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Chenetal. Infectious Diseases of Poverty (2022) 11:117
https://doi.org/10.1186/s40249-022-01046-z
RESEARCH ARTICLE
Transmissibility oftuberculosis
amongstudents andnon-students:
anoccupational-specic mathematical
modelling
Qiuping Chen1,2,3†, Shanshan Yu1†, Jia Rui1,2,3†, Yichao Guo1, Shiting Yang1, Guzainuer Abudurusuli1,
Zimei Yang1, Chan Liu1, Li Luo1, Mingzhai Wang4, Zhao Lei1, Qinglong Zhao5, Laurent Gavotte6, Yan Niu7*,
Roger Frutos2* and Tianmu Chen1*
Abstract
Background: Recently, despite the steady decline in the tuberculosis (TB) epidemic globally, school TB outbreaks
have been frequently reported in China. This study aimed to quantify the transmissibility of Mycobacterium tuber-
culosis (MTB) among students and non-students using a mathematical model to determine characteristics of TB
transmission.
Methods: We constructed a dataset of reported TB cases from four regions (Jilin Province, Xiamen City, Chuxiong
Prefecture, and Wuhan City) in China from 2005 to 2019. We classified the population and the reported cases under
student and non-student groups, and developed two mathematical models [nonseasonal model (Model A) and
seasonal model (Model B)] based on the natural history and transmission features of TB. The effective reproduction
number (Reff) of TB between groups were calculated using the collected data.
Results: During the study period, data on 456,423 TB cases were collected from four regions: students accounted
for 6.1% of cases. The goodness-of-fit analysis showed that Model A had a better fitting effect (P < 0.001). The aver-
age Reff of TB estimated from Model A was 1.68 [interquartile range (IQR): 1.20–1.96] in Chuxiong Prefecture, 1.67 (IQR:
1.40–1.93) in Xiamen City, 1.75 (IQR: 1.37–2.02) in Jilin Province, and 1.79 (IQR: 1.56–2.02) in Wuhan City. The average
Reff of TB in the non-student population was 23.30 times (1.65/0.07) higher than that in the student population.
Conclusions: The transmissibility of MTB remains high in the non-student population of the areas studied, which
is still dominant in the spread of TB. TB transmissibility from the non-student-to-student-population had a strong
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Open Access
†Qiuping Chen, Shanshan Yu and Jia Rui contributed equally to this study
*Correspondence: niuyan@chinacdc.cn; frutossmt@gmail.com;
13698665@qq.com
1 State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics,
School of Public Health, Xiamen University, Xiamen, Fujian, People’s Republic
of China
2 CIRAD, URM 17, Intertryp, Montpellier, France
7 Chinese Center for Disease Control and Prevention, 155 Changbai Road,
Changping District, Beijing, China
Full list of author information is available at the end of the article
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Chenetal. Infectious Diseases of Poverty (2022) 11:117
Background
Despite widespread implementation of control measures,
including pharmaceutical therapy and vaccination, tuber-
culosis (TB) remains a major cause of disease and death
in most high-burden countries. In 2021, most TB cases
occurred in the 30 high-burden countries (87%), in which
8 countries account for two-thirds, with China (7.4%)
ranking after India (28%) and Indonesia (9.2%) [1]. China
is also on the three lists of high-burden countries for TB,
HIV-associated TB, and multidrug resistant tuberculosis
(MDR-TB) of the World Health Organization (WHO).
Despite the difficulties that remain, such as the emer-
gence of drug-resistant strains of Mycobacterium tuber-
culosis (MTB) and the coinfection of TB with the human
immunodeficiency virus (HIV)/acquired immune defi-
ciency syndrome (AIDS), the global incidence of tuber-
culosis is estimated to slowly decline by 1.6% per year,
far from the 4–5% estimated to be required to reach the
objectives of the WHO’s End TB Strategy [2]. Due to the
emergence of the COVID-19 pandemic, there is a large
global drop in people newly diagnosed with TB and
reported in 2020, compared to 2019[3].
In China 2021, the number of reported TB cases is
ranked second highest after viral hepatitis, and in terms
of death is the second highest after AIDS [4]. ere are
about 250 million students in China (about 20% of the
population). e reported TB cases in students account
for about 4–6% of the total reported TB cases [5]. TB
cases in the 15–24-year age group accounted for about
85% of the total reported TB cases in students, which
means the number of TB cases in high school and col-
lege students is higher, especially in the 18-year-old age
group [6–8]. When MTB spreads in schools, it can be
transmitted rapidly and have a major impact on young
people simply because of cluster. erefore, it is one of
the reasons whyschool TB outbreaks have been reported
frequently in China, despite the steady global decline of
the TB incidence trend [9–13]. Moreover, MDR-TB out-
breaks have also been reported in schools, making TB
control in schools much more difficult [14, 15].
eoretical epidemiology, also known as the math-
ematical model of epidemiology, uses mathematical for-
mulas to express the law of disease prevalence explicitly
and quantitatively between cause, host and environment,
and to theoretically explore the effects of different con-
trol measures. Mathematical modelling has become a
powerful tool for analysing epidemiological character-
istics [16], which is used to reveal the characteristics of
the internal spread of infectious diseases. Transmission
dynamic models are commonly used in infectious dis-
ease models, including Susceptible-infectious-recovered
model, Susceptible-exposed-symptomatic-recovered
(SEIR) model, and Autoregressive integrated moving
average model. Some studies use models to analyse TB,
such as TB intervention assessments [17], analysis of
vaccine control effectiveness [18, 19], and TB treatment
[20–23]. Different models have been developed to treat
latent TB infections (LTBI) that incorporate certain fac-
tors such as drug-resistant strains [24], coinfection with
HIV [25], and TB reinfection [26], and to study the epi-
demiology of TB [27]. Specific targeted sub-populations
have been defined, including age-specific subgroups [28],
adults and children [29], and smokers and non-smokers
[30]. However, only a few studies have used occupational
mathematical models to study TB transmission in China.
e construction of TB models which are used to explore
the dynamics of TB transmission between students and
non-students is unclear.
e prevention and control of TB in schools has been
improved with the efforts of medical personnel staff at all
levels. In the past 10years, control measures have been
continuously strengthened and improved, but the trans-
mission characteristics of TB in schools are still unclear.
e aim of this study is to establish a mathematical model
of TB between students and non-students, to analyse
and explore the transmissibility of MTB in schools, and
then to take reasonable and effective measures to control
TBin schools.
Methods
Study design
In this study, based on the reported and observed mor-
bidity characteristics, we developed a SEIR model with
two occupational groups (students and non-students).
We investigated the role of occupation in the transmis-
sion process and evaluated feasible control strategies
to achieve the objectives outlined in the WHO End TB
Strategy [3]. Furthermore, this study classified active
TB patients into high or low transmissibility groups
according to their pathogenic status [31].
Firstly, in this study, a dataset was constructed, includ-
ing basic information (sex, age, occupation, and location)
influence on students. Specific interventions, such as TB screening, should be applied rigorously to control and to pre-
vent TB transmission among students.
Keywords: Tuberculosis, Transmission, Compartmental model, Occupational-specific dynamics, Student, Non-
student, China
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Chenetal. Infectious Diseases of Poverty (2022) 11:117
and case classification (positive and negative cases of
pathogen). Demographic data was obtained from the
Chinese Statistical Yearbook [32–35], including the total
population, the total student population, birth rate, and
the death rate for each city (Additional file1: TableS1).
Secondly, two mathematical models (Models A and B
refer to non-seasonal and seasonal models, respectively)
were constructed to simulate the reported TB cases of
the four regions in China, based on the natural history
and seasonality of TB. In each model, we divided the col-
lected data into four subpopulations of active diseases in
two dimensions. e first dimension for all calculations
and outputs was the occupation of students (1 subscript)
or non-students (2 subscript), while the second dimen-
sion was the pathogenic status, including pathogen posi-
tive (p subscript) and pathogen negative (n subscript)
pulmonary disease. In addition, goodness-of-fit was con-
ducted to evaluate the effectiveness of model fitting.
Finally, we simulated the sub-population-to-sub-
population transmission process, to determine the
combination with the most distinctive impact, via calcu-
lating effective reproduction number (Reff) and perform-
ing knock-out analysis. is enabled the formulation of
effective and targeted control measures for TB transmis-
sion in China, in accordance with occupation-specific
prevention and control (Fig.1).
Data collection
We collected year-based TB incidence data from the
China Public Health Science Data Center (http:// www.
phsci enced ata. cn/ Share/ index. jsp) from January 1, 2005
to December 31, 2017 for each province in China (not
included Hong Kong, Macao, and Taiwan) [36]. After we
calculated the average annual incidence rate and plotted
the incidence map (Fig.2), we found an inequality in the
disease burden.
Fig. 1 Study design for analysing the transmissibility of TB among students and non-students. The four subscripts are denoted as follows: TB
transmission in student groups (11 subscript), TB transmission of student-to-non-student groups (12 subscript), TB transmission in non-student
groups (22 subscript), TB transmission of non-student-to-student groups (21 subscript)
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Chenetal. Infectious Diseases of Poverty (2022) 11:117
Considering the geographic position, the TB incidences
in four regions (located in the north, south, southwest,
and middle of China) are average incidences compared to
those of the other regions in China, which is consistent
with the population distribution [37], which means that
the selection can better reflect the TB epidemiological
characteristics in geographical differences.
is study collected data on reported TB cases, popu-
lations, and areas in four regions [Jilin Province, Wuhan
City in Hubei Province, Chuxiong Yi Autonomous Pre-
fecture (Chuxiong Prefecture) in Yunnan Province,
and Xiamen City in Fujian Province] (Additional file1:
Table S1), which are from the Health Commission of
each region, the Statistics Bureau of each region and data
mentioned in some researches [38, 39], etc. erefore,
the TB results from these four regional analyses are effec-
tively representative of TB epidemiological characteris-
tics in China.
Fig. 2 Regional and national distribution of reported TB incidence. Reported TB incidence in different regions (not included Hong Kong, Macao,
and Taiwan) in China from 2005 to 2017
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Chenetal. Infectious Diseases of Poverty (2022) 11:117
Classication ofTB patients
Reported TB cases included in this study consisted of
laboratory confirmed pulmonary tuberculosis (PTB), and
clinical diagnostic PTB. Since the Chinese government
implemented the National Notifiable Disease Surveil-
lance System (NNDSS) for infectious diseases in 2004,
the diagnostic criteria for TB has changed several times
(“WS288–2008 Diagnosis of tuberculosis” [40] with the
adjusted notice in 2017 [41], and the “WS 288–2017
Diagnosis of Tuberculosis” [42] and the “WS 196–2017
Classification of Tuberculosis” standards [43], with the
adjusted notice in 2018 [31]). All TB cases were classi-
fied on the base of the following criteria. e confirmed
PTB cases were denoted as people with possible PTB
symptoms, such as a continuous cough for more than 2
weeks, hemoptysis, and night sweat, and confirmed by
a sputum smear and/or a sputum culture with the result
of detectable acid-fast bacilli or positive result from a
rapid molecular diagnostic instrument (e.g., GeneXpert).
e clinical diagnosis of PTB was defined as people with
obviously abnormal chest radiography along with no
curative effect from anti-inflammatory treatment under
the circumstance of negative results from laboratory tests
or absence of related results [44–46].
e PTB cases are classified as follows, based on patho-
genic findings: sputum smear positive, sputum smear
negative, sputum culture positive, sputum culture nega-
tive, molecular biology positive, and without sputum
PTB [42]. In the latest notice published in 2018, the clas-
sification of TB cases, which must be reported in the
NNDSS, was adjusted to “pathogen positive (including
sputum smear positive and only sputum culture posi-
tive PTB)”, “pathogen negative (including sputum smear
negative PTB)”, “rifampicin resistant”, “no pathogenic
findings (including without sputum PTB and tubercu-
lous pleurisy” [31]. We have reclassified all historical data
according to the new classification notice for consistency
(see detail in Additional file2: TableS2).
Diagnosis criteria ofPTB patients
e diagnosis of PTB is based on a pathogenic exami-
nation (including bacteriology and molecular biology),
combined with epidemiological history, clinical manifes-
tations, chest imaging, relevant auxiliary examinations,
differential diagnosis, and other comprehensive analyses
[47]. Pathogenic and pathological results were used as
the basis for diagnosis. erefore, the following inclusion
criteria were TB cases with pathogen positive [“positive
cases with MTB detected by sputum smear, culture-con-
firmed or molecular biology (nucleic acid of MTB)”] and
negative [“TB cases without MTB detected (including
patients with negative sputum smear and without spu-
tum)”].e rifampicin resistance category was officially
reported in 2019 and represented a small percentage
(< 5%) of the total data collected.erefore, to maintain
the consistency of the overall data, we excluded these
data from the analysis.
Occupational‑specic transmission model ofTB
Based on the model, the total population (N) was divided
into the following five compartments: susceptible popula-
tion (S), exposed population (or low-risk latent tubercu-
losis infection, LTBI) (E), pathogen positive tuberculosis
population (Ip), pathogen negative tuberculosis popula-
tion (In), and recovered or removed population (R).
1) Susceptible population (S): people who have not
been exposed to MTB or those who experienced
self-clearance by their own immune system. e lat-
ter is a state in which the bacteria in the body cannot
replicate to the extent that self-clearance occurs due
to the strong immunity of the body after exposure,
a state in which the body has a sustained immune
response to MTB antigen stimulation.
2) Exposed population (or low-risk LTBI) (E): A suscep-
tible population is exposed to MTB through contact
with a highly infectious or less infectious population
and is in an MTB carrier state but is temporarily
noninfectious.
3) Pathogen positive TB population (Ip): positive cases
with MTB detected by sputum smear, culture-con-
firmed, or molecular biology (nucleic acid of MTB).
4) Pathogen negative TB population (In): TB cases with-
out MTB detected (including patients with negative
sputum smear and no sputum), with low infectious-
ness.
5) Recovered or removed population (R): is is a state
of cure or recovery, noninfectious and asymptomatic,
referring to the population undergoing successful
treatment, including the treatment success for the
“pathogen negative” population and the “pathogen
positive” population (both the cured and the treat-
ment success population).
Based on the natural history of TB, we developed a
mathematical model Susceptible-exposed-symptomatic
(pathogen positive)-symptomatic (pathogen negative)-
recovered (SEIpInR) model to investigate the transmission
process of TB. e proposed SEIpInR model is based on
the following facts and assumptions:
1) Births and natural deaths change the total population
(N); the birth rate and the death rate are br and dr,
respectively. e entire birth population enters group
2(the non-student group).
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Chenetal. Infectious Diseases of Poverty (2022) 11:117
2) is population is generally susceptible to MTB
infection. When an infected individual is exposed,
the exposed population (E) progresses to the active
TB-infected population (I) at a rate of β. Since the
transmissibility of the pathogen positive TB popu-
lation (Ip) is higher than the pathogen negative TB
population (In), the transmissibility of In is set to be a
к times (к < 1) compared to Ip.
3) Approximately 5–10% of the exposed population
(E) infected with MTB will develop symptoms and
become Ip or In; both belong to the active TB-infected
population and will receive treatment. Most exposed
people do not develop symptoms, but undergo a pro-
cess of self-clearance and become a susceptible popu-
lation (S). If E progresses to Ip at a rate of ω1 (incuba-
tion period coefficient) with a scale factor of q, and
E progresses to In at a rate of ω2 (latent period coef-
ficient) with a scale factor of (1-q). e progression
from E to S occurs at a rate of θ (early self-clearance
rate) on a scale factor of m. At time t, the progression
from E to Ip, from E to In and from E to S is propor-
tional to the number of exposed populations, which
is qω1E, (1-q)ω2E, and mθE, respectively.
4) Studies have shown that the proportion of patients
with TB cured by the directly observed treatment
and short course chemotherapy (DOTS) who require
retreatment in the next 1–2years is 2 to 7% [48, 49].
Patients who are retreated can be broadly divided
into two categories: those who were not successfully
cured following treatment, and those who relapsed
after being cured.
5) ere were two outcomes for the Ip compart-
ment: First, a certain proportion of treatment suc-
cessindividuals (1-λ) transform into a recovered or
removed population (R), while another proportion
of treatment failureindividuals (λ) transform into an
exposed population (E). At time t, the rate of devel-
opment from Ip to R, which is proportional to the
Ip population, is given as (1-λ)γ1Ip, while the rate of
development from Ip to E, which is proportional to
the E population, is given as λμ1Ip. γ1 is the removal
coefficient, whereas μ1 is the coefficient of develop-
ment from Ip to E. Similarly, the rate of development
to R in the In compartment is given as (1-λ)γ2In, while
the rate of development to E is given as λμ2In. γ2 is
the removal coefficient and μ2 is the coefficient of
development of In to E.
6) e people in the Ip and In compartments recover
or are removed (R) after successful treatment (com-
pletion of treatment for In and Ip [50]). Reinfection
occurs after the completion of treatment or cured,
that is, the active TB-infected population (Ip, In)
returns to the exposed population (E).
7) Reactivation (or relapse) is often associated with
immunodeficiency, such as the onset of disease due
to HIV/TB coinfection or low resistance, such as
severe cold. If people in the R compartment develop
into E with a relapse ratio a where τ represents the
relapse coefficient, the rate of development from R to
E at time t is proportional to R, which is aτR.
8) e pathogen positive TB population (Ip) and the
pathogen negative TB population (In) die of disease,
in addition to natural deaths. Suppose the fatality
rates for Ip were f1 and that for In were f2; then, at time
t, the death rates for Ip and In are f1Ip and f2In, respec-
tively.
9) e student population was set as S1, E1, Ip1, In1, and
R1, whereas the non-student population was set as
S2, E2, Ip2, In2, and R2. Interactions were observed
between students and non-students. We defined
the relative transmission rate of student-to-student
as β11, non-student-to-non-student as β22, student-
to-non-student as β12, and non-student-to-student
as β21. erefore, the number of newly emerging
infections was β11S1(Ip1 + кIn1) from the student-to-
student population, β22S2(Ip2 + кIn2) from the non-
student-to-non-student population, β12S2(Ip1 + кIn1)
from the student-to-non-student population, and
β21S1(Ip2 + кIn2) from the non-student-to-student
population.
A framework diagram of the SEIpInR model is shown
in Fig.3. e mathematical expression of the differential
equation of the SEIpInR model are as follows:
dS
1
dt
=−β11S1Ip1+κIn1+mθE1−β21 S1Ip2+κIn2−drS
1
dE
1
dt=β11S1Ip1+κIn1−mθE1+β21 S1Ip2+κIn2
−drE1−qω1E1+µ1Ip1−(1−q)ω2E1
+
µ
2
In
1+
ατR
1
dI
p1
dt
=qω1E1−µ1Ip1−(1−)γ1Ip1−(dr +f1)Ip
1
dI
n1
dt
=(1−q)ω2E1−µ2In1−(1−)γ2In1−(dr +f2)In
1
dR
1
dt
=(1−)γ1Ip1+(1−)γ2In1−ατR1−drR
1
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Chenetal. Infectious Diseases of Poverty (2022) 11:117
d
S2
dt=brN −drS2−β22S2Ip2+κIn2
+mθE2−β12S2(I
p
1+κI
n
1)
dE
2
dt=β22S2Ip2+κIn2−mθE2+β12 S2(Ip1+κIn1
)
−drE2−qω1E2+µ1Ip2−(1−q)ω2E2
+
µ
2
In
2+
ατR
2
d
Ip2
dt
=qω1E2−µ1Ip2−(1−)γ1Ip2−f1Ip2−drIp
2
dI
n2
dt
=(1−q)ω2E2−µ2In2−(1−)γ2In2−f2In2−drIn
2
dR
2
dt
=(1−)γ1Ip2+(1−)γ2In2−ατR2−drR
2
Fig. 3 Flowchart of the SEIpInR model. The four occupational compartments are denoted as follows: pathogen positive students (Ip1 subscript),
pathogen positive non-students (Ip2 subscript), pathogen negative students (In1 subscript) and pathogen negative non-students (In2 subscript)
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Chenetal. Infectious Diseases of Poverty (2022) 11:117
Parameter estimation
Fourteen parameters were obtained from references or
actual data in this model: birth rate (br), death rate (dr),
transmission relative rate (β), proportion of early clear-
ance (m), rate of early clearance (θ), transmission relative
rate between pathogen negative and positive TB popula-
tion (к), proportion of exposed to TB population (q), rate
of exposed to TB population (ω), proportion of treatment
failure (λ), rate from TB population to exposed popula-
tion (μ), TB population removal coefficient (γ), Case
fatality rate of TB population (f), recurrence ratio- pro-
portion of recovered or removed population developing
into exposed population (a), and reciprocal of time to
recurrence rate at which recovered or removed popula-
tion progresses to exposed population (τ).
Parameter β was derived from the curve-fitting
results. Some parameters (br, dr, and q) were obtained
from actual data, while other parameters were obtained
from the literature. e description of each variable and
parameter in this model is detailed in Table1.
1) Early self-clearance (early clear) was defined as a
persistent negative interferon-gamma release test
(IGRA) (patients with pathogenically positive TB
were tested at baseline and after 14weeks). Studies in
Indonesia have shown that early self-clearance is 25%
[51]. e time to self-clearance was set at 14weeks,
which is the interval between the two IGRA tests;
thus θ = 1/ (14/4) = 0.286.
2) Treatment failure: e WHO 2021 TB report [3]
showed the treatment success rate was 95.9% in 2019
and 95.7% in 2020. Previous reports revealed that
this value did not change much between 2000 and
2020. erefore, we considered the treatment suc-
cess rateas 95% and set the treatment failure rate
(λ) to 5%, that is λ = 0.050. e conventional treat-
ment course was 6 months. erefore, the time to
complete the treatment was set as 6months, that is,
μ1 = μ2 = 1/6 = 0.167 [52].
3) Relapse: Studies [53–55] in China showed the relapse
rate was 5.3–6.9%. erefore, the median was cho-
sen and the relapse proportion was set at a = 0.062
(recurrence ratio). A domestic study [53] showed
that the median time from the first attack to relapse
in TB patients was 1.3 years [interquartile range
(IQR) 0.6–2.8years]. erefore, the relapse rate was
established at 1/(1.3*12), i.e., τ = 0.064 (reciprocal of
time to recurrence).
Table 1 The description and features of estimated parameters
E for the exposed population (or low-risk latent tuberculosis infection, LTBI), Ip for pathogen positive tuberculosis population, In for pathogen negative tuberculosis
population, I1 for student tuberculosis population, I2 for non-student tuberculosis population, and R for recovered or removed population
Parameter Description Unit Value Source
br Birth rate 1 Null Reported data
dr Death rate 1 Null Reported data
βTransmission relative rate Per person. per month Null Curve fitting
β11 Transmission relative rate among students Per person. per month Null Curve fitting
β22 Transmission relative rate among non-students Per person. per month Null Curve fitting
β12 Transmission relative rate from students to non-students Per person. per month Null Curve fitting
β21 Transmission relative rate from non-students to students Per person. per month Null Curve fitting
κTransmission relative rate between population In and population Ip1 0.2 Reference [56]
mProportion of early clearance 1 0.25 Reference [51]
θRate of early clearance Per month 0.286 Reference [51]
qProportion from E to Ip1 Null Reported data
1-q Proportion from E to In1 Null Reported data
ω1Rate from E to I1Per month 0.667 Reference [93]
ω2Rate from E to I2Per month 0.667 Reference [93]
λProportion of treatment failure 1 0.05 Reference [3]
μ1Rate from I1 to E (reciprocal time to retreatment) Per month 0.167 Reference [52]
μ2Rate from I2 to E (reciprocal time to retreatment) Per month 0.167 Reference [52]
γ1I1 removal coefficient Per month 0.286 Reference [57]
γ2I2 removal coefficient Per month 0.286 Reference [57]
f1Case fatality rate of I11 0.1284 References [94, 95]
f2Case fatality rate of I21 0.1284 Reference [94, 95]
aProportion of R developing into E (recurrence ratio) 1 0.062 References [53–55]
τRate at which R progresses to E (reciprocal of time to recurrence) Per month 0.064 Reference [53]
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Chenetal. Infectious Diseases of Poverty (2022) 11:117
4) e transmissibility coefficient of In relative to Ip was
set as κ to 0.2 [56] with reference to the actual data
and the relevant literature.
5) After approximately two weeks of effective treat-
ment, TB cases with a nondrug-resistant active infec-
tion usually do not remain infectious to others and
become low in infection status [57]. Short-course
(3-to 4-month) rifamycin-based treatment regimens
are preferred over longer-course (6 to 9months) iso-
niazid monotherapy for the treatment of low-infec-
tion cases of TB [8]. erefore, we set the duration
of the illness at 14weeks (average value 3–4month),
γ1 = γ2 = 1/(14/4) = 0.286.
6) e birth rate (br) and the death rate (dr) for each
year in each region were obtained from the statistical
offices of each study area.
Transmissibility index
e basic reproduction number (R0) is an important
parameter for determining the infectiousness of a dis-
ease. R0 refers to the number of new cases expected from
an infected case in a susceptible population during an
average infectious period. We set Reff as the evaluation
index, which denotes R0 after intervention measures were
taken, to evaluate the impact of intervention measures on
the relative transmissibility of MTB in the population.
In this study, Reff was calculated using the next-gener-
ation matrix method, and all source codes are accessible
at GitHub (https:// github. com/ rorsc hachk wok/ tb_ reff).
In this study, Reff1 represents the transmissibility of the
population of students with active TB [sum of transmissi-
bility from student cases to student cases (Reff11) and from
student cases to non-student cases (Reff12)], while Reff2
represents the transmissibility of the population of non-
student active TB cases [sum of transmissibility from
non-student cases to non-student cases (Reff22) and from
non-student cases to student cases (Reff21)].
Simulation methods andstatistical analysis
Berkeley Madonna 8.3.18 (developed by Robert Macey
and George Oster of the University of California in
Berkeley. Copyright ©1993–2001 Robert I. Macey &
George F. Oster) was used to fit the curves of the inci-
dence data. e estimated model coefficients and the
simulation of the intervention effects were also generated
using this software. e curving fit was performed using
the fourth order Runge–Kutta method to obtain the key
parameter values: student-to-student (β11), non-student-
to-non-student (β22), student-to-non-student (β12), and
non-student-to-student (β21) transmission rates.
To consider the potential seasonality transmission of
TB, although seasonality remains unclear, we developed
two models in this study, which are described as follows:
Model A: seasonality excluded.
In Model A, the epidemic curve for each year was
divided into ascending and descending periods accord-
ing to the characteristics of the reported number of TB
cases (Fig. 1). e SEIpInR model without seasonality
was adopted to fit the data in each period, and the cor-
responding transmission relative rates (β, β11, β12, β22,
and β21), the ascending and descending Reff (Reff(asc) and
Reff(des), respectively) were calculated.
Model B: seasonality included.
In Model B, we used a seasonality function in the SEIp-
InR model to fit the reported TB epidemic curve (Fig.1),
which is shown as follows:
In this equation, βt, β0, c, and T refer to the transmis-
sion rate at time t, the transmission rate at time = 0, the
correcting value of time (month) and the potential sea-
sonality cycle, respectively.
e goodness-of-fit test was performed between the
fitted results and the collected data by calculating the
R2 and P values. Key parameters (β11, β12, β22, β21) were
knocked out, and the cumulative number of cases was
calculated to assess the main parameter affecting trans-
missibility. SPSS Statistics for Windows, version 13.0
(SPSS Inc., Chicago, Ill, USA) was used to perform statis-
tical analyses. e coefficient of determination (R2) was
used to evaluate the curve fitness.
Results
Epidemiology oftuberculosis inthefour regions
e age range of the TB patients was between 15 and
90years, with two peaks in the incidence of TB: a large
and a small peak in the age groups 35–90 and 15–35,
respectively. In Wuhan City, Jilin Province and Chux-
iong Prefecture, there were two age distribution peaks of
non-student TB patients (15–35 and 45–60years group),
while in Xiamen City, there was only one peak (15–
35years group). Student patients with TB were among
15–25year group (Fig.4A). Most patients with TB in the
four regions were male, with a male-to-female ratio of 7∶3
(Fig. 4B). e Chinese Infectious Disease Report Card
categorises cases into 18 categories, andthe top six occu-
pations (88.4% of the total cases) in the four regions were:
farmers, housework and unemployment, others, work-
ers, students, and retirees. Among these four regions,
the top three occupations of TB patients in Wuhan were
β
t=β0
1+sin
2π
(
t
−
c
)
T
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Chenetal. Infectious Diseases of Poverty (2022) 11:117
domestic and unemployed (23.2%), farmers (22.2%), and
retirees (12.1%). e top three occupations of TB patients
were farmers (50.3%), domestic and unemployed (18.6%),
and others (8.0%) in Jilin Province; workers (22.5%),
farmers (15.3%), and others (10.6%) in Xiamen; farmers
(87.2%), retirees (4.5%), and students (2.2%) in Chuxiong
Prefecture (Fig.4C). e ranking of students with TB was
sixth, sixth, eighth and third in Wuhan, Jilin, Xiamen, and
Chuxiong Prefecture, respectively.
e number of reported TB cases in Wuhan City and
Jilin Province showed a decreasing annual trend, while
the number of reported TB cases in Xiamen City and
Chuxiong Prefecture showed a slight fluctuation trend
(Fig. 5). e incidence in the student population was
Fig. 4 Age, gender, and occupation distributions: A Wuhan City, B Jilin Province, C Xiamen City, and D Chuxiong Prefecture
Fig. 5 Temporal distribution by month: A Wuhan City, B Jilin Province, C Xiamen City, and D Chuxiong Prefecture
Page 11 of 22
Chenetal. Infectious Diseases of Poverty (2022) 11:117
distinctly low during the winter holidays (January–Febru-
ary, approximately 30days) and summer vacation (July–
August, approximately 60days), with one or two distinct
peaks after returning to school (the remaining months of
the year). ere were slight differences between regions
in the time of occurrence of these peaks: Wuhan (March
and September–October), Xiamen (March and Octo-
ber), Jilin (April and September), and Chuxiong (April
and October). However, for the non-student population,
there were no clear lows or peaks.
Most cases had either positive or negative pathogen
results (87.3%), andthe ratio was 1∶1.13. e propor-
tion of cases without pathogenic findings was 12.6%;
rifampicin resistant results accounted for 0.1%. e num-
ber of pathogen positive and negative cases was essen-
tially the same in Jilin Province, while the other three
regions reported more pathogen negative cases than
positive. e proportion of patients without pathogenic
findings was the lowest in Xiamen City and the highest
in Chuxiong Prefecture. Very few cases of resistance to
rifampicin were reported in any region (Fig.6).
Curve tting
We conducted goodness-of-fit tests for the two models
based on the case datasets from the four regions (Wuhan
City, Jilin Province, Chuxiong Prefecture, and Xiamen
City) (Figs. 7 and 8). R2 values were calculated for the
four model groups (pathogen positive cases in the stu-
dent group, Ip1; pathogen negative cases in the student
group, In1; pathogen positive cases in the non-student
group, Ip2; and pathogen negative cases in the non-stu-
dent group, In2). e values showed that, although the
two established TB models fitted well with the trend of
TB incidence rates (Table2), Model A had better fitting
results than Model B.
Transmissibility forinteractions amongthefour groups
e results of Reff among and between the different popu-
lations in each region are shown in Fig.9.
In Wuhan City, the median Reff for TB among the
mixed population was 1.79 (IQR: 1.56–2.02). Most TB
transmissions occurred due to the high transmission in
non-student populations, including among non-student
populations [median Reff22 was 1.57 (IQR: 1.41–1.72)] and
non-student-to-student populations [median Reff21 was
0.14 (IQR: 0.11–0.15)], with a median Reff2 of 1.71(IQR:
1.54–1.87). e values of Reff22 and Reff21 slowly descended
from 2005 to 2018. e values of Reff12 and Reff11 were
nearly zero excluding in 2006 (Reff12 was 6.39, Reff11 was
0.19) and 2013 (Reff11 was 0.58) (Table3).
In Jilin Province, the median Reff for TB among the
mixed population was 1.75 (IQR: 1.37–2.02). Most
TB transmission occurred due to the high transmis-
sion in the non-student population, including among
non-student populations [median Reff22 was 1.57, (IQR:
1.27–1.77)] and from non-student-to-student popula-
tions [median Reff21 was 0.09, (IQR: 0.07–0.11)], with a
median Reff2 of 1.66 (IQR: 1.35–1.89). e Reff21 and Reff12
values maintained stable fluctuations at values lower than
1 from 2007 to 2019. e value of Reff11 was nearly zero
excluding in 2009, when it reached 1.19 (Reff(asc) 1.17,
Reff(des) 0.02) (Table4).
In Chuxiong Prefecture, the median Reff of TB among
the mixed population was 1.68 (IQR: 1.20–1.96). Most
TB transmissions occurred due to the high transmis-
sion in non-student populations with a median Reff22
1.59 (IQR: 1.14–1.80), and the other three values (Reff11,
Reff21, Reff12) were nearly zero each year. e values of Reff2
and Reff1 fluctuated smoothly from 2008 to 2018, with a
median Reff2 of 1.63 (IQR: 1.17–1.82) and a median Reff1 of
0.05 (IQR: 0.02–0.09), respectively (Table5).
In Xiamen City, we excluded data analysis in 2019 for
only 3 months data collection from January to March,
which was not a valid TB representation for the entire
year. Except that the median Reff for TB among the mixed
population was 1.67 (IQR: 1.40–1.93). Most TB transmis-
sions of occurred due to the high transmission in non-
student populations, with a median Reff22 of 1.58 (IQR:
1.32–1.80). Reff2 values slowly decreased between 2005
and 2018, with a median Reff2 of 1.61 (IQR: 1.35–1.85).
Although there were several values of Reff12 higher than
0.10 in student-non-student transmission (Reff (des) in
2005, 2012, 2016, and Reff (asc) in 2006, 2008, 2010), the
overall transmissibility was annual decreasing with a
median Reff12 of 0.04 (IQR: 0.00–0.07) (Table6).
A similar transmission relationship among and between
student and non-student populations was calculated in
Model B. However, the model revealed exceedingly high
values of Reff over many years in the four regions, which
indicates that the findings of Model B may be unsuitable
to show the characteristics of TB. Additional details of
the results are provided in Additional file3: Tables S3,
Fig. 6 Proportions of patients reporting pulmonary tuberculosis in
the four study areas
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Chenetal. Infectious Diseases of Poverty (2022) 11:117
Additional file 4: Table S4, Additional file 5: TableS5,
Additional file6: TableS6.
Cumulative incidence rate aftertheknock‑out‑pathways,
β11, β12, β22, andβ21
According to the knock-out results (Fig.10), the number
of TB cases among students was significantly reduced
by more than half (60–70%) when the transmissibility of
non-student-to-student populations (β21) was knocked
out. When TB transmission among non-students (β22)
was blocked, the number of TB cases was reduced by
approximately 67% (65–70%) among non-students and
by approximately 28% (25–30%) among students. ere
was only a 5% reduction (2–12%) among students when
TB transmission among students (β11) was blocked, and
TB reported cases had barely changed (less than 1%)
while TB transmission from non-student-to-students
(β12) was blocked.
Discussion
is study is the first to address the occupational-specific
transmission dynamics of TB and emphasise the impor-
tance of control between student groups, which can
Fig. 7 Plot of goodness-of-fit results for the non-seasonal model (Model A)
Page 13 of 22
Chenetal. Infectious Diseases of Poverty (2022) 11:117
increase our understanding of the characteristics of TB
transmission in different occupational groups.
Analysis ofepidemiological characteristics
e incidence rate of TB decreased in the study regions,
which is in good agreement with previous global reports
[3], but was unevenly distributed between these four
regions. is phenomenon may be attributed to sev-
eral reasons. First, the inclusion of previously untreated
patients in the management after several years of contin-
uous active screening led to a certain decline in the num-
ber of subsequent patient detection cases. Second, since
2017, a nationwide survey of underreporting and under-
registration of TB [58] and a diagnostic review [59] were
carried out, which improved the quality of TB reporting
and diagnosis in each region. However, the reported inci-
dence rate of TB is still higher in areas less economically
developed than in the west, such as Chuxiong Prefecture,
although the attention and support of governments and
health administrations, as well as the support for precise
health poverty alleviation have been undertaken at all
levels [9].
Remarkably, the reported incidence of TB in the stu-
dent population has increased. e reported data also
Fig. 8 Plot of goodness-of-fit results for the seasonal model (Model B)
Page 14 of 22
Chenetal. Infectious Diseases of Poverty (2022) 11:117
confirmed that the proportion of student patients has
increased from 4.0% in 2015 to 6.2% in 2019, with a dif-
ference of 2.2% [9]. is is mainly due to the fact that
early warning of individual cases of TB in schools has
been included in the National Automatic Early Warn-
ing System for Infectious Diseases since July 2018 [60].
Furthermore, disease control agencies at all levels have
strengthened the information verification process of
school-age patients and improved the sensitivity of the
surveillance of student patients [61]. Schools have also
strengthened medical examinations and handled clusters
of TB outbreaks [62]. Our results highlight an obvious
incidence peak among students at the beginning of the
semester. ere are several explanations for this observa-
tion. Students are in close contact with social residents
and are exposed to TB patients in the community dur-
ing holidays. Considering LTBI [8, 63], students infected
with MTB on vacation do not become ill immediately,
Table 2 Goodness-of-fit test results of the two models (Models A and B) in the study areas
Correlation between the simulated and observed data was tested using R2 and p values. We divided all the compartments representing active diseases (I) into four
occupational compartments: pathogen positive students (Ip1 subscript), pathogen positive non-students (Ip2 subscript), pathogen negative students (In1 subscript) and
pathogen negative non-students (In2 subscript)
Region Ip1In1Ip2In2
R2P R2P R2P R2P
Model A
Wuhan City 0.882 < 0.001 0.55 < 0.001 0.49 < 0.001 0.857 < 0.001
Jilin Province 0.939 < 0.001 0.834 < 0.001 0.781 < 0.001 0.823 < 0.001
Xiamen City 0.105 < 0.001 0.156 < 0.001 0.202 < 0.001 0.708 < 0.001
Chuxiong Prefecture 0.971 < 0.001 0.971 < 0.001 0.752 < 0.001 0.619 < 0.001
Model B
Wuhan City 0.236 < 0.001 0.083 < 0.001 0.337 < 0.001 0.761 < 0.001
Jilin Province 0.961 < 0.001 0.615 < 0.001 0.489 < 0.001 0.559 < 0.001
Xiamen City 0.039 0.009 0.009 0.202 0.239 < 0.001 0.399 < 0.001
Chuxiong Prefecture 0.978 < 0.001 0.977 < 0.001 0.952 < 0.001 0.818 < 0.001
Fig. 9 The chart of effective regeneration number plotted according to the two models
Page 15 of 22
Chenetal. Infectious Diseases of Poverty (2022) 11:117
but become ill after returning to school when they are
exposed to several inducements, including high pressure
and cold, among others. Farmers always had the highest
reported incidence rates. However, this is not surpris-
ing if we consider that the rural population represents
most of the total population of China, and the allocation
of medical and health resources in rural areas is inad-
equate, resulting in unequal access to medical resources
for urban and rural residents [13]. Furthermore, it may be
related to the lower level of education of farmers, poorer
living conditions, and lack of awareness of health protec-
tion [64].
e diagnosis results in the study areas, which had
a low pathogen positive rate of less than 50%. A previ-
ous report showed the pathogen positive rate for PTB
reported in China in 2020 was 57%, up from 45.03% in
2019 [3]. However, a gap still exists when this is com-
pared with surveillance results based on laboratory path-
ogenic diagnostic evidence in other countries worldwide.
Both the TB laboratory diagnostic and the TB imaging
detection capacity need to be improved in primary care
institutions in China, which is consistent with the out-
comes of one diagnostic and therapeutic survey on TB
sentinel medical institutions [65] and on the current
Table 3 Reff of Model A between students and non-students (Wuhan City)
Re11 denotes the transmissibility of MTB from student cases to student cases. Re 12 denotes the transmissibility of MTB from student cases to non-student cases. Re
21 denotes the transmissibility of MTB from non-student cases to student cases. Re 22 denotes the transmissibility of MTB from the non-student cases to non-student
cases. Re1 represents the transmissibility of the population of students with active TB cases (sum of Re11 and Re 12), whereas Re 2 represents the transmissibility of the
population of non-student active TB cases (sum of Re 22 and Re 21)
asc denotes the ascending Re (Re(asc)). des denotes the descending Re (Re(des))
Year Re11 Re 12 Re 22 Re 21 Re1 Re2
2005 (asc) 0.04 0.00 2.12 0.24 0.04 2.37
2005 (des) 0.08 0.00 1.73 0.13 0.08 1.87
2006 (asc) 0.05 0.36 1.62 0.16 0.41 1.79
2006 (des) 0.14 0.00 1.75 0.16 0.14 1.91
2007 (asc) 0.06 0.00 1.86 0.14 0.07 2.00
2007(des) 0.04 0.00 1.49 0.13 0.04 1.62
2008 (asc) 0.02 0.00 2.27 0.42 0.02 2.68
2008 (des) 0.02 0.00 1.41 0.14 0.02 1.55
2009 (asc) 0.10 0.00 1.79 0.27 0.10 2.05
2009 (des) 0.00 0.00 1.12 0.14 0.00 1.26
2010 (asc) 0.06 0.00 1.75 0.20 0.06 1.94
2010 (des) 0.03 0.00 1.20 0.12 0.03 1.32
2011 (asc) 0.03 0.00 1.62 0.15 0.03 1.77
2011(des) 0.02 0.00 1.43 0.11 0.02 1.54
2012 (asc) 0.02 0.00 1.57 0.10 0.03 1.67
2012 (des) 0.02 0.00 1.45 0.08 0.02 1.53
2013 (asc) 0.55 0.01 1.61 0.08 0.56 1.69
2013 (des) 0.03 0.00 1.57 0.12 0.03 1.69
2014 (asc) 0.02 0.00 1.59 0.12 0.02 1.71
2014 (des) 0.02 0.00 1.25 0.12 0.02 1.37
2015 (asc) 0.00 0.02 1.57 0.15 0.02 1.72
2015 (des) 0.02 0.00 1.29 0.09 0.02 1.38
2016 (asc) 0.00 0.00 1.62 0.13 0.00 1.75
2016 (des) 0.06 0.00 1.41 0.15 0.06 1.56
2017 (asc) 0.11 0.01 1.71 0.16 0.11 1.87
2017 (des) 0.00 0.03 1.36 0.12 0.03 1.47
2018 (asc) 0.00 0.07 1.56 0.04 0.07 1.60
2018 (des) 0.02 0.00 1.21 0.07 0.02 1.28
Median 0.06 0.02 1.57 0.14 0.07 1.71
P25 0.02 0.00 1.41 0.11 0.02 1.54
P75 0.06 0.00 1.72 0.15 0.07 1.87
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Chenetal. Infectious Diseases of Poverty (2022) 11:117
status of TB diagnostic capacity at county-level TB sen-
tinel medical institutions in China [66]. To achieve the
goal of "reaching a pathogenic positivity rate of more
than 50% by 2022" as required by the Action Plan to Stop
TB (2019–2022) [67], it is still necessary to continue to
strengthen the quality of laboratory work [68].
Analysis ofTB transmission dynamicscharacteristics
In this study, two mathematical models of TB were con-
structed according to the transmission characteristics of
TB: Models A and B. Although there may be seasonal
fluctuations in the actual incidence of TB in some areas,
Model A fitted better than Model B. erefore, we believe
that the analysis results of Model A can better reflect the
real situation of TB incidence.
erefore, the following interpretations were made
according to the results of the Reff calculation of Model A
and results of a knock-out analysis:
A) Overall, the average values of Reff in the four regions
showed that a single TB case could effectively spread
to one or two people. TB transmissibility among non-
students (Reff2) was 23.30 times (IQR: 1.94–7.24) higher
than among students (Reff1). TB transmission remained
dominant in the non-student population. is find-
ing also existed in the knock-out analysis. Transmission
among non-students increased the number of reported
TB cases in all four groups (67% in non-students and 28%
in students). e non-student population was large, and
included 17 occupations, different locations with active
cases, and a wide range of age groups. In high-burden
Table 4 Reff of Model A between students and non-students (Jilin Province)
Re11 denotes the transmissibility of MTB from student cases to student cases. Re 12 denotes the transmissibility of MTB from student cases to non-student cases. Re
21 denotes the transmissibility of MTB from non-student cases to student cases. Re 22 denotes the transmissibility of MTB from the non-student cases to non-student
cases. Re1 represents the transmissibility of the population of students with active TB cases (sum of Re11 and Re 12), whereas Re 2 represents the transmissibility of the
population of non-student active TB cases (sum of Re 22 and Re 21)
asc denotes the ascending Re (Re(asc)). des denotes the descending Re (Re(des))
Year Re11 Re 12 Re 22 Re 21 Re1 Re2
2007 (asc) 0.00 0.00 1.27 0.03 0.00 1.30
2007 (des) 0.01 0.00 1.22 0.11 0.01 1.33
2008 (asc) 0.05 0.00 1.80 0.18 0.05 1.98
2008 (des) 0.00 0.00 1.23 0.11 0.00 1.35
2009 (asc) 1.17 0.02 1.60 0.00 1.19 1.60
2009 (des) 0.02 0.09 1.16 0.09 0.10 1.26
2010 (asc) 0.02 0.00 1.53 0.12 0.02 1.65
2010 (des) 0.00 0.02 1.25 0.09 0.02 1.34
2011 (asc) 0.05 0.00 1.99 0.15 0.05 2.14
2011 (des) 0.02 0.00 1.28 0.08 0.02 1.36
2012 (asc) 0.03 0.00 1.68 0.10 0.03 1.78
2012 (des) 0.02 0.00 1.25 0.06 0.02 1.31
2013 (asc) 0.03 0.00 1.84 0.09 0.03 1.93
2013 (des) 0.03 0.00 1.68 0.09 0.03 1.76
2014 (asc) 0.04 0.00 2.02 0.11 0.04 2.12
2014 (des) 0.02 0.07 1.54 0.05 0.10 1.59
2015 (asc) 0.04 0.00 2.04 0.10 0.04 2.13
2015 (des) 0.02 0.00 1.56 0.06 0.02 1.62
2016 (asc) 0.04 0.00 2.16 0.13 0.04 2.30
2016 (des) 0.03 0.00 1.58 0.06 0.03 1.64
2017 (asc) 0.03 0.00 2.02 0.09 0.03 2.11
2017 (des) 0.03 0.00 1.54 0.09 0.03 1.63
2018 (asc) 0.02 0.08 1.60 0.06 0.10 1.66
2018 (des) 0.02 0.00 1.48 0.11 0.02 1.59
2019 (asc) 0.00 0.40 1.40 0.09 0.40 1.49
2019 (des) 0.02 0.00 1.04 0.09 0.02 1.13
Median 0.07 0.03 1.57 0.09 0.09 1.66
P25 0.02 0.00 1.27 0.07 0.02 1.35
P75 0.03 0.00 1.77 0.11 0.05 1.89
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Chenetal. Infectious Diseases of Poverty (2022) 11:117
areas, such as China, most TB transmission occurs out-
side the home (< 20% of household transmission) which
is not necessarily attributable to known close contacts
[69, 70]. e probability of TB transmission to others
by a TB patient is determined by many factors, includ-
ing socioeconomic, environmental, high or low regional
disease burden, infectiousness of the case, MTB strain,
and host susceptibility. Determining the specific site of
TB transmission outside the home is difficult. e poten-
tial for airborne transmission even during brief contact,
combined with variable incubation periods, makes it
exceptionally difficult to establish a specific TB trans-
mission link. Despite these challenges, certain specific
settings have been identified as important contributors
to TB risk, such as nasal transmission [71–74], hospi-
tal-associated transmission [75], homeless shelters [76],
prisons [77, 78], public transportation [79], churches
[80], schools [69, 81] and slums [82–84]. is is pre-
cisely because the places where students study and live
are close, providing good conditions for the spread of
TB; therefore, the implementation of TB control policies
in schools is especially important. e presence of these
factors has contributed to the high rates of acquired TB
in this group over the years.
Furthermore, the concentration of TB transmission in
certain settings and subpopulations also leads to hetero-
geneity of transmission, which can serve to increase Reff
and may make it more difficult to control transmission
[85]. Moreover, adults in their most active age groups are
more likely to be infected with TB due to their close con-
tact with each other [86]. To explain why transmission
among the non-student populations increased the num-
ber of infected patients among non-students, it may be
assumed that household and unnoticed transmissions in
the community contribute simultaneously [87].
B) e results from knock-out analysis indicated that
non-student-to-student transmission increased the num-
ber of reported TB cases in the student group (either
pathogen positive or negative), and transmission among
non-students increased the number of reported TB cases
Table 5 Reff of Model A between students and non-students (Chuxiong Prefecture)
Re11 denotes the transmissibility of MTB from student cases to student cases. Re 12 denotes the transmissibility of MTB from student cases to non-student cases. Re
21 denotes the transmissibility of MTB from non-student cases to student cases. Re 22 denotes the transmissibility of MTB from the non-student cases to non-student
cases. Re1 represents the transmissibility of the population of students with active TB cases (sum of Re11 and Re 12), whereas Re 2 represents the transmissibility of the
population of non-student active TB cases (sum of Re 22 and Re 21)
asc denotes the ascending Re (Re(asc)). des denotes the descending Re (Re(des))
Year Re11 Re 12 Re 22 Re 21 Re1 Re2
2008 (des) 0.01 0.00 0.80 0.02 0.01 0.82
2009 (asc) 0.02 0.01 1.96 0.02 0.03 1.98
2009 (des) 0.00 0.04 0.87 0.02 0.05 0.89
2010 (asc) 0.02 0.00 1.97 0.03 0.02 2.01
2010 (des) 0.00 0.00 1.04 0.02 0.01 1.06
2011 (asc) 0.01 0.00 1.52 0.04 0.01 1.56
2011 (des) 0.00 0.08 1.11 0.01 0.08 1.12
2012 (asc) 0.02 0.00 1.26 0.03 0.02 1.29
2012 (des) 0.01 0.05 1.72 0.02 0.06 1.74
2013 (asc) 0.00 0.01 0.24 0.05 0.02 0.29
2013 (des) 0.00 0.09 1.71 0.02 0.09 1.73
2014 (asc) 0.02 0.00 1.14 0.04 0.02 1.17
2014 (des) 0.00 0.09 1.78 0.04 0.09 1.82
2015 (asc) 0.03 0.02 2.82 0.13 0.05 2.95
2015 (des) 0.03 0.00 1.73 0.05 0.03 1.78
2016 (asc) 0.06 0.00 3.58 0.09 0.06 3.66
2016 (des) 0.00 0.13 1.52 0.02 0.13 1.54
2017 (asc) 0.00 0.16 1.80 0.00 0.16 1.80
2017 (des) 0.00 0.09 1.58 0.03 0.09 1.61
2018 (asc) 0.03 0.00 2.01 0.12 0.04 2.12
2018 (des) 0.00 0.09 1.26 0.04 0.09 1.30
Median 0.01 0.04 1.59 0.04 0.05 1.63
P25 0.00 0.00 1.14 0.02 0.02 1.17
P75 0.02 0.09 1.80 0.04 0.09 1.82
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Chenetal. Infectious Diseases of Poverty (2022) 11:117
in all the four groups. ere may be several reasons for
this. First, the home–school transmission route may be
one of the reasons. TB is actively transmitted by house-
hold exposure [88], and a prospective case–control study
found that previous exposure to TB in a household could
cause an infected student to spread TB to their class-
mates [89]. Second, we believe that the school commu-
nity transmission route is important due to increased
exposure to other occupations during vacations.
C) Although TB transmission is spread mainly by non-
students, the transmissibility of student-to-non-students
in some years and in some regions, is particularly high,
such as the Reff12 of Chuxiong Prefecture in 2016 (Reff12:
0.13), 2017 (Reff12: 0.16), and that of Wuhan City in 2006
(Reff12: 0.36), etc. is could be due to TB outbreaks in
schools [90]. Once TB transmission occurs in schools,
the spread of TB will exceed beyond the public due to
the frequent contact between students and cause wide-
spread TB in schools. Due to this particularity of TB
school transmission, the TB reporting system of China is
more sensitive to the population of student occupation.
A national single-case warning system is used to iden-
tify the student tuberculosis patients. When a student is
diagnosed, close contacts screening, isolation and treat-
ment of the TB patients are implemented in the short-
est time. ese measures make the control of student
Table 6 Reff of Model A between students and non-students (Xiamen City)
Re11 denotes the transmissibility of MTB from student cases to student cases. Re 12 denotes the transmissibility of MTB from student cases to non-student cases. Re
21 denotes the transmissibility of MTB from non-student cases to student cases. Re 22 denotes the transmissibility of MTB from the non-student cases to non-student
cases. Re1 represents the transmissibility of the population of students with active TB cases (sum of Re11 and Re 12), whereas Re 2 represents the transmissibility of the
population of non-student active TB cases (sum of Re 22 and Re 21)
asc denotes the ascending Re (Re(asc)). des denotes the descending Re (Re(des))
Year Re11 Re 12 Re 22 Re 21 Re1 Re2
2005 (asc) 0.04 0.00 2.21 0.03 0.04 2.24
2005 (des) 0.00 0.22 2.03 0.03 0.22 2.06
2006 (asc) 0.02 0.11 2.71 0.01 0.13 2.72
2006 (des) 0.02 0.00 1.77 0.05 0.02 1.83
2007 (asc) 0.04 0.00 1.81 0.04 0.04 1.84
2007 (des) 0.03 0.01 2.00 0.04 0.04 2.04
2008 (asc) 0.00 0.15 1.86 0.03 0.15 1.89
2008 (des) 0.03 0.00 1.54 0.03 0.03 1.57
2009 (asc) 0.00 0.09 1.75 0.02 0.09 1.77
2009 (des) 0.04 0.01 1.69 0.05 0.05 1.73
2010 (asc) 0.04 0.15 1.60 0.03 0.19 1.62
2010 (des) 0.03 0.01 1.33 0.02 0.04 1.36
2011 (asc) 0.03 0.00 2.01 0.05 0.03 2.06
2011 (des) 0.01 0.00 1.39 0.03 0.01 1.42
2012 (asc) 0.02 0.00 1.36 0.02 0.02 1.39
2012 (des) 0.00 0.12 1.31 0.03 0.12 1.34
2013 (asc) 0.06 0.00 1.79 0.08 0.06 1.87
2013 (des) 0.02 0.00 1.28 0.02 0.02 1.29
2014 (asc) 0.01 0.04 1.16 0.01 0.05 1.18
2014 (des) 0.01 0.02 1.19 0.03 0.03 1.22
2015 (asc) 0.02 0.00 1.48 0.02 0.02 1.50
2015 (des) 0.01 0.02 1.21 0.05 0.03 1.27
2016 (asc) 0.02 0.01 1.34 0.02 0.03 1.37
2016 (des) 0.00 0.10 1.11 0.01 0.10 1.12
2017 (asc) 0.02 0.00 1.38 0.02 0.02 1.40
2017 (des) 0.01 0.06 1.43 0.01 0.07 1.45
2018 (asc) 0.02 0.01 1.32 0.02 0.03 1.35
2018 (des) 0.02 0.00 1.22 0.02 0.02 1.25
Median 0.02 0.04 1.58 0.03 0.06 1.61
P25 0.01 0.00 1.32 0.02 0.03 1.35
P75 0.03 0.07 1.80 0.03 0.07 1.85
Page 19 of 22
Chenetal. Infectious Diseases of Poverty (2022) 11:117
TB outbreaks much more effective, and then reduce the
tuberculosis cases of this outbreak. But in the real world,
if the epidemic was not dealt with promptly, a widespread
TB outbreak in schools will be inevitable.
Prevention andcontrol ofTB amongstudents
e relevant authorities must continue to strengthen
the prevention and control of TB in student populations
in the future [91]. ere are shortcomings at all levels,
including schools, medical institutions and TB control
institutions, and improvements are needed. For schools,
the implementation of a system to trace the causes of
absence from school to detect patients in a timely and
proactive manner is effective. Medical institutions should
keep the epidemic information channels open with
schools and TB control institutions, and provide timely
information about confirmed students to schools and TB
control institutions. TB prevention and control institu-
tions should perform timely information verification and
close contact follow-up after the detection of the infected
student.
In addition, we suggest that more attention should be
paid to men, farmers, and young and middle-aged peo-
ple; and the bacteriological diagnosis of TB should be
strengthened. More data collection from social con-
tact surveys is required to provide information on how
individual behaviors drive disease dynamics at the popu-
lation level.
In particularly, several limitations may have influenced
the results obtained. e first is selection bias due to
inconsistency at the administrative levels in our study
areas, which includes three cities and one province. e
second is that we only included cases that were diagnosed
as “bacteriologically confirmed positive or negative” and
excluded those that were diagnosed as “rifampicin resist-
ant” when processing the initial data. e latter could
also contribute to TB transmission. Furthermore, com-
plete immunity does not occur in patients with TB after
recovery. However, partial immunity has been observed
in previously infected individuals, which can prevent
reinfection (risk ratio = 0.5) [92]. e last limitation of
our methodology is that it was not possible to subdivide
the 17 non-student occupations to better articulate the
mechanisms of transmission between different occupa-
tions and quantify the impact of different non-student
occupations on the student population.
Conclusions
is study has the potential to improve our understand-
ing of the features of TB transmission in different occu-
pational groups. e transmission of MTB was high in
non-student populations, and that in the non-student
population was 23.30 times higher than in the student
Fig. 10 Knockout analysis in the four study areas: A Wuhan Cit y, B Jilin Province, C Xiamen City, and D Chuxiong Prefecture. The subscripts refer
to the occupation of the students (1 subscript) or non-students (2 subscript). The initial state is denoted as the default. The performance of the
knockout for each of the transmission pathway was determined by setting the beta value to zero. For example, kb11 stands for getting rid of the
student-to-students’ transmission pathway
Page 20 of 22
Chenetal. Infectious Diseases of Poverty (2022) 11:117
population. It had the strongest influence among non-
student groups. It not only increases the incidence of
TB among non-students, but also among students.
e incidence of TB among students has been on the
rise and is the fourth highest in occupational distribu-
tion (especially in economically developed areas with
a high number of students), despite the incidence of
TB in China showing a downward trend annually. e
TB outbreak among students can rapidly improve the
transmissibility of TB in a short time, which will affect
the prevalence of TB in other groups. TB screening
should be performed rigorously at the beginning of the
school semesters, when returning to school, to detect
patients with LTBI. is implies the need for the imple-
mentation of more control measures such as strength-
ening the school TB management efforts and timely
management of identified TB-infected students, after
the academic year begins.
Abbreviations
SEIR: Susceptible-exposed-symptomatic-recovered; Reff: Effective reproduction
number; MTB: Mycobacterium tuberculosis; TB: Tuberculosis; PTB: Pulmonary
tuberculosis; MDR-TB: Multidrug resistant tuberculosis; HIV: Human immuno-
deficiency virus; AIDS: Acquired immune deficiency syndrome; LTBI: Latent
TB infections; NNDSS: National Notifiable Disease Surveillance System; IGRA
: Interferon-gamma release assays; R0: Basic reproduction number; DOTS:
Directly observed treatment and short course chemotherapy.
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s40249- 022- 01046-z.
Additional le1: TableS1. The basic information of the four regions.
Additional le2: TableS2. Two different classifications of tuberculosis in
National Notifiable Disease Surveillance System (NNDSS).
Additional le3: TableS3. Estimation of transmissibility between stu-
dents and non-students (Wuhan City).
Additional le4: TableS4. Estimation of transmissibility between stu-
dents and non-students (Jilin Province).
Additional le5: TableS5. Estimation of transmissibility between stu-
dents and non-students (Chuxiong Perfecture).
Additional le6: TableS6. Estimation of transmissibility between stu-
dents and non-students (Xiamen City).
Acknowledgements
The authors thank Miss Qiao Liu for her helpful comments.
Author contributions
QC, SY, JR, YG, SY, GA, ZY, CL, LL, MW, ZL, QZ, LG, YN, RF, and TC had full access
to all the data in the study and take responsibility for the integrity of the data
and the accuracy of the data analysis. YN, RF, and TC were responsible for
study conception and design. MW, and QZ collected the data. QC, SY, JR, YG,
SY, GA, ZY, CL, LL, LG, and ZL were responsible for data analysis and interpreta-
tion. QC, SY and JR drafted the manuscript. YN, RF, and TC critically revised the
manuscript for intellectual content. YN, RF, and TC contributed equally to this
study. All authors read and approved the final manuscript.
Funding
This study was supported by the Bill and Melinda Gates Foundation (Grant
Number: INV-005834). The funder had no role in the study design, data collec-
tion and analysis, decision to publish, or manuscript preparation.
Availability of data and materials
The datasets used and/or analysed during the current study are available from
the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was approved by the Ethics Committee of the School of Medicine,
Xiamen University. Consent requirement, either verbal or written, was waived
by the thics Committee of the School of Medicine on the following grounds:
(1) only anonymized records were used without the need for direct involve-
ment nor active participation of patients; (2) neither medical intervention nor
biological samples were involved; (3) study procedures and results would not
affect clinical management of patients in any form.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1 State Key Laboratory of Molecular Vaccinology and Molecular Diagnos-
tics, School of Public Health, Xiamen University, Xiamen, Fujian, People’s
Republic of China. 2 CIRAD, URM 17, Intertryp, Montpellier, France. 3 Université
de Montpellier, Montpellier, France. 4 Xiamen Center for Disease Control
and Prevention, Xiamen, Fujian, People’s Republic of China. 5 Jilin Provincial
Center for Disease Control and Prevention, Changchun, Jilin, People’s Republic
of China. 6 Espace-Dev, Université de Montpellier, Montpellier, France. 7 Chinese
Center for Disease Control and Prevention, 155 Changbai Road, Changping
District, Beijing, China.
Received: 27 June 2022 Accepted: 17 November 2022
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