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. 2020 Oct 6;61(3):E304–E312. doi: 10.15167/2421-4248/jpmh2020.61.3.1530

Determine the most common clinical symptoms in COVID-19 patients: a systematic review and meta-analysis

YOUSEF ALIMOHAMADI 1,2, MOJTABA SEPANDI 3,4,✉, MARYAM TAGHDIR 3,5, HADISEH HOSAMIRUDSARI 6
PMCID: PMC7595075  PMID: 33150219

Summary

Introduction

COVID-19 is an emerging infectious disease. The study about features of this infection could be very helpful in better knowledge about this infectious disease. The current systematic review and meta-analysis were aimed to estimate the prevalence of clinical symptoms of COVID-19 in a systematic review and meta-analysis.

Methods

A systematic review using Medline/PubMed, Scopus, and Google scholar has been conducted. In the current systematic review and meta-analysis, the articles published in the period January 1, 2020, to April 2, 2020, written in English and reporting clinical symptoms of COVID-19 was reviewed. To assess, the presence of heterogeneity, the Cochran’s Q statistic, the I2 index, and the tau-squared test were used. Because of significant heterogeneity between the studies the random-effects model with 95% CI was used to calculate the pooled estimation of each symptom prevalence.

Results

The most common symptoms in COVID-19 patients include: Fever 81.2% (95% CI: 77.9-84.4); Cough: 58.5% (95% CI: 54.2-62.8); Fatigue 38.5% (95% CI: 30.6-45.3); Dyspnea: 26.1% (95% CI: 20.4-31.8); and the Sputum: 25.8% (95% CI: 21.1-30.4). Based on the meta-regression results, the sample size used in different studies did not have a significant effect on the final estimate value (P > 0.05).

Conclusions

Considering the main symptoms of COVID-19 such as Fever, Cough, Fatigue, and Dyspnea can have a key role in early detection of this disease and prevent the transmission of the disease to other people.

Keywords: Clinical symptoms, COVID-19, Meta-analysis

Introduction

The World Health Organization (WHO) described Coronavirus 2019 (COVID-19) as a public health emergency. The international concern of COVID-19 is more in comparison to Severe Acute Respiratory Syndrome (SARS), which previously was pandemic in 2003 [1]. Coronaviruses are important pathogens that can affect the lower respiratory tract in humans and can cause diseases ranging from a simple cold to severe infection with up to 50% lethality [2]. The COVID-19 is a highly contagious infectious disease and one infected person can infect an average of three other people [3] which is higher than that for SARS (1.7-1.9) and MERS (< 1), suggesting that SARS-CoV-2 has a greater potential for being outbreak. Evidence suggests that there are many similarities between COVID-19 and SARS. About 79.5% of the similarities in the genome sequence of these two viruses have been reported [4]. COVID-19 can spread in the community more easily than MERS and SARS because of the less severe clinical picture of it [5]. Although the disease is mild in most people, in some patients, especially those with other underlying diseases, there may be a respiratory failure, arrhythmias, shock, Kidney failure, cardiovascular damage, or liver failure [6, 7]. Currently, there is no effective antiviral treatment for the disease and only supportive care may be helpful [7] The case fatality rate (CFR) of COVID-19 was reported to be 3.8% but it can differ in patients who have comorbidities [8]. The CFR of COVID-19 is lower than that of SARS and that of MERS[5]. The most common symptoms are fever, cough, and myalgia or fatigue [9]. Although the clinical symptoms of the disease are nonspecific, understanding the symptoms is essential. Patient with fever and upper respiratory tract symptoms with lymphopenia or leukopenia should be considered as suspected [9] Patients may present with diarrhea a few days before the fever. A slight number of patients may report a headache [10]. Diarrhea is more common in SARS [5].

Combining the results of studies that have focused on the prevalence of COVID-19 related symptoms could be helpful in the best identification and diagnosis of infection. Because of the importance of symptoms in the identification of COVID-19 infection the current study was aimed to estimate the prevalence of Clinical Symptoms of COVID-19 in a systematic review and meta-analysis.

Materials and methods

ELIGIBILITY CRITERIA

All articles published in the period January 1, 2020, to April 2, 2020, written in English and reporting clinical symptoms of COVID-19 was reviewed. Review articles as well as articles that lacked original data or reported incomplete data were excluded.

INFORMATION SOURCES AND SEARCH STRATEGY

We conducted a systematic review using Medline/PubMed, Scopus and Google scholar. The following search terms used: “Clinical features”, “COVID-19”, “coronavirus disease 2019”, “coronavirus disease-19”, “2019 novel coronavirus disease”, “severe acute respiratory syndrome coronavirus”, “clinical symptoms”, “clinical characteristics” and “clinical manifestations”. The searches were concluded by April 2, 2020, and two researchers independently assessed search results. References of related papers were also searched for other relevant articles to enhance the search strategy.

STUDY SELECTION

After performing the search strategy some records were excluded because of Duplicates and unrelated. After that, the records screened based on abstracts and titles. The full text of related articles was then assessed according to the inclusion and exclusion criteria. Observational studies that reported clinical symptoms were included in the meta-analysis.

DATA COLLECTION PROCESS AND DATA ITEMS

Data including the type and date of publication, country, the sample size, age, and clinical symptoms of COVID-19 were extracted independently by two authors. A third person checked the article list and data extractions to ensure there were no duplicate articles and also resolved discrepancies about study inclusion.

ASSESSMENT OF METHODOLOGICAL QUALITY

To assess the study quality of the case series studies the Institute of Health Economics (IHE) was used [11]. Also, the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist for observational studies was used assessment quality of the cross-sectional and cohort studies.

META-REGRESSION ANALYSIS

To assess the effect of sample size on pooled estimations the meta-regression analysis was used.

STATISTICAL APPROACH

To assessment, the presence of heterogeneity, the Cochran’s Q statistic, the I2 index, and the tau-squared test were used. Due to the difference in the age of patients, we perform subgroup analyzes in different age groups. Because of the presence, the significant heterogeneity between the studies the random-effects model with 95% CI was used to calculate the pooled estimation of symptoms prevalence. The data were analyzed using stata version 11.0.

Results

In the current systematic review and meta-analysis, 54 studies that estimated the symptoms related to COVID-19 were included in the final analysis (Tab. I). After searching PubMed and Google Scholar electronic databases, 1,498 possibly relevant articles were identified; 1,397 articles were removed due to unrelated to study purpose and duplication. Of the remaining 101 articles, 45 were excluded after screening based on abstract and title and 2 articles removed because of lack of needed information. Finally, 54 articles were included in the final meta-synthesis (Fig. 1).

Tab. I.

Characteristics of the included studies on effective factors on mortality COVID-19, 2020.

Id First author Country Design Sample size
1 Dawei Wang [12] China Case series 138
2 Chaolin [13] China Cross-sectional 41
3 Chen [14] China Cross-sectional 99
4 Chung [15] China Cross-sectional 21
5 Chen [16] China Cross-sectional 29
6 Wang [12] China Cross-sectional 138
7 Kui [17] China Cross-sectional 137
8 Chang [18] China Cross-sectional 13
9 COVID-19 team Australia [19] Australia Cross-sectional 15
10 Li et al. [20] China Case series 24
11 Feng [21] China Case series 21
12 Zhang [22] China Case series 9
13 Feng [23] China Case series 15
14 Wang [24] China Cross-sectional 34
15 Xiaobo[25] China Cross-sectional 52
16 Jiong Wu et al. [26] China Cross-sectional 80
17 Zonghao Zhao [27] China Cross-sectional 77
18 Wen Zhao [28] China Cohort study 77
19 Wenjie Yang [29] China Cohort study 85
20 Matt Arentz [30] USA Case series 21
21 Ying Huang [31] China Retrospective 36
22 G Jian-ya Lei Liu [32] China Retrospective 51
23 Tao Chen [4] China Cohort 274
24 jin Zhang [33] China Cross-sectional 242
25 Shijiao Yan [34] China Retrospective 168
26 Jian Wu [35] China Retrospective 80
27 Yang Xu [36] China Retrospective 69
28 Fei Zhou [37] China Retrospective 191
29 Zenghui Cheng [38] China Retrospectively 11
30 Youbin Liu [39] China Retrospective 291
31 Yanli Liu [40] China Retrospective 109
32 Yonghao Xu [41] China Retrospective 45
33 Lang Wang [42] China Cohort 339
34 Zhichao Feng [43] China Cohort 141
35 Guo-Qing Qian [44] China Retrospective 91
36 BarnabyEdward Young [45] Singapore Case series 18
37 Ying Wen [46] China Retrospective 417
38 Jiaqiang Liao [47] China Retrospective 46
39 Xu Chen [48] China Cohort 291
40 Penghui Yang [49] China Cohort 55
41 Jie Liu [50] China Retrospective 64
42 Hang Fu [51] China Cross-sectional 52
43 Heshui Shi [52] China Cross-sectional 81
44 Wei Zhao [53] China Retrospective 101
45 Hua Fan [54] China Cohort 47
46 Ling Hu [55] China Retrospective 323
47 X. Zhao [56] China Cross-sectional 80
48 Zhaowei Chen [57] China Retrospective 89
49 Huijun Chen [58] China Retrospective 9
50 Rachael Pung [59] Singapore Retrospective 17
51 Wanbo Zhu [60] China Retrospective 116
52 Xiaoping Chen [61] China Retrospective 123
53 W. Guan [62] China Cross-sectional 1,099
54 Xi Xu[ 63] China Retrospective 90
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Fig. 1.

Fig. 1.

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PRISMA Flow Diagram for included studies in the current meta-analysis.

According to the results of the analysis, the most common symptoms in patients with coronavirus include:

  • Fever 81.2% (95% CI: 77.9-84.4);

  • Cough: 58.5% (95% CI: 54.2-62.8);

  • Fatigue: 38.5% (95% CI: 30.6-45.3);

  • Dyspnea: 26.1% (95% CI: 20.4-31.8);

  • and the presence of Sputum: 25.8% (95% CI: 21.1-30.4).

Other results are shown in Table II and Figure 2. Figure 2 presents the pooled estimation of some symptoms among COVID-19 patients.

Tab. II.

The prevalence of different symptoms among COVID-19 patients according to age groups.

Symptom Number of studies Sample size Pooled estimation I2
(%)		 P T2
< 40 years of old > 40 years of old Total
Chest tightness 14 1,967 8.1
(3.7-12.6)		 20.1
(9.6-30.6)		 17
(13.1-25.4)		 96.8 < 0.001 0.01
Cough 54 6,380 53.5
(44.3-62.7)		 61.2
(56.3-66.1)		 58.5
(54.2-62.8)		 91.7 < 0.001 0.02
Diarrhea 36 4,995 3.5
(2.1-4.9)		 8.6
(6.5-10.6)		 7.6
(5.9-9.2)		 83.9 < 0.001 0.001
Dyspnea 27 3,388 8.8
(2.6-15)		 31.4
(24-38.7)		 26.1
(20.4-31.8)		 97.4 < 0.001 0.02
Fatigue 22 3,803 30.5
(21.9-39.1)		 38.6
(29.9-47.2)		 38.5
(30.6-45.3)		 95.5 < 0.001 0.02
Fever 53 5,298 78.1
(73.3-82.8)		 83
(79.1-86.9)		 81.2
(77.9-84.4)		 92.6 < 0.001 0.01
Hemoptysis 9 1,998 1.9
(0-4.6)		 1.8
(0.008-2.9)		 1.7
(0.008-2.6)		 46.9 < 0.001 0.05
Headache 34 5,129 9.2
(5.4-13.1)		 9.5
(7.1-12.0)		 9.5
(7.5-11.6)		 88.7 < 0.001 0.002
Myalgia 37 4,676 19
(14-23.9)		 19.4
(14.9-24.0)		 20.1
(16.5-23.7)		 91.5 < 0.001 0.009
Shortness of breath 13 1,828 17.3
(3.6-30.1)		 19.3
(11.2-27.5)		 18.5
(12-24.9)		 93.3 < 0.001 0.01
Sore throat 29 3,906 15
(9.6-20.4)		 14.5
(10.9-18.2)		 15
(12.1-18.0)		 86 < 0.001 0.004
Sputum production 28 3,677 21
(15.4-26.7)		 28
(22-34.1)		 25.8
(21.1-30.4)		 91 < 0.001 0.01
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Fig. 2.

Fig. 2.

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The forest plots of some symptoms among COVID-19 patients.

META-REGRESSION ANALYSIS

Based on the meta-regression results, the sample size used in different studies did not have a significant effect on the final estimate value (P > 0.05).

The distribution of the estimated prevalence of different symptoms according to sample sizes as shown in Figure 3.

Fig. 3.

Fig. 3.

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The distribution of estimated prevalence of symptoms according to different sample sizes (the X and Y axes are the sample size and estimated prevalence respectively).

The diagrams below show the percentage distribution of symptom estimation based on the volume of different samples.

Based on these charts, the estimated amount of chest pain, cough, dyspnea, hemoptysis, and fever with decreasing sample size showed a decreasing trend, while other symptoms showed an increasing trend with increasing sample size.

Discussion

The COVID-19 is a new highly contagious infection that threatens people of all countries [64].

The clinical presentation of COVID-19 is wide, from asymptomatic infection to severe fatal diseases [14] Considering the shortage of diagnostic kits around the world this systematic review seems necessary, to find the clinical presentation of COVID-19 and using them in early diagnosis of this infection [13]. Unfortunately, there is no treatment for this virus, and patients’ treatment is just focused on supportive care. On the other hand, the limited number of critical care centers and mechanical ventilation in the world culminates in high concern for the health care system [7]. To date, over 1,607,912 cases have been reported worldwide and from different countries [65]. To deal with such an emerging infectious disease, there an urgent need to identify and determine factors associated with the evolution of the disease and its outcomes. In this Systematic Review and Meta-Analysis study, we reported the clinical symptoms of COVID-19. Although the 2019-nCoV sequence is not the same as the other two viruses (SARS-CoV and MERS-CoV) that were pandemic in 2003 and 2012, respectively, they are somewhat similar in pathogenesis [66, 67]. Cytokines may play a role in human coronavirus infection. Indirect evidence suggests that in the second phase of 2019-nCoV infection: high fever, pneumonia, and hypoxemia occur despite a significant reduction in viral load [68]. In this systematic review and meta-analysis study, the clinical symptoms of COVID-19 were examined to provide a better understanding of the disease. The most common symptoms were fever and, cough, and fatigue that was consistent with the general symptoms of a viral infection and pneumonia. Similar to previous studies [25, 62], the present study showed that fever in 81.2% of cases, cough in 58.5% of cases, and fatigue in 38.5% of cases. Fever is the most common symptom in patients with COVID-19, but not all patients had fever [13, 69]. The fever is an alarming sign of the disease, vomiting, and fever (above 39 degrees) are usually associated with more severe illness and more length of stay in the hospital. Fever is less common in COVID-19 than in SARS and MERS [34, 70]. Therefore, more attention should be paid to COVID-19 patients who do not have fever as a source clue of infection, and if the surveillance system relies only on fever in patients, then some patients will be missed [71]. Diarrhea, myalgia, hemoptysis, and sore throats were less common symptoms in this review, these results were similar to those obtained for other viruses, such as SARS and MERS [26]. This may indicate that COVID-19 can also be classified as a similar infection to SARS and MERS infection, which targets the cells of the lower respiratory tract system. Although nasopharynx is theoretically the first organ infected with the COVID-19, a recent study [13] showed that infected individuals rarely show present upper respiratory symptoms at the onset of the infection. This suggests that the virus mostly targets the cells of the lower respiratory tract cells [72]. Research and clinical findings suggest that SARS-CoV-2 may be colonized in the nasopharynx but the immune system cannot identify COVID-19 in the early stages. Therefore, the virus can be removed from the body with its through natural reactions, including sneezing and runny nose. This demonstrates the importance of accurately identifying COVID-19 symptoms at admission. Especially considering that studies have suggested the possibility of transmission of the disease by a healthy carrier [73]. This may be one of the reasons why COVID-19 is more contagious than SARS.

On the other hand, the lower incidence of early respiratory symptoms may be due to the presence of a pathogenic latency of SARS-CoV-2. Although gastrointestinal symptoms, especially diarrhea, were rare in the current study, the results of a study have shown that the SARS-CoV-2 virus can be isolated from the fecal samples of patients with gastrointestinal symptoms [74]. In another study, the SARS-CoV-2 virus has been isolated in a rectal swab of patients whose RT-PCR test results were negative with a throat swab sample [44]. Therefore, simultaneously sampling from throat and rectal may be useful, especially in patients with gastrointestinal symptoms. This review has some limitations which should be considered when interpreting the results. Most of the available studies for inclusion are from China. However the present study was done without any language restrictions and based on a comprehensive search strategy, only English electronic databases were searched; thus, it is likely that some related non-English papers have been missed.

Conclusions

Due to the rapid spreading of this infection, the lack of diagnostic tools, and limited intensive care units in the world, the use of other factors such as the clinical features of COVID19 can serve to give early warning for the appropriate interventions and decrease the number of death of COVID-19. So considering the main symptoms of COVID-19 such as Fever, cough Fatigue and Dyspnea can have a key role in early detection of this disease.

Figures and tables

Acknowledgements

We thank all authors involved in this manuscript.

Funding sources: this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Footnotes

Conflict of interest statement

The authors declare no conflict of interest.

Authors’ contributions

YA and MS formulated the research questions, designed the study, developed the preliminary search strategy, conducted the quality assessment, methodology, formal analysis, prepare drafts of the manuscript, review and editing, MT and HH refined the search strategy by conducting iterative database queries and incorporating new search terms, searched and collected the articles, re-viewed the manuscript content. Data extraction and prepare drafts of the manuscript. All authors critically reviewed the manuscript content. All authors have read and approved the final version of the manuscript.

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