《【病毒外文文献】2017 High reproduction number of Middle East respiratory syndrome coronavirus in nosocomial outbreaks_ Mathematical mode》由会员分享，可在线阅读，更多相关《【病毒外文文献】2017 High reproduction number of Middle East respiratory syndrome coronavirus in nosocomial outbreaks_ Mathematical mode（24页珍藏版）》请在金锄头文库上搜索。
1、Accepted ManuscriptHigh reproduction number of Middle East respiratory syndrome coronavirus innosocomial outbreaks: Mathematical modelling in Saudi Arabia and South KoreaSunhwa Choi, Eunok Jung, Bo Youl Choi, Young Joo Hur, Moran KiPII:S0195-6701(17)30526-1DOI:10.1016/j.jhin.2017.09.017Reference:YJH
2、IN 5231To appear in:Journal of Hospital InfectionReceived Date: 4 July 2017Accepted Date: 20 September 2017Please cite this article as: Choi S, Jung E, Choi BY, Hur YJ, Ki M, High reproduction number of MiddleEast respiratory syndrome coronavirus in nosocomial outbreaks: Mathematical modelling in Sa
3、udiArabia and South Korea, Journal of Hospital Infection (2017), doi: 10.1016/j.jhin.2017.09.017.This is a PDF file of an unedited manuscript that has been accepted for publication. As a service toour customers we are providing this early version of the manuscript. The manuscript will undergocopyedi
4、ting, typesetting, and review of the resulting proof before it is published in its final form. Pleasenote that during the production process errors may be discovered which could affect the content, and alllegal disclaimers that apply to the journal pertain.MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 1 Hi
5、gh reproduction number of Middle East respiratory syndrome coronavirus in nosocomial 1 outbreaks: Mathematical modelling in Saudi Arabia and South Korea 2 3 Short title: High reproduction numbers of MERS-CoV 4 5 Sunhwa Choi,1 Eunok Jung,2 Bo Youl Choi,1 Young Joo Hur,3 Moran Ki4* 6 7 1Department of
6、Preventive Medicine, Hanyang University Medical College, Seoul, Korea 8 2Department of Mathematics, Konkuk University, Seoul, Korea 9 3Center for Infectious Disease Control, Korea Centre for Disease Control and Prevention, Cheongju, Korea 10 4Department of Cancer Control and Population Health, Gradu
7、ate School of Cancer Science and Policy, 11 National Cancer Centre, Goyang, Korea 12 13 *Corresponding author: Moran Ki, M.D., Ph.D. 14 Department of Cancer Control and Policy, Graduate School of Cancer Science and Policy 15 National Cancer Centre, 323 Ilsan-ro, Ilsandong-gu, Goyang 10408, Korea 16
8、Tel: +82-31-920-2736, Fax: +82-50-4069-4908, E-mail: morankincc.re.kr 17 18 Competing interests: None. 19 20 21 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 2 Data availability: All relevant data are available at http:/rambaut.github.io/MERS-22 Tools/cases2.html. 23 24 Funding: This work was supported by
9、the National Cancer Centre Grant (NCC-1710141-1). 25 26 Keywords: nosocomial infection; basic reproduction number; epidemiology; Middle East 27 respiratory syndrome coronavirus; mathematical modelling; South Korea 28 29 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 3 Summary 30 Background: Effective counte
10、rmeasures against emerging infectious diseases require an 31 understanding of transmission rate and basic reproduction number (R0). The R0 for severe acute 32 respiratory syndrome (SARS) is generally considered to be 1, whereas that for Middle East 33 respiratory syndrome (MERS) is considered to be
11、1. However, this does not explain the large-34 scale outbreaks of MERS that occurred in Kingdom of Saudi Arabia (KSA) and South Korean 35 hospitals. 36 Aim: To estimate R0 in nosocomial outbreaks of MERS. 37 Methods: R0 was estimated using the incidence decay with an exponential adjustment model. 38
12、 The KSA and Korean outbreaks were compared using a line listing of MERS cases compiled using 39 publicly available sources. Serial intervals to estimate R0 were assumed to be 68 days. Study 40 parameters (R0 and countermeasures d) were estimated by fitting a model to the cumulative 41 incidence epi
13、demic curves using Matlab. 42 Findings: The estimated R0 in Korea was 3.9 in the best-fit model, with a serial interval of 6 days. 43 The first outbreak cluster in a Pyeongtaek hospital had an R0 of 4.04, and the largest outbreak 44 cluster in a Samsung hospital had an R0 of 5.0. Assuming a 6-day se
14、rial interval, the KSA 45 outbreaks in Jeddah and Riyadh had R0 values of 3.9 and 1.9, respectively. 46 Conclusion: The R0 for the nosocomial MERS outbreaks in KSA and South Korea was estimated 47 to be in the range of 25, which is significantly higher than the previous estimate of 1. 48 Therefore,
15、more comprehensive countermeasures are needed to address these infections. 49 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 4 Introduction 50 The emergence of infectious diseases associated with Middle East respiratory syndrome (MERS), 51 severe acute respiratory syndrome (SARS), and Ebola has created unpr
16、ecedented public health 52 challenges. These challenges are complicated by the lack of basic epidemiological data, which 53 makes it difficult to predict epidemics. Thus, it is important to quantify actual outbreaks as 54 novel infectious diseases emerge. Disease severity and rate of transmission ca
17、n be predicted by 55 mathematical models using the basic reproduction number (R0).1 For example, R0 has been 56 extensively used to assess pathogen transmissibility, outbreak severity, and epidemiological 57 control.2-4 58 59 In previous studies, the R0 for MERS has ranged from 0.42 to 0.92,5-8 whic
18、h suggests that the 60 MERS coronavirus (MERS-CoV) has limited transmissibility. However, these studies typically 61 considered community-acquired MERS infections. In this context, nosocomial infections can 62 exhibit different reproduction numbers, as the transmission routes for community-acquired
19、and 63 nosocomial infections often differ.9 Recent studies have also examined large healthcare-64 associated outbreaks of MERS-CoV infection in Jeddah and Riyadh within the Kingdom of Saudi 65 Arabia (KSA). One study reported higher healthcare-acquired R0 values than those from 66 community-acquired
20、 infections when using the incidence decay with exponential adjustment 67 (IDEA) model, which yielded values of 3.56.7 in Jeddah and 2.02.8 in Riyadh.10 The IDEA 68 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 5 model is simple because it does not consider the population-level immune status, which makes 6
21、9 it especially useful for modelling emerging infectious diseases in resource-limited settings. 70 The MERS outbreak in South Korea was associated with hospital-acquired infections. At that 71 time, the Korea Centre for Disease Control and Prevention (KCDC) assumed that the outbreak 72 had an R0 1.
22、Thus, the initial countermeasures were not sufficiently aggressive to prevent the 73 spread of MERS-CoV infection to other hospitals. Therefore, we used the IDEA model to 74 evaluate and compare the MERS R0 values from the outbreaks in both the KSA and South Korean 75 hospitals. 76 77 78 MANUSCRIPT
23、ACCEPTEDACCEPTED MANUSCRIPT 6 Methods 79 Data source 80 The KSA data were obtained using a line listing of MERS-CoV cases that was maintained by 81 Andrew Rambaut (updated on 19 August 2015). The line listing was created using data from the 82 KSA Ministry of Health and World Health Organization rep
24、orts (WHO).10 Since only 44% of the 83 cases in the KSA listing included the onset date, hospitalization dates or reported dates were 84 used instead. The Korean data were obtained from the KCDC. Among the 186 MERS cases, 178 85 had confirmed onset dates. The eight cases with unknown dates of onset
25、were assigned dates 86 based on those of laboratory confirmations. All cases in the KSA and Korea were confirmed 87 based on laboratory findings. Study parameters (R0 and countermeasures d) were estimated 88 by fitting a model to the cumulative incidence epidemic curves using Matlab software 89 (Mat
26、hworks, Natick, MA, USA). 90 91 The data were narrowed down to only the hospital infection cases. Cases with unknown 92 transmissions were considered to be hospital infections if a) the patient was in contact with a 93 healthcare worker and/or hospitalized patients, or b) the patient was a healthcar
27、e worker. Cases 94 were excluded if they could not be verified as hospital infections (e.g., zoonotic transmission, 95 family contact, or community infection). 96 97 98 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 7 Model 99 We used the IDEA model to estimate the R0 as reported previously,11 together with
28、 publicly 100 available data. The IDEA model is based on the concept that the number of incident cases (?) in 101 an epidemic generation (?) that can be counted as: 102 ?(?) = ?. (1) When an outbreak occurs, epidemic control measures can be implemented, which can, in turn, 103 change the R0. Therefo
29、re, the relationship between I and R0 with countermeasures (?) is defined 104 as follows: 105 ?(?) = ?(1 + ?)?. (2) The R0 and d parameters are estimated by fitting ? from model (2) to the observed cumulative 106 incidence data of MERS using the least-squares data-fitting method. Since the IDEA mode
30、l is 107 parameterized using epidemic generation time, in the present study, incidence case counts were 108 aggregated at serial intervals of 6, 7, and 8 days.10 109 We considered two large outbreaks in each country studied: the outbreaks in Riyadh and 110 Jeddah for the KSA, and those in Pyeongtaek
31、 St. Marys Hospital, and Samsung Seoul Hospital 111 for South Korea. The term resnorm is defined as the norm of the residual, which is the squared 112 2-norm of the residual; it measures the difference between observed data and the fitted value 113 provided by a model. However, since residuals can b
32、e positive or negative, a sum of residuals is 114 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 8 not a good measure of overall error in the fit. Therefore, a better measure of error is the sum of 115 the squared residuals (E), which is calculated as follows: 116 117 E = (?(?,?data?) ?data?)?. 118 (3) 119
33、120 The given input data (xdata), the observed output data, (ydata), and F(x, xdata) are the 121 functions we wanted to fit, where xdata was an epidemic generation, ydata was the observed 122 cumulative incidence data, and F(x, xdata) was equation (2). 123 Since the generation times and the estimate
34、d values differ according to serial interval times, the 124 resnorm changes accordingly. Therefore, to compare the resnorm with the serial interval time, 125 the relative resnorm was defined as follows: 126 E = (?(?,?data?)?data?)?data?. (4) 127 128 The IDEA model was fitted to the cumulative South
35、Korean MERS-CoV case data from the onset 129 date of the first case to the onset date of the last case. The outbreak start date was defined as 130 11 May 2015 because that was the symptom onset date for Patient Zero, who was the index 131 case and caused the outbreak in the Pyeongtaek hospital. MERS
36、 patient no. 14 caused the 132 outbreak at the Samsung hospital, and his symptom onset date was 21 May 2015. The last case 133 of the MERS outbreak in South Korea was observed on 4 July 2015. The KSA MERS outbreak 134 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 9 model was fitted using the cumulative inc
37、idence data from 28 March 2014 to 2 June 2014 in 135 Jeddah and from 20 March 2014 to 29 May 2014 in Riyadh. 136 137 Ethical Considerations 138 All data used in these analyses were de-identified publicly available data obtained from the 139 WHO, the KSA Ministry of Health website, or KCDC datasets.
38、As such, these data were deemed 140 to be exempt from institutional review board assessment. 141 142 143 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 10 Results 144 The KSA outbreaks were relatively large, with 180 cases (over the course of 67 days) in Jeddah 145 and 142 cases (over the course of 71 days)
39、 in Riyadh. The Korean outbreaks involved 186 cases 146 (over the course of 55 days), including 36 cases (over the course of 23 days) in the Pyeongtaek 147 hospital, and 91 cases (over the course of 45 days) in the Samsung hospital. Most Korean cases 148 (180) were hospital acquired, with the except
40、ion of four cases acquired by household 149 transmission and two cases with unknown modes of transmission. In the KSA, only two cases 150 involved confirmed zoonotic transmission, while a large number of unknown transmissions 151 (Jeddah: 99 cases; Riyadh: 69 cases) and hospital exposures (Jeddah: 8
41、0 cases; Riyadh: 70 cases) 152 were observed (Table I). 153 154 The IDEA model was fitted to the daily KSA and Korea MERS-CoV case data according to the 155 onset date. Figure 1 displays the cumulative MERS-CoV case data for the 2014 KSA and the 2015 156 South Korea MERS outbreaks. Patient Zeros sym
42、ptom-onset date was 11 May 2015; however, 157 he was admitted to the Pyeongtaek hospital on 15 May 2015. Therefore, the outbreak was 158 assumed to start on 15 May 2015 via a simulation of the Pyeongtaek hospital outbreak. The 159 outbreak start date for the Samsung hospital was determined to be 25
43、May 2015, following the 160 same logic (Figure 1). 161 162 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 11 Figure 2 shows the results of the 2014 KSA outbreak. Squares (), circles (), and asterisks (*) 163 represent data aggregation of the number of cases by serial intervals of 6, 7, and 8 days; the 164 c
44、urves represent model fits for best-fit parameters. Our estimated R0 values for Jeddah and 165 Riyadh were in the range of 3.956.68 and 1.922.52, respectively, using serial intervals of 68 166 days. The estimated R0 values for the Korea MERS outbreak were 3.96, 4.91, and 5.95 for serial 167 interval
45、s of 6, 7, and 8 days, respectively (Figure 3). Since most cases were related to hospital-168 acquired infections, the R0 for each hospital was also considered. The outbreak in the Samsung 169 hospital was larger than that in the Pyeongtaek hospital (the first Korean outbreak). The 170 Pyeongtaek ho
46、spital exhibited best-fit R0 values of 4.04, 4.23, and 4.39 for serial intervals of 6, 7, 171 and 8 days, respectively, while the Samsung hospital exhibited greater R0 values of 5.0, 6.8, and 172 8.11 for serial intervals of 6, 7, and 8 days, respectively. Figure 3 shows that the IDEA model 173 prov
47、ided well-fitted curves for the cumulative data regarding South Korean MERS symptom-174 onset dates for all cases. 175 176 Although the IDEA model seemed to be appropriate, the original data never precisely fit the 177 model. Therefore, the appropriateness of the model was assessed. Error was evalua
48、ted using 178 the relative resnorm to find the best-fit parameters. The results indicated that the best-fit R0 179 and serial interval values were 4.9 and 7 days for all cases, 4.39 and 8 days for the Pyeongtaek 180 hospital, and 5.0 and 6 days for the Samsung hospital, respectively. Countermeasures
49、 (termed 181 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 12 “d”) increased with each serial interval because the daily effort of countermeasures was 182 aggregated by serial interval. 183 184 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 13 Discussion 185 The clusters of MERS-CoV cases in KSA healthcare facilit
50、ies occurred from late March to late 186 May 2014, while the Korean outbreaks occurred from mid-May to early July in 2015. These 187 hospital-based outbreaks exhibited characteristics different from those of community-based 188 outbreaks (higher R0 values and case fatality rates).12, 13 189 190 The
51、estimated R0 is a basic epidemiological variable that is important for selecting appropriate 191 countermeasure efforts. However, an emerging infectious disease often has an unknown 192 epidemiology, making it difficult to mathematically model. Several methods have been 193 proposed to address this
52、issue, including the IDEA model. The Richards model can also estimate 194 the R0 using the cumulative daily number of cases and the outbreak turning point (or the peak, 195 ?).14 In this context, Hsieh used the Richards model to estimate the R0 values for the Korean 196 outbreak as 7.019.3. Yet, the
53、 Richards model does not consider any countermeasures 197 implemented during an outbreak; therefore, it can only be used after an outbreak has peaked. 198 199 The present study used the IDEA model to estimate the R0 values from the MERS outbreaks in 200 the KSA and South Korea. The IDEA model exhibi
54、ted a good fit: the estimated R0 values for South 201 Korea were 3.98.0, and the best-fit R0 was 4.9 for a serial interval of 7 days. Conversely, the R0 202 values for Riyadh and Jeddah were 1.92.5 and 3.96.9, respectively, using serial intervals of 6203 8 days. Majumder et al.10 used the IDEA model
55、 and estimated very similar R0 values of 2.02.8 204 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 14 for Riyadh and 3.56.7 for Jeddah, with serial intervals of 68 days. However, the estimated R0 205 values from the present study were much higher than the previously reported values of 1 for 206 MERS (the th
56、reshold for an epidemic).15 Regardless, the Korean government assumed that the 207 outbreak had an R0 value of 1 based on the previous research. The initial criterion for 208 quarantine, therefore, was limited to cases of “close contacts,” which were defined as people 209 who were within 2 metres of
57、 a MERS patient for 1 hour.16 These quarantinesestablished 210 using an incorrectly assumed R0resulted in more MERS patients and greater hospital-to-211 hospital transmission.16 212 213 A serial interval is the interval between successive cases of an infectious disease. This time 214 period depends
58、on the temporal relationship between the infectiousness of the disease, the 215 clinical onset of the source case, and the incubation period of the receiving case.17 As MERS 216 becomes infectious with the onset of clinical symptoms, the MERS latency period equals the 217 incubation period. Therefor
59、e, the shortest serial interval could be the same as the incubation 218 period, and the longest serial interval could be the sum of the incubation period and the 219 maximum duration of infectiousness. During the Korean MERS outbreak, several super-220 spreading events occurred because the MERS case
60、s were not immediately isolated upon 221 presentation of clinical symptoms.18 Thus, these cases contacted susceptible individuals for up 222 to 1 week after the onset of their clinical symptoms. However, most MERS cases with laboratory 223 confirmation were isolated immediately after clinical-sympto
61、m onset.19, 20 In this study, since 224 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 15 the incubation period was 214 days (median: 6 days), the serial interval was slightly longer 225 than the incubation period. The IDEA model with several serial intervals (412 days) was used 226 and found that intervals
62、 of 68 days provided the best fit. For the KSA data, even though the 227 reported date was used instead of the onset date, the R0 was not affected because aggregated 228 data by serial intervals was used in the analysis. 229 230 The IDEA model is limited by the fact that the countermeasures term (d)
63、 cannot be compared 231 with the d of another model. In this context, an increasing d in accordance with increasing serial 232 intervals indicates that the countermeasure efforts are increasing. However, the size of d cannot 233 be compared between two or more models of different outbreaks. Neverthe
64、less, the strength of 234 the IDEA model is its simplicity because the R0 value can be estimated using only the cumulative 235 number of cases according to the serial interval. 236 237 Conclusions 238 The estimated R0 values from the KSA outbreaks (Riyadh and Jeddah) ranged from 1.9 to 6.9, 239 wher
65、eas the estimated values from the South Korean outbreaks ranged from 3.9 to 8.0. Based 240 on these findings, it appears that nosocomial MERS-CoV outbreaks in the KSA and South Korea 241 had higher R0 values than the previously assumed values of 1. Although community-acquired 242 infections are caus
66、ed by contact, nosocomial infections are caused by a combination of contact 243 and aerosol transmission; therefore, R0 values for hospital infections can be higher than those 244 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 16 for community-acquired infections. Hence, more comprehensive countermeasures a
67、re needed 245 to address nosocomial MERS infection and prevent its spread. 246 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 17 References 247 1 Chowell G, Sattenspiel L, Bansal S, Viboud C. Mathematical models to characterize 248 early epidemic growth: A review. Phys Life Rev 2016; 18: 66-97. 249 2 Riley
68、S, Fraser C, Donnelly CA, Ghani AC, Abu-Raddad LJ, Hedley AJ, et al. Transmission 250 dynamics of the etiological agent of SARS in Hong Kong: impact of public health interventions. 251 Science 2003; 300: 1961-6. 252 3 Kaplan EH, Craft DL, Wein LM. Emergency response to a smallpox attack: the case fo
69、r 253 mass vaccination. Proc Natl Acad Sci U S A 2002; 99: 10935-40. 254 4 Velasco-Hernandez JX, Gershengorn HB, Blower SM. Could widespread use of 255 combination antiretroviral therapy eradicate HIV epidemics? Lancet Infect Dis 2002; 2: 487-93. 256 5 Kucharski AJ, Althaus CL. The role of superspre
70、ading in Middle East respiratory 257 syndrome coronavirus (MERS-CoV) transmission. Euro Surveill 2015; 20: 14-8. 258 6 Breban R, Riou J, Fontanet A. Interhuman transmissibility of Middle East respiratory 259 syndrome coronavirus: estimation of pandemic risk. Lancet 2013; 382: 694-9. 260 7 Fisman DN,
71、 Leung GM, Lipsitch M. Nuanced risk assessment for emerging infectious 261 diseases. Lancet 2014; 383: 189-90. 262 8 Cauchemez S, Fraser C, Van Kerkhove MD, Donnelly CA, Riley S, Rambaut A, et al. 263 Middle East respiratory syndrome coronavirus: quantification of the extent of the epidemic, 264 sur
72、veillance biases, and transmissibility. Lancet Infect Dis 2014; 14: 50-6. 265 9 Nishiura H, Endo A, Saitoh M, Kinoshita R, Ueno R, Nakaoka S, et al. Identifying 266 determinants of heterogeneous transmission dynamics of the Middle East respiratory syndrome 267 (MERS) outbreak in the Republic of Kore
73、a, 2015: a retrospective epidemiological analysis. BMJ 268 Open 2016; 6: e009936. 269 10 Majumder MS, Rivers C, Lofgren E, Fisman D. Estimation of MERS-Coronavirus 270 Reproductive Number and Case Fatality Rate for the Spring 2014 Saudi Arabia Outbreak: Insights 271 from Publicly Available Data. PLo
74、S Curr 2014; 6. 272 11 Fisman DN, Hauck TS, Tuite AR, Greer AL. An IDEA for short term outbreak projection: 273 nearcasting using the basic reproduction number. PLoS One 2013; 8: e83622. 274 12 Kim KM, Ki M, Cho SI, Hong JK, Cheong HK, Kim JH, et al. Epidemiologic features of the 275 first MERS outb
75、reak in Korea: focus on Pyeongtaek St. Marys Hospital. Epidemiol Health 2015; 276 37: e2015041. 277 13 Majumder MS, Kluberg SA, Mekaru SR, Brownstein JS. Mortality Risk Factors for 278 Middle East Respiratory Syndrome Outbreak, South Korea, 2015. Emerg Infect Dis 2015; 21: 279 2088-90. 280 14 Hsieh
76、YH. 2015 Middle East Respiratory Syndrome Coronavirus (MERS-CoV) 281 nosocomial outbreak in South Korea: insights from modeling. PeerJ 2015; 3: e1505. 282 15 Lee J, Chowell G, Jung E. A dynamic compartmental model for the Middle East 283 respiratory syndrome outbreak in the Republic of Korea: A retr
77、ospective analysis on control 284 interventions and superspreading events. J Theor Biol 2016; 408: 118-26. 285 16 Ki M. 2015 MERS outbreak in Korea: Hospital-to-Hospital Transmission. Epidemiol 286 Health 2015; doi 10.4178/epih/e2015033. 287 17 Fine PE. The interval between successive cases of an in
78、fectious disease. Am J Epidemiol 288 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 18 2003; 158: 1039-47. 289 18 Kim SW, Park JW, Jung HD, Yang JS, Park YS, Lee C, et al. Risk factors for transmission of 290 Middle East respiratory syndrome coronavirus infection during the 2015 outbreak in South Korea. 291
79、 Clin Infect Dis 2016; doi 10.1093/cid/ciw768. 292 19 Park GE, Ko JH, Peck KR, Lee JY, Lee JY, Cho SY, et al. Control of an Outbreak of Middle 293 East Respiratory Syndrome in a Tertiary Hospital in Korea. Ann Intern Med 2016; 165: 87-93. 294 20 Cho SY, Kang JM, Ha YE, Park GE, Lee JY, Ko JH, et al.
80、 MERS-CoV outbreak following a 295 single patient exposure in an emergency room in South Korea: an epidemiological outbreak 296 study. Lancet 2016; 388: 994-1001. 297 298 299 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 19 Tables 300 Table I. Characteristics of selected MERS outbreaks in Saudi Arabia and
81、South Korea 301 Saudi Arabia South Korea Jeddah Riyadh Total Pyeongtaek Hospital Samsung Hospital Outbreak Onset date 28/3/2014 20/3/2014 11/5/2015 15/5/2015 25/5/2015 Duration (day) 67 71 55 23 45 No. of cases 180 142 186 36 91 Exposure Hospital 801 701 180 36 88 Household 4 0 3 Zoonotic 1 1 0 0 0
82、Unknown 99 69 2 0 0 Status2 Healthcare worker 40 8 39 3 15 Patient 82 20 36 Family or visitor 63 13 40 Unknown 140 134 2 0 0 Date3 Onset date 75 66 178 36 85 Hospitalized date 85 79 186 36 91 Reported date 180 142 186 36 91 1 Hospital exposure cases included healthcare workers and individuals who we
83、re in contact with a healthcare 302 worker or hospitalized patients. 303 2 The status of cases when they were exposed to MERS. 304 3 The number of cases with information for onset date, hospitalization date, and reported date of MERS. 305 306 307 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT 20 Figures 308
84、 Legends 309 Figure 1. Epidemic curves of cumulative cases by selected MERS outbreaks in Saudi Arabia and 310 South Korea. 311 Figure 2. Best-fit Ro by serial intervals of MERS in Jeddah and Riyadh, Saudi Arabia, 2014, using 312 the IDEA model. 313 Figure 3. Best-fit Ro by serial intervals of MERS i
85、n South Korea, 2015, using the IDEA model. 314 MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT Figure 1. Epidemic curves of cumulative cases by selected MERS outbreaks in Saudi Arabia and South Korea. 050100150200Reported dateNo. of case 20 Mar 1429 Mar 1407 Apr 1416 Apr 1425 Apr 1404 May 1413 May 1422 May 1
86、402 Jun 14050100150200No. of case 15 May 1524 May 1502 Jun 1511 Jun 1520 Jun 1529 Jun 1504 Jul 15JeddahRiyadhTotalPyeongtaekSamsungMANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT Serial Interval ? Resnorm Jeddah Riyadh Jeddah Riyadh 6 3.9463 1.9168 2.7971 23.8599 7 5.0505 2.3247 5.6315 32.9805 8 6.6806 2.525
87、2 6.4178 14.3884 Figure 2. Best-fit Ro by serial intervals of MERS in Jeddah and Riyadh, Saudi Arabia, 2014, using the IDEA model. 0123456789101112020406080100120140160180GenerationCumulative incidence Jeddah-dataJeddah (6)Jeddah-dataJeddah (7)Jeddah-dataJeddah (8)Riyadh-dataRiyadh (6)Riyadh-dataRiy
88、adh (7)Riyadh-dataRiyadh (8)MANUSCRIPT ACCEPTEDACCEPTED MANUSCRIPT Serial Interval ? Resnorm Total Pyeongtaek Hospital Samsung Hospital Total Pyeongtaek Hospital Samsung Hospital 6 3.9555 4.0426 5.0000 22.6323 14.8974 27.9525 7 4.9125 4.2315 6.8006 40.5951 27.8792 46.7812 8 5.9531 4.3935 8.1151 34.0529 36.2232 64.0210 Figure 3. Best-fit Ro by serial intervals of MERS in South Korea, 2015, using the IDEA model. 0123456789020406080100120140160180200GenerationCumulative incidence Total (6)Total (7)Total (8)Pyeongtaek (6)Pyeongtaek (7)Pyeongtaek (8)Samsung (6)Samsung (7)Samsung (8)
