016b). The six climatic zones included: 1) hot humid summer (HHS) zone

including equatorial, tropical and some parts of subtropical areas; 2) warm humid summer

(WHS) zone including some parts of subtropical areas; 3) hot dry summer and mild winter

(HDMW) zone including most desert areas; 4) hot dry summer and cold winter (HDCW)

zone including grassland and some parts of desert areas; 5) warm summer and cool winter

(WSCW) zone located in a warm temperate area; and 6) mild warm summer and cold winter

(MSCW) zone including a cool temperate area. The numbers of postal locations were 168,

239, 39, 584, 1926 and 187 in HHS, WHS, HDMW, HDCW, WSCW and MSCW zones,

respectively.

Statistical analysis and modelling

Weekly influenza data were collected by each postal location and were categorized into three

age groups (0-14, 15-64 and 65+ years) over 157 consecutive weeks from 1st January 2011 to

31st December 2013 across the six climatic zones. Weather conditions for a postal zone were

assumed to be as per the climatic zone described by the Bureau of Meteorology. Hence, local

weather factors were not involved in the analysis in the study.

To detect spatial variations in the epidemics of influenza A and B across Australia, we

mapped the spatial distributions of overall mean weekly notification rates by each postal

location for the three age groups over the study period. Heatmaps were used to display the

seasonal patterns of influenza A and B epidemics. Using ANOVA and Dunnett-Tukey-


6

Kramer (DTK) Pairwise Multiple Comparison Test significant differences in mean weekly

influenza notification rates among the six climatic zones in each age group were determined.

Epidemic duration for each climatic zone was defined as the number of weeks in the calendar

year in which influenza cases exceeded 2.5% of the annual number of influenza cases in each

climatic zone. We also tested a 5% epidemic threshold to test the sensitivity of the epidemic

durations, but 2.5% threshold appeared to show a reliable definition of epidemic period in the

study. Annual peak timing of influenza epidemics was defined as the week with the highest

overall mean weekly notification rate at each climatic zone in each age group in each year.

Bayesian space-time models based on a spatial conditional autoregressive (CAR) model

(Besag, York, & Mollie, 1991) and a susceptible, infectious and removed (SIR) model were

used to examine the dynamic transmission rates of both influenza A and B across the six

climatic zones in each age group over the study period, except for the ≥ 65 age group for

influenza B owing to very small weekly number of influenza B counts across postal locations

over the study period.

Let yij be observed weekly age-specific laboratory-confirmed cases of influenza A and B at

postal location i under a climatic zone and in week j, (i=2513, j=157). A discrete form SIR

model for fraction of susceptibles in a local age-specific population at postal location i and in

week (j+1) is expressed as , where S and I denote numbers in the age-

specific susceptible and infected populations, respectively. We assumed that the number of

susceptibles in the first week was Si1 = 65% of population size for each postal location

(Dorigatti, Cauchemez, & Ferguson, 2013; He, Dushoff, Eftimie, & Earn, 2013). Let Iij be

defined as a Poisson distribution, )(~ ijij PoisI . The expected count ij is given by a function

of the number of infected cases in week (j-1) and the susceptible population size in week j

(Huang et al., 2016; Lawson & Song, 2010):

(1)


7

(2)

where βij is weekly transmission rate of influenza at location i and in week j; b0 is the

intercept for the model which represents the log-transformed baseline transmission rate

across all postal locations; b1, b2, b3 and b4 are the regression coefficients for the harmonic

terms to describe the seasonal patterns of the influenza A and B; tij is a random effect to take

account of spatiotemporal variation in transmission rate at each postal location; random effect

ui captures the effect of unmeasured or unobserved factors with spatial pattern using a CAR

model which is built as a function of its first-order neighbourhood; random effect vi

corresponds to geographically unstructured heterogeneity in transmission rate that captures

measurement error or micro-scale variation. Normally distributions were adopted for all

terms ui, νi and tij. The Bayesian analysis was performed using WinBUGS software version

1.4. Posterior distributions for βij were obtained through Markov chain Monte Carlo (MCMC)

sampling. Convergence was assessed by checking the trace plots for the parameters. We ran

15,000 MCMC iterations and discarded the first 3,000 MCMC iterations as burn-in.

Finally, we used linear regression models to roughly capture the trends of the estimated

transmission rates from starting influenza activity month to peak month or from peak month

to off peak season in order to investigate the temporal evolutions of seasonal epidemics of

influenza A and B. The slope coefficients αi that were estimated from linear regression

models (i.e. βij = αi × (week)j+bi) were used to compare the magnitudes of the linear growth

rates (from starting influenza activity in May to peak month in August) and the linear decay

rates (from September to off-peak season in December) for influenza A and B transmission

among the six climatic zones in each climatic zone.


8

Results

1. Epidemics of influenza A and B at a nationwide level

There was a statistically significant difference in overall means of weekly notification rates

between influenza A (mean=2.11 per 100,000) and influenza B (mean=0.9 per 100,000)

(ANOVA test, p<0.001) during the study period in Australia. The overall mean weekly

notification rates of influenza A for each age group were also significantly greater than those

of influenza B in the respective 0-14 (p<0.0149), 15-64 (p<0.001) and ≥65 (p<0.0219) age

groups. Influenza A and B activities exhibited similar seasonal patterns during the study

period, which the peak timings for the highest overall monthly notification rates of both

influenza A and B occurred in winter (August), except for influenza A in July in 2012. Figure

2 shows the spatial distributions of mean weekly notification rates by a postcode level across

Australia. There were substantial spatial variations in the mean weekly notification rates of

influenza A in the three age groups, while large spatial variation only presented in the 0-14

age group for influenza B.

[Figure 1 about here]

2. Influenza Epidemic Characteristics in the six climatic zones

2.1 Magnitude of influenza Epidemics

Table 1 shows the summary of observed mean weekly notification rates of influenza A and B

by a postcode level in the six climatic zones. In the 0-14 age group, the highest averages of

mean weekly notification rates were observed in WHS zone for influenza A (mean = 4.91 per

100,000 population) and in HDMW zone for influenza B (mean = 3.25 per 100,000

population), while the smallest averages of mean weekly notification rates were found in

MSCW zone for both influenza A (mean=1.9 per 100,000 population) and B (mean=0.78 per


9

100,000 population). In the 15-64 age group, the highest averages of mean weekly

notification rates presented in HDMW zone for both influenza A (mean=3.29 per 100,000

population) and B (mean=1.21 per 100,000 population), followed by HHS zone for both

influenza A (mean=3.0 per 100,000 population) and B (mean=1.1 per 100,000 population),

while the smallest averages of mean weekly notification rates occurred in MSCW zone for

both influenza A (mean=1.44 per 100,000 population) and B (mean=0.42 per 100,000

population). In the ≥65 age group, the highest averages of mean weekly notification rates

presented in HHS zone for influenza A (mean=3.14 per 100,000 population) and in MSCW

zone for influenza B (mean=0.79 per 100,000 population). The smallest averages of mean

weekly notification rates exhibited in HDMW zone for influenza A (mean=1.58 per 100,000

population) and B (mean=0.41 per 100,000 population).

The DTK test indicated that there were statistically significant differences in mean weekly

notification rates of influenza A and B between different climatic zones in each age group,

except for influenza B in the ≥ 65 age group (Figure 3). For example, in the 0-14 age group,

mean weekly notification rates of influenza A and B were significantly greater in WHS zone

than in HDCW, WSCW and MSCW zones, but significantly smaller in MSCW zone than in

HHS and WSCW zones. Mean weekly notification rates were significantly greater in WHS

zone than in HDMW zone for influenza A and in HDCW zone than in MSCW zone for

influenza B. There were no differences in means of weekly notification rates between any

other pairs of climatic zones. In the 15-64 age group, mean weekly notification rates of

influenza A and B were significantly smaller in MSCW zone than in HHS, WHS and HDCW

zones. In the ≥ 65 age group, mean weekly notification rate of influenza A was only

significantly greater in WHS zone than in WSCW zone. There were no significant difference

in mean of weekly notification rates of influenza B among the six climatic zones.

[Table 1 about here]


10


2.2 Seasonal pattern

Heatmaps show the temporal patterns of mean weekly notification rates of influenza A and B

during the study period (Figure 4). Although influenza A and B activities concentrated in

winter, the heatmaps depicted a diversity of seasonal patterns among the six climatic zones in

each age group. HHS was likely to experience a semi-annual and annual influenza patterns

for influenza A and B in the three age groups, particularly for influenza A. There were no

clear annual seasonal patterns in HDMW zone for either influenza A or B for the 15-64 and ≥

65 age groups. WHS, HDCW, WSCW and MSCW zones were more likely to present clear

annual seasonal patterns for influenza A and B in the three age groups.


2.3 Epidemic duration and peak timing

Table 2 shows the epidemic durations of each year using a 2.5% epidemic threshold in each

climatic zone for each age group. There were large variations in epidemic durations between

the three age groups in the six climatic zones. In the 0-14 age group, the longest mean

durations were observed in HHS zone for influenza A (mean=28 weeks) and influenza B

(mean=27.7 weeks), while the smallest mean duration was 13 weeks (range 10 to 16 weeks)

for influenza A in WSCW zone and 11.7 weeks (range 10 to14 weeks) for influenza B in

WHS zone. In the 15-64 age group, the longest mean duration was found in HHS zone for

influenza A (mean=28 weeks) and in HDMW zone for influenza B (mean=37 weeks), while

the smallest mean duration was 10.7 weeks in WHS zone for influenza A and 13.7 weeks in

WSCW zone for influenza B. In the ≥ 65 age group, the longest mean duration presented in

HDMW zone for influenza A (mean=39.3 weeks) and in HHS zone for influenza B

(mean=41.3 weeks), while the smallest mean duration occurred in WSCW zone with an


11

average of 15.3 weeks for influenza A and in WSCW zone with an average of 17.3 weeks for

influenza B. Here, influenza B in HDMW zone (mean=13 weeks) was omitted from

comparison of the smallest mean duration due to missing data in 2013.

Table 3 shows the annual peak timings of influenza A and B epidemics in each year among

the six climatic zones in each age group. Although 77.6% of the peak mean weekly

notification rates of influenza A and B were observed in winter months between the middle

of June and the end of August among the six climatic zones in the three age groups during the

three years, the peak weeks of each year were not well synchronized. For example, the annual

peak timings for influenza A and B in HHS zone were found across four seasons in the three

age groups, particularly in 2013.

[Tables 2 and 3 about here]

2.4 Transmission Rate

Table 4 shows the averages of posterior mean weekly transmission rates of influenza A and B

in each climatic zone and each age group during the study period. There were significant

differences in means of estimated weekly transmission rates for influenza A and B among the

six climatic zones in each age groups (ANOVA test: p<0.001). In the 0-14 age group, the

highest means of estimated weekly transmission rates for both influenza A and B exhibited in

WHS zone, while the lowest averages of estimated weekly transmission rates for both

influenza A and B were found in MSCW zone. In the 15-64 age group, the highest averages

of estimated weekly transmission rates for both influenza A and B appeared in HDMW zone,

while the lowest means of estimated weekly transmission rates for both influenza A and B

presented in MSCW zone. In the ≥65 age group, the highest averages of estimated weekly

influenza A transmission rates was found in WHS and HHS zones, while the lowest mean of

estimated weekly influenza A transmission rate showed in HDMW zone.


12

[Table 4 about here]

2.5 Linear growth and decay of transmission rate

The linear regression models in Figures 5 and 6 demonstrated marked variations in the linear

growth and decay rates of mean estimated weekly transmission rates for influenza A and B

among the six climatic zones in each age group. For the linear growth rates from May to

August in Figure 5, in the 0-14 age group, the highest linear growth rates were found in WHS

zone for both influenza A (α = 0.0037, p=0.001) and B (α = 0.0029, p<0.001), while the

lowest linear growth rates for mean weekly transmission rates presented in MSCW for both

influenza A (α =0.0012, p=0.166) and B (α =0.0006, p<0.001), followed by HHS zone for

influenza A (α=0.0017, p=0.03) and B (α=0.0007, p=0.005). In the 15-64 age group, the

highest linear growth rates for mean weekly transmission rates showed in HDMW zone for

both influenza A (α=0.0022, p=0.01) and B (α=0.001, p=0.046), while the lowest linear

growth rates for mean weekly transmission rates were founded in MSCW zone for both

influenza A (α=0.001, p=0.004) and B (α=0.0004, p<0.001). In the ≥65 age group, influenza

A presented the highest linear growth rates for mean weekly transmission rates in WHS zone

(α=0.0027, p<0.001), followed by HHS (α=0.0026, p<0.001), while HDMW zone showed the

lowest linear growth rate (α=0.0004, p=0.143).

For the linear decay rates of mean estimated weekly transmission rates from September to

December in Figure 6, in the 0-14 age group, the highest linear decay rates occurred in

HDMW zone for both influenza A (α = -0.0022, p=0.007) and B (α =-0.0014, p<0.001),

while the lowest linear decay rates were found in MSCW zone for influenza A (α =-0.0003,

p<0.001) and in HDMW zone for influenza B (α =-0.0003, p=0.022). In the 15-64 age group,

the highest linear decay rates presented in HDMW zone for influenza A (α=-0.0011, p=0.009)

and in WHS zone for influenza B (α=-0.0007, p<0.001), while the lowest linear decay rates


13

were found in MSCW zone for influenza A (α=-0.0002, p<0.001), and in HHS (α=-0.0002,

p<0.001) and MSCW (α=-0.0002, p=0.009) zones for influenza B. In the ≥65 age group,

influenza A showed the highest linear decay rates for mean weekly transmission rate in WHS

zone (α=-0.001, p=0.001), followed by HHS (α=-0.0009, p=0.005) and HDCW (α=-0.0008,

p<0.001), while the lowest linear decay rates were found in HDMW (α=-0.0001, p=0.664)

and MSCW (α=-0.0004, p=0.106) zones.

[Figures 4 and 5 about here]

Discussion

Our study demonstrated significant differences in epidemiological features of influenza A

and B outbreaks when population were stratified by climatic zones and age group.

Statistically significant differences in mean weekly notification rates for both influenza A and

B were observed between the six climatic zones in each age group (Figure 2). It is notable

that the 0-14 and 15-64 age groups exhibited relatively higher mean weekly notification rates

of influenza A and B in WHS zone compared with HDCW, WSCW and MSCW zones. Mean

weekly notification rates of influenza A and B in the 0-14 and 15-64 age groups were

significantly smaller in MSCW zone than in HHS zone and in HDCW zone except for

influenza A in the 0-14 age group and in WSCW zone excluding influenza A in the 15-64 age

group. Moreover, WHS zone had a significantly higher mean weekly notification rate of

influenza A than that in WSCW zone in the ≥65 age group. In Australia, WSCW and MSCW

zones are categorized as temperate climate regions. WSCW zone has a warm summer and a

cool winter, while MSCW zone is a relative colder region with a mild/warm summer and a

cold winter in Australia. HHS zone includes equatorial, tropical and subtropical regions and

WHS may only include subtropical region. HHS typically experiences a hot humid summer

and a wet-dry climate while WHS has a warm humid summer and no dry season. HDMW


14

zone has a hot dry summer and a mild winter due to mostly desert environmental. HDCW

experiences a dry summer and cold winter zone with some parts of desert and grassland

environmental condition (Australian Government Bureau of Meteorology, 2016a, 2016b). As

stated above, we found that the climatic zones with a warm winter or a mild winter were

more likely to have higher influenza notification rates than relatively colder regions, such as

MSCW zone. Taken together, we hypothesised that people may be easily infected by

influenza virus due to ignoring themselves keeping warm or staying indoors in a warm winter,

which may induce a human behaviour that increase the notification rates in these areas.

Interestingly, there were no spatial variations in mean weekly notification rates of influenza

A and B in the ≥65 age group among the six climatic zones, with the exception of the WHS

zone for influenza A. This result suggests the intensity of influenza epidemics in the ≥65 age

group may not vary significantly geographically, as has previously been reported (Huang et

al., 2017). The Australian National Immunisation Program offers free influenza vaccination

to individuals aged 65+ years (Australian Government Department of Health, 2015), with a

73% of seasonal influenza vaccine uptake rate in this age group (National Centre for

Immunisation Research & Surveillance, 2017). This could help elderly people to battle

against influenza virus infection due to an increase in their anti-influenza antibody titres

(Bernstein et al., 1999; Osterholm, Kelley, Sommer, & Belongia, 2012).

This study found the HHS zone to present semi-annual and annual epidemic patterns. The

WHS, HDCW, WSCW and MSCW zones were more likely to have annual influenza season

for influenza A and B in the three age groups, which is consistent with the global circulation

of seasonal influenza activity that there are two distinct influenza seasons in some of tropical

and subtropical regions and one influenza season in temperate region reported by previous

studies (Azziz Baumgartner et al., 2012; Soebiyanto, Adimi, & Kiang, 2010; Yazdanbakhsh

& Kremsner, 2009; Yu et al., 2013). The epidemic season of influenza could occur therefore


15

earlier in HHS zones than in other climatic zones due to experiencing two distinct influenza

seasons. Although WHS zone is classified as subtropical area with no dry season, only

annual influenza season occurred in WHS zone for influenza A and B in the three age groups.

Moreover, it was striking that HDMW with a desert environment presented more complex

and sporadic inter-seasonal sustained sporadic patterns of influenza A and B activity. A

previous study in Maricopa County, Arizona which has a desert climate showed a pattern of

two distinct influenza seasons yearly (Soebiyanto et al., 2010). Our results suggest that the

epidemic patterns of seasonal influenza varied not only in the traditional climatic zones, but

also in small geo-climatic regions within the same traditional climatic zones. The different

seasonal influenza patterns might be used to guide public health authorities to adjust timing

of influenza vaccination and prevention strategies in some specific areas.

Our study demonstrated that mean durations and peak timings of influenza A and B

epidemics are not well synchronized in the three age groups or in the six climatic zones. HHS

and HDMW zones were more likely to exhibit longer mean epidemic durations for influenza

A and B, regardless of age, excluding the ≥ 65 age group in HDMW zone for influenza B. It

would be expected that the complex seasonal patterns in HHS and HDMW zones could

extend their epidemic durations. In contrast, relatively shorter mean annual epidemic

durations occurred in WHS and WSCW zones for the 0-14 age group and in WHS and

MSCW zones for the 15-64 age group and in WSCW zone for the ≥ 65 age group for

influenza A, and in WHS zone for the 0-14 age group and in WHS and WSCW zones for the

15-64 age group and in HDMW and WSCW zones for the ≥65 age group for influenza B.

The epidemic durations of influenza A and B in WHS zone in the six climatic zones were

more likely to be relatively short in the 0-14 and 15-64 age groups, suggesting that influenza

activity may not last long in warmer regions with one annual influenza season.


16

Although WSCW and MSCW zones were categorised as temperate zones, MSCW zone had

relatively longer mean annual epidemic durations among the two zones, except for influenza

A in the 15-64 age group. We suggested that cold-temperate regions might have long-lasting

influenza activity compared with mild-temperate regions. Importantly, although the ≥65 age

group had a high seasonal influenza vaccine uptake rate, our study found that the ≥65 age

group had relatively longer mean epidemic durations for influenza A and B compared with

the respective climatic zones in the 0-14 and 15-64 age groups. It is commonly known that

individuals ≥65 years of age are more susceptible due to reduced immunity and increased

prevalence of chronic diseases (DiazGranados et al., 2014; McElhaney et al., 2013). Despite

existing variations in the peak timings of influenza A and B epidemics among the six climatic

zones most of the peak timings occurred in winter. HHS and HDMW zones presented more

diverse ranges of annual peak timings due to their complex seasonal patterns, particularly for

influenza A in the ≥65 age group, in which peak timings sometimes occurred in the early

autumn and summer or the end of summer and spring. The different epidemic durations and

peak timings could help public health authorities to make more practical and flexible control

strategies (e.g. medical supplies allocation and timing of interventions) for seasonal influenza

according to specific areas.

Bayesian space-time models revealed the differences in means of estimated weekly

transmission rates of influenza A and B among the six climatic zones in each age group.

MSCW zone showed relatively smaller averages of posterior mean weekly transmission rates

of influenza A and B in the 0-14 and 15-64 age groups, while HHS and WHS zones were

likely to present relatively higher averages of posterior mean weekly transmission rates of

influenza A in the three age groups and influenza B in WHS zone for in the 0-14 age group. It

is noted that although low temperatures was positively associated with influenza transmission


17

in temperate regions (J. D. Tamerius et al., 2013), the MSCW zone, with a cold winter in

Australia, presented relatively lower averages of posterior mean weekly transmission rates in

the study. By contrast, the warm zones, such as the HHS, WHS and HDMW zones, exhibited

relatively higher averages of posterior mean weekly transmission rates for influenza A and B.

A previous study proposed that increased temperature or humidity leads to enhanced

efficiency of influenza transmission by a contact in a warm region, and supposed that the

routes of influenza infection were primarily through by aerosol transmission in temperate

regions and by direct or indirect contact in tropical regions (A. Lowen & Palese, 2009). If this

hypothesis is true, we suggest that influenza prevention and control interventions may need to

be implemented year-round in warm regions, and that health agencies may need to focus

further on public education of influenza transmission routes, as well as personal hygiene.

Our most interesting findings were that the temporal evolutions of influenza A and B

epidemics were not well synchronized among the six climatic zones in each age group. The

magnitudes of the linear growth and decay rates of overall posterior mean weekly

transmission rates varied by different climatic zones and age groups. WHS and HDMW

zones tended to have relatively higher linear growth and decay rates for both influenza A and

B in the 0-14 and 15-64 age groups, except for the linear decay rate for influenza B in

HDMW zone in 0-14 age group. Averages of posterior mean weekly transmission rates of

both influenza A and B were more likely to increase (from starting influenza season (May) to

peak month (August)) and decrease (from September to December) linearly slowly in cold

regions (i.e. MSCW) compared to other climatic zones in the 0-14 and 15-64 age groups,

excluding the decay rate of influenza B in the 0-14 age group. Less geographic variations in

the linear growth and decay rates among the six climatic zones in the 15-64 age group

suggested that the temporal evolutions of influenza A and B epidemics were likely to be

synchronous than the other age groups. Moreover, although HHS was likely to have a semi-


18

annual pattern of influenza activity, the magnitudes of the growth rates for influenza A and B

showed a relatively modest increase in weekly transmission rates in HHS zone compared to

the rest climatic zones in the 0-14 and 15-64 age groups. Finally, the findings indicated that

the linear growth rates of influenza A were relatively high in HHS and MSCW zones in the

≥65 age group compared to other two age groups, while their linear decay rates were

relatively fast. Overall, our study is likely to identify the differences in the temporal

evolutions of influenza A and B epidemics in different age groups and climatic zones. This

result may translate into timing of influenza control and resource allocation.

The limitations of the study should be acknowledged. Firstly, the use of laboratory-confirmed

notification data tends to underestimate actual case numbers as it only includes a proportion

of the total cases of influenza occurring in the community, that is, only those cases for which

health care was sought, a test conducted and a diagnosis made, followed by a notification to

health authorities. Although this might have led to underestimating the risk of influenza A

and B epidemics, the study should reasonably mirror the relative magnitudes of influenza A

and B epidemics due to a strong positive linear relationship between the number of the

laboratory-confirmed cases and the number of infected cases. Secondly, HDMW zone has a

low population density due to mostly desert environmental condition. Thus, a small number

of influenza cases might generate a relative higher notification rate in a postal-level location

with a low population density. Thirdly, interactions between the three age groups in the same

postal locations could exist. However, the interaction might be explained by spatial

correlation of geographical neighbourhoods. Although the transmission rates were estimated

separately by the three Bayesian space-time models, the random effect ui in the models could

capture the effects of unobserved factors with spatial pattern to adjust for the estimated

transmission rates. Fourthly, social environmental factors would have largely varies in a long-

term dataset. To reduce the impact, the data only included three years in our project. Further


19

analysis incorporating social and environmental factors needs to expand the data in recent

years to better improve our understanding of seasonal influenza epidemics.

Conclusion

This is first study to comprehensively investigate seasonal influenza characteristics by virus

type, age group and climatic zones on the basis of air temperature and humidity across

Australia. Our analyses showed that overall mean weekly notification rate of influenza A was

greater than that of influenza B, although they had a similar seasonal epidemic pattern at a

nationwide level during the study period. However, there were substantial differences in the

characteristics of seasonal influenza A and B epidemics among the six climatic zones and

three age groups. Our findings provide important information that can be used to guide public

health authorities in developing more flexible strategies for prevention and control of

seasonal influenza for a specific age group and a specific climatic zone in Australia.


20

Notes

Conflicts of interest

The authors declare that no competing interests exist.

Acknowledgements:

We would like to express our gratitude to the Office of Health Protection, Australian

Government Department of Health for providing laboratory confirmed influenza notification

data on behalf of the Communicable Diseases Network Australia. We would like to express

our sincere thanks to Office of Health Protection, Australian Government Department of

Health for their helpful comments and suggestions that helped us to improve the quality of

the manuscript. W.H was supported by an Australian Research Council Future Fellowship

(FT140101216).

Additional information

Competing financial interests: The authors declare no competing financial interests.


21

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Figure legends:

Figure 1. The spatial variations in the observed mean weekly notification rates of influenza A

and B for the three age groups by the postal areas across Australia during the study period

Figure 2. The line plots (with 95% confidence intervals) show the significant differences ( red

lines) in the mean of weekly notification rates of influenza A and B between the different

climatic zones in each age group using Dunnett-Tukey-Kramer (DTK) Pairwise Multiple

Comparison Test adjusted for unequal variances and unequal sample sizes.

Figure 3. Heatmaps for the observed overall mean weekly notification rates of influenza A

and B among the six climatic zones in each age group during the study period.

Figure 4. The scatter plots with linear growth regression lines from the linear regression

models for means of estimated transmission rates of influenza A and B in each climatic zones

and each age group during the study period.

Figure 5. The scatter plots with linear decay regression lines from the linear regression

models for means of estimated transmission rates of influenza A and B in each climatic zones

and each age group during the study period.


24

Table 1. Summary of the observed mean weekly notification rates of influenza A and B by

postal level for the three age groups in the six climatic zones during the study period.

Climate zones

0-14 years 15-64 years ≥ 65 years

Mean SD Min Max Mean SD Min Max Mean SD Min Max

Influenza A

HHS 3.69 5.97 0.0 43.32 3.0 6.12 0.0 75.32 3.14 5.73 0.0 55.56

WHS 4.91 3.57 0.0 22.03 2.69 1.61 0.0 8.8 2.78 2.29 0.0 13.04

HDMW 2.51 3.61 0.0 16.18 3.29 4.76 0.0 24.49 1.58 2.48 0.0 8.21

HDCW 2.57 5.24 0.0 41.84 2.01 3.67 0.0 45.64 2.13 4.23 0.0 34.72

WSCW 2.83 5.92 0.0 112.75 1.78 3.01 0.0 57.17 2.09 3.4 0.0 58.09

MSCW 1.90 2.92 0.0 19.36 1.44 1.46 0.0 7.39 2.16 3.01 0.0 16.81

Influenza B

HHS 2.08 3.35 0.0 25.16 1.1 1.84 0.0 14.29 0.49 1.1 0.0 9.9

WHS 2.41 1.71 0.0 7.18 0.74 0.59 0.0 5.26 0.61 0.81 0.0 6.52

HDMW 3.25 6.04 0.0 29.62 1.21 2.15 0.0 10.04 0.41 0.95 0.0 3.15

HDCW 1.73 4.35 0.0 43.63 0.67 1.41 0.0 12.72 0.45 2.05 0.0 31.95

WSCW 1.85 3.36 0.0 50.82 0.65 1.1 0.0 13.74 0.58 1.89 0.0 37.58

MSCW 0.78 1.34 0.0 10.31 0.42 0.68 0.0 4.73 0.79 6.74 0.0 91.28


25

Table 2. Summary of influenza epidemic duration of the year in the three age groups by the

six climatic zones during 2011 to 2013.

Climatic zones 0-14 years 15-64 years ≥ 65 years

Mean SD range Mean SD Range Mean SD Range

Influenza A

HHS 28 16.6 9-40 28 16.4 10-42 30.7 18.8 9-42

WHS 13.7 4.6 11-19 10.7 0.6 10-11 20.7 15 11-38

HDMW 22.3 19.1 8-44 26.3 19.2 9-47 39.3 5.5 34-45

HDCW 14.7 6.4 11-22 12 1.7 10-13 29 18 9-44

WSCW 13 3 10-16 13 2 11-15 15.3 3.2 13-19

MSCW 14.7 6.1 8-20 11.3 3.1 8-14 28.7 18.9 8-45

Influenza B

HHS 27.7 10.1 17-37 32 16.5 13-43 41.3 13.3 26-49

WHS 11.7 2.1 10-14 14 3.5 12-18 34 5.3 28-38

HDMW 17.3 0.6 17-18 37 18.2 16-48 13 1.4 12-14

HDCW 15 4.4 12-20 24.3 20.6 11-48 39.3 15.5 22-52

WSCW 15.7 6.1 10-22 13.7 0.6 13-14 17.3 4 15-22

MSCW 18.3 2.5 16-21 20 12.1 13-34 32 3.6 29-36


26

Table 3. Influenza epidemic peak timing of the year by the six climatic zones in the three age

groups during 2011 to 2013. The peak timing was defined as the week with the highest

overall mean weekly notification rates by climatic zone in each year.

Climatic zone 0-14 years 15-64 years ≥ 65 years

2011 2012 2013 2011 2012 2013 2011 2012 2013

Influenza A

HHS 31 32 11 31 33 11 7 33 12

WHS 31 32 35 31 32 35 31 33 35

HDMW 31 33 38 31 33 33 51 28 49

HDCW 33 32 34 35 32 35 34 33 37

WSCW 32 27 35 31 28 35 38 28 34

MSCW 29 28 34 29 29 35 32 31 39

Influenza B

HHS 32 31 50 33 29 41 36 38 47

WHS 32 35 34 34 35 35 34 37 39

HDMW 22 33 31 14 33 35 25 28 --

HDCW 36 31 48 31 32 34 31 31 36

WSCW 33 33 35 31 36 35 29 33 35

MSCW 33 37 35 30 28 35 31 32 36


27

Table 4. Averages of posterior means and 95% credible intervals for weekly transmision rates

across the six climatic zones from the Bayesian space-time models for the three age groups

during the study period.

Climate zone 0-14 years 15-64 years ≥ 65 years

Mean 95% Credible interval Mean 95% Credible interval Mean 95% Credible interval

Influenza A

HHS 0.0103 0.0013 – 0.2015 0.0083 0.0008 – 0.1407 0.0093 0.0008 – 0.2439

WHS 0.0147 0.0017 – 0.1805 0.0079 0.0008 – 0.0949 0.0094 0.0009 – 0.1715

HDMW 0.0075 0.0006 – 0.2056 0.0088 0.0006 – 0.1765 0.0035 0.0003 – 0.2021

HDCW 0.0076 0.0006 – 0.1704 0.0057 0.0005 – 0.1277 0.0056 0.0006 – 0.1701

WSCW 0.0091 0.0008 – 0.1578 0.0058 0.0005 – 0.0966 0.0061 0.0005 – 0.1496

MSCW 0.0055 0.0005 – 0.1109 0.0043 0.0004 – 0.0853 0.0056 0.0006 – 0.1307

Influenza B

HHS 0.0049 0.0004 – 0.1329 0.0027 0.0002 – 0.0641

WHS 0.0086 0.0008 – 0.1332 0.0028 0.0002 – 0.045

HDMW 0.0051 0.0001 – 0.1762 0.0031 0.0002 – 0.0734

HDCW 0.0042 0.0004 – 0.12 0.0016 0.0001 – 0.0509

WSCW 0.0058 0.0005 – 0.1174 0.0019 0.0001 – 0.0443

MSCW 0.0019 0.0001 – 0.0611 0.0013 0.0001 – 0.0327


28

Figure 1


29

Figure 2


30

Figure 3


31

Figure 4


32

Figure 5


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