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\begin{document}

\title{Co-infection and clinical characteristics of SARS-CoV-2 and influenza
among COVID-19 cases: A-meta analysis}



\author[1]{Reza Alizadeh}%
\author[1]{Monireh Golpur}%
\author[1]{Reza Valadan}%
\author[1]{Msoumeh Rezaei}%
\author[1]{Mohammadreza Haghshenas}%
\author[1]{tahoora mousavi}%
\affil[1]{Mazandaran University of Medical Sciences}%


\vspace{-1em}



  
  \date{June 04, 2024}


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\selectlanguage{english}
\begin{abstract}
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), that
causes coronavirus disease 2019 (COVID-19) is a public health problem
and may have co-infection with other pathogens such as influenza
virus.This study aims to assess the co-infection of SARS-CoV-2 with
influenza among COVID-19 cases.The all relevant studies were collected
from international databases. For improving the quality of the present
literature, the all studies were evaluated by two reviewers in order to
confirm all of the studies have inclusion criteria. Finally, all
articles with sufficient quality scores were included in meta-analysis.
Assessment of heterogeneity among the studies of primary studies was
performed using the statistic chi-squared test (Cochran's Q) and I2
index. In this results, random or fixed effect model were used for
determination of heterogeneity test. All statistical analyses were
performed using Comprehensive Meta-Analysis (CMA), V.2 software.This
meta- analysis included 9 primary studies investigating the co-infection
of SARS-CoV-2 with influenza among COVID-19 cases. Pooled prevalence
(95\% confidence interval) of co-infection is shown that the prevalence
of influenza A is higher than influenza B. 2.3(0.5-9.3) vs 0.1
(0.4-3.3). Using the fixed effect model the frequency of fever was
(80.6\% {[}95\% CI 76.1--84.40, p \textless{} 0.153{]}) and it is shown
that fever is the most prevalent symptom in patients.Patients admitted
to hospital with COVID-19 also infected with influenza virus. Thus, the
current research provides a better understanding about the control and
treatment of co-infection with SARS-CoV-2 and the influenza virus.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Coronavirus is a~family~of RNA viruses that can cause of common cold,
Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory
Syndrome (MERS) with the mortality rate of 10\% and 37\%, respectively
(1). The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a
novel coronavirus, that causes coronavirus disease 2019 (COVID-19) was
discovered in Wuhan, China and expanded in all over the world from 31
December and spread across the continents (2, 3). The earliest countries
(China, South Korea, and Iran) announced the COVID-19 outbreak as a
public health problem(4).

The coronavirus's 3000 nucleotide genome encodes four structural protein
such as, Spike (S) protein, Nucleocapsid (N) protein, Membrane (M)
protein, envelop (E) protein and several non-structural proteins (nsp)
(5). Spike (S) protein consist of transmembrane (TM) domain which is
able to bind a host receptor. Nuclear capsid or N-protein which is bound
to the virus single positive strand RNA, is located inside in the
capsid. Nucleoprotein gene plays key role in virus's replication and
transcription; it allows the virus to hijack human cells and turn them
into virus factories (6). M protein is the most abundant protein in the
viral surfaces which is the central organizer for the virus protein. The
E-protein is a small membrane protein, plays an important role in virus
assembly subunits, membrane penetrance of the host cell and interaction
between viruses and host cells (7).

The symptoms of COVID-19 can include fever, cough, sore throat, fatigue,
shortness of breath and gastrointestinal symptoms such as diarrhea and
nausea (8, 9). Coronaviruses have been responsible for the common cold
by a long time and it is reported that the symptoms of SARS-CoV-2
disease in human is similar to the common cold or influenza; but the
infection and mortality rate of the SARS-CoV-2 is higher than other
respiratory infections. SARS-CoV-2 is a contiguous virus and can be
transmit by the infected person breathed, coughed, or sneezed (10).
Study shows that SARS-CoV-2 may have co-infection with other pathogens
such as viruses, bacteria, and fungi which are related to increase in
hospitalization rate and mortality. It is reported that the most
co-infection occur with influenza virus (11). Influenza is a respiratory
illness with the sign of fever, chills, body aches, sore throats, nasal
congestion, fatigue, vomiting, abdominal pain, and diarrhea, and seems
to have similar transmission character with COVID-19 (12, 13). Recently
study have clarified that there are Immunopathological similarities
between influenza and SARS-CoV-2 (14). Several studies from United Sate
of America (15, 16), china (17) and Iran (18) show that there is
co-infection with SARS-CoV-2 and influenza A and B virus. In addition
some researches indicate that the co-infection of SARS-CoV-2 with
influenza in patients suffering from pneumonia, sinus infection,
bronchitis and cardiovascular disease~(CVD) promote the mortality rate
(18-20).

Researchers found that patients admitted to hospital with COVID-19 also
infected with influenza virus. Thus, the current research provides a
better understanding about the control and treatment of co-infection
with SARS-CoV-2 and the influenza virus. So, This study aims to assess
the co-infection of SARS-CoV-2 with influenza among COVID-19 cases.

\textbf{Material and methods}

\textbf{Search strategy}

The all published studies were searched using ISI, Science direct,
Scopus, Pubmed, Google scholar, and Wiley online international databases
from December 2019 to July 2021. The search strategy was done using the
following key words: ``co-infection'', ``COVID-19 virus'',
``Influenza'', ``influenza virus'', ``Human flu'', ``SARS-COV-2'',
``severe acute respiratory syndrome coronavirus 2'', ``coronavirus
disease 2019 virus'', ``2019 novel coronavirus''. AND'/`OR' operators
were used to identify the articles. For improving the quality of the
present literature, the references of studies were examined. In
addition, all titles and abstracts were randomly evaluated~by two
reviewers in order to confirmed~all of the studies have inclusion
criteria and their results were compared with each other.

\textbf{Inclusion/exclusion criteria}

All articles with sufficient quality scores were included in
meta-analysis following inclusion criteria: 1) All English studies. 2)
Patients with confirmed diagnosis of COVID-19. 3) Studies based on
prevalence of influenza among COVID -19 patients. 4) Reported prevalence
of clinical characterization among co-infected patients. 5) Result of
abnormalities in~chest~among co-infected patients.

The excluded studies were as follows: 1) Articles with no access to the
full-text. 2) Case reports or case series studies. 3) Duplicated
studies. 4) Studies published in languages other than English. 5)
Abstracts, cases, and review studies. 6) Studies that did not report
data on co-infection.

\textbf{Quality assessment}

At first, after identification and screening of studies, articles were
assessed for eligibility, and finally suitable studies were included to
the Meta-analysis review. In the present study, The STROBE checklist was
used for evaluation of the quality of the selected articles based on
title and contents. The STROBE checklist composed of 22 items covering
all of the aspects of the methodology such as collection methods, tools,
type of the study, definition of the variables, statistical analysis
tests, study objectives, sample size and study population.

Depending on the quality analysis, each question was assigned to score
one point. In this checklist, the final scores for each study could
range from 0 to 44, respectively. Based on the results of the quality
assessment, studies were divided into three categories: Low
(\textless{}15.5), average (15.5--29.5) and high quality
(\textgreater{}30) and studies with low-quality scores were excluded
from the final meta-analysis.

\textbf{Data extraction}

The following variables were extracted from the appropriate articles:
first author, publication date, type of study, geographical regions,
study language, number of SARS-CoV-2 confirmed patients, total
influenza, influenza A, influenza B, co-infected patients with clinical
characterization (fever, cough, fatigue, diarrhea, difficult breathing),
radiological data (Chest CT). The information preparation was done in
Microsoft excel spreadsheet and all statistical analyses were carried
out via Comprehensive Meta Analysis V.2 software.

\textbf{Statistical analysis}

In our research, the primary outcome was the prevalence of influenza in
COVID-19 patients and the secondary outcome was the prevalence of
clinical characterization in co-infected patients. Assessment of
heterogeneity among the studies of primary studies was performed using
the statistic chi-squared test (Cochran's Q) and
I\textsuperscript{2}index. In this results, random or fixed effect model
were used for determination of heterogeneity test and more than 50\%
were considered as the degree of heterogeneity. For study heterogeneity
evaluation, a forest plot estimated with confidence intervals of 95\%.
(CIs; horizontal lines). All statistical analyses were performed using
Comprehensive Meta-Analysis (CMA), V.2 software.

\textbf{Results}

A total of 1031 articles were obtained using the electronic strategy
search. After removing of 78 duplicate articles, 1453 study were
remained for screening. Then, the full-text of the articles was
evaluated and 1289 irrelevant articles were excluded. At the eligibility
check stage, 164 articles were examined and 155 of them were omitted.
Finally, 9 articles were included in this meta-analysis review,
according to PRISMA (Preferred reporting items for systematic review and
meta-analysis) guideline. Figure 1 shows the review process for the
included studies.

Accordingly, this Meta-analysis shows the estimation of the influenza
prevalence among patients infected with COVID-19 (Table 1). In nine
cross sectional studies, the influenza prevalence among patients
infected with COVID-19 varied from 0.08 in Nowak study to 49.46\% in
Simin Ma study. In the present study the clinical characterization among
patients infected with COVID-19 and influenza is shown in Table 2.

\textbf{Co-infection of SARS-COV-2 and influenza}

Generally, with the compounding of the results, the influenza prevalence
among co-infected patients with the confidence interval of 95 \% and
with based on random effect model is (I\textsuperscript{2}:95.948\%) and
it is shown that heterogeneity was observed among the primary results of
the studies (Fig 2). Significant statistical heterogeneity based on
random effect model are found in the analysis of the influenza (A, B)
prevalence among co-infected patients (I\textsuperscript{2} = 95.977\%),
and (I\textsuperscript{2} = 77.350) respectively. The current result is
shown that the prevalence of influenza A is higher than influenza B.
2.3(0.5-9.3) vs 0.1 (0.4-3.3). (Figure 3, 4).

\textbf{Prevalence of clinical characterization and chest radiography
among co-infected patients}

The forest plot analyses were performed for fever, chest CT
abnormalities, diarrhea, fatigue, cough, and difficult breathing.

\textbf{Fever analysis}

Figure 5 (A) shows that most co-infected patients had fever. In the
present study, the prevalence of fever is reported in 5 articles
comprising 291patients and ranged between 69.56-100\%. Combination of
these studies in the same manner as for fever, using the fixed effect
model revealed that the frequency of fever is (80.6\% {[}95\% CI
76.1--84.40, p \textless{} 0.153{]}).

\textbf{Cough analysis}

A total of 5 articles including 124 patients reported on the prevalence
of cough, which ranged from 24.83-100\%. Due to the heterogeneity in the
results of the primary studies, the random-effects model used for
assessment. By combining these 6 articles, it is revealed that cough is
the second most common symptom presenting in co-infected patients
(43.3\% {[}95\% CI 24.1--64.8, p = 0.000{]}). Figure 5 (B).

\textbf{Fatigue analysis}

The prevalence of fatigue is assessed in 5 articles including 37
patients and the rate of fatigue varied between 3.26 and 40\%. Based on
the homogeneity between the results of the primary studies the fixed
effects model was used for assessment. By revealing of articles, the
frequency of fatigue in patients is (13.8\% {[}95\% CI 5.6--30.3, p
=0.000{]}). Figure 5 (C).

\textbf{Diarrhea analysis}

The prevalence of diarrhea is reported in 4 articles comprising 35
patients and ranged from 3.92--40\% respectively. Based on the
heterogeneity between the results of the primary studies, the
random-effects model was used for assessment. By combining of these
articles, diarrhea is determined to have a prevalence of (12.2\% {[}95\%
CI 3.9--32.3, p = 0.000{]}). Figure 5 (D).

\textbf{Difficult breathing analysis}

Difficult breathing was less common in the patients of COVID-19 and
influenza. The prevalence of difficult breathing assessed in 4 articles
comprising 27 patients and is reported to range from 6.87--100\%. Due to
the heterogeneity in the results of the primary studies, the
random-effects model was employed. Combined analysis of these articles
revealed that difficult breathing occurred in (9.3\% {[}95\% CI
3.7--21.5, p \textless{} 0.010{]}). Figure 5 (E).

\textbf{Chest radiography}

Among the selected studies, the prevalence of CT abnormalities is
reported in 5 articles comprising 272 patients and ranged from
83--100\%. By combining the results of these 3 studies, the frequency of
CT abnormalities in co-infected patients is (66.8\% {[}95\% CI
29.4--90.7, p \textless{} 0.001{]}). As there is heterogeneity between
the results for CT abnormalities, the random-effects model was used for
assessment. Figure 5 (F).

Figure 1: Flowchart of review process for the included studies

Table 1: Estimation of the influenza prevalence among patients infected
with COVID-19\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Influenza B & Influenza A & Total influenza & Number of SARS-CoV-2
patients & Language & Authors & Reference\tabularnewline
\midrule
\endhead
N/A & N/A & 19 (37.25) & 51 & English & Castillo (2020) &
(21)\tabularnewline
23 (7.49) & 153 (49.83) & 131 (42.67) & 307 & English & Yue(2020) &
(22)\tabularnewline
1 (0.4) & 2 (0.8) & 22 (8.8) & 250 & English & Ma(2020) &
(23)\tabularnewline
N/A & 1 (0.08) & 1 (0.08) & 1204 & English & Nowak(2020) &
(24)\tabularnewline
2 (1.73) & 3 (2.60) & 5 (4.34) & 115 & English & Ding(2020) &
(25)\tabularnewline
N/A & 23 (21.90) & 49 (46.66) & 105 & English & Hashemi1 (2020) &
(26)\tabularnewline
N/A & N/A & 46 (49.46) & 93 & English & Simin Ma(2020) &
(27)\tabularnewline
5 (1.94) & 2 (0.77) & 34 (13.22) & 257 & English & Zhua(2020) &
(28)\tabularnewline
0 & N/A & 1 (4.16) & 24 & English & Yanjun(2020) & (29)\tabularnewline
\bottomrule
\end{longtable}

Table 2: Estimation of clinical characterization among patients infected
with COVID-19 and influenza.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllll@{}}
\toprule
Chest CT, Abnormalities & Difficult breathing & Diarrhea & Fatigue &
Cough & Fever & Number of patients & Area & Language & Authors &
Reference\tabularnewline
\midrule
\endhead
122 (93.12) & 9 (6.87) & 11 (8.39) & 13 (9.92) & 40 (30.53) & 104
(79.38) & 131 & Wuhan & English & Yue(2020) & (22)\tabularnewline
127 (83) & 11 (7.18) & 6 (3.92) & 5 (3.26) & 38 (24.83) & 132 (86.27) &
153 & Wuhan & English & Yue(2020) & (22)\tabularnewline
23 (100) & 2 (8.69) & 0 (0) & 3 (13.04) & 6 (26.08) & 16 (69.56) & 23 &
Wuhan & English & Yue(2020) & (22)\tabularnewline
N/A & 5 (100) & 2 (40) & 2 (40) & 5 (100) & 5 (100) & 5 & China &
English & Ding(2020) & (25)\tabularnewline
N/A & N/A & 16 (34.78) & 14 (30.43) & 35 (76.08) & 34 (73.91) & 46 &
China & English & Simin Ma(2020) & (27)\tabularnewline
272 & 27 & 35 & 37 & 124 & 291 & 358 & Total of samples & Total of
samples & Total of samples &\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 2: Estimation of total influenza prevalence among patients
infected with COVID-19\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 3: Estimation of influenza (A) prevalence among patients infected
with COVID-19\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 4: Estimation of influenza (B) prevalence among patients infected
with COVID-19\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 5 (A): Estimation of fever among co-infected patients\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 5 (B): Estimation of cough among co-infected patients\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Figure 5 (C): Estimation of fatigue among co-infected patients\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

Figure 5 (D): Estimation of diarrhea among co-infected patients\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

Figure 5 (E): Estimation of difficult breathing among co-infected
patients\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

Figure 5 (F): Estimation of difficult CT abnormalities among co-infected
patients

\textbf{Discussion}

SARS-CoV-2 and influenza infection are associated with respiratory
disorders, and signs in patients can vary from moderate to severe
morbidity and mortality (30). Some studies and case reports indicate
that influenza co-infection with COVID-19 may be important for the
infected patient's severity (31-34). In this study, the prevalence of
co-infection SARS-CoV-2 with influenza A and B, clinical
characterization, and chest radiography in co-infected patients with
confirmed SARS-CoV-2 infection was meta-analyzed. The results indicated
that the prevalence of influenza A is higher than influenza B
co-infection in COVID-19 patients (2.3(0.5-9.3) vs. 0.1 (0.4-3.3)). Our
findings show that fever and cough were the most common clinical
symptoms (86\% and 46\%, respectively) in co-infected SARS-CoV-2
patients with influenza A or B. In addition, Fatigue, Diarrhea, and
Difficult Breathing were the less common clinical findings among
co-infected patients (13.8\%, 12.2\%, and 9.3\%, respectively).

Influenza and SARS-Cov-2 viruses are transmitted by contact, droplets,
and contaminated surfaces and cause respiratory diseases with a broad
range of moderate to severe symptoms (35, 36). Based on the basic
reproduction number R zero (R0), SARS-Cov-2 viruses can infect more
people than influenza (1.5--5.7 for SARS-Cov-2, 0.9--2.1 for influenza)
(36, 37). Several studies have confirmed co-infection of SARS-CoV-2 with
influenza A and influenza B, such as studies in the United States (15,
16), China (17), and Iran (18). Some articles have shown that
respiratory viruses such as influenza can lead to complications of the
disease and even patient death in confirmed cases of COVID-19 (18-20).
Some other research had the contrary view they assume that competitive
advantage in virus connection can play an essential role in SARS-CoV-2
interactions with other viruses, such as influenza, during co-infection
(38, 39). Moreover, different immune response mechanisms can give rise
to a competitive advantage between SARS-COV-2 and other co-infecting
viruses; therefore, in patients with SARS-CoV-2, the co-infection rate
with other viruses, such as influenza, is much lower (38, 39).

The research limitations are the number of studies, small sample sizes,
publication bias, heterogeneity of the study, poor quality analysis and
reporting in some of the included studies.

Due to the low prevalence of SARS-COV-2, co-infected influenza patients,
and many differences between COVID-19 and influenza, such as
transmissibility, mortality rate, laboratory diagnosis, and clinical
symptoms (30, 40), these results only suggest that consider the
influenza viruses in COVID-19 suspected patients. As a consequence, this
approach will help to select the best treatment protocol for the
management of COVID-19 patients and reduce the severity of the disease.
People should also be vaccinated against seasonal influenza to reduce
the risk of co-infection in the recent pandemic.

\textbf{Author Contributions}

TM, RA conducted the literature search and data extraction. MG, RV
performed the statistical analysis and drafted the manuscript. MH and MR
revised the final manuscript. All authors reviewed and approved the
final version of the manuscript.

\textbf{Competing interests}

All authors declare that they have no conflicts of interest.

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