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Open Access Full Text Article ORIGINAL RESEARCH Trend Dynamics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission in 16 Cities of Province, China

This article was published in the following Dove Press journal: Clinical Epidemiology

Muhammad Fawad 1,2 Objective: A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was detected Sumaira Mubarik 3 by researchers from a patient in , Hubei province, China, in December 2019, and Saima Shakil Malik 4 broke out in January 2020. Then, the pandemic was detected in countries around the world. Yangyang Hao2 Therefore, precise estimates of its current and future trends are highly required for future Chuanhua Yu 3 policy implications. Methods: We retrieved data from the Health Commission of Hubei, China. Logistic-S curve Jingli Ren 1 model was used to estimate the current and future trends of SARS-CoV-2-infected cases 1Henan Academy of Big Data, among 16 cities of Hubei, China from Jan-11 to Feb-24, 2020. ’ University, Zhengzhou 450052, People s fi Republic of China; 2School of Results: Out of 64,287 con rmed cases of SARS-CoV-2 infection in Hubei, higher percen- Mathematics and Statistics, Zhengzhou tage of cases were in Wuhan and . The highest death percentage was found in ’ University, Zhengzhou 450001, People s Wuhan and Qianjiang. A significant percentage of cures were found in Enshi Prefecture and Republic of China; 3Department of Epidemiology and Biostatistics, School of , while Wuhan showed the lowest percentage of cures. Rising trends in infected Health Sciences, , cases were observed throughout the study period, particularly in Wuhan, and a higher trend ’ Wuhan, Hubei 430071, People s Republic was observed after 12-Feb. Gradual decline trend of SARS-CoV-2 cases was observed during of China; 4Department of Zoology, University of Gujrat, Gujrat 50700, Feb-25 to Mar-15 in Hubei Province. Future forecast showed that the average number of Pakistan SARS-CoV-2-infected cases might be decreased or stable in Hubei in the coming 20 days. Conclusion: The public must take precautionary measures in order to control and prevent disease spread and avoid extra travelling. Keywords: SARS-CoV-2, death, trends, cities, Wuhan, China

Introduction In the last two decades, coronavirus has produced two large-scale pandemics, including Severe acute respiratory syndrome (SARS) and the Middle East respira- tory syndrome (MERS).1,2 Since the outbreak of SARS some eighteen years ago, various severe acute respiratory syndrome-related Coronaviruses (SARS-CoV) have been revealed in bats being their natural host reservoir1,3-6 and have potential to cause future disease outbreaks.7,8 Literature has shown that some of these Coronaviruses are capable of infecting humans and can lead to severe health conditions. In December 2019, a series of pneumonia cases of unknown cause appeared in Wuhan, Hubei, China, with viral pneumonia relating clinical presenta- Correspondence: Jingli Ren tion. Sequencing of samples from the lower respiratory tract of infected patients Academy of Big Data, Zhengzhou University, Zhengzhou 450052, People’s confirmed the emergence of a novel virus named as Severe Acute Respiratory Republic of China Syndrome Coronavirus 2 (SARS-CoV-2).9 As of 2nd June 2020, 84,597, cases Tel +86-13653827917 10 Email [email protected] and 4645 deaths were reported only in China. Due to inadequate quarantine, cases submit your manuscript | www.dovepress.com Clinical Epidemiology 2020:12 699–709 699 DovePress © 2020 Fawad et al. This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms. – http://doi.org/10.2147/CLEP.S254806 php and incorporate the Creative Commons Attribution Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). Fawad et al Dovepress have been reported in many other countries including Commission of the People’s Republic of China, and the Nepal, Thailand, South Korea, Japan, Taiwan, Singapore, Health Commission of each Province, which publish daily Nepal, United States, Vietnam, Iran and Pakistan.11–19 epidemic data. (http://wjw.hubei.gov.cn/bmdt/ztzl/ Clinicians and WHO (World Health Organization) have fkxxgzbdgrfyyq/xxfb/202002/t20200224_2146325.shtml). reported human to human transmission leading to a much We have included confirmed cases (64,287), cured wider spread of the disease. The genome of SARS-CoV-2 cases (16,738) and death (2495) cases, recorded from 11 encoded endonuclease; therefore, the rate or chances of Jan 2020 to 24 Feb 2020. The total number of cases varied mutation are much lower than other viruses making this from 194 to 46,607 among 16 different cities of Hubei zoonotic pathogen more virulent by effective person to province. person transmission.7 Unfortunately, no vaccine or drug has been approved 20 Analytical Analysis yet to treat human coronaviruses completely. Numerous Descriptive analysis included frequencies and percentages options can be envisioned like vaccines, oligonucleotide- of collected cases for each parameter of interest. Summary based therapies, monoclonal antibodies, small molecule- trends were analyzed by time for 16 cities separately. based drugs, interferon therapies and peptides either to Further, the logistic S-curve model was used to estimate 8,20-22 prevent or control the emerging (SARS-CoV-2). the trends of SARS-CoV-2 cases among 16 cities of Hubei However, new interventions needed more extended peri- province China. Mean Absolute Percentage Error ods to develop spanning over months to years. Therefore, (MAPE), Mean Absolute Deviation (MAD) and Mean due to urgency of the novel SARS-CoV-2 outbreak, scien- Squared Deviation (MSD) were used to evaluate the pre- tists and clinicians are focusing in using the potential of dicted models. Logistic S-curve has many applications in existing antiviral agents (approved or in the process of different fields; for example, biologists have used it to approval) based on their therapeutic experiences with illustrate the growth of a specie under competition. 23,24 SARS and MERS. The most commonly used anti- Similarly, it has been used in medicine to show the spread viral agents all over the world include chloroquine, lopi- of epidemic diseases.30,31 The fascinating part of the logis- navir/ritonavir, hydroxychloroquine, remdesivir and tic S-curve fitting is the ability to forecast from early sizes 25–27 favipiravir. Two other drugs named ivermectin and the last maximum, a reality that frequently stuns and nitazoxanide have recently been reported to show an activ- occasionally vexes individuals, with its intrinsic compo- ity against SARS-CoV-2 in vitro and are approved/ nent of predetermines. licensed for the treatment of some other human The logistic S-curve was initially proposed by Pierre- 28,29 infections. François,32 in the context of population growth. It is a type Recently, various articles have been published report- of curve that displays the growth of the understudy vari- ing individual cases, hazardous health effects, poor quar- able in terms of another variable, often expressed as a unit antine, and risk of pandemics, genetic make-up, clinical of time. Common characteristics of Logistic S-curve, at interventions and disease manifestations. Here, we will first, are that the growth is approximately exponential accurately report the incidence of SARS-CoV-2 in 16 (geometric), then slows down to linear (arithmetic), fol- different cities of Hubei, China, with particular emphasis lowed by rapid growth and followed by slow growth again on the incidence of infected cases in different cities of to an asymptotic maximum. The three parameters L, β and

Hubei province with respect to time (Jan-11 to Feb-24, x0 describing a logistic S-curve form: 2020). In addition, we plan to estimate the current and ðÞ¼ L future trends of SARS-CoV-2-infected cases with logistic fx βðÞ (1) 1 þ e x x0 S-curve model among 16 different cities of Hubei pro- vince, China to present the exact situation of this disease. where L is the maximum value of the curve, e is the natural logarithm base, β is the logistic growth rate, and x0 is the x-value of the sigmoid’s midpoint. The change in

Materials and Methods values of β and x0 in the above equation (1) affects the Data Source exact shape of the logistic S-curve. β affects how steeply The data of cases diagnosed with SARS-CoV-2 were the function rises as it passes through its midpoint; how- retrieved from the Official website of the National Health ever, the x0 is computed at which time the midpoint

700 submit your manuscript | www.dovepress.com Clinical Epidemiology 2020:12 DovePress Dovepress Fawad et al occurs. Accuracy of the forecast are measure by using, Table 1 Summary of SARS-CoV-2-Infected Cases in 16 Cities of Mean Absolute Percentage Error (MAPE), Mean Absolute Hubei Province Deviation (MAD), Mean Squared Deviation (MSD) and Cities Total Number of Death Cured expressed as: Name Number of Existing Cases Cases ðÞ ^ yt yt = ∑ yt Wuhan 46,607 35,674 1987 8946 MAPE ¼ 100 ðy Þ0Þ (2) n t (76.54%) (4.26%) (19.19%) Xiaogan 3465 2180 108 1177 n jjy ^y MAD ¼ ∑ t t (3) (62.91%) (3.12%) (33.97%) t¼1 n Huanggang 2904 1142 103 1659 (39.33%) (3.55%) (57.13%) n jjy ^y 2 MSD ¼ ∑ t t (4) 1574 798 42 734 t¼1 n (50.70%) (2.69%) (46.63%) In the above equations (from 2 to 4), yt is the actual value 1383 878 40 465 (variable understudy) at time t, ^y denotes the fitted value, t (63.48%) (2.89%) (33.62) and n is the number of observations. MAPE, MAD and MSD measure the accuracy of fitted time series values. 1303 719 33 551 (55.18%) (2.53%) (42.29%) MAPE expresses accuracy as a percentage, where MAD measures accuracy in the same units as data, which helps 1174 624 29 521 in conceptualizing the amount of error. On the other hand, (53.15%) (2.47%) (44.38%) MSD is always calculated using the same denominator, n, 1005 485 30 490 and it is a more sensitive measure of an unusually large (48.26%) (2.98%) (48.76%) forecast error than MAD. 924 591 29 304 (63.96%) (3.14%) (32.9%)

Results 920 494 37 389 Descriptive Analysis (53.69%) (4.02%) (42.28%) A summary of infected cases of SARS-CoV-2 in 16 dif- 836 380 11 445 ferent cities located in the Hubei province is presented in (45.45%) (1.31%) (53.23%) Table 1. Wuhan has the highest death percentage (4.26%), Shiyan 669 374 2 293 while Qianjiang (4.12%) and Jingmen (4.02%). Similarly, (55.90%) (0.30%) (43.80%) the lowest death percentage is reported in Shiyan (0.30%), 573 286 19 268 Xianning (1.31%), and Enshi Prefecture (1.59%). (49.91%) (3.31%) (46.77%) Substantially higher percentage of cures is observed among Enshi Prefecture (58.17%), Huanggang (57.13%), 494 223 13 258 Xianning (53.23%), and Tianmen (52.23%). However, (45.14%) (2.63%) (52.23%) Wuhan has the lowest cure percentage (19.19%). Enshi 251 101 4 146 Prefecture (40.24%) (1.59%) (58.17%)

Current Trends of SARS-CoV-2-Infected Qianjiang 194 104 8 82 Cases with Time (53.61%) (4.12%) (42.27%) Figure 1 shows the trend of the total, existing, deceased and cured number of SARS-CoV-2-infected cases in the Hubei province of China from 11-Jan to 24-Feb 2020. The to decline slowly with forwarding days. Overall, the trend rapid increase in SARS-CoV-2-infected cases was of deceased cases in Hubei province was slightly higher observed from 27-Jan and onwards, and the two-fold after 16-Feb, but this number was lower than that of cured increase was reported after 12-Feb 2020. Recovery of cases. patients was slightly improved after 14-Feb, and the To explore differences in the incidence of infected respective trend of remaining or existing patients started cases from SARS-CoV-2 among different cities, we have

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Hubei Province

Total Existing Death Cure 70000 60000 50000 40000 30000 20000

Total confirmed cases Total confirmed 10000 0 11-Jan 15-Jan 19-Jan 23-Jan 27-Jan 31-Jan 04-Feb 08-Feb 12-Feb 16-Feb 20-Feb 24-Feb Days

Figure 1 Actual trend of SARS-CoV-2-infected cases for Hubei province. plotted the same trends as described above for Hubei closely follows the actual data, indicating that the logis- province for each city separately. Figure 2 depicts the tic S-curve model fits the data well. The estimated trends in total, existing, deceased, and cured SARS-CoV- number of SARS-CoV-2-infected cases in Wuhan was 2-infected cases. A higher rate/incidence of all parameters higher than in all other cities. We observed that the was observed in Wuhan, while Xiaogan was ranked sec- growth of cases increased smoothly over the entire ond, taking into account the total number of SARS-CoV-2 period and was higher in Wuhan, Xianning, Qianjiang, cases. On the other hand, the lowest rates were observed in and Enshi Prefecture. The forecast for these cities also Qianjiang and Enshi Prefecture, respectively. A significant indicated a slightly declining stable trend of cases from increase in the trend of SARS-CoV-2-infected cases in 25-Feb to onward, while the future trend of other cities Wuhan was identified on and after 29-Jan. In other cities, showed almost declined stable trend during the forecast the total number of infected cases were also increased in period (Figure 4). the same duration, but in Wuhan and Xiaogan, the trend Moreover, we compared the average number of was increased significantly over the period compared to SARS-CoV-2-infected cases before and after 25-Feb; other cities (Figure 2). and found that during the specified period average fore- cast number of cases steadily declined from 25-Feb to Estimation and Forecasting of SARS-CoV- 15-Mar for both Hubei province and Wuhan city. In 2 Cases in 16 Cities of Hubei Province order to check the precision of our forecast, we also reported the actual number of SARS-CoV-2-infected Estimated present and future forecasted trends in the num- cases per day from 25-Feb to 29-Feb by the Health ber of SARS-CoV-2-infected cases are shown in Figure 3. Commission of Hubei Province, China, which verified We noted that during the observed time, the growth of our findings of a decrease in the future number of virus- SARS-CoV-2 case has increased (β ¼ 0:75; p<0:05Þ in infected cases in Hubei province and Wuhan city Hubei province. Besides, the forecast trend indicated that (Table 2). in the next 20 days, the trend of SARS-CoV-2 cases might start to decline slightly or remain consistent from 25-Feb to 15-Mar (Figure 3). Discussion Figure 4 depicted the detailed city-wise trend in the This is an extensive study reporting the exact number number of SARS-CoV-2-infected cases across 16 differ- of existing, deceased and cured SARS-CoV-2 cases in ent cities of Hubei province. We observed from 16 different cities of Hubei, China, from 11-Jan to Figure 4, that almost for all cities, the fitted trend 24-Feb 2020. In addition, estimation and forecasting

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Huanggang Wuhan Xiaogan Total Existing Death Cure Total Existing Death Cure 50000 3500 4000 3000 40000 3500 2500 30000 3000 2500 2000 20000 2000 1500 1500 10000 1000 1000 500 Total confirmed cases Total confirmed 0 500 cases Total confirmed

Total confirmed cases Total confirmed 0 0 11-Jan 15-Jan 19-Jan 23-Jan 27-Jan 31-Jan 04-Feb 08-Feb 12-Feb 16-Feb 20-Feb 24-Feb 21-Jan 23-Jan 25-Jan 27-Jan 29-Jan 31-Jan 02-Feb 04-Feb 06-Feb 08-Feb 10-Feb 12-Feb 14-Feb 16-Feb 18-Feb 20-Feb 22-Feb 24-Feb 24-Jan 27-Jan 30-Jan 02-Feb 05-Feb 08-Feb 11-Feb 14-Feb 17-Feb 20-Feb 23-Feb Days Days Days Jingzhou Ezhou Suizhou Total Existing Death Cure Total Existing Death Cure Total Existing Death Cure 1800 1600 1400 1600 1400 1200 1400 1200 1200 1000 1000 1000 800 800 800 600 600 600 400 400 400 200 200 200 Total confirmed cases Total confirmed Total confirmed cases Total confirmed 0 0 cases Total confirmed 0 25-Jan 27-Jan 29-Jan 31-Jan 02-Feb 04-Feb 06-Feb 08-Feb 10-Feb 12-Feb 14-Feb 16-Feb 18-Feb 20-Feb 22-Feb 24-Feb 25-Jan 27-Jan 29-Jan 31-Jan 23-Jan 25-Jan 27-Jan 29-Jan 31-Jan 16-Feb 18-Feb 20-Feb 22-Feb 24-Feb 02-Feb 04-Feb 06-Feb 08-Feb 10-Feb 12-Feb 14-Feb 06-Feb 08-Feb 10-Feb 12-Feb 02-Feb 04-Feb 14-Feb 16-Feb 18-Feb 20-Feb 22-Feb 24-Feb Days Days Days Yichang Xiangyang Huangshi Total Existing Death Cure Total Existing Death Cure Total Existing Death Cure 1200 1400 1000 1000 1200 900 800 800 1000 700 800 600 600 600 400 500 400 400 200 300 200 Total confirmed cases Total confirmed 0 200

Total confirmed cases Total confirmed 0 100 Total confirmed cases Total confirmed 0 25-Jan 27-Jan 29-Jan 31-Jan 02-Feb 04-Feb 08-Feb 10-Feb 16-Feb 18-Feb 20-Feb 22-Feb 24-Feb 06-Feb 12-Feb 14-Feb 25-Jan 27-Jan 29-Jan 31-Jan 04-Feb 10-Feb 12-Feb 14-Feb 16-Feb 02-Feb 06-Feb 08-Feb 18-Feb 20-Feb 22-Feb 24-Feb 24-Jan 26-Jan 28-Jan 30-Jan Days 01-Feb 03-Feb 05-Feb 07-Feb 09-Feb 11-Feb 13-Feb 15-Feb 17-Feb 19-Feb 21-Feb 23-Feb Days Days Jingmen Xianning Shiyan Total Existing Death Cure Total Existing Death Cure 1000 Total Existing Death Cure 1000 900 800 800 800 700 700 600 600 600 500 500 400 400 400 300 300 200 200 200 Total confirmed cases Total confirmed

100 cases Total confirmed 100 Total confirmed cases Total confirmed 0 0 0 26-Jan 28-Jan 30-Jan 23-Jan 25-Jan 27-Jan 29-Jan 31-Jan 24-Jan 26-Jan 28-Jan 30-Jan 15-Feb 17-Feb 01-Feb 03-Feb 05-Feb 07-Feb 09-Feb 11-Feb 13-Feb 19-Feb 21-Feb 23-Feb 02-Feb 04-Feb 06-Feb 08-Feb 10-Feb 12-Feb 16-Feb 18-Feb 20-Feb 22-Feb 24-Feb 14-Feb 01-Feb 03-Feb 05-Feb 07-Feb 09-Feb 11-Feb 13-Feb 15-Feb 17-Feb 19-Feb 23-Feb 21-Feb Days Days Days Xiantao Enshi Prefecture Tianmen

Total Existing Death Cure Total Existing Death Cure Total Existing Death Cure 700 300 600 600 250 500 500 400 200 400 300 150 300 200 100 200 100 50

Total confirmed cases 100 Total confirmed cases Total confirmed

Total confirmed cases Total confirmed 0 0 0 25-Jan 27-Jan 29-Jan 31-Jan 02-Feb 04-Feb 06-Feb 08-Feb 10-Feb 12-Feb 14-Feb 16-Feb 18-Feb 20-Feb 22-Feb 24-Feb 25-Jan 27-Jan 29-Jan 31-Jan 02-Feb 04-Feb 06-Feb 08-Feb 10-Feb 12-Feb 14-Feb 16-Feb 18-Feb 20-Feb 22-Feb 24-Feb 26-Jan 28-Jan 30-Jan 24-Jan 07-Feb 09-Feb 11-Feb 13-Feb 15-Feb 17-Feb 19-Feb 21-Feb 23-Feb 01-Feb 03-Feb 05-Feb Days Days Days Qianjiang

Total Existing Death Cure 250

200

150

100

50 Total confirmed cases Total confirmed 0 27-Jan 29-Jan 31-Jan 04-Feb 06-Feb 08-Feb 02-Feb 10-Feb 12-Feb 14-Feb 16-Feb 18-Feb 20-Feb 22-Feb 24-Feb Days

Figure 2 Actual trend of SARS-CoV-2-infected cases among 16 different cities of Hubei province. representing trend and incidence of SARS-CoV-2- It illustrates the latest status of SARS-CoV-2 infection infected cases were presented to get an idea about the in China and adds details about its spread with respect disease spread and the success of treatment strategies. to time.

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Trend Analysis Plot for Hubei S-Curve Trend Model Yt = (10**6) / (17.0581 + 69194.1*(0.754518**t))

70000 Variable Actual 60000 Fits Forecasts

es 50000

s Curve Parameters Intercept 14.4 40000 A sy mptote 58623.1 Asym. Rate 0.8 30000 Accuracy Measures MAPE 23 20000 MAD 2196 MSD 11778417

Total confirmed10000 ca

0 61 12 18 24 30 6054484236 Days

Figure 3 Estimated and forecasted trend of SARS-CoV-2-infected cases for Hubei province.

Coronavirus is one of the leading pathogens of respira- consider the rate of cured cases from this viral outbreak, it tory infection. SARS-CoV and MERS-CoV are two extre- was observed that Enshi Prefecture, Huanggang, Xianning, mely pathogenic viruses that cause a severe respiratory and Tianmen have a significantly higher cure percentage syndrome in humans. SARS-CoV outbreaks in 2002–2003 compared to Wuhan. have infected 8422 cases in 29 different countries across the Furthermore, we have shown the trend of existing, globe4,33 whereas, in 2012, MERS-CoV has emerged in deceased, and cured numbers of SARS-CoV-2 cases in Middle Eastern countries but has been imported to China the Hubei province of China from 11-Jan to 24-Feb due to inadequate quarantine by the Middle East.34–36 The 2020. Results from the present study showed a rapid emerging SARS-CoV-2 also caused severe lower respiratory increase in SARS-CoV-2-infected cases from 27-Jan and tract infections and was declared as a global emergency by onwards with a two-fold increase after 12-Feb 2020. It was the WHO.33,37 High through-put sequencing has confirmed observed that patients start getting better after 14-Feb, and that SARS-CoV-2 is quite different from the already reported the respective trend of remaining or existing patients six Coronavirus sub-types. Both MERS-CoV and SARS- started to decline slowly with forwarding days. Overall, CoV originated in bats and are capable of transmitting the trend of deceased cases was mildly higher after 16- directly to human beings from dromedary camels and civets, Feb, but this number was found to be lower than that of respectively,38,39 but the origin of SARS-CoV-2 still needs cured cases in Hubei. Differences in the incidence of in-depth investigations. The transmission rates of SARS- SARS-CoV-2-infected cases among different cities were CoV-2 are unknown yet, but there strong evidence of analyzed by plotting the results individually for all the human–to–human transmission does exist.33 cities. Again, the trend remains the same, and a higher The present study has shown the occurrence of SARS- rate of disease prevalence was observed in Wuhan. In CoV-2-infected cases of 16 different cities located in Hubei contrast, Xiaogan was ranked in the second position, and province. It was observed that Wuhan has the highest death the lowest rate was found in Qianjiang and Enshi percentage with this viral infection, followed by Qianjiang Prefecture. A significantly increasing trend of SARS- and Jingmen. Whereas some of the cities in Hubei have very CoV-2 cases in Wuhan was identified on 29-Jan and low death rates, including Shiyan, Xianning, and Enshi onwards. We have explored that number of SARS-CoV- Prefecture as compared to Wuhan. These findings are in 2-infected cases also increase among other studies in the accordance with the latest reports highlighting the spread of same duration but increasing trend in Wuhan and Xiaogan SARS-CoV-2 in China, specifically in Wuhan.7,33 If we was much higher throughout the study period.

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Our findings were supported by the reports of the It is conceivably that the reporting and detection of the National Health Commission, which states the quick SARS-CoV-2 infection have gained greater importance increase of this viral infection from 20 to 25 Jan 2020.40 since it was reported countrywide. Spring Festival travel

Trend Analysis Plot for Wuhan Trend Analysis Plot for Xiaogan S-Curve Trend Model S-Curve Trend Model Yt = (10**6) / (18.5290 + 57721.7*(0.789708**t)) Yt = (10**5) / (29.5053 + 3733.14*(0.707966**t)) 60000 Variable 3500 Variable Actual Actual Fits 50000 Fits 3000 Forecasts Forecasts s e es Curve Parameters 2500 Curve Parameters

as 40000 Intercept 17.3 Intercept 26.58 cas A sy mptote 3389.22 A sy mptote 53969.5 d 2000 30000 Asym. Rate 0.8 Asym. Rate 0.71 rmed c

fi Accuracy Measures 1500 Accuracy Measures n MAPE 13.5 o MAPE 18 c 20000 MAD 89.7 l MAD 1155

a 1000 MSD 3802626 MSD 13290.6 otal confirme ot T T 10000 500

0 0 61 605448423630241812 51 10 2015 25 30 35 4540 50 Days Days

Trend Analysis Plot for Huanggang Trend Analysis Plot for Jingzhou S-Curve Trend Model S-Curve Trend Model Yt = (10**4) / (3.48761 + 2558.67*(0.655421**t)) Yt = (10**4) / (6.47894 + 2379.31*(0.670275**t))

3000 Variable 1600 Variable Actual Actual Fits 1400 Fits 2500 Forecasts Forecasts s s

e Curve Parameters e 1200 Curve Parameters 2000 Intercept 3.90 Intercept 4.19 cas cas A sy mptote 2867.30 1000 A sy mptote 1543.46 d d A sy m. Rate 0.66 Asym. Rate 0.67 1500 800 irme

f Accuracy Measures Accuracy Measures MAPE 20.4 600 MAPE 15.7 1000 MA D 123.0 MAD 69.6 lcon a MSD 29673.6 400 MSD 11925.3 ot otal confirme T 500 T 200

0 0 51 55504540353025201510 51 10 30252015 35 40 45 50 Days Days

Trend Analysis Plot for Ezhou Trend Analysis Plot for Suizhou S-Curve Trend Model S-Curve Trend Model Yt = (10**4) / (7.49627 + 11252.0*(0.615447**t)) Yt = (10**4) / (7.74747 + 513.590*(0.703503**t))

1400 Variable 1400 Variable Actual Actual 1200 Fits 1200 Fits Forecasts Forecasts s e 1000 Curve Parameters 1000 Curve Parameters Intercept 0.89 Intercept 19.18 cas

d 800 A sy mptote 1334.00 800 A sy mptote 1290.74 A sy m. Rate 0.62 Asym. Rate 0.70 rmed cases irme f 600 Accuracy Measures fi 600 Accuracy Measures MAPE 37.1 n

o MAPE 18.99

MA D 105.2 c lcon

l MA D 28.31 a 400 MSD 17964.7 a 400

t MSD 1466.55 ot T 200 To 200

0 0 51 504540353025201510 51 10 2015 25 3530 40 5045 Days Days

Trend Analysis Plot for Xiangyang Trend Analysis Plot for Huangshi S-Curve Trend Model S-Curve Trend Model Yt = (10**4) / (8.58994 + 1675.64*(0.636101**t)) Yt = (10**4) / (9.95930 + 623.840*(0.717527**t))

1200 Variable 1000 Variable Actual Actual Fits Fits 1000 Forecasts Forecasts

s s 800 e Curve Parameters e Curve Parameters 800 Intercept 5.94 Intercept 15.78 cas cas A sy mptote 1164.15 A sy mptote 1004.09 d d 600 A sy m. Rate 0.64 Asym. Rate 0.72 600 irme

f Accuracy Measures Accuracy Measures MAPE 43.78 400 MA PE 5.236 400

lcon MA D 50.15 MAD 15.886

a MSD 5452.60 MSD 713.613 ot otal confirme T 200 T 200

0 0 51 504540353025201510 51 10 2015 25 3530 40 5045 Days Days

Figure 4 Continued.

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Trend Analysis Plot for Yichang Trend Analysis Plot for Jingmen S-Curve Trend Model S-Curve Trend Model Yt = (10**4) / (10.8960 + 1200.11*(0.699228**t)) Yt = (10**4) / (10.9516 + 7121.58*(0.634981**t))

Variable Variable 900 Actual 900 Actual 800 Fits 800 Fits Forecasts Forecasts s s

700 Curve Parameters e 700 Curve Parameters se Intercept 8.258 Intercept 1.402 cas ca 600 600

Asymptote 917.765 d A sy mptote 913.106 d

e 500 Asym. Rate 0.699 500 Asym. Rate 0.635 400 Accuracy Measures 400 Accuracy Measures MAPE 90.81 MAPE 30.65 onfirm c MA D 27.56 MA D 62.26 l 300 300 a MSD 1908.19 MSD 7993.11 t

o 200 otal confirme 200 T T 100 100 0 0

51 3025201510 35 40 45 50 51 10 2015 25 30 4035 5045 Days Days

Trend Analysis Plot for Xianning Trend Analysis Plot for Shiyan S-Curve Trend Model S-Curve Trend Model Yt = (10**4) / (10.6062 + 201.829*(0.814090**t)) Yt = (10**4) / (15.2977 + 723.785*(0.754699**t))

Variable 700 Variable 900 Actual Actual Fits 600 Fits 800 Forecasts Forecasts s 700 Curve Parameters 500 Curve Parameters se Intercept 47.073 Intercept 13.530 ca 600 Asymptote 942.848 d 400 A sy mptote 653.695 e Asym. Rate 0.814 500 Asym. Rate 0.755 rm rmed cases i

Accuracy Measures fi 300 Accuracy Measures 400 MAPE 11.01 n onf o MAPE 72.940 c MA D 45.32 c l MAD 19.529

300 MSD 2999.62 a 200 tal t MSD 566.462 o T 200 To 100 100 0 0 51 353025201510 40 5045 51 1510 20 3025 4035 45 50 Days Days

Trend Analysis Plot for Xiantao Trend Analysis Plot for Tianmen S-Curve Trend Model S-Curve Trend Model Yt = (10**4) / (17.7717 + 2260.92*(0.716428**t)) Yt = (10**4) / (18.6473 + 1701.36*(0.770792**t))

600 Variable 600 Variable Actual Actual 500 Fits 500 Fits Forecasts Forecasts

Curve Parameters Curve Parameters 400 Intercept 4.388 400 Intercept 5.814 cases Asymptote 562.693 d A sy mptote 536.269 e Asym. Rate 0.716 Asym. Rate 0.771

m 300 300 rmed cases

Accuracy Measures fi Accuracy Measures n

MAPE 15.096 o MAPE 22.236

200 c 200

MAD 13.867 l MAD 19.647 a

MSD 283.203 t MSD 561.340

Total confir100 To 100

0 0 51 3025201510 35 40 45 50 51 10 2015 25 30 454035 50 Days Days

Trend Analysis Plot for Enshi Prefecture Trend Analysis Plot for Qianjiang S-Curve Trend Model S-Curve Trend Model Yt = (10**3) / (3.81782 + 60.8443*(0.799438**t)) Yt = (10**3) / (4.42043 + 179.563*(0.807550**t))

Variable 250 Variable 250 Actual Actual Fits Fits Forecasts 200 Forecasts s 200 Curve Parameters

se Curve Parameters Intercept 15.465 Intercept 5.435 ca Asymptote 261.929 d 150 A sy mptote 226.222

e 150 Asym. Rate 0.799 Asym. Rate 0.808 rmed cases

Accuracy Measures fi Accuracy Measures n 100

onfirm MA PE 10.2725 o MAPE 17.774 c 100 c MAD 8.1258 l MAD 11.420

MSD 86.7628 a t MSD 193.977 Total 50 To 50

0 0 51 3025201510 35 4540 50 51 10 2015 3025 454035 Days Days

Figure 4 Estimated and forecasted trend of SARS-CoV-2-infected cases among16 cities of Hubei province. rush would be another reason for this rapid spread, as The number of SARS-CoV-2-infected cases during the thousands of people from different cities and countries specified time was estimated using the logistic S curve visit Wuhan.41 model. After applying and evaluating the accuracy

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Table 2 Comparison of Past and Future Predicted Number of viral drugs that are already licensed for other viral diseases, Cases for Hubei Province and Wuhan although various clinical trials are in progress to prepare Hubei Wuhan vaccines as a definitive treatment. The United States Center for Disease Control and Prevention has released an updated Average number of cases (11-Jan to 1652 1192 42 24-Feb) guideline along with a patient under investigation (PUI) 43 Average number of forecast cases 338 366 form for suspected cases. So, anyone who has a previous (from 25-Feb to15-March) history of traveling to Wuhan or has met someone from a Actual number of cases of 5 days from 499, 404, 464, 370, visitor to China must follow these guidelines and visit clin- Heath Commission of Hubei Province, 409, 318, 383, 313, icians if they have any symptoms related to the disease, such China (from 25-Feb to 29-Feb) 423 420 as cough or difficulty breathing. China has very well mana- ged to cope up with disease spread by implementing a strict measurement of different time series models, the logistic lockdown in the Province Hubei, and specifically, more S-curve trend model was found to be a suitable fit for mea- attention is given to the Wuhan as having the highest number fi suring the trends in the number of SARS-CoV-2 cases. The of con rmed cases. China has imposed travel restrictions to present study showed that during the observed time growth control the transmission of disease, followed later by many of infected cases had significantly increased in Hubei pro- other countries around the globe. Regular checking of tem- vince. In addition to this forecast trend over the next 20 days, perature and maintenance of social distancing has helped it has been noted that the trend of infected cases may begin to China to recover from this pandemic. China has announced new measures to avoid a second wave of infection as it decline slowly or remain consistent from 25-Feb to 15-Mar. progressively reopens its economy and releases citizens We observed that almost all cities, fitted trends closely follow from coronavirus lockdowns. New measures include social the actual data, which confirms the suitability of the logistic distancing, personal hygiene, and, more precisely, civilized S-curve model to the data. The estimated number of SARS- behavior. Globally, nations are much more unified than ever CoV-2 cases in Wuhan was higher than in all other cities. The because emerging pathogens do not care about geopolitical growth of infected cases increased smoothly during the entire boundaries. Proactive investment in medical and public period and was higher in Wuhan, Xianning, Qianjiang, and health infrastructure is mandatory to respond effectively to Enshi Prefecture. The forecast for these cities also indicated the current coronavirus pandemic. It is important not only to the slightly decreasing stable trend of cases from 25-Feb to sustain and enhance international collaboration, surveillance, onward, while the future trend of other cities has shown communication, and cooperation on this major SARS-CoV-2 almost declined stable trend during the forecast period giving outbreak but also to be well prepared for future public health a hope that the situation will be better in the coming future. 44 threats. Also, a comparison of infected cases before and after 25-Feb showed that during the specified period average forecast Conclusion number of cases steadily declined from 25-Feb to 15-Mar The SARS-CoV-2 infection is transmitting very quickly for both Hubei province and Wuhan city. The accuracy of our with an increasing number of infected cases around the forecast was assessed by the actual number of SARS-CoV-2- globe as well. The future development regarding its spread infected cases per day from 25-Feb to 29-Feb collected by the and definitive treatment is not clear yet; therefore, the Health Commission of Hubei province, China, which con- public should take precautionary measures in dealing firmed our results of a reduction in the future number of cases with the virus, since it may become very contagious and in Hubei province, and specifically in Wuhan city, which is take their lives at risk of death. A higher prevalence of facing a significant challenge in the fight against this pan- disease was reported in Wuhan as compared to other cities demic. But this reduction can only be observed after taking of China, so, if someone has the previous history of accurate preventive measures and establishing fully equipped traveling to Wuhan or meet any infected person and quarantines for infected cases. Otherwise, it would become experience lower respiratory problems, must go for screen- difficult to deal with this global challenge. ing at earliest. The death rate is declining in different cities There is currently no definitive treatment available for of Hubei, but still, it is prevailing and spreading globally, this viral infection; therefore, precautions measures must be so anyone must be careful when travelling and attending taken to prevent ourselves. Clinicians are using several anti- gatherings because it could cause disease to spread.

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Initial public health response and interim clinical guidance for the 2019 novel coronavirus outbreak United States, December 31, 2019 February 4, 2020. Morbidity Mortality Weekly Rep. 2020;69(5):140. doi:10.15585/mmwr.mm6905e1 Funding 15. Sookaromdee P, Wiwanitkit V. Imported cases of 2019-novel coro- This research is supported by the National Natural Science navirus (2019-nCoV) infections in Thailand: mathematical modelling Foundation of China (Grant No. 11771407) and CAE of the outbreak. Asian Pac J Trop Med. 2020;13:3. doi:10.4103/1995- 7645.277516 Advisory Project (No. 2020-ZD-16). The funders had no 16. Yasri S, Wiwanitkit V. Exported wuhan novel coronavirus infection: role in the study design, data collection, analysis, decision an expected rate with reference to main destination of Chinese tourist, Thailand. Int J Prev Med. 2020;11(1):28. doi:10.4103/ijpvm. to publish, or preparation of the manuscript. The APC was IJPVM_39_20 funded by Jingli Ren. 17. 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