1. Introduction {#sec1}
===============

In December 2019, the new COVID-19 coronavirus was recognized as a cause of respiratory illness. The first reports of pneumonia were from people who worked or lived in the Huanan seafood wholesale market in Wuhan, China raising concerns about a zoonotic viral infection \[[@bib1],[@bib2]\]. Phylogenetic analysis showed that the COVID-19 belong to the beta-coronavirus \[[@bib1]\]. Epidemiological studies have shown that the virus is spread relatively easily and can be transmitted by aerosol, droplets, and through infected surfaces \[[@bib3]\]. The COVID-19 has now spread to more than 50 countries from December 2019 to February 2020 \[[@bib4]\]. Most symptoms are non-specific in patients with respiratory disease. According to the latest WHO report, out of 83,652 confirmed cases of COVID-19 worldwide, 2791 deaths occurred in China and 67 deaths is recorded in other countries \[[@bib4]\].

Thus far, 6 coronaviruses that are able to infect humans have been identified, coronavirus infections are typically asymptomatic or associated with mild respiratory symptoms \[[@bib1]\]. The first coronavirus to cause severe disease in humans was the Severe Acute Respiratory Syndrome virus (SARS), which was appeared in the Guangdong province of southern China in 2002, there were 8098 reported case and 774 deaths \[[@bib5]\]. In Saudi Arabia in 2012, the Middle East respiratory syndrome coronavirus (MERS-CoV), which was transmitted from the camels to humans, caused 2458 infections with 848 deaths \[[@bib6]\].

Clinical studies have shown that COVID-19 can rapidly cause pulmonary damage and severe respiratory symptoms \[[@bib3]\]. There is no vaccine or targeted treatment currently available for COVID-19 infection. Treatment is largely supportive although multiple experimental antiviral medications are being evaluated \[[@bib7],[@bib8]\]. Thus, prevention and rapid diagnosis of infected patients is crucial. To date, the published clinical studies are quite small and give variable findings. With this in mind, here we evaluate the clinical features and laboratory findings using a large sample size of COVID-19 infected patients in order to assist in its understanding, prevention and treatment.

2. Methods {#sec2}
==========

2.1. Search strategy {#sec2.1}
--------------------

This study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) guidelines \[[@bib9]\]. We searched all studies published up to February 28, 2020 from the following databases: Embase, Scopus, PubMed, Web of Science and the Cochrane library. Search medical subject headings (MeSH) terms used were: "COVID-19", "Coronavirus", "severe acute respiratory syndrome coronavirus", and all their synonyms like "Wuhan Coronavirus", "SARS-CoV-2", and "COVID-19". Moreover, we searched for unpublished and grey literature with Google scholar, Center for Disease Controls (CDC) and WHO databases. We also examined references of included articles to find additional relevant studies. There was no language restriction and all included studies are written in English or Chinese languages, the latter were translated by <https://translate.google.com/>. Additional search strategy details are provided in [Table S1](#appsec1){ref-type="sec"} (supplementary material) \[[@bib10]\].

2.2. Study selection {#sec2.2}
--------------------

Duplicate studies were removed using EndNote X7 (Thomson Reuters, New York, NY, USA). Records were initially screened by title and abstract by independently two authors (AP, SG). The full-text of potentially eligible records was retrieved and examined. Any discrepancies were resolved by consensus.

2.3. Inclusion criteria {#sec2.3}
-----------------------

Studies had to fulfil the following pre-determined criteria to be eligible for inclusion in our meta-analysis. Studies were included if they reported the number of confirmed cases of patients with demographic data, \[AND\] \[OR\] clinical data, \[AND\] \[OR\] laboratory data, \[AND\] \[OR\] risk factor data. Confirmed patients were defined as any patient with positive nucleic acid testing (most of the studies with Real-Time PCR) or those meeting CDC and WHO criteria at the time of their publication.

2.4. Exclusion criteria {#sec2.4}
-----------------------

Studies were excluded if they did not report number of confirmed cases, were letters to the editor or individual case reports or reviews. News reports were also excluded.

2.5. Data extraction {#sec2.5}
--------------------

All included publications were published in 2020 and all patients are from China. The following items were extracted from each article: first author, Center and study location in China, sample collection time period, patient follow-up time, reference standard for infection confirmation, number of confirmed cases, and all demographic, clinical, laboratory data, and risk factor data. Two of our authors (AP and SG) independently extracted data and differences were resolved by consensus.

2.6. Quality assessment {#sec2.6}
-----------------------

Quality assessments of studies were performed by two reviewers independently according to the Critical Appraisal Checklist recommended by the Joanna Briggs Institute \[[@bib11]\], and disagreements were resolved by consensus. The checklist is composed of nine questions that reviewers addressed for each study. The 'Yes' answer for each question received one point. Thus, final scores for each study could range from zero to nine ([Table S2](#appsec1){ref-type="sec"} in Supplementary Material).

2.7. Analysis {#sec2.7}
-------------

Data cleaning and preparation was done in Microsoft Excel 2010 (Microsoft©, Redmond, WA, USA) and further analyses were carried out via Comprehensive Meta-Analysis Software Version 2.0 (Biostat, Englewood, NJ). Determination of heterogeneity among the studies was undertaken using the chi-squared test (Cochran\'s Q) to assess the appropriateness of pooling data. We used Random effect model (M − H heterogeneity) for pooled results \[[@bib12]\]. P values reflect study heterogeneity with \<0.05 being significant. We also used the Begg\'s and Egger\'s tests based on the symmetry assumption to detect publication bias.

3. Results {#sec3}
==========

3.1. Characteristics of included studies {#sec3.1}
----------------------------------------

The process of study selection is displayed in [Fig. 1](#fig1){ref-type="fig"} . A total of 36,115 reports were screened for the analysis of patients with COVID-19, 36,014 were excluded after title and abstract screening and the full text of 342 reports were reviewed in full text. We excluded studies that did not report sufficient data and finally 80 studies met the inclusion criteria ([Fig. 1](#fig1){ref-type="fig"}). Characteristics of the selected articles are summarized in [Table 1](#tbl1){ref-type="table"} . Of the 80 studies that were included in the analysis, 79 studies were in English and the one of them was in the language of Chinese \[[@bib13]\]. All studies were retrospective, published in 2020, and all patients were from China.Fig. 1Flow Diagram of Literature Search and Study Selection (PRISMA flow chart).Fig. 1Table 1Characterization of Included Studies with total 61, 742 COVID-19 Confirmed Patients. All Studies are Retrospective, from China, and Published in 2020.Table 1First AuthorSampling CenterSample collection timePatient follow up (days)N Confirmed PatientsMean age in years (IQR)N sex (male)Reference standardNanshan Chen \[[@bib14]\]Wuhan Jinyintan HospitalJan 1 to Jan 20, 20205--249955·567RT-PCR(21--82)Kaiyuan Sun \[[@bib30]\]MulticenterJan 20- Jan 29, 2020422884962.3CDC guideline(2--89)Jie Li \[[@bib31]\]Dazhou Central Hospital22 January- February 10, 20201--211745.19RT-PCR(32--65)Dawei Wang \[[@bib15]\]Zhongnan Hospital of WuhanJanuary 1-January 28, 20206--341385675RT-PCR(42--68)Chaolin Huang \[[@bib16]\]Jin Yintan Hospital (Wuhan)Dec 31, 2019-UNNA414930RT-PCR(41--58)Weijie Guan \[[@bib17]\]MulticenterNANA109947640RT-PCR(35--58)Yang Yang \[[@bib32]\]NANA51 days4021492211NALei Chen (Chinese) \[[@bib13]\]Tongji hospital in WuhanJanuary 14--29, 202015 day295621RT-PCR(26--79)Adam Bernheim \[[@bib3]\]MulticenterJanuary 18-February 2, 202012 days1214561RT-PCR & CT scan(18--80)Feng Pan \[[@bib33]\]Union Hospital12 Jan-6 Fen 2020NA214015RT-PCR(25--63)jin Zhang \[[@bib18]\]No.7 hospital of WuhanJan 16th to Feb 3rd, 2020NA1405771RT-PCR(25--87)Yichun Cheng \[[@bib19]\]Tongji hospital in WuhanJanuary 28-February 11, 202010 (7--13)71063374RT-PCR(51--71)Ming-Yen \[[@bib34]\]Hong Kong-Shenzhen HospitalNANA215613RT-PCR(37--65)Sijia Tian \[[@bib35]\]Beijing Emergency Medical ServiceJan 20 to Feb 10, 2020Feb.10 2026247.5127RT-PCR(1--94)Qun Li \[[@bib20]\]NANANA42515--89240WHO guideline(26--82)De Chang \[[@bib36]\]3 hospitals in BeijingJanuary 16- January 29, 2020Feb.4\
2020133410NA(34--48)Xiao-Wei Xu \[[@bib21]\]Zhejiang province10 January −26 January 202010 days624136WHO guideline(32--52)Fengxiang Song \[[@bib22]\]Center for Disease Control, ShanghaiJanuary 20- January 27, 2020NA514925CT scan & nucleic acid test(16--76)Michael Chung \[[@bib37]\]MulticenterJanuary 18--27, 2020NA215113CT scan, NA(29--77)Zunyou Wu (CDC) \[[@bib38]\]Multicenterthrough February 11, 202015 days44,67230--7922,981nucleic acid test resultBicheng Zhang \[[@bib39]\]hospitalized deathJanuary 11, 2020 to February 1030 day8272.554rt-pcrBing-Liang Lin \[[@bib40]\],MulticenterJanuary 20 to February 19,29 day915052rt-pcrBo Hu \[[@bib41]\]MulticenterJanuary 8 to February 920 day506234rt-pcrChuansheng Zheng \[[@bib42]\]Union Hospital, Wuhan16 Jan 2020 to 15 Feb,30 day643523rtpcrLin Fu \[[@bib43]\]Union HospitalJanuary 1 to January 3030 day20099rtpcrFei Zhou \[[@bib44]\]Multicenter19156119rtpcrGuo-Qing Qian \[[@bib45]\]Multicenteras of 11 FebruaryNA915037rt-pcr and clinicalGuqin Zhang \[[@bib46]\]Zhongnan Hospitalanuary 2 to February 10,NA22155108rtpcrQiannan Guo \[[@bib47]\]Tongji HospitalUNUN1157.559rtpcrHang Fu \[[@bib48]\]Chengdu, hospitalJan 1 to Feb 20,NA5244.5rtpcrHeshui Shi \[[@bib49]\]Union HospitalDec 20, 2019, and Jan 23NA8149·542rtpcrHuijun Chen \[[@bib50]\]Multicenter20-JanNA926--40rtpcrJian Wu \[[@bib51]\]Multicenter22-JanNA8046.139rtpcrJianlei Cao \[[@bib52]\]Multicenter3-JanNA102rtpcrJie Liu \[[@bib53]\]Union Hospital16 Jan 2020 to 15 FebNA643523rtpcrJing Yuan \[[@bib54]\]Shenzhen hospitalJan 23 23rd 2020 to Feb 21 21stNA25288rtpcrJinjun Zhang \[[@bib55]\]MulticenterJan 20 to Feb 20,30 DAY47846.9238rtpcrJin-Wei Ai \[[@bib56]\]HubeiUNUN10250.3852rtpcrJiong Wu \[[@bib57]\]Yancheng City22-JanNA804442rtpcrJun Chen \[[@bib58]\]ShanghaiJan 20 to Feb 6,14 DAY24951126rtpcrKaiyuan Sun \[[@bib59]\]MulticenterJan 13 and Jan 31NA50746281rtpcrKaiyue Diao \[[@bib60]\]WuhanJanuary 17th to February 5th30 DAY647.53rtpcrKenneth W. Tsang \[[@bib61]\]Hong KongFebruary 22, 2003, and March 2230 DAY1052.55rtpcrKui Liu \[[@bib62]\]MulticenterDecember 30, 2019 to January 2424 DAY1375761rtpcrL. Zhang \[[@bib63]\]MulticenterJan 13, 2020, to Feb 2640 DAY286517rtpcrLei Liu \[[@bib64]\]Hospital in ChongqingJanuary 20 to February 3,14 DAY514532rtpcrlei shu \[[@bib65]\]Wuhan Stadium Cabin HospitalFeb 13 to Feb 29,16 DAY54550264rtpcrLei Wang \[[@bib66]\]Zhengzhou UniversityJan 21 to Feb 05, 2020,14 DAY183910rtpcrLi Yan \[[@bib67]\]Tongji HospitalJanuary 10th to February 18th18 DAY37558.83220rtpcrLi-Li Ren \[[@bib68]\]wuhanDecember 18 to December 29, 201912 DAY5UN3rtpcrLin Fu \[[@bib69]\]Union HospitalJanuary 1 to January 3030 DAY200UN99rtpcrXiang Li \[[@bib70]\]Multicenter24-Feb-20NA29247·83134rtpcrMatt Arentz \[[@bib71]\]Evergreen hospitalFebruary 20, 2020, and March 515 DAY217011rtpcrNaibin Yang \[[@bib72]\]Zhejiang25th January to 28th FebruaryNA10333rtpcrPing Wu \[[@bib73]\]Yichang Central People\'s HospitalFebruary 9 to 15NA3865.825rtpcrQifang Bi \[[@bib74]\]Shenzhen,January 14 to February 1225 DAY39145187rtpcrQiurong Ruan \[[@bib75]\]Multicenter150rtpcrTao Yao \[[@bib76]\]Renmin hospitalNA5570.737rtpcrWen Zhao \[[@bib77]\]Beijing YouAn Hospital21st Jan and 8th February14 DAY775234rtpcrYani Kuang \[[@bib78]\]ZhejiangJanuary 17,NA1434777rtpcrYani Kuang \[[@bib79]\]Zhejiang,1-JanNA94447.4476rtpcrWan Chen \[[@bib80]\]Hospital of Guangxi Zhuang15-JanNA854134rtpcrXiaomin Luo \[[@bib81]\]Renmin hospitalJan 30 to Feb 2525 DAY40356193rtpcrXiaoyu Han \[[@bib82]\]Union Hospital,December 20 th and February 212 DAY17405rtpcrXun Li \[[@bib83]\]wuhanAs of February 13NA2571.4810rtpcrYan Deng \[[@bib84]\]wuhanJanuary 1,NA22554124rtpcrYang Wu \[[@bib85]\]wuhan13-JanNA14595ct and rtpcrYangli Liu \[[@bib86]\]Guangdong,December 8, 2019,NA13rtpcrYanli Liu \[[@bib87]\]Hospital of WuhanJanuary 2 to FebruaryNA1095559rtpcrYing Huang \[[@bib88]\]wuhanJan 21 and Feb 1020 DAY3669.2225rtpcrYing Wen \[[@bib89]\]MulticenterNA41745.4197rtpcrYingjie Wu \[[@bib90]\]wuhan12-JanNA402198rtpcrYuhui Wang \[[@bib91]\]wuhanJanuary 16 to February 1730 DAY904533rtpcrZhibing Lu \[[@bib92]\]MulticenterJanuary 1 to February1515 DAY12357.7861rtpcrZhiliang Hu \[[@bib93]\]Multicenterfrom Jan 28 to Feb 9, 202019 DAY24rtpcrPing Yu \[[@bib94]\]Shanghai7-Jan-20NA474.25ct scanAli Aminian \[[@bib95]\]tehran9-FebNA463.5ct scanHui Yu \[[@bib96]\]wuhanFeb. 1 to Mar. 3,NA1051--16 year64ct scanMatthieu Million \[[@bib97]\]France, multi centerMarch 3rd to March 31sNA106143.6\
14--95492Ct scan/rt pcrBai shaoliGansu Prov center22-januaryNA853.714Rt pcr[^2]

3.2. Quality assessment {#sec3.2}
-----------------------

Quality assessment of included studies were performed based on the Critical Appraisal Checklist and the final scores for quality of included studies are represented in [Table S2](#appsec1){ref-type="sec"} (in supplementary material). In brief, studies by Chen \[[@bib14]\], Wang \[[@bib15]\], Huang \[[@bib16]\], Guan \[[@bib17]\], Zhang \[[@bib18]\], Cheng \[[@bib19]\], Li \[[@bib20]\], Wei Xu \[[@bib21]\], and Song \[[@bib22]\] had the highest quality of the studies available in the purpose of this study.

3.3. Demographics, baseline characteristics, and clinical characterization {#sec3.3}
--------------------------------------------------------------------------

[Table 2](#tbl2){ref-type="table"} shows that 61, 742 confirmed patients with COVID-19 infection were included in the Meta-analysis, of which 55% (95% CI 50--57.5, p \< 0.001) were male. The most of the patients had fever 87% (95% CI 73--93, p \< 0.001) and cough 68% (95% CI 55.5--74, p \< 0.001). A much smaller proportion of patients had sore throat 14% (95% CI 7.8--17, p 0.06), headache 14% (95% CI 8.3--18, p \< 0.001), diarrhea 8% (95% CI 4.6--11.4, p \< 0.001), rhinorrhea 7% (95% CI 3--12, p 0.43) or nausea and vomiting 6.5% (95% CI 2.7--13, p \< 0.001). Most patients required hospitalization 81% (95% CI 68--94, p \< 0.001), 25.6% (95% CI 6.7--48, p \< 0.001) were deemed to be in critical condition and the mortality rate was 6% (95% CI 4--8.5, p \< 0.001) between all infected patients. [Table 3](#tbl3){ref-type="table"} shows that case fatality rate (CFR) in \<15 years old age groups was 0.6% (95% CI 0--0.9, p 1), \>50 years old was 39.5% (95% CI 28.5--52, p \< 0.001) ([Fig. 2](#fig2){ref-type="fig"} ), all range group was 6% (95% CI 4--8.5, p \< 0.001) ([Fig. 3](#fig3){ref-type="fig"} ).Table 2Demographics, baseline characteristics, and clinical outcomes of patients with confirmed COVID-19.Table 2Clinical presentation\*Confidence interval 95%Heterogeneity test, I2 (%)\*\*Heterogeneity test, P Value\*\*Number of StudiesAge, years48 (mean)43--5098\<0.00123Sex (Male)55 (%)50--57.588.4\<0.00124Fever87 (%)73--9398\<0.00118Cough68 (%)55.5--7486\<0.00118Fatigue39 (%)29--52.593\<0.00114Sputum production/Expectoration31 (%)19--3992\<0.0019Myalgia24 (%)14--4392\<0.0019Dyspnea24 (%)12.6--3292\<0.00111Sore throat14 (%)7.8--17520.069Headache14 (%)8.3--1877\<0.00116Diarrhea8 (%)4.6--11.470\<0.00118Rhinorrhea7 (%)3--1200.436Nausea and vomiting6.5 (%)2.7--1384\<0.0016OutcomeHospitalized81 (%)68--9495\<0.0017Critical condition/ICU25.6 (%)6.7--4899\<0.0018CFR (all age group)6 (%)4--8.589.6\<0.00149[^3]Table 3Meta-analysis on clinical presentation of case fatality rate (CFR) in different age groups of confirmed COVID-19 cases.Table 3Age groups (year)CFR (%)Confidence IntervalpatientsHeterogeneity test\*Lower limit (%)Upper limit (%)Number StudiesIncluded patientsI-squaredP-valueAll Range648.54954,25289.6\<0.001\>5039.528.55214193597\<0.001\<150.600.918201[^4]Fig. 2Forest plot of the meta-analysis on clinical presentation of case fatality rate (CFR) in different age groups of confirmed COVID-19 cases.Fig. 2Fig. 3Forest plot of the meta-analysis on clinical presentation of case fatality rate (CFR) in all age groups of confirmed COVID-19 cases.Fig. 3

3.4. Clinical characteristics, and Comorbid conditions of patients infected with COVID-19 {#sec3.4}
-----------------------------------------------------------------------------------------

The majority of patients, 62.5% (95% CI 54.5--79, p \< 0.001), had a history of recent travel endemic area or contact with them. A significant minority of patients (39.5%, 95% CI 20--56, p \< 0.001) had a history of chronic diseases and 26.5% (95% CI 9.6--49, p \< 0.001) had exposure at the seafood market(s) ([Table 4](#tbl4){ref-type="table"} ).Table 4Clinical Characteristics and Comorbid Conditions of patients with confirmed COVID-19.Table 4Risk FactorPatients with risk factor (%)Confidence interval 95%Heterogeneity test, I2 (%)\*Heterogeneity test, P Value\*Number of Studies reportingHistory of recent travel endemic area or contact with them62.554.5--7996\<0.00111Chronic diseases39.520--5695\<0.0016Exposure to seafood market26.59.6--4995\<0.0018Sick contacts with respiratory illness184.5--39.697\<0.0017Hypertension188.5--24.697.5\<0.00117ARDS17.54--26.795.7\<0.0018Diabetes94--1596\<0.00111Current smoker8.23.7--15690.018Chronic liver disease73.8--8.460.3812Digestive system disease4.52.5--4.995\<0.0018Health care worker162--4.6790.00812Past smoker41.1--7.5800.026Cardiovascular and cerebrovascular diseases3.32.2--2.598\<0.00114Chronic respiratory disease3.20.6--893\<0.0017Cancer2.70.4--7.496.3\<0.0019[^5]

3.5. Laboratory findings of patients infected with COVID-19 {#sec3.5}
-----------------------------------------------------------

The laboratory analysis and features showed that the most infected patients had increased platelets 61% (95% CI 41--78, p \< 0.001), and CRP 79% (95% CI 65--91, p \< 0.001), while others showed decreased lymphocytes, 57.5% (95% CI 42--79, p \< 0.001) ([Table 5](#tbl5){ref-type="table"} ).Table 5Laboratory features for confirmed patients with COVID-19.Table 5Confidence interval 95%normal rangeTotal Patient NumberNumber of Studies**Leucocytes (WBCs) (mean)**6.2 ( × 10⁹ per L)5.3--6.93.5--9.5296117*Increased*[a](#tbl5fna){ref-type="table-fn"}18.3 (%)6.4--25.6*Decreased*28 (%)21--33**Neutrophils (mean)**4.6 ( × 10⁹ per L)3.1--5.11.8--6.3121212**Lymphocytes (mean)**0.94 ( × 10⁹ per L)0.9--1.061.1--3.2316118*Decreased*57.5 (%)42--79**Platelets (mean)**196.5 ( × 10⁹ per L)167--205125--350290015*Decreased*13 (%)5--30*Increased*61 (%)41--78**CRP**[a](#tbl5fna){ref-type="table-fn"}**(mean)**32 (mg/L)19.7--46.50--0.588010*Increased*79 (%)65--91**Hemoglobin (mean)**113 (g/L)106--132130--175286212**ESR**\*\***(mean)**44 (mm/h)46--570--153204Albumin (mean)36.8 (g/L)24.5--4640--554205*Decreased*81%72--87**Interleukin-6 (mean)**8.1 (pg/mL)6.8--8.60.0--75096*Increased*56%42--61**LDH**\*\*\* **(mean)**286268--294120--250238312*Increased*69.3 (%)58--83[^6][^7]

3.6. Chest X-ray and CT scan findings in patients infected with COVID-19 {#sec3.6}
------------------------------------------------------------------------

Analysis showed that the most abnormality which finding with Chest X-ray and CT are bilateral involvement of chest radiography 81% (95% CI 62.5--87, p \< 0.001), consolidation 73.5% (95% CI 50.5--91, p \< 0.001), and ground-glass opacity 73.5% (95% CI 40--90, p \< 0.001) ([Table 6](#tbl6){ref-type="table"} ).Table 6Chest X-ray and CT scan Findings in Patients with Confirmed COVID-19.Table 6Abnormality (%)Confidence interval 95%Heterogeneity test, I2 (%)[a](#tbl6fna){ref-type="table-fn"}Heterogeneity test, P Value[a](#tbl6fna){ref-type="table-fn"}Number of StudiesBilateral involvement of chest radiography8162.5--8793\<0.00118Consolidation73.550.5--9189\<0.0019Ground-glass opacity73.540--9097\<0.00116Unilateral involvement of chest radiography18.58.5--29.594\<0.0019[^8]

4. Discussion {#sec4}
=============

COVID-19 belongs to the Coronaviridae family and is the newest serious zoonotic virus after the related viruses SARS and MERS \[[@bib23],[@bib24]\]. Prior to 2002, coronaviruses were associated with mild respiratory illness, but with the emergence of SARS in 2002, MERS in 2012, and now in late 2019, COVID-19, establishes that coronaviruses can be associated with severe respiratory disease. Genetic variation and phylogenetic analysis of these viruses show that the COVID-19 virus has 84% homology to other beta-coronaviruses, 96% sequence similarity at the whole genome level to a bat coronavirus and 79.5% similarity to the SARS virus \[[@bib8],[@bib25]\]. These results suggest that bats are important coronavirus reservoirs.

A study by Adam Bernheim et al. showed that among 121 COVID-19 patients, fever, cough and sputum production were the most common clinical symptoms \[[@bib3]\]. Our study found utilizing data from 52,251 patients with COVID-19 infection, that in additional to these, fatigue and myalgia (muscle soreness) were also common.

The large data set here finds that 81% of patients required hospitalization, 25.6% were found to be in critical condition and the mortality rate was 6% between all infected patients. The mortality rate is lower than some studies (for example, 11% in Nanshan et al. \[[@bib14]\]), but still higher than many viral infections. It should be recognized that these numbers are bias due to the data set including publications related to screening practices (e.g. only those with symptoms being screened) increased the % value. The true mortality rate from COVID-19 is almost certainly much lower than that found in this study. As more data emerges from screening asymptomatic or mildly symptomatic individuals in China and around the world, the true mortality rate will be better understood. Additionally, at the time of submission of this manuscript only \~50% of reported infected patients had recovered ([gisanddata.maps.arcgis.com](http://gisanddata.maps.arcgis.com){#intref0015}). Lymphopenia, age, multilobular infiltration, smoking history, hypertension, and bacterial co-infection have been reported as mortality risk factors. Underlying cardiovascular disease (40%) and bilateral pneumonia (81%) were common among those who have died. Recent travel endemic area or contact with them, exposure to persons with respiratory symptoms, and seafood market exposures were common amongst those contracting COVID-19. Among 2361 COVID-19 patients with laboratory data available, leukocytosis was found in 18.3% and leukopenia in 28% with lymphocytopenia in 57.5%. Among 2200 patients, thrombocytosis occurred in 61% and in a smaller sample (n = 290) CRP was increased in 79%.

A study by Yu Zhao et al. showed that ACE2 is a COVID-19 virus receptor and that it is normally expressed on pulmonary alveolar epithelial cells \[[@bib26]\]. ACE2 activates the RAS cascade, which can lead to hypertension. The pathology in this pathway can also stimulate fibrogenesis, inflammation, cell hypertrophy, and cell proliferation \[[@bib27],[@bib28]\]. ACE2 expression is increased in people with pulmonary ARDS and acute respiratory injury \[[@bib29]\]. The data collected here shows that ARDS occurred in 17.5% of reported patients with COVID-19 infection.

4.1. Limitations {#sec4.1}
----------------

Several limitations of this study exist. Publication bias and study heterogeneity are unavoidable in this type of study, therefore it should be considered when interpreting the outcomes of the reports and our final data set. Further, this study likely overestimates disease severity due to lack of screening of asymptomatic or mildly symptomatic individuals and subsequent publication bias related to these factors. It is very likely that many infected persons have not been detected, thus falsely elevating the rates of hospitalization, critical condition, and mortality.

5. Conclusions {#sec5}
==============

Fever and cough are the most common symptoms of COVID-19 infection in the literature published to date. Thombocytosis, lymphopenia, and increased CRP were common lab findings although most patients included in the overall analysis did not have laboratory values reported. The most common radiographic sign was bilateral involvement in and consolidation. Among Chinese patients with COVID-19, rates of hospitalization, critical condition, and hospitalization were high in this study, but these findings may be biased by reporting of only confirmed cases.

Declaration of competing interest
=================================

The authors have declared that no competing interests exist.

Funding {#appsec3}
=======

Dr. Bahr receives funding from the 10.13039/100000065National Institute of Neurological Disorders and Stroke of the National Institutes of Health, K23 NS110470.

Author contributions {#appsec4}
====================

Conceived and designed the study: AP, SG, Comprehensive research: SG, AK, AP, Analyzed the data: A P, MAM, Wrote and revised the paper: AP, SG, BB, AK, RT, MAM, NB, DK, JPI, Participated in data analysis and manuscript editing: AP, SG, BB, AK, RT, MAM, NB, DK, JPI.

Ethical statement {#appsec5}
=================

The manuscript is a systematic review, so the ethical approval was not required for the study.

Appendix A. Supplementary data {#appsec1}
==============================

The following is the Supplementary data to this article:Multimedia component 1Multimedia component 1

None.

Supplementary data to this article can be found online at <https://doi.org/10.1016/j.micpath.2020.104390>.

[^1]: Equally first author.

[^2]: NA = not known, RT-PCR= Real Time Polymerase Chain Reaction, CDC= Centers for Disease Control and Prevention, WHO= World Health Organization, CT scan = CT scan of chest, N = number, IQR = interquartile range.

[^3]: \*Age is an exception, presented in mean age in years. \*\* Greater than 50% is considered high heterogeneity, less than 50% is considered low heterogeneity. A low p value (\<0.05) is consistent with high heterogeneity. Case fatality rate (CFR).

[^4]: Case fatality rate (CFR), \* Greater than 50% is considered high heterogeneity, less than 50% is considered low heterogeneity. A low p value.

[^5]: ARDS = acute respiratory distress syndrome \* Greater than 50% is considered high heterogeneity, less than 50% is considered low heterogeneity. A low p value (\<0.05) is consistent with high heterogeneity.

[^6]: CRP= C Reaction Protein, ESR = Erythrocyte sedimentation rate. WBCs= White blood cells.

[^7]: Increased or Decreased refers to values above or below the normal range.

[^8]: Greater than 50% is considered high heterogeneity, less than 50% is considered low heterogeneity. A low p value (\<.05) is consistent with high heterogeneity. CT scan = CT scan.
