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Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: A systematic review of the literature

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Background: The potential impact of an influenza pandemic can be assessed by calculating a set of transmissibility parameters, the most important being the reproduction number (R), which is defined as the average number of secondary cases generated per typical infectious case. Methods: We conducted a systematic review to summarize published estimates of R for pandemic or seasonal influenza and for novel influenza viruses (e.g. H5N1). We retained and summarized papers that estimated R for pandemic or seasonal influenza or for human infections with novel influenza viruses. Results: The search yielded 567 papers. Ninety-one papers were retained, and an additional twenty papers were identified from the references of the retained papers. Twenty-four studies reported 51 R values for the 1918 pandemic. The median R value for 1918 was 1.80 (interquartile range [IQR]: 1.47-2.27). Six studies reported seven 1957 pandemic R values. The median R value for 1957 was 1.65 (IQR: 1.53-1.70). Four studies reported seven 1968 pandemic R values. The median R value for 1968 was 1.80 (IQR: 1.56-1.85). Fifty-seven studies reported 78 2009 pandemic R values. The median R value for 2009 was 1.46 (IQR: 1.30-1.70) and was similar across the two waves of illness: 1.46 for the first wave and 1.48 for the second wave. Twenty-four studies reported 47 seasonal epidemic R values. The median R value for seasonal influenza was 1.28 (IQR: 1.19-1.37). Four studies reported six novel influenza R values. Four out of six R values were <1. Conclusions: These R values represent the difference between epidemics that are controllable and cause moderate illness and those causing a significant number of illnesses and requiring intensive mitigation strategies to control. Continued monitoring of R during seasonal and novel influenza outbreaks is needed to document its variation before the next pandemic.
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Estimates of the reproduction number for seasonal,
pandemic, and zoonotic influenza: a systematic
review of the literature
Matthew Biggerstaff1*
* Corresponding author
Email: mbiggerstaff@cdc.gov
Simon Cauchemez2
Email: simon.cauchemez@pasteur.fr
Carrie Reed1
Email: creed1@cdc.gov
Manoj Gambhir3
Email: manoj.gambhir@monash.edu
Lyn Finelli1
Email: lfinelli@cdc.gov
1 Epidemiology and Prevention Branch, Influenza Division, National Center for
Immunization and Respiratory Diseases, Centers for Disease Control and
Prevention, 1600 Clifton Road NE MS A-32, Atlanta 30333, Georgia
2 Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris,
France
3 National Center for Immunization and Respiratory Diseases, CDC, Atlanta,
Georgia
Abstract
Background
The potential impact of an influenza pandemic can be assessed by calculating a set of
transmissibility parameters, the most important being the reproduction number (R), which is
defined as the average number of secondary cases generated per typical infectious case.
Methods
We conducted a systematic review to summarize published estimates of R for pandemic or
seasonal influenza and for novel influenza viruses (e.g. H5N1). We retained and summarized
papers that estimated R for pandemic or seasonal influenza or for human infections with
novel influenza viruses.
Results
The search yielded 567 papers. Ninety-one papers were retained, and an additional twenty
papers were identified from the references of the retained papers. Twenty-four studies
reported 51 R values for the 1918 pandemic. The median R value for 1918 was 1.80
(interquartile range [IQR]: 1.47–2.27). Six studies reported seven 1957 pandemic R values.
The median R value for 1957 was 1.65 (IQR: 1.53–1.70). Four studies reported seven 1968
pandemic R values. The median R value for 1968 was 1.80 (IQR: 1.56–1.85). Fifty-seven
studies reported 78 2009 pandemic R values. The median R value for 2009 was 1.46 (IQR:
1.30–1.70) and was similar across the two waves of illness: 1.46 for the first wave and 1.48
for the second wave. Twenty-four studies reported 47 seasonal epidemic R values. The
median R value for seasonal influenza was 1.28 (IQR: 1.19–1.37). Four studies reported six
novel influenza R values. Four out of six R values were <1.
Conclusions
These R values represent the difference between epidemics that are controllable and cause
moderate illness and those causing a significant number of illnesses and requiring intensive
mitigation strategies to control. Continued monitoring of R during seasonal and novel
influenza outbreaks is needed to document its variation before the next pandemic.
Keywords
Reproductive number, Influenza, Pandemics, Zoonotic influenza
Background
Annual influenza epidemics occur worldwide and cause substantial morbidity and mortality
[1]. In the United States between 5% and 20% of the population are infected with influenza
every year [2], resulting in between 3,000 and 49,000 influenza-associated deaths [3].
Influenza viruses are constantly changing either through the collection of minor point
mutations or through major antigenic shifts. These major shifts can result in the introduction
of novel influenza viruses into the human population to which humans have little or no
immunity, causing pandemics [1]. Four influenza pandemics have occurred since the
beginning of the 20th century and have ranged widely in transmissibility and clinical severity
[1,4].
Recognizing that the characteristics of future pandemics will be difficult to predict given the
mutability of the influenza virus and the range of morbidity and mortality experienced in
previous pandemics, an approach to the early assessment of influenza pandemics has been
developed relying on standardized measures of transmissibility and clinical severity [5]. An
important transmissibility parameter identified is the reproduction number (R), which is
defined as the average number of secondary cases generated per typical infectious case [6,7].
R describes on average how many persons a case will infect, and a value of R greater than 1
indicates that the infection may grow or persist in the population while a value of R less than
1 indicates that the infection will decline in the population, although exceptions exist[7,8].
Many methods to calculate R have been described that allow for the use of epidemiologic
data from different epidemic time points [7]. Some examples include estimating R using the
growth rate of the epidemic, the epidemic curve’s size and shape, the final attack rate, or by
direct observation of disease transmission from one generation to the next [7]. The population
susceptibility to the infection also affects the interpretation of R. If R is calculated in a
population entirely susceptible to infection (or where an assumption about population
susceptibility to infection is made), then R is known as the basic reproduction number (R0).
In contrast, the effective reproduction number (RE) is calculated in a population with
underlying immunity and accounts for a population’s reduced susceptibility to infection [9].
The value of R characterizes the final number infected in the absence of an intervention in
homogeneously mixed populations, the herd immunity threshold, and, when coupled with the
generation time, defined as the interval between infections in two consecutive generations, or
the serial interval, defined as the interval between the onset of symptoms in two consecutive
generations), the speed with which the disease spreads in the population [10-12]. Therefore,
the magnitude of R plays an important role in the selection and aggressiveness of
countermeasures (e.g. social distancing, treating ill individuals, or vaccination) required to
slow transmission of the disease [10,13].
Because R is used as a measure of transmissibility and informs the selection of different
mitigation strategies, it is important to understand the range and uncertainty of published R
values. In this paper, we investigate whether published estimates of R differ between
pandemic, seasonal, and novel influenza, we compare values of R calculated in differing
geographic regions and settings, and we explore the assumptions and limitations of the
estimation methods of R.
Methods
We performed a literature search using the PubMed database from 1950 to January 16, 2013.
The following key terms were searched: “reproduction number and influenza”, “reproductive
number and influenza”, “R0 and influenza”, “reproduction rate and influenza”, and
“reproductive rate and influenza”. We limited our search to articles in English. We retained
articles that estimated R for pandemic or seasonal influenza or for human infections with
non-human influenza viruses (e.g. H5N1). For all studies retained, we abstracted the date of
publication, the year, the geographic location where the data were collected, the influenza
subtype, the study population, whether it was a confined setting, the wave of the observation
(if during a pandemic), the estimated value of R, the method to identify influenza cases, and
whether it was a R0 or RE. If multiple R values were provided, we provide the median and
range. Since methods to estimate the reproduction number often require a value for the
generation time or the serial interval, we also report those values [14]. We classified the
method used to determine influenza-associated cases into two categories: laboratory
confirmed, which required the use of confirmatory testing of respiratory or blood specimens,
or unconfirmed, which relied on syndromic case definitions to identify cases and required no
laboratory confirmation of illnesses.
Median R values and interquartile ranges (IQR) were reported for each pandemic and for the
group of inter-pandemic seasonal epidemics. If a range of values was given for an individual
study instead of a point estimate, the middle value of the range was used in the pandemic or
epidemic median calculations.
Results
The search strategy initially identified 567 papers (Figure 1). Ninety-one papers were
retained that estimated R for pandemic or seasonal influenza or for human infections with
non-human influenza viruses (e.g. H5N1). Twenty additional papers were contributed by the
references of the papers identified through the original search. In all, 111 articles were
retained that presented original estimates of the reproduction number (summarized in Tables
1, 2, 3, 4, 5 and 6). Data provided in the tables are also available as .csv files in Additional
files 1, 2, 3, 4, 5 and 6.
Figure 1 PRISMA flowchart of the article selection for the reproductive number and
influenza literature review.
Table 1 Reproduction Numbers from the 1918 Influenza A/H1N1 Pandemic
Location Wave
a
Study
Population
Mean
GT/SIb Reproduction Number
(R)
95% CI
c
Basic or
Effective
Case definition Reference
Year
Published
Australia 1st community 2.6 1.80 1.6–2.0 basic unconfirmed
hospitalizations/deaths [115] 2008
Brazil 1st community 4 2.68 basic unconfirmed illness [116] 2007
Canada 1st community 3 1.50 1.5–1.5 basic unconfirmed deaths [117] 2011
Canada 1st community 6 2.1 2.1–2.1 basic unconfirmed deaths [117] 2011
Colombia 1st community 3 1.4–1.5 effective unconfirmed deaths [118] 2012
Colombia 1st community 4 1.5–1.7 effective unconfirmed deaths [118] 2012
Denmark 1st community 2.6 2.2–2.4 effective unconfirmed illness [119] 2008
Denmark 1st community 4 2.8–3.0 effective unconfirmed illness [119] 2008
Denmark 1st community 2.6 2.8–4.0 effective unconfirmed hospitalizations [119] 2008
Denmark 1st community 4 3.6–5.4 effective unconfirmed hospitalizations [119] 2008
Italy 1st community 3 1.03 1.00–
1.08 basic unconfirmed hospitalizations [120] 2011
Mexico 1st community 3 1.30 effective unconfirmed deaths [121] 2010
Peru 1st community 3 1.38 1.37–
1.40 effective unconfirmed deaths [122] 2011
Switzerland 1st community 3.11 1.49 1.45–
1.53 basic unconfirmed hospitalizations [123] 2006
Switzerland 1st community 3.4 1.50 basic unconfirmed deaths [124] 2009
United
Kingdom 1st community 2.6 1.7 basic unconfirmed deaths [10] 2006
United
Kingdom 1st community 4.1 2.10 effective unconfirmed illness [36] 2006
United
Kingdom 1st community 6 2.00 basic unconfirmed illness [37] 2005
United
Kingdom 1st community NR 1.16–2.94 effective unconfirmed illness [125] 2010
United
Kingdom 1st students NR 1.43–5.36 effective unconfirmed illness [125] 2010
USA 1st community 4 1.34–3.21 effective unconfirmed illness [126] 2008
Various 1st community 4 1.2–3.0 effective unconfirmed illness [127] 2007
Various 1st community 4 2.1–7.5 effective unconfirmed illness [127] 2007
1st sailors 4 4.97 effective unconfirmed illness [126] 2008
Canada 2nd community 3.6 2.26 1.95–
2.63 basic unconfirmed illness [128] 2010
Canada 2nd community 3.6 1.49 1.42–
1.55 basic unconfirmed illness [128] 2010
Canada 2nd community 3 2.40 2.4–2.5 basic unconfirmed deaths [117] 2011
Canada 2nd community 6 4.3 4.2–4.4 basic unconfirmed deaths [117] 2011
Denmark 2nd community 2.6 1.22–1.24 effective unconfirmed illness [119] 2008
Denmark 2nd community 4 1.29–1.33 effective unconfirmed illness [119] 2008
Denmark 2nd community 2.6 1.2–1.3 effective unconfirmed hospitalizations [119] 2008
Denmark 2nd community 4 1.3–1.4 effective unconfirmed hospitalizations [119] 2008
Germany 2nd community 1 1.58 0.03–
10.3 basic unconfirmed deaths [129] 2007
Germany 2nd community 3 2.52 0.75–
5.85 basic unconfirmed deaths [129] 2007
Germany 2nd community 5 3.41 1.91–
5.57 basic unconfirmed deaths [129] 2007
Italy 2nd community 3 1.38 1.3–1.5 basic unconfirmed hospitalizations [120] 2011
Mexico 2nd community 3 1.30 effective unconfirmed deaths [121] 2010
New Zealand 2nd military >1.5 1.3–3.1 basic unconfirmed hospitalizations [130] 2006
Switzerland 2nd community 2.28 3.75 3.6–3.9 effective unconfirmed hospitalizations [123] 2006
Switzerland 2nd community 3.4 2.40 basic unconfirmed deaths [124] 2009
United
Kingdom 2nd community 3 1.39 1.36–
1.43 effective unconfirmed deaths [131] 2008
United
Kingdom 2nd community 6 1.84 1.75–
1.92 effective unconfirmed deaths [131] 2008
United
Kingdom 2nd community 6 1.55 basic unconfirmed illness [37] 2005
United
Kingdom 2nd community 2.6 1.50 basic unconfirmed deaths [10] 2006
USA 2nd community 2.5 2.14 basic unconfirmed deaths [132] 2009
USA 2nd community NR 2.20 1.55–
2.84 effective unconfirmed illness [30] 2007
USA 2nd community 4 2.00 1.7–2.3 effective unconfirmed deaths [133] 2004
USA 2nd community 2.85 1.73 effective unconfirmed deaths [14] 2007
United
Kingdom 3rd community 3 1.39 1.29–
1.49 effective unconfirmed deaths [131] 2008
United
Kingdom 3rd community 6 1.82 1.61–
2.05 effective unconfirmed deaths [131] 2008
United
Kingdom 3rd community 6 1.70 basic unconfirmed illness [37] 2005
Median reproduction number for the 1918 pandemic: 1.80; Interquartile range 1.47–2.27
a The first wave of illnesses began in the Northern Hemisphere in the spring 1918 [1]. A second wave of more intense transmission occurred
concurrently in North America, Europe, and Africa in the Fall of 1918 while a third and final wave of activity occurred in some areas of the
world during the winter of 1919 [15].
b The generation time (GT) or serial interval (SI) assumed in the reproduction number estimation.
c Confidence interval.
NR = Not reported.
This table is also available as a .csv file as Additional file 1.
Table 2 Reproduction Numbers from the 1957 Influenza A/H2N2 Pandemic
Location Wave
Study
Population
Mean GT/SI
b
Reproduction Number (R) 95% CI
c
Basic or Effective
Case definition Reference
Year Published
Netherlands 2nd community 3 1.39 basic unconfirmed deaths [34] 2010
United Kingdom 2nd community 2.6 1.70 basic unconfirmed deaths [10] 2006
United Kingdom 2nd community 3 1.5–1.6 basic unconfirmed illness [35] 2008
United Kingdom 2nd community 4 1.7–1.8 basic unconfirmed illness [35] 2008
United Kingdom 2nd community 4.1 1.50 effective unconfirmed illness [36] 2006
United Kingdom 2nd community NR 1.65 basic serology confirmed infection [37] 2005
USA 2nd community 4 1.70 basic unconfirmed illness [38] 2004
Median reproduction number for the 1957 pandemic: 1.65; Interquartile range 1.53–1.70
a The 1957 influenza A/H2N2 pandemic began in February 1957 in southern China and spread to Singapore and Hong Kong in April [1]. The
virus was first isolated in the United States in June 1957 and was associated with a mild first wave of illnesses [1,17]. The peak of the pandemic
occurred during the second wave in the Northern Hemisphere in October 1957 and was followed by a third wave in January 1958.
b The generation time (GT) or serial interval (SI) assumed in the reproduction number estimation.
c Confidence interval.
NR = Not reported.
This table is also available as a .csv file as Additional file 2.
Table 3 Reproduction Numbers from the 1968 Influenza A/H3N2 Pandemic.
Location Wave
a
Study Population
Mean GT/SI
b
Reproduction Number (R)
95% CI
c
Basic or Effective
Case definition Reference
Year Published
Hong Kong 1st community 2.95 1.89 basic unconfirmed illness [39] 1986
various 1st community 4 1.06–2.06 basic serology; laboratory confirmed illness; unconfirmed illness [18] 2010
various 1st confined settings 4 1.08–1.62 basic serology; laboratory confirmed illness; unconfirmed illness [18] 2010
United Kingdom
1st community 4.1 1.80 effective unconfirmed illness [36] 2006
United Kingdom
2nd community NR 1.85 effective serology confirmed infection [37] 2005
various 2nd community 4 1.08–2.02 effective serology; laboratory confirmed illness; unconfirmed illness [18] 2010
various 2nd confined settings 4 1.43 1.23–1.63
effective serology; laboratory confirmed illness; unconfirmed illness [18] 2010
Median reproduction number for the 1968 pandemic: 1.80; Interquartile range 1.56–1.85.
a The 1968 influenza A/H3N2 pandemic began in Hong Kong in July 1968. Large single waves of illness were reported in the Northern
Hemisphere between September 1968 and April 1969 (with peaks occurring in December 1968–January 1969). Large single waves of illnesses
were reported in the Southern Hemisphere between June and September 1969. Some countries in the Northern Hemisphere, such as the United
Kingdom, did not have an outbreak of H3N2 until the winter of 1969–70.
b The generation time (GT) or serial interval (SI) assumed in the reproduction number estimation.
c Confidence interval.
NR = Not reported.
This table is also available as a .csv file as Additional file 3.
Table 4 Reproduction Numbers from the 2009 Influenza A/H1N1 Pandemic
Location Wave
a
Study Population
Mean GT/SI
b
Reproduction Number (R) 95% CI
c
Basic or Effective
Case definition Reference
Year Published
Mexico 0 community 1.91 1.25 0.76–1.74
basic laboratory confirmed illness [19] 2011
Australia 1st community 2.8 1.50 1.50–2.70
effective laboratory confirmed illness [40] 2010
Australia 1st community 2.8 1.20 1.0–1.4 effective laboratory confirmed illness [40] 2010
Australia 1st community 2.9 2.40 2.3–2.4 effective laboratory confirmed illness [41] 2009
Australia, rural 1st community 2.9 1.28 1.26–1.30
effective laboratory confirmed illness [42] 2011
Australia, urban 1st community 2.9 1.26 1.22–1.30
effective laboratory confirmed illness [42] 2011
Canada 1st community 1.91 1.30 1.12–1.47
basic laboratory confirmed illness [43] 2010
Canada 1st community 2.78 2.21 1.98–2.50
basic laboratory confirmed illness [44] 2012
Canada 1st community 3.6 1.63 1.31–1.96
basic laboratory confirmed illness [43] 2010
Canada 1st community 4.31 1.31 1.25–1.38
basic laboratory confirmed illness [45] 2010
Chile 1st community 2.5 1.80 1.6–2.0 effective unconfirmed emergency room visits [46] 2010
Chile, central 1st community 3 1.32 1.27–1.37
effective unconfirmed hospitalizations [47] 2012
Chile, northern 1st community 3 1.19 1.13–1.24
effective unconfirmed hospitalizations [47] 2012
Chile, southern 1st community 3 1.58 1.45–1.72
effective unconfirmed hospitalizations [47] 2012
China 1st community 2.6 1.25 1.22–1.28
effective laboratory confirmed illness [48] 2012
China 1st community 4.31 1.53 1.45–1.60
basic laboratory confirmed illness [49] 2012
China 1st community NR 1.68 basic laboratory confirmed illness [50] 2011
Hong Kong 1st community 3 1.70 1.6–1.8 effective laboratory confirmed illness [51] 2010
Hong Kong 1st community 3.2 1.45 1.4–1.5 effective laboratory confirmed illness [52] 2010
Israel 1st community 2.92 1.06 0.97–1.16
effective laboratory confirmed illness [53] 2011
Italy 1st community 2.6 1.30 1.23–1.32
effective unconfirmed illness [54] 2012
Japan 1st school 1.9 2.30 2.0–2.6 effective laboratory confirmed illness [55] 2009
Japan 1st community 2.7 1.28 1.23–1.33
effective laboratory confirmed illness [55] 2009
Mexico 1st community 1.91 1.58 1.34–2.04
basic unconfirmed illness [56] 2009
Mexico 1st community 1.96 1.42 basic unconfirmed illness [57] 2010
Mexico 1st community 2.6 1.40 1.2–1.9 basic laboratory confirmed illness [56] 2009
Mexico 1st community 2.6 1.22 1.05–1.60
basic laboratory confirmed illness [56] 2009
Mexico 1st community 3 1.80 1.78–1.81
effective unconfirmed illness [58] 2011
Mexico 1st community 3 1.43 1.29–1.57
effective laboratory confirmed illness [59] 2009
Mexico 1st community 3.1 2.20 2.1–2.4 effective laboratory confirmed illness [60] 2009
Mexico 1st community 3.5 2.30 2.1–2.5 basic laboratory confirmed illness [11] 2009
Mexico 1st community 3.6 1.75 1.6–1.9 basic seeding from Mexico [61] 2009
Mexico 1st community 4.1 3.10 2.9–3.5 effective laboratory confirmed illness [60] 2009
Mexico City 1st community 3 1.72 basic laboratory confirmed illness [62] 2009
Morocco 1st community 2.3 1.44 1.32–1.56
basic laboratory confirmed illness [63] 2012
Morocco 1st community 2.7 1.40 1.34–1.48
basic laboratory confirmed illness [63] 2012
Netherlands 1st community 3 0.50 effective laboratory confirmed illness [64] 2009
New Zealand 1st community 2.7 1.25 1.07–1.47
effective laboratory confirmed illness [65] 2011
New Zealand 1st community 2.8 1.96 1.80–2.15
effective laboratory confirmed illness [66] 2009
New Zealand 1st community 2.8 1.55 1.16–1.86
effective laboratory confirmed illness; unconfirmed illness [67] 2010
North America 1st community 2.7 1.3–2.1 basic laboratory confirmed illness [68] 2010
Peru 1st community 2.8 1.37 1.33–1.41
effective laboratory confirmed illness [69] 2009
Peru 1st community 3 1.30 1.3–1.3 effective unconfirmed illness [70] 2011
Peru, Lima 1st community 3 1.70 1.6–1.7 effective unconfirmed illness [70] 2011
Singapore 1
st
dance club 1.91 1.9–2.1 basic laboratory confirmed illness [71] 2010
Singapore 1st military NR 1.91 1.50–2.36
effective laboratory confirmed and unconfirmed illness [72] 2010
South Africa 1st community 2.3 1.43 1.38–1.49
effective laboratory confirmed illness [73] 2012
South Africa 1st community 2.78 1.47 1.30–1.72
effective laboratory confirmed illness [73] 2012
South Africa 1st community 2.78 1.42 1.20–1.71
effective laboratory confirmed illness [73] 2012
Southern Hemisphere 1st community 1.9 1.16–1.53 effective laboratory confirmed illness [74] 2010
Southern Hemisphere 1st community 2.60 1.33 1.28–1.45
basic laboratory confirmed and unconfirmed illness [75] 2011
Taiwan 1st community 1.91 1.14 1.04–1.25
effective laboratory confirmed illness [76] 2011
Taiwan 1st community NR 1.16 0.98–1.34
effective serology confirmed infection [77] 2011
Thailand 1st community 1.9 1.78 1.67–1.89
basic laboratory confirmed illness [78] 2009
Thailand 1st community 2.6 2.07 1.92–2.22
basic laboratory confirmed illness [78] 2009
United Kingdom 1st school 2.2 1.33 1.11–1.56
effective laboratory confirmed illness [79] 2012
United Kingdom 1st community 2.5 1.44 1.27–1.63
effective laboratory confirmed illness [80] 2009
USA 1st community 2.2 1.70 1.4–2.1 basic laboratory confirmed illness [29] 2009
USA 1st community 2.6 2.20 1.4–2.5 basic laboratory confirmed illness [29] 2009
USA 1st school 2.7 3.30 3.0–3.6 effective unconfirmed illness [31] 2009
USA 1st community 3.5 1.3–2.0 1.0–2.2 basic laboratory confirmed illness [11] 2009
USA 1st camp attendees 7 2.20 1.4–3.3 effective unconfirmed illness [81] 2011
Vietnam 1st community 1.9 1.50 1.5–1.6 basic laboratory confirmed illness [82] 2010
Vietnam 1st community 3.6 2.00 1.9–2.2 basic laboratory confirmed illness [82] 2010
worldwide 1st community 2.67 1–2 effective laboratory confirmed illness [83] 2011
China 2nd community 4 1.66 1.27–2.05
effective confirmed hospitalizations [84] 2012
China 2nd community 4.3 1.70 1.4–1.9 effective laboratory confirmed illness [85] 2010
France 2nd military 2.9 1.5–1.6 effective unconfirmed illness [86] 2012
Iran 2nd school NR 1.28 1.05–1.54
basic unconfirmed illness [87] 2012
Italy 2nd community 2.5 1.33 effective unconfirmed illness [88] 2011
Japan 2nd community 3 1.48 1.41–1.56
effective unconfirmed illness [89] 2012
Mexico 2nd community 3 1.62 1.61–1.63
effective unconfirmed illness [58] 2011
Reunion Island 2nd community 2.8 1.26 1.08–1.49
effective unconfirmed illness [90] 2010
Taiwan 2nd community 1.91 1.02 1.01–1.02
effective laboratory confirmed illness [76] 2011
Taiwan 2nd community NR 1.87 1.68–2.06
effective serology confirmed infection [77] 2011
United Kingdom 2nd community 2.5 1.30 1.2–1.5 effective laboratory confirmed illness [91] 2010
Mexico 3rd community 3 1.24 1.23–1.24
effective unconfirmed illness [58] 2011
various community NR 1.30 1.1–1.4 effective serology confirmed infection [92] 2012
Median reproduction number for the 2009 pandemic: 1.46; Interquartile range 1.30–1.70
a The 2009 influenza A/H1N1 pandemic began in Mexico in the late winter or early spring of 2009 [19]. The United States and the United Kingdom experienced a first wave of illnesses in the Spring of 2009 followed
by a second wave during the Fall of 2009 [4]. However, unlike these three countries, a number of countries, especially in the Southern Hemisphere, only experienced a single wave of illnesses [20].
b The generation time (GT) or serial interval (SI) assumed in the reproduction number estimation.
c Confidence interval.
NR = Not reported.
This table is also available as a .csv file as Additional file 4.
Table 5 Reproduction Numbers from Seasonal Influenza Epidemics
Year(s) Type/Subtype Study Population
Mean GT/SI
a
Reproduction Number (R) 95% CI
b
Basic or Effective Case definition Reference Year Published
1889–1890 H3N8? USA & Europe 2.6 2.10 1.9–2.4 basic unconfirmed deaths [93] 2010
1948–1949 H1N1 Canada 4.1 1.30 effective unconfirmed illness [36] 2006
1949–1950 H1N1 Canada 4.1 1.50 effective unconfirmed illness [36] 2006
1950–1951 H1N1 Canada & UK 4.1 2.00 1.9–2.5 effective unconfirmed deaths [36] 2006
1958–1973 H2N2; H3N2; B United Kingdom 4.48 3.9–7.1 effective unconfirmed illness [94] 1979
1972–2002 H1N1/H3N2/B Australia 5.5 1.30 effective unconfirmed deaths [95] 2008
1972–2002 H1N1/H3N2/B France 5.5 1.30 effective unconfirmed deaths [95] 2008
1972–2002 H1N1/H3N2/B USA 5.5 1.30 effective unconfirmed deaths [95] 2008
1972–2002 H1N1/H3N2/B USA; France; Australia 5.5 1.30 1.2–1.4 effective unconfirmed deaths [95] 2008
1975–2004 H1N1/H3N2/B Norway 6 1.06–1.69 effective unconfirmed deaths [96] 2010
1976–1981 H1N1/H3N2/B USA 2.6 1.70 basic serology confirmed infection [10] 2006
1976–1981 H1N1/H3N2/B USA 4.1 1.16 basic serology confirmed infection [97] 2000
1977–1978 H1N1 United Kingdom 2.2 4.38 basic unconfirmed illness [33] 2005
1977–1978 H1N1 United Kingdom 3 21.00 basic unconfirmed illness [13] 2004
1977–1978 H1N1 United Kingdom 4.70 16.90 basic unconfirmed illness [33] 2005
1984–1985 H1N1/H3N2 France 2.49 1.37 effective unconfirmed illness [98] 1988
1985–2005 H1N1/H3N2/B United Kingdom 2.2 1.6–2.1 basic unconfirmed illness [99] 2010
1985–2005 H1N1/H3N2/B United Kingdom 2.7 1.6–2.5 basic unconfirmed illness [100] 2012
1985–2006 H1N1/H3N2/B France 2.4 1.4–1.7 1.3–1.8 basic unconfirmed illness [101] 2008
1996–2006 H1N1/H3N2/B Brazil 3 1.03 1.02–1.04 effective unconfirmed deaths [102] 2010
1998–1999 H3N2 Israel 3 1.14 effective unconfirmed illness [103] 2011
1998–1999 H3N2 Israel 3 1.16 effective unconfirmed illness [103] 2011
1998–1999 H3N2 USA 3 1.18 1.05–1.25 effective laboratory confirmed illness [104] 2009
1998–2009 H1N1/H3N2/B Israel 2.5 1.17–1.62 effective unconfirmed illness [105] 2012
1999–2000 H3N2 Israel 3 1.16 effective unconfirmed illness [103] 2011
1999–2000 H3N2 Israel 3 1.18 effective unconfirmed illness [103] 2011
1999–2006 seasonal H1N1 Taiwan 2 1.19 0.76–1.86 basic confirmed and unconfirmed illness [106] 2010
1999–2006 H3N2 Taiwan 3 1.41 0.92–2.19 basic confirmed and unconfirmed illness [106] 2010
1999–2006 B Taiwan 3 1.07 0.69–1.69 basic confirmed and unconfirmed illness [106] 2010
2000–2001 H1N1 Israel 3 1.12 effective unconfirmed illness [103] 2011
2000–2009 H1N1/H3N2/B Italy 4 1.17–1.36 effective unconfirmed illness [107] 2012
2001–2002 H3N2 Israel 3 1.25 effective unconfirmed illness [103] 2011
2001–2002 H3N2 Israel 3 1.27 effective unconfirmed illness [103] 2011
2003–2004 H3N2 Israel 3 1.19 effective unconfirmed illness [103] 2011
2003–2004 H3N2 Israel 3 1.21 effective unconfirmed illness [103] 2011
2003–2004 H3N2 Switzerland 2.6 1.2–1.3 effective unconfirmed illness [108] 2011
2004–2005 H3N2 Israel 3 1.25 effective unconfirmed illness [103] 2011
2004–2005 H3N2 Israel 3 1.25 effective unconfirmed illness [103] 2011
2004–2005 unspecified Taiwan 4.1 1.00 effective unconfirmed deaths [109] 2010
2004–2005 H3N2 USA 7 1.20 1.1–1.3 effective laboratory confirmed illness [110] 2008
2006–2007 H3N2 Israel 3 1.28 effective unconfirmed illness [103] 2011
2006–2007 H3N2 Israel 3 1.33 effective unconfirmed illness [103] 2011
2007–2008 H3N2 Israel 3 1.25 effective unconfirmed illness [103] 2011
2007–2008 H3N2 Israel 3 1.29 effective unconfirmed illness [103] 2011
2011/12 H1N1 Mexico 3 1.20 effective laboratory confirmed hospitalizations [111] 2012
2011/12 H1N1 Mexico 3 1.20 effective laboratory confirmed hospitalizations [112] 2012
2011/12 H1N1 Mexico 4 1.30 effective laboratory confirmed hospitalizations [112] 2012
Median reproduction number for seasonal influenza: 1.28; Interquartile range 1.19–1.37
a The generation time (GT) or serial interval (SI) assumed in the reproduction number estimation
b Confidence interval
NR = Not reported
This table is also available as a .csv file as Additional file 5.
Table 6 Reproduction Numbers from Novel Influenza Outbreaks
Year(s) Subtype
Study Population
Mean GT/SI
a
Reproduction Number (R)
95% CI
b
Basic or Effective
Case definition
Reference
Year Published
1976 H1N1 New Jersey 1.9 1.20 1.1–1.4 basic serologically confirmed illness
[23] 2007
2004–2006
H5N1 Vietnam 7 0.00 0–0.42 effective laboratory confirmed illness [110] 2008
2004–2006
H5N1 Indonesia 7 0.00 0–0 effective laboratory confirmed illness [110] 2008
2005 H5N1 Turkey 9.5 <1 basic laboratory confirmed illness [113] 2007
2005–2009
H5N1 Indonesia 6 0.1–0.25 0–0.4 effective laboratory confirmed illness [114] 2012
2006 H5N1 Indonesia 9.5 1.14 0.61–2.14
basic laboratory confirmed illness [113] 2007
Median reproduction number for novel influenza outbreaks: 0.34; Interquartile range 0.05–0.98
a The generation time (GT) or serial interval (SI) assumed in the reproduction number estimation.
NR = Not reported.
b Confidence interval.
This table is also available as a .csv file as Additional file 6.
1918 influenza pandemic
The origins of the 1918 influenza A/H1N1 pandemic are unknown, and illnesses are thought
to have occurred in three waves [1,15]. The first wave began in the Northern Hemisphere in
the spring 1918 [1]. A second wave of more intense transmission occurred concurrently in
North America, Europe, and Africa in fall 1918, and a third and final wave occurred in some
areas of the world during winter 1919 [15,16]. The 1918 pandemic was the most deadly
pandemic ever recorded, and an estimated 675,000 deaths occurred in the United States
during the pandemic period. In contrast to seasonal influenza, which disproportionately
affects the very young and old, those aged 20–40 years were especially affected [15].
Twenty-four studies reported 51 separate 1918 pandemic values of R (Table 1; Figure 2). The
median point estimate of R in the community setting for all waves of illness was 1.80 (IQR:
1.47–2.27) (Table 1). A higher median R value (R = 3.82; IQR: 2.68–4.84) was reported in
confined settings, such as ships, military camps, and schools. The median values of R were
similar between the first and subsequent waves of illness: the median value of R was 1.81
(IQR: 1.50–2.28) for the 1st wave, 1.73 (IQR: 1.39–2.33) for the second wave, and 1.70 (IQR:
1.55–1.76) for the third wave (Table 1).
Figure 2 Estimates of the reproduction number for the 1918 influenza A/H1N1
pandemic according to location, wave of illness, setting, and the serial interval or
generation time assumed in the estimation method. For individual studies, the single
estimate or median of multiple estimates is shown as a circle for basic reproduction numbers
or a square for effective reproduction numbers, and the range or confidence interval is
denoted by brackets. Estimates of the reproduction number are color coded based on the
generation time or serial interval used in calculations: red (<3 days), blue (3 days), or black
(not reported or not used).
The majority of 1918 pandemic values for R were calculated for populations in Europe,
which accounted for 58% of the R estimates included in this analysis. The mean generation
time or serial interval used in the calculations to estimate R had a median value of 3.3 days,
and the mean ranged from 1.5–6 days. Because the influenza virus was not discovered until
1931[1], all studies included in this review relied on reports of unconfirmed illness to identify
those ill. A majority (65%) used pneumonia-and-influenza-related hospitalizations and deaths
as the case ascertainment source (Table 1).
1957 influenza pandemic
The 1957 influenza A/H2N2 pandemic began in February 1957 in southern China and spread
to Singapore and Hong Kong in April[1]. The virus was first isolated in the United States in
June 1957 and was associated with a first wave [1,17]. The peak of the pandemic occurred
during the second wave in the Northern Hemisphere in October 1957 and was followed by a
third wave in January 1958. An estimated 115,000 deaths occurred in the United States
during the pandemic period [15].
Six studies reported seven separate 1957 pandemic values of R (Table 2; Figure 3). The
median point estimate of R in the community setting for the second wave of illnesses was
1.65 (IQR: 1.53–1.70). No R values were reported for confined settings or for the 1st or 3rd
waves of illness.
Figure 3 Estimates of the reproduction number for the 1957 influenza A/H2N2 and the
1968 influenza A/H3N2 pandemics according to location, wave of illness, setting, and the
serial interval or generation time assumed in the estimation method. For individual
studies, the single estimate or median of multiple estimates is shown as a circle for basic
reproduction numbers or a square for effective reproduction numbers, and the range or
confidence interval is denoted by brackets. Estimates of the reproduction number are color
coded based on the generation time or serial interval used in calculations: red (<3 days), blue
(3 days), or black (not reported or not used).
A majority (86%) of 1957 pandemic R values were calculated for populations in Europe. The
mean generation time or serial interval used in the calculations to determine R had a median
value of 3.5 days, and the mean ranged from 2.6–4.1 days. All studies but one included in this
review relied on an unconfirmed illnesses to identify those ill. The other study relied on the
final attack rate as determined by serological methods (Table 2).
1968 influenza pandemic
The 1968 influenza A/H3N2 pandemic began in Hong Kong in July 1968. Large single
waves were reported in the Northern Hemisphere between September 1968 and April 1969
(with peaks occurring in December and January) and in the Southern Hemisphere between
June and September 1969. Some countries in the Northern Hemisphere, such as the United
Kingdom, did not have an outbreak of H3N2 until the winter of 1969–70. In all, an estimated
110,000 deaths occurred in the United States during the pandemic period [15].
Four studies reported seven separate 1968 pandemic values of R (Table 3; Figure 3). The
median point estimate of R in the community setting for all waves of illness was 1.80 (IQR:
1.56–1.85) (Table 3). Only two values for R in confined settings were reported, and the
median value was 1.39. Two values of R were reported in a community setting during the
first wave and three during the second wave. The median value of R during the 1st wave was
1.56 and 1.68 during the 2nd wave (Table 3).
The 1968 pandemic values for R were calculated among populations in diverse geographic
locations, mainly because of one study that calculated separate values for over 25 locations,
such as Africa, Asia, and South America (the overall estimate for R is included in Table 3)
[18]. The mean generation time or serial interval used in the calculations to determine R had
a median value of 4 days with little variation. The studies for the 1968 pandemic included in
this review relied on a mix of laboratory-confirmed, unconfirmed illnesses, or serologically-
confirmed infections to identify those ill (Table 3).
The 2009 influenza pandemic
The 2009 influenza A/H1N1 pandemic began in Mexico in the late winter or early spring
2009 [19]. The United States and the United Kingdom experienced a first wave of illnesses in
the spring followed by a second wave during the fall [4]. However, a number of other
countries, especially in the Southern Hemisphere, only experienced a single wave of illnesses
[20]. In all, an estimated 12,000 deaths occurred in the United States during the first year of
pandemic circulation [21].
Fifty-seven studies reported 78 separate 2009 pandemic values of R (Table 4; Figure 4). The
median point estimate of R in the community setting for all waves of illness was 1.46 (IQR:
1.30–1.70) while a higher median R value (R = 1.96; IQR: 1.50–2.23) was reported in
confined settings, such as military or summer camps, schools, and night clubs. The value of R
was similar across the two distinct waves of illness: the median value of R was 1.47 (IQR:
1.31–1.71) for the first wave and 1.48 (IQR: 1.30–1.66) for the second wave (Table 4).
Figure 4 Estimates of the reproduction number for the 2009 Influenza A/H1N1
pandemic according to location, wave of illness, setting, and the serial interval or
generation time assumed in the estimation method. For individual studies, the single
estimate or median of multiple estimates is shown as a circle for basic reproduction numbers
or a square for effective reproduction numbers, and the range or confidence interval is
denoted by brackets. Estimates of the reproduction number are color coded based on the
generation time or serial interval used in calculations: red (<3 days), blue (3 days), or black
(not reported or not used).
A majority of 2009 pandemic values for R were calculated for populations in North America
(30%) and Asia (26%). The mean generation time or serial interval used in the calculations to
determine R had a median value of 2.8 days, and the mean ranged from 1.9–7 days (Table 4).
A majority of the studies included for the 2009 pandemic relied on either laboratory-
confirmed illnesses (71%) or unconfirmed illnesses (24%) to identify those ill (Table 4).
Seasonal influenza
Seasonal influenza causes sustained epidemics in the non-tropical areas of the Northern
Hemisphere and Southern Hemisphere during their respective late fall to early spring months.
Epidemics in the tropical regions occur sporadically but can be associated with the rainy
season [1]. The mortality burden from influenza varies by season, and from 1976–2007,
estimates of annual influenza-associated deaths in the United States from respiratory and
circulatory causes ranged from 3,000 to 49,000 [3].
Twenty-four studies reported 47 separate seasonal epidemic values of R (Table 5; Figure 5).
The median point estimate of R in the community setting for seasonal influenza was 1.27
(IQR: 1.19–1.37) while a higher median R value (R = 16.9) was reported in a British
boarding school during the 1977–78 influenza season (Table 5). R values for seasons where
H3N2 (R = 1.25; IQR: 1.18–1.27) or H1N1 (R = 1.25; IQR: 1.18–1.35) predominated were
equivalent (Table 5).
Figure 5 Estimates of the reproduction number in the community for seasonal influenza
epidemics according to location, wave of illness, and the serial interval or generation
time assumed in the estimation method. For individual studies, the single estimate or
median of multiple estimates is shown as a circle for basic reproduction numbers or a square
for effective reproduction numbers, and the range or confidence interval is denoted by
brackets. Estimates of the reproduction number are color coded based on the generation time
or serial interval used in calculations: red (<3 days), blue (3 days), or black (not reported or
not used).
A majority of seasonal influenza values for R were calculated for populations in Israel (35%),
Europe (25%), and North America (21%). The mean generation time or serial interval used in
the calculations to determine R had a median value of 3.0 days, and the mean ranged from
2.0–7.0 days (Table 5). A majority of the studies included for seasonal influenza relied on
unconfirmed illnesses or deaths (79%); the reminder relied on either laboratory-confirmed
illnesses or hospitalizations or serologically-confirmed infections (Table 5).
Human infections with non-human influenza viruses
Human infections with novel or non-human influenza viruses (also known as zoonotic
influenza viruses) are rare but can result in a pandemic if sustained person-to-person
transmission occurs and the population has little or no pre-existing population immunity to
the virus. Therefore, instances of infection with non-human influenza viruses are investigated
thoroughly to assess the transmissibility of the virus. The largest number of novel influenza
cases at the time of this review was from the ongoing influenza A/H5N1 outbreak centered in
Southeast Asia and the Middle East. From January, 1, 2003 to February 15, 2013, 620
laboratory-confirmed cases have been reported to the WHO, of which 367 have died [22].
Another large outbreak of novel influenza occurred in 1976 in Fort Dix, New Jersey, which
was caused by an influenza A/H1N1 virus similar to those found circulating in swine [23].
Four studies estimated the values of R for the A/H5N1 and A/H1N1 outbreaks (Table 6).
Four out of six estimates (67%) of R were less than one, and the highest R estimate (R = 1.2)
was for the 1976 A/H1N1 outbreak in a New Jersey military camp (a confined setting) (Table
6).
A majority of novel A virus R values were calculated for populations in Southeast Asia
(67%), indicative of where the bulk of A/H5N1 bird-to-human transmission occurs. The
mean generation time or serial interval used in the calculations to determine R had a median
value of 7.0 days, and the mean ranged from 1.9–9.5 days (Table 6). All studies relied on
either laboratory-confirmed illness or serological-confirmed infection (Table 6).
Discussion
In this review, the median R values reported for the four pandemics and seasonal influenza
varied between 1.27–1.8 while R values for novel influenza were generally below 1. We
found the highest median reproduction number associated with the 1918 and the 1968
influenza pandemics (both 1.8), followed by the 1957 pandemic (1.65), the 2009 pandemic
(1.46), seasonal influenza epidemics (1.27), and novel influenza outbreaks. A majority of R
values published were for either the 1918 pandemic or the 2009 pandemic; the 1957 and 1968
pandemics had the fewest published studies. Researchers calculated values for R for a variety
of locations and utilized many different case definitions, ascertainment methods, and
assumptions about the generation time or serial interval.
The approximate basic reproductive numbers for some common infectious diseases range
from 12–18 for measles, 12–17 for pertussis, and 4–7 for mumps, polio, rubella, and
smallpox [12]. These values are much higher than what has been reported for influenza, and
most R values reported in this review ranged from 1.0–2.0. However, the overall clinical
attack rate and peak daily incidence of an outbreak, which measures the potential burden on
healthcare services and school and workplace absenteeism, are very sensitive to changes in
the value of R within this range. Past research utilizing a number of assumptions on the
symptomatic ratio, contact patterns, and seeding has estimated that the cumulative clinical
attack rates for a pandemic when R = 1.3 ranged from 15%–21% and increased to 34%–42%
for R = 2.0 [10,11]. Similarly, the peak daily attack rate is 0.5% for R = 1.3 and 2.2% for R =
2.0 [10]. Therefore, with only an absolute difference in R of 0.7, the clinical attack rates in
these studies more than doubled and the peak daily incidence more than quadrupled.
Differences in the value of R within this range also affect the evaluation of potential
mitigation strategies (e.g., school closures, vaccination, household isolation) for influenza
pandemics. Analysis of strategies to mitigate an influenza pandemic have found that the
effectiveness of non-travel-related control policies, such as school closures, household
quarantine, and vaccination, would decrease as the value of R increases from 1.0 to 2.0 [10].
The success of various vaccination strategies would also be more likely for values of R < 1.7
[10,11]. Therefore, the small variations in pandemic R estimates found in this analysis can
have important implications for the overall impact and success of mitigation efforts for an
influenza pandemic. This finding highlights the importance of making precise estimates of R
early in a pandemic. Further research should focus on refining methods that allow for early,
robust estimates of R.
The results of this analysis reinforce the idea that R is a measure that captures the
transmissibility of an influenza virus in the population under study and is not an intrinsic
value. The inputs for its calculation can include the population contact rate, the probability of
infection per contact, the duration of illness, and the percentage of the population that is
susceptible which is affected by the characteristics of the population under study. Therefore,
the variations in the value for R for the same pandemic or seasonal outbreak are expected and
may be due to the underlying social and socio-demographic factors of the population studied,
public health interventions, and geographical or climatic factors of the location. These
variations include the percentage of the source’s population under 18 years old; differences in
contact patterns between age groups, which vary by country [24,25]; and differences in
population susceptibility profiles, which varied by age group for the 2009 pandemic [26].
Another important factor that may contribute to the variation is the season from which data
used to estimate R is collected. While the effect of weather on the transmissibility of
influenza has not been fully explored, some studies have shown that the level of absolute
humidity is inversely correlated with influenza transmissibility [27,28]. Therefore, estimates
of R should be interpreted in the context of the population under study and the season in
which data was collected and direct comparisons of R between populations should be
undertaken with caution.
Variations in the estimated values of R may also be driven by changes in surveillance
intensity in the same country over time. If a country suddenly improves its surveillance
system in response to a pandemic and is better able to identify cases, then the number of
cases being reported will increase, even though the actual number of cases occurring will not
have changed. This increase in the reported number of cases may increase the estimated R as
the growth rate of the outbreak will increase [29]. Conversely, the value of R could be
artificially lowered if countries implement changes in surveillance practices that result in a
lower number of identified cases, such as reducing screening recommendations, or have their
surveillance systems overwhelmed. This effect was seen in the United States during the 2009
pandemic, when influenza testing for every case became unfeasible and testing
recommendations were changed [4].
One of the more important methodological assumptions that can have a large impact on the
estimated value of R is the length of the serial interval or generation time used during the
estimation of R. Longer serial intervals have previously been associated with higher estimates
of R when compared to estimates from the same dataset using shorter serial intervals [9]. In
this analysis, estimates of R from the 1918, 1957, and 1968 pandemics utilized higher serial
interval values than were used for the 2009 pandemic or for seasonal influenza. Additionally,
higher values of R from the 2009 pandemic often were estimated using a generation time or
serial interval of 3 days or more (Figure 4). Therefore, the estimates of R included in this
analysis should be interpreted in the context of the serial intervals or generation times used in
the estimation method. Like R, the values for the generation time or the serial interval can
vary by the source population. Therefore, researchers estimating the values of R should strive
to use standard estimates of the serial interval or generation time for influenza or at least
include common values in a sensitivity analysis. This will help with the comparability of R
values across studies and may aid in the correct interpretation of R estimates. An additional
way in which estimates of R may be biased up or down lies in the choice of estimation
procedure itself. Chowell et al. showed that estimates of R obtained using simple epidemic
mathematical models varied considerably as the model increased in complexity (e.g. the
addition of a period of infection latency or an age-structured population) [30].
Although we found no difference in the value of R for studies using confirmed cases versus
unconfirmed cases in the estimation method, the trade-off between the accuracy of the less
specific but more efficient and cost effective syndromic data compared to laboratory-
confirmed influenza infections is unknown. The incubation periods of non-influenza
respiratory pathogens that co-circulate with influenza (e.g. respiratory syncytial virus or
rhinovirus) range from a median of 1.9–5.6 days; estimates of R for influenza could either be
overestimated or underestimated during periods of co-circulation, depending on the intensity
and identity of the co-circulating respiratory pathogen [31]. Future research should focus on
estimation of R using laboratory-confirmed cases and hospitalizations and should provide
estimates from syndromic data for comparison.
Most studies included in this analysis focused on 1918 or the 2009 pandemic. Only a small
number of estimates of the reproduction number have been reported for the two other
pandemics of the 20th century (1957 and 1968). As a consequence, there is still insufficient
information to fully clarify the transmission dynamics of the 1957 and 1968 pandemics.
Because historical data are available for these pandemics, future research should focus on
estimations of R for the 1957 and 1968 pandemics to better understand the characteristics of
these pandemics.
This study generally found higher reproduction numbers for confined settings, such as
schools, military bases, or night clubs, except for estimates from the 1968 pandemic. Because
confined settings increase the intensity of transmission by increasing contact rates among
those ill and well, the values of R presented for outbreaks in confined settings are likely to be
much higher than values of R estimated for the community and should be interpreted
accordingly. While the estimation of R in confined settings may be useful for the assessment
of the upper bounds of transmissibility, its value is not directly comparable to estimates of R
made in the community setting.
This review found, with one exception, a high degree of consistency in the estimated values
of R for seasonal influenza epidemics. The only notable exception was the extremely high R
values estimated for an outbreak of influenza A (H1N1) in 1978 at a small British boarding
school with 763 male students aged 10–18 who were mostly full boarders [32]. The results of
this analysis suggest that the extreme R values reported for this outbreak are not typical of
seasonal or pandemic influenza and instead may be the result of the lack of pre-existing
immunity among the students to the strain of influenza A (H1N1) that caused the outbreak,
the extremely high contact rates likely among a group of boarded students, or a study artifact
related to the small number of students in the study population [13,32,33]. Additionally, the
median R value of seasonal influenza (R = 1.27) is well below the median values seen during
the four pandemics examined in this report. The consistency of seasonal R values is even
more remarkable given the wide variety of estimation methods, data sources, and
assumptions used in the studies included here. However, the majorities of seasonal influenza
estimates were from a small number of countries. Estimates of R from countries in Africa,
Asia, and South America are also needed to determine if values of R for seasonal influenza
epidemics are affected by geographic and social factors.
This systematic review is subject to at least three limitations. First, we combined estimates
for the basic and effective reproductive numbers when presenting the median estimates in this
study. Even though these values measure transmission in populations with differing levels of
underlying population immunity, some papers included in this review did not clearly
differentiate between basic and effective reproductive numbers or state the required
population immunity assumptions when reporting basic reproductive numbers. Therefore, we
choose to present summary values for the basic and effective reproductive numbers together
to simplify the results. The tables include whether the reproductive number estimate was
reported as basic or effective for each study. Second, we did not assess included studies for
the type or quality of their methodology or the risk of study bias. Finally, we only included
published estimates of the reproductive number, which may not be representative of
unpublished reproductive number values.
Conclusions
In this review, we explored the ranges and uncertainty of the values of R estimated for
seasonal, pandemic, and novel influenza. We found that values of R changed over the course
of a pandemic but the effect of the waves varied. The value of R is not constant and may be
affected by mitigation strategies, the season, and the population under study. The values of R
found in this analysis represent the difference between a pandemic that is controllable with
less intensive mitigation strategies and would cause moderate amounts of illness to a
pandemic that would require very intensive mitigation strategies and would cause greater
amounts of illness. Continued monitoring of R during outbreaks of human infections with
non-human influenza viruses and in various settings throughout future pandemics will be
required to fully understand the effects of mitigation, geography, and season.
Competing interest
The authors declare that they have no financial or non-financial competing interests with the
publication of this manuscript.
Authors’ contributions
MB led the data collection, analysis, and the writing of the article. SC led the editing of the
article and assisted with data interpretation. CR and MG contributed significantly to data
interpretation and reviewed multiple drafts of the article. LF contributed to the design of the
study, data interpretation, and reviewed multiple drafts of the article. All authors read and
approved the final manuscript.
Acknowledgements
We are particularly grateful for the assistance in the preparation and editing of the manuscript
given by Alejandro Perez and Dr. Claudia Campbell.
Disclaimer
The findings and conclusions in this report are those of the authors and do not necessarily
represent the official position of the Centers for Disease Control and Prevention.
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Additional files
Additional_file_1 as CSV
Additional file 1 Reproduction Numbers from the 1918 Influenza A/H1N1 Pandemic.
Additional_file_2 as CSV
Additional file 2 Reproduction Numbers from the 1957 Influenza A/H2N2 Pandemic.
Additional_file_3 as CSV
Additional file 3 Reproduction Numbers from the 1968 Influenza A/H3N2 Pandemic.
Additional_file_4 as CSV
Additional file 4 Reproduction Numbers from the 2009 Influenza A/H1N1 Pandemic.
Additional_file_5 as CSV
Additional file 5 Reproduction Numbers from Seasonal Influenza Epidemics.
Additional_file_6 as CSV
Additional file 6 Reproduction Numbers from Novel Influenza Outbreaks.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Additional files provided with this submission:
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Additional file 2: 1087449605126674_add2.csv, 1K
http://www.biomedcentral.com/imedia/6171285401416534/supp2.csv
Additional file 3: 1087449605126674_add3.csv, 1K
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Additional file 4: 1087449605126674_add4.csv, 9K
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Additional file 5: 1087449605126674_add5.csv, 5K
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Additional file 6: 1087449605126674_add6.csv, 1K
http://www.biomedcentral.com/imedia/1526934972141653/supp6.csv
... The R0 figure for Covid-19 has been estimated to be 2.63, and thus means that each case or person infected with COVID-19 will infect on average 2.63 people [8]. The R0 figure of COVID-19 (2.63) is much higher than that of the seasonal influenzas which have a median R0 of 1.65 [9]. In the early phases of the pandemic a true definitive mode and route of transmission had not been established and thus further aided the spread of the virus as it compounded the difficulty in trying to contain and diminish the spread of the virus. ...
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Mauritius recorded its first three cases of SARS-CoV-2 on March 18, 2020, all of which had an international travel history. On March 23, 2020 a six-week complete national lockdown of the island nation which has since been praised by the WHO commenced. A total of 335 cases were detected until June 02, 2020 with a total of 10 deaths being recorded. The island nation has since been a COVID "safe" country, with no known community cases circulating within the general populous. The enhanced indomitability of the virus to survive on inanimate objects is believed to have caused the now current second wave of SARS-CoV-2 infections in Mauritius. Mauritius lost its COVID "safe" status after a cluster of SARS-CoV-2 cases were detected in a fruit importation enterprise. The virus is believed to have been imported on stock from another country, consequently infecting employees and causing an outbreak with multiple secondary clusters. The cases are expected to continue rising as further contact tracing takes place. The current situation developing in Mauritius should be used as a global case study as Mauritius is an example of how a nation can be shifted from COVID "safe" (with no circulating active cases within the general populous) to a full scale second wave of infections due to the importation and subsequent transmission of the virus through its importation. It is critical to assess what measures can be put into place to prevent such importation of the virus from occurring.
... Furthermore, the spread of the epidemic is similarly located in regions over time for different predominant influenza viruses 8 . For example, during the pandemic caused by the novel swine influenza variant A(H1N1)pdm in 2009, epidemic clustering and transmission similar to that of seasonal influenza was observed 8,[10][11][12][13][14][15][16][17] . This event highlighted the importance of understanding epidemic trends to estimate the risk of an epidemic. ...
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We analysed 2006–2016 national influenza surveillance data in Japan with regards to age-, sex-, and predominant virus-related epidemic patterns and the prevalence of serum influenza virus antibodies. We found a significant increase in influenza prevalence in both children (≤ 19 years old) and adults (≥ 20 years old) over time. The influenza prevalence was higher in children (0.33 [95% CI 0.26–0.40]) than in adults (0.09 [95% CI 0.07–0.11]). Additionally, the mean prevalence of antibodies for A(H1N1)pdm09 and A(H3N2) was significantly higher in children than in adults, whereas the mean prevalence of antibodies for B lineages was relatively low in both children and adults. There was a biennial cycle of the epidemic peak in children, which was associated with a relatively higher prevalence of B lineages. The female-to-male ratios of the influenza prevalence were significantly different in children (≤ 19 years old; 1.10 [95% CI:1.08–1.13]), adults (20–59 years old; 0.79 [95% CI 0.75–0.82]), and older adults (≥ 60 years old; 1.01 [95% CI 0.97–1.04]). The significant increase in influenza prevalence throughout the study period suggests a change of immunity to influenza infection. Long-term surveillance is important for developing a strategy to monitor, prevent and control for influenza epidemics.
... We have used the effective reproductive number (R eff ) to assess the transmissibility and risk of transmission of COVID-19 (27). R eff is the average number of secondary infections caused by an infected person during one period of infection after the implementation of an intervention. ...
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Background In September 2021, there was an outbreak of coronavirus disease 2019 (COVID-19) in Xiamen, China. Various non-pharmacological interventions (NPIs) and pharmacological interventions (PIs) have been implemented to prevent and control the spread of the disease. This study aimed to evaluate the effectiveness of various interventions and to identify priorities for the implementation of prevention and control measures. Methods The data of patients with COVID-19 were collected from 8 to 30 September 2021. A Susceptible-Exposed-Infectious-Recovered (SEIR) dynamics model was developed to fit the data and simulate the effectiveness of interventions (medical treatment, isolation, social distancing, masking, and vaccination) under different scenarios. The effective reproductive number ( R eff ) was used to assess the transmissibility and transmission risk. Results A total of 236 cases of COVID-19 were reported in Xiamen. The epidemic curve was divided into three phases ( R eff = 6.8, 1.5, and 0). Notably, the cumulative number of cases was reduced by 99.67% due to the preventive and control measures implemented by the local government. In the effective containment stage, the number of cases could be reduced to 115 by intensifying the implementation of interventions. The total number of cases ( TN ) could be reduced by 29.66–95.34% when patients voluntarily visit fever clinics. When only two or three of these measures are implemented, the simulated TN may be greater than the actual number. As four measures were taken simultaneously, the TN may be <100, which is 57.63% less than the actual number. The simultaneous implementation of five interventions could rapidly control the transmission and reduce the number of cases to fewer than 25. Conclusion With the joint efforts of the government and the public, the outbreak was controlled quickly and effectively. Authorities could promptly cut the transmission chain and control the spread of the disease when patients with fever voluntarily went to the hospital. The ultimate effect of controlling the outbreak through only one intervention was not obvious. The combined community control and mask wearing, along with other interventions, could lead to rapid control of the outbreak and ultimately lower the total number of cases. More importantly, this would mitigate the impact of the outbreak on society and socioeconomics.
... [3] As a comparison, the R 0 of the seasonal influenza is about half the one of the SARS-CoV-2 Alpha variant, being generally close to 1.3. [4] The measles virus is six and twelve times more infectious than most SARS-CoV-2 variants and influenza, respectively, and is among the most contagious pathogen worldwide. With an estimated R 0 often found in the 12-18 range, similar to what is estimated for the SARS-CoV-2 Omicron variant, [5,6] measles is an airborne virus that can be spread exceptionally easily among non-vaccinated people. ...
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Measles is one of the most infectious airborne viruses worldwide. With a basic reproduction rate between 12–18, this virus is six times more infectious than the SARS‐CoV‐2 Alpha variant and similar to the SARS‐CoV‐2 Omicron variant. Even though a cheap and effective vaccine is available, measles is still common in developing countries. To date, sporadic outbreaks are also reported in developed countries, primarily due to non‐vaccinated people. This work presents a point‐of‐care (POC) biosensing device capable of detecting measles virions (MV) in human saliva. The device is a surface‐acoustic‐wave (SAW) based lab‐on‐a‐chip (LOC), smaller than a €1‐cent coin, in which SAWs are used both for sensing and fluid recirculation. The biosensing detection performance of this system is tested and device sensitivity and selectivity are assessed. The SAW‐LOC with MV loaded in healthy, whole human saliva is finally validated. The experimental results also highlight a crucial aspect of the biosensing process: the interactions between probing and target species during incubation with or without fluid mixing. The presented findings are promising for realizing a POC platform for measles diagnosis and may serve as a guideline for designing new microfluidics‐based biosensing systems.
... In addition, a very high infection rate facilitates transmission to new hosts. For HMV the average number of secondary cases arising from a single infected individual is six times higher than for influenza viruses (Biggerstaff et al., 2014;Guerra et al., 2017;Sakai et al., 2013). However, immunity is cross protective making species jumping less likely if a population has previously been infected, even sub-clinically, by a related morbillivirus. ...
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This chapter highlights the potential of an integrated palaeopathological approach for unravelling the evolutionary history of shared human and animal pathogens. The transfer of pathogens between animals and humans has occurred for millennia and remains a public health issue today, as evidenced by the Covid-19 pandemic. Although the focus has traditionally centred on pathogens transmitted from animals to humans (zoonoses), the direction of transmission is also often the other way (reverse zoonoses). The best way to understand the cross-species transfer of pathogens is by investigating the conditions that make it possible for a new host, whether human or animal, to be infected. As societies transitioned from hunting and foraging to herding and farming, and from living in rural to urban settings, and in increasingly industrialised environments, dramatic changes occurred in their relationships with animals. These changes had major impacts on pathogen evolution. In particular, the new opportunities for transmission affected pathogen virulence and distribution. This chapter investigates the factors driving transmission of Taenia tapeworms, Mycobacteria, Plasmodium falciparum, Brucella, Burkholderia mallei and the morbilliviruses in domestic environments. It draws evidence from palaeopathology, historical sources, molecular analysis, paleoenvironmental data and medical/veterinary clinical literature. This approach helps explain why these pathogens became problematic when and where they did, and it identifies factors that are likely to contribute to future outbreaks. The chapter concludes with a reflection on the ways an interdisciplinary understanding of past diseases provides insights for current and future epidemics.
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Influenza and coronaviruses, zoonotic respiratory RNA viruses, cause global pandemics with major public health issues. These viruses exist as quasispecies due to the rapid evolution driven by their error-prone viral RNA polymerases and/or genomic organizations. They also show similar waves of infections/cases during the pandemic. However, there are some dissim-ilarities like severe disease in coronaviruses is due to cytokine-induced hyperactivity of the immune system, while secondary bacterial infection is a significant cause of death in influenza. Furthermore, unlike coronavirus, the segmented nature of influenza virus genome makes it easier for new strains to emerge through genetic reassortment, making its prevention and control more difficult. In this mini-review, we summarize the historical events of influenza and coronavirus pandemics or epidemics and the roles played by RNA viral genomes and pathogenesis in modulating viral evolution and generation of pandemic strains. Collectively, influenza and coronavirus diagnostics, vaccination, and other measures are critical for mitigating and controlling future pandemics. These pandemics might be regarded as a wake-up call to prepare us for future disasters.
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Since the emergence of COVID-19, concerted worldwide efforts have taken place to minimize global spread of the contagion. Its widespread effects have also facilitated evolution of new strains, such as the delta and omicron variants, which emerged toward the end of 2020 and 2021, respectively. While these variants appear to be no more deadly than the previous alpha, beta, and gamma strains, and widespread population vaccinations notwithstanding, greater virulence makes the challenge of minimizing spread even greater. One of the peculiarities of this virus is the extreme heath impacts, with the great majority of individuals minimally affected, even sometimes unaware of infection, while for a small minority, it is deadly or produces diverse long-term effects. Apart from vaccination, another approach has been an attempt to identify treatments, for those individuals for whom the virus represents a threat of particularly severe health impact(s). These treatments include anti-SARS-CoV-2 monoclonal antibodies, anticoagulant therapies, interleukin inhibitors, and anti-viral agents such as remdesivir. Nutritional factors are also under consideration, and a variety of clinical trials are showing promise for the use of specific fatty acids, or related compounds such as fat-soluble steroid vitamin D, to mitigate the more lethal aspects of COVID-19 by modulating inflammation and by anti-viral effects. Here we explore the potential protective role of fatty acids as a potential prophylactic as well as remedial treatment during viral infections, particularly COVID-19. We present a multiplexed method for the analysis of free and phospholipid bound fatty acids, which may facilitate research into the role of fatty acids as plasma biomarkers and therapeutic agents in minimizing pre- and post-infection health impacts.
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New antiviral influenza treatments can effectively alleviate illness while reducing viral shedding. However, how such effects can translate into lower population infections of seasonal influenza in China remains unknown. To shed light on the public health impacts of novel antiviral agents for influenza, we constructed a dynamic transmission model to simulate the seasonal influenza epidemics in China. Two antivirus treatments, baloxavir and oseltamivir, were evaluated by estimating their impacts on the incidences of influenza infection in a single flu season. In the base‐case analysis of 10% antiviral treatment uptake rate, 2,760 and 3,420 per 10,000 persons contracted influenza under the treatment of baloxavir and oseltamivir, respectively. These incidence rates amounted to a 18.90% relative risk reduction (RRR) of infection associated with baloxavir in relation to oseltamivir. The corresponding RRR was 82.16% when the antiviral treatment uptake rate was increased to 35%. In addition, the peak of the prevlance of infected individuals (per 10,000 persons) under the baloxavir treatment was 177 (Range: 93 to 274) fewer than that of oseltamivir. Our analyses suggest that the baloxavir treatment strategy reduces the incidence of influenza in China compared with oseltamivir in the setting of a seasonal flu epidemic. Also, increasing the uptake rate of antiviral treatment can potentially prevent millions of infections during a single flu season. This article is protected by copyright. All rights reserved.
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Background: Since the outbreak of COVID-19 emerged in Wuhan, China, in December 2019, the epidemic has spread worldwide and posed a great threat to society. Despite great achievements in COVID-19 research, few studies have focused on the similarities and differences between SARS-CoV-2 and influenza viruses. Results: Through a review of the literature on SARS-CoV-2 and influenza viruses, we found that influenza occurs every year, and influenza pandemics occur irregularly. The uncomplicated human influenza viruses primarily affect the larger airways and rarely the alveoli. However, SARS-CoV-2 mainly involves the deep airways and lungs and can cause DAD, leading to severe hypoxemia. In general, SARS-CoV-2 is no less infectious than the influenza virus. However, its destructive power to the lungs is no less than the avian influenza virus. There is currently no clinical vaccine and specific inhibitor against SARS-CoV-2. Conclusions: SASR-CoV-2 damages lung function more severely than the influenza virus, with higher morbidity, mortality, and severe disease rates. Controlling the source of infection, cutting off the route of transmission, and protecting susceptible populations are critical to the fight against SARS-CoV-2.
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We compare several popular methods of estimating the basic reproduction number, R 0 , focusing on the early stages of an epidemic, and assuming weekly reports of new infecteds. We study the situation when data is generated by one of three standard epidemiological compartmental models: SIR, SEIR, and SEAIR; and examine the sensitivity of the estimators to the model structure. As some methods are developed assuming specific epidemiological models, our work adds a study of their performance in both a well-specified (data generating model and method model are the same) and miss-specified (data generating model and method model differ) settings. We also study R 0 estimation using Canadian COVID-19 case report data. In this study we focus on examples of influenza and COVID-19, though the general approach is easily extendable to other scenarios. Our simulation study reveals that some estimation methods tend to work better than others, however, no singular best method was clearly detected. In the discussion, we provide recommendations for practitioners based on our results.
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Background The novel influenza A H1N1 (2009) virus, identified in mid-2009, spread rapidly in Guangdong Province. The accurate estimation of epidemiological parameters is of vital significance in decision-making for coping with pandemic influenza. Methods We used influenza A H1N1 epidemic data from local cases in Guangdong Province, China, in conjunction with a complex SEIR model (susceptible, exposed, infectious, recovered) to estimate the basic reproduction number. The transmission rate was obtained by fitting the model to the cumulative number of local daily infected cases using the nonlinear ordinary least squares method. The latent period and duration of infectiousness were obtained from the published literature, and the proportion of symptomatic infected cases was obtained from the serological survey conducted by the Center for Disease Control and Prevention of Guangdong Province. We determined the variance of model parameters via a simulation study. Results The model was in keeping with the observed epidemic data (coefficient of determination = 0.982). The basic reproduction number was estimated preliminarily to be R0 = 1.525 (95% confidence interval 1.448–1.602), with the possible range of true R0 being 1.30–1.85. We estimated the transmission rate β to be between 0.390 and 0.432. Conclusions With the help of the serological survey, useful estimates of key epidemiological parameters for the influenza A H1N1 outbreak in Guangdong Province were obtained. The sensitivity analysis suggests that different latent periods and infectious periods, which specify different mean durations of generation time, have a significant impact on R0. Our proposed model and findings provide a relevant contribution towards understanding the characteristics of influenza A H1N1 in Guangdong Province.
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The effects of influenza on a population are attributable to the clinical severity of illness and the number of persons infected, which can vary greatly between seasons or pandemics. To create a systematic framework for assessing the public health effects of an emerging pandemic, we reviewed data from past influenza seasons and pandemics to characterize severity and transmissibility (based on ranges of these measures in the United States) and outlined a formal assessment of the potential effects of a novel virus. The assessment was divided into 2 periods. Because early in a pandemic, measurement of severity and transmissibility is uncertain, we used a broad dichotomous scale in the initial assessment to divide the range of historic values. In the refined assessment, as more data became available, we categorized those values more precisely. By organizing and prioritizing data collection, this approach may inform an evidence-based assessment of pandemic effects and guide decision making.
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Background: Following the emergence of 2009 pandemic influenza A virus subtype H1N1 (A[H1N1]pdm09) in the United States and Mexico in April 2009, A(H1N1)pdm09 spread rapidly all over the world. There is a dearth of information about the epidemiology of A(H1N1)pdm09 in Africa, including Morocco. We describe the epidemiologic characteristics of the A(H1N1)pdm09 epidemic in Morocco during 2009-2010, including transmissibility and risk factors associated with fatal disease. Methods: We implemented influenza surveillance for patients presenting with influenza-like illness (ILI) at 136 private and public clinics for patients with severe acute respiratory illness (SARI) at 16 regional public hospitals from June 2009 through February 2010. Respiratory samples and structured questionnaires were collected from all enrolled patients, and samples were tested by real-time reverse-transcription polymerase chain reaction for influenza viruses. We estimated the risk factors associated with fatal disease as well as the basic reproduction number (R(0)) and the serial interval of the pandemic virus. Results: From June 2009 through February 2010, we obtained 3937 specimens, of which 1452 tested positive for influenza virus. Of these, 1398 (96%) were A(H1N1)pdm09. Forty percent of specimens from ILI cases (1056 of 2646) and 27% from SARI cases (342 of 1291) were positive for A(H1N1)pdm09. Sixty-four deaths occurred among laboratory-confirmed A(H1N1)pdm09 SARI cases. Among these cases, those who had hypertension (age-adjusted odd ratio [aOR], 28.2; 95% confidence interval [CI], 2.0-398.7), had neurological disorders (aOR, 7.5; 95% CI, 1.5-36.4), or were obese (aOR, 7.1; 95% CI, 1.6-31.1), as well as women of gestational age who were pregnant (aOR, 2.5; 95% CI, 1.1-5.6), were at increased risk of death. Across the country, elevated numbers of locally acquired infections were detected 4 months after the detection of the first laboratory-confirmed case and coincided with the expected influenza season (October-January) in Morocco. We obtained an R(0) estimate of 1.44 (95% CI, 1.32-1.56) and a mean serial interval (±SD) of 2.3 ± 1.4 days (95% CI, 1.6-3.0). Conclusion: Widespread but delayed community transmission of A(H1N1)pdm09 occurred in Morocco in 2009, and A(H1N1)pdm09 became the dominant influenza virus subtype during the 2009-2010 influenza season. The transmissibility characteristics were similar to those observed in other countries.
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Describing transmissibility parameters of past pandemics from diverse geographic sites remains critical to planning responses to future outbreaks. We characterize the transmissibility of influenza A(H1N1)pdm09 (hereafter pH1N1) in South Africa during 2009 by estimating the serial interval (SI), the initial effective reproductive number (initial R(t)) and the temporal variation of R(t). We make use of data from a central registry of all pH1N1 laboratory-confirmed cases detected throughout South Africa. Whenever date of symptom onset is missing, we estimate it from the date of specimen collection using a multiple imputation approach repeated 100 times for each missing value. We apply a likelihood-based method (method 1) for simultaneous estimation of initial R(t) and the SI; estimate initial R(t) from SI distributions established from prior field studies (method 2); and the Wallinga and Teunis method (method 3) to model the temporal variation of R(t). 12,360 confirmed pH1N1 cases were reported in the central registry. During the period of exponential growth of the epidemic (June 21 to August 3, 2009), we simultaneously estimate a mean R(t) of 1.47 (95% CI: 1.30-1.72) and mean SI of 2.78 days (95% CI: 1.80-3.75) (method 1). Field studies found a mean SI of 2.3 days between primary cases and laboratory-confirmed secondary cases, and 2.7 days when considering both suspected and confirmed secondary cases. Incorporating the SI estimate from field studies using laboratory-confirmed cases, we found an initial R(t) of 1.43 (95% CI: 1.38-1.49) (method 2). The mean R(t) peaked at 2.91 (95% CI: 0.85-2.91) on June 21, as the epidemic commenced, and R(t)>1 was sustained until August 22 (method 3). Transmissibility characteristics of pH1N1 in South Africa are similar to estimates reported by countries outside of Africa. Estimations using the likelihood-based method are in agreement with field findings.
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The history of influenza so far as it is known, that is, for several centuries, comprises a series of long cycles in which great pandemics alternate with periods of relative quiescence, the length of cycles as measured by the intervals between pandemics being usually a matter of decades. The special characteristics of influenza pandemics are their wide and rapid extension, their high attack rates, and great effect on general mortality rates. Since these cycles are undoubtedly of fundamental significance in the natural history of influenza any proper discussion of the epidemiology of the disease should cover at least one full cycle, preferably the last, from 1889 to the present. The material for such a discussion must, however, be collected from many and diverse sources and laboriously fitted together, since there is no concrete specific and continuous record of the prevalence or mortality of influenza during such a period of years.