Turning Points and Climatic Impacts on the Multi-wave H7N9

Outbreaks in Guangdong Province, China

Piaopiao Zhou

, Wenwen Li

, Xingfeng Huang

, Yuting Wang

and Tian Tang

4,5,* e

Zhong De College, Zhengzhou Institute of Engineering and Technology, Zhengzhou, China

Department of Public Basic Education, Henan vocational university of science and technology, Zhoukou, China

School of Mathematics and Computer Science, Guilin University of Electronic Technology, Guilin, China

National Demonstration Center for Experimental Electronic Circuit Education, Guilin University of Electronic

Technology, Guilin, China

Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology, Guilin,

China

Keywords: H7N9, Reproduction Number, Turning Point, Climate Factor.

Abstract: The constant emergence of avian influenza A (H7N9) epidemics posed a huge threat to public health in China

recently. Based on the data of the human reported cases and climate from October 2013 to May 2017, we

employed Richard model, generalized linear model (GLM) and generalized additive model (GAM) to infer

the H7N9 transmission potentials in Guangdong Province, which is one of the important H7N9 epicenter. The

results indicated that the turning points occurred around mid-January, one week after closing live poultry

markets. It was further found that temperature and relative humidity are negatively proportional to the number

of H7N9 cases with the lags of 1-3 and 1-5 weeks, respectively. The number of H7N9 cases has a non-linear

relationship with rainfall and air pressure. These discoveries can provide practical information for risk

assessment and intervention implementation against H7N9 transmissi.

1 INTRODUCTION

Human infections of influenza A (H7N9) virus were

first identified in eastern China in March 2013 (Zhou

2015). Thereafter, five waves of human influenza A

(H7N9) epidemic (including 1533 human cases with

592 deaths) have been reported in China. Guangdong

Province is one of the high-risk areas, where a total of

258 cases have been reported as of December 2017.

Current frequent emergence and rapid expansion of

avian influenza A (H7N9) virus pose a huge threat to

public health in China.

More and more studies have been conducted on

the epidemiology and transmission dynamics of

H7N9 in recent years. Previous studies have found

that human H7N9 infections were associated with the

exposure of live poultry markets (PLMs) (Zhou 2015,

https://orcid.org/0000-0003-2583-2252

https://orcid.org/0000-0001-8373-0647

https://orcid.org/0000-0003-2391-2494

https://orcid.org/0000-0001-9051-0810

https://orcid.org/0000-0003-2291-3997

Chen 2016, Chen 2013, Bao 2013, Li 2014, Han

2013, Wu 2013), hence LPMs can support the

maintenance, amplification and dissemination of

H7N9 virus (Chen 2016, Chen 2013, Bao 2013, Peiris

2014, Sims 2012). In view of this, closing LPMs has

become the most commonly implemented measures

to control H7N9 transmission. Some studies

evaluated the effects of LPM closure on H7N9-

incidence in humans and found that closing LPMs in

different cities of China can reduced the risk of

human infections by 73%-

99%

(Fourni´e 2014,

Vittoria 2016, Yu 2014, Peiris 2016, Wu 2016, Yuan

2015, Wu 2017, Adam 2015). On the other hand,

several studies were conducted to infer the

associations between H7N9 infection with climate

and environment, but the results exhibited geographic

heterogeneity. For example, Liu et al. found that

Zhou, P., Li, W., Huang, X., Wang, Y. and Tang, T.

Turning Points and Climatic Impacts on the Multi-wave H7N9 Outbreaks in Guangdong Province, China.

DOI: 10.5220/0011197000003438

In Proceedings of the 1st International Conference on Health Big Data and Intelligent Healthcare (ICHIH 2022), pages 11-16

ISBN: 978-989-758-596-8

temperature and absolute humidity were negatively

associated with H7N9 infection in five Chinese

regions during 2013-2016 (Tao 2018), similar results

was observed in (Li 2015). Yi et al. indicated that

minimum and maximum temperature but not

humidity and daily temperature differences

contributed to human H7N9 infections in China

during 2013/14 seasons (Zhang 2015). Hu et al.

claimed that H7N9 incidence rate in Shanghai during

the spring of 2013 was linked with fortnightly mean

temperature and fortnightly mean rainfall (Hu 2015).

However, to our knowledge, we have not found

related studies on the turning point and

transmissibility of human H7N9 infection in

Guangdong, as well as the climatic effects.

To fill the knowledge gap, in this paper, we went

a further step to explore the transmission dynamics

and the risk factors contributed to H7N9 transmission

in Guangdong Province during 2013-2017. We first

established a Richard model to fit the weekly H7N9

cases to detect the turning point for the outbreak.

Turning points have epidemiological implications,

which is the threshold between acceleration and

deceleration. Identifying the turning points can help

us to examine the impact of intervention measures

relating to the turning point. We then used a

generalized linear model (GLM) and generalized

additive model (GAM) to explore the associations

between the climates and human H7N9 infection,

which enable us to clarify the long-term effects of

climate on human H7N9 infections.

2 MATERIALS AND METHODS

2.1 Study Site

Guangdong Province was selected as the study area

because it was one of the hardest-hit areas by H7N9

in China. This province is situated in the southern

China, with an area of 179,800 square kilometers and

about 100.7 million inhabitants. The climate is

subtropical humid, with short, mild, dry winters and

long, hot, wet summers. The annual mean

temperature is 21.8 centigrades and the annual

accumulate precipitation is 1,789 mm.

2.2 Data

Human H7N9 cases from October 2013 to April 2017

in Guangdong was used in this study. The data was

ex-tracted from the Center for Health Protection of

HongKong

(https://www.chp.gov.hk/sc/resources/29/332.html),

regarding to the number of reported apparent and

confirmed human cases per week. The temperature

and relative humidity during the study period were

downloaded from Guangdong Meteorological

Service (http://www.grmc.gov.cn/).

Figure 1: Time series of weekly relative humidity and

average temperature during October 2013 and April 2017.

2.3 Richards Model

The Richards model was widely used to analyze the

development trend and transmission potentials of

infectious diseases (Hsieh 2018, Hsieh 2009, Hsieh

2006), which is written as

)(

]1/[)(

ttr

eKtI

−−

(1)

where I(t) is the cumulative number of notification

cases at time t, k is the maximum case number over

the course of the outbreak, r is the per capita growth

rate of the infected population, and α is the deviation

exponent of,

is related to the turning point

the epidemic (or the inflection point of the cumulative

case curve) by the simple formula

./)(ln ratt

This parameter has important

epidemiological significance, which indicates the

beginning of a popular phase (from deceleration to

acceleration) or end (from acceleration to

deceleration)

(Hsieh 2018, Hsieh 2009, Hsieh 2006).

The Richards model is a phenomenological model

allowed to describe the evolution of the cumulative

case number

(Hsieh 2018, Hsieh 2009, Hsieh 2006). By

using standard software with nonlinear least-squares

(NLS) approximation tool, the parameters K , r , α and

was estimated by fitting the Richards model to the

epidemic curve in each epidemic season (October to

next April).

2.4 Generalized Linear Model

Generalized linear models (GLM) have been proved

to be an effective approach allowed to analyze the

associations between disease outbreaks and the

predictor variables. GLM is designed to model

response variables that may follow a general

ICHIH 2022 - International Conference on Health Big Data and Intelligent Healthcare

distribution, which is viewed as a unification of

linear and nonlinear regression models. It includes

three components: (1) a response variable

distribution, (2) a linear predictor that involves a

number of independent variables, and (3) a link

function that connects the predictor with the response

variable (Joseph 2006). Here, we used GLM to infer

the effects of climate on H7N9 emergence. It is

assumed that human reported cases follow Possion

distribution, in that the infection of H7N9 is a small

probability event. We specified the expected number

of human H7N9 cases as follows:

01 2

ttt

yRHTEM

ττ

ββ β

−−

=+ +

(2)

where RH and TEM represent the relative humidity

and temperature, respectively; and are the time lag

of variables. The statistical analyses were performed

using the statistical software R.

2.5 Generalized Additive Model

Generalized additive model (GAM) is a

nonparametric extension of Generalized linear model

which can effectively address the complex nonlinear

relationship between independent variables and

dependent variables. GAM is characterized by

smooth function fitting for some or all independent

variables to reduce errors caused by linear model

assumptions and hence the requirements for model

samples are more relaxed. We applied GAM to infer

the impact of climate on the emergence of H7N9

virus. Its basic form is as follows:

(()) ( , )

gEY a S X df



(3)

where S(.) represent the spline function of the

independent variable that has nonlinear relationship

with the dependent variable. Statistical software R

was also used for analysis.

3 RESULTS

Between October 2013 and June 2017, a total of 258

cases of human infection were reported in Guangdong

Province (Fig. 2). The incidence rate is 0.00024%,

and the mortality rate is 38.76%. The epidemic curve

exhibited a strong seasonal cycle, as human infections

usually occurred in the winter/spring and reached a

peak in January.

The 2013/14 epidemic season possessed the most

number of human infections (109 cases), followed by

2014/15 season (72 cases) and 2013/14 season (63

cases). The H7N9 epidemic in 2015/16 season is

relatively soft with 12 reported cases. In response to

the outbreak, many LPMs were closed by local

government. Such intervention was usually

implemented in January.

Figure 2: Time series of weekly human H7N9 cases

reported during October 2013 and April 2017. The blue bar

and the yellow curve correspond to the new and the

cumulative cases, respectively.

Fitting the cumulative number of human H7N9

case to the previously described Richards model

procedure, we obtained the parameter estimates of the

four waves (Table 1), with the corresponding

theoretical epidemic curve 3). It is found that the

model preformed well in terms of fitting the real-

world observations. We found that the four turning

points occurred on January 17, 2014, January 16,

2015, February 3, 2016 and December 30, 2016,

which is corresponded the the timing of closing live

poultry markets. It should be noted that after the

periods of the turning points, the outbreak started to

ease, reversing the initial exponential growth.

Table 1: Estimation results of the Richards model

parameters with the 95% CI for the four H7N9 waves in

Guangdong Province during 2013-2017.

Time period

2013.10-2014.04 16.50 0.32 104.51 0.81

2014.10-2015.04 16.47 0.65 72.31 1.41

2015.10-2016.04 19.33 0.75 13.14 3.85

2016.10-2017.04 13.75 0.28 64.05 0.07

Based on the GLM analysis, we found that the

average temperature and the relative humidity were

entered into the model, where the detailed

information of coefficients is presented in Table 4 and

2. The results indicated that the relative humidity and

the average temperature have significant influence on

the human H7N9 infections, where human cases are

most negatively correlated with the changes of

relative humidity and average temperature at lags of

1-5 and 1-3 weeks, respectively.

Turning Points and Climatic Impacts on the Multi-wave H7N9 Outbreaks in Guangdong Province, China

Figure 3: Fitting the cumulative H7N9 infections in

Guangdong during the four epidemic seasons by using the

Richards model.

Figure 4: The correlation between the number of H7N9

cases and temperature, as well as relative humidity.

Table 2: Results from the generalized linear model.

Estimate Std.error Z-Value Pr(>lZl)

Intercept 6.8612 0.4791 14.32 < 2e-16

RH -0.0391 0.0073 -5.35 8.8e-08

TEM -0.2022 0.0148 -13.70 < 2e-16

The correlation coefficients between human

infections and meteorological factors are shown in

Table 3. It is found that infection is negatively and

strongly relevant to average, highest and lowest

temperature, with correlation coefficient around -

0.66. Infection is also positively associated with

average air pressure, with correlation coefficient as

0.45. Yet the linear relation between infection and

humidity, wind speed, rainfall, sunlight is not

significant, which indicates that it may exist nonlinear

connection between them.

Table 3: The correlation between the number of H7N9

cases and climatic impacts

Climatic factor R-Value P-Value

Average temperature -0.66 0

Highest temperature -0.62 0

lowest temperature -0.66 0

rainfall -0.29 0

Average relative

humidit

-0.33 0

Average wind speed 0.18 0.09

Average air pressure 0.45 0

Average sunlight -0.15 0.21

Based on GAM analysis, the results showed that

there was a complex nonlinear relationship between

climate factors and the incidence of H7N9. As shown

in Figure 5, the number of H7N9 cases gradually

decreased with the increase of temperature. When the

average temperature reached 20℃, the number of

cases was almost zero. When the rainfall was more

than 200mm, the number of cases increased with

increasing rainfall. The risk of H7N9 infection peaks

when relative humidity reaches 80 percent. When the

air pressure is close to 1005hPa, it is most favorable

for human infections of H7N9 virus.

Figure 5: Effect chart of relationship between climatic

factors and the number of H7N9 cases.

4 DISCUSSION

The recent emergence of avian influenza A (H7N9)

virus has becoming a big public health issue in China.

Exploring the risk factors and evaluating the

intervention strategies are the priority for controlling

ICHIH 2022 - International Conference on Health Big Data and Intelligent Healthcare

and preventing H7N9 epidemics. In this study, we

used a Richards model, generalized linear model and

generalized additive model to investigate the

associations between incidence decline and LPM

closure, and to infer the effects of climatic factors on

H7N9 transmission. Two insights arising from our

results could provide meaningful clues for

policymakers to implement effective interventions on

H7N9 infection.

First, we found that the turning points of the four

epidemic waves occurred exactly at one week after

closing LPMs. Before the turning points, human cases

had the potential to increase exponentially. After that,

the numbers began to decelerate. During 2013/14 and

2014/15 epidemic seasons in Guangdong, human

infections were sporadically reported in December.

The local governments successively closed LPMs in

early January. In this cases, we found that the turning

points occurred in mid-January. The H7N9 epidemic

in 2015/16 season is less serious, and only a few

LPMs were closed in January 2016. We found that the

turning points was relatively late, occurring on

February 3. H7N9 outbreak in 2016/17 season was

much earlier, where human cases were recorded from

mid-December. The Guangdong government

responded very quickly and instructed the local

authorities to close LMPs. Consequently, we found

that the turning point occurred in late December. In

short, we found that the turning points occurring after

about one week of LPM closure. Such time lag could

be related to the latent period of H7N9 virus in human

and poultry. Our results indicated that closing LPMs

can effectively reduce human H7N9 infections, which

is consistent with previous findings (Yu 2014, Wu

2014, Adam 2015, Zhu 2021).

Second, we found that the change in human H7N9

infections appears to be most closely correlated with

change in temperature at lags of 1-3 weeks,

meanwhile the changes in relative humidity seems to

be most correlated with change in H7N9 case number

at lag of 1-5 weeks. Our findings are consistent with

previous analysis, where they claimed that

temperature and humidity are the dominant variables

for H7N9 transmission (Tao 2018, Li 2015, Zhang

2015, Hu 2015). This can be explained by changes in

virus activity under different climate conditions. Low

temperature and humidity favoured the survival and

transmission of H7N9 viruses during its outbreak, and

can also directly/indirectly affect people’s behaviour,

making them more vulnerable to H7N9 viruses (Tao

2018, Hu 2015). Further understanding of the impact

of socio-ecological factors on the incidence of H7N9

with the development of early warning system can be

useful and important in the control and prevention of

H7N9.

In summary, we have detected the turning points

of the four H7N9 epidemic waves, and clarified the

potential relationship between human cases and

temperature as well as relative humidity. Our results

indicated that closing LPMs can significantly reduce

human infections, and LPM closure and climatic

factors played a role in the seasonality of H7N9

transmission.

In addition to climate, human activities and

contact with live poultry could be the important

factors contributed in the spread of H7N9.

Government regulation toward live poultry market

can modify the transmission pattern of H7N9. These

factors should be considered in future studies fur

guiding H7N9 control.

ACKNOWLEDGEMENTS

This article is supported by Guangxi Key Laboratory

of Cryptography and Information Security

(GCIS201707).

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