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      SARS-CoV-2 and Rohingya Refugee Camp, Bangladesh: Uncertainty and How the Government Took Over the Situation

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          Year-long, every human race is fighting against SARS-CoV-2 with their all resources. Since 2017, the Bangladeshi government is providing shelter to a huge number of Rohingya refugees, and now in this COVID-19 pandemic, the government is to provide all necessities and medical supports to this population, while the country hardly can fulfill all rights of her own population. This study analyzes the SARS-CoV-2 situation in the Rohingya refugee camps at Cox’s Bazar and found that the authority has done a great job taking control over the murrain compared to the host and over-all the worldwide situation. Although taken precautions are good enough till now, more detailed and pragmatic preparedness should be adopted for the worst scenario in case. Last but not least, this success would not be possible without the help of other non-governmental and international voluntary and professional organizations.

          Abstract

          Background: Bangladesh hosts more than 800,000 Rohingya refugees from Myanmar. The low health immunity, lifestyle, access to good healthcare services, and social-security cause this population to be at risk of far more direct effects of COVID-19 than the host population. Therefore, evidence-based forecasting of the COVID-19 burden is vital in this regard. In this study, we aimed to forecast the COVID-19 obligation among the Rohingya refugees of Bangladesh to keep up with the disease outbreak’s pace, health needs, and disaster preparedness. Methodology and Findings: To estimate the possible consequences of COVID-19 in the Rohingya camps of Bangladesh, we used a modified Susceptible-Exposed-Infectious-Recovered (SEIR) transmission model. All of the values of different parameters used in this model were from the Bangladesh Government’s database and the relevant emerging literature. We addressed two different scenarios, i.e., the best-fitting model and the good-fitting model with unique consequences of COVID-19. Our best fitting model suggests that there will be reasonable control over the transmission of the COVID-19 disease. At the end of December 2020, there will be only 169 confirmed COVID-19 cases in the Rohingya refugee camps. The average basic reproduction number ( R 0 ) has been estimated to be 0.7563. Conclusions: Our analysis suggests that, due to the extensive precautions from the Bangladesh government and other humanitarian organizations, the coronavirus disease will be under control if the maintenance continues like this. However, detailed and pragmatic preparedness should be adopted for the worst scenario.

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          Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study

          Summary Background In December, 2019, a pneumonia associated with the 2019 novel coronavirus (2019-nCoV) emerged in Wuhan, China. We aimed to further clarify the epidemiological and clinical characteristics of 2019-nCoV pneumonia. Methods In this retrospective, single-centre study, we included all confirmed cases of 2019-nCoV in Wuhan Jinyintan Hospital from Jan 1 to Jan 20, 2020. Cases were confirmed by real-time RT-PCR and were analysed for epidemiological, demographic, clinical, and radiological features and laboratory data. Outcomes were followed up until Jan 25, 2020. Findings Of the 99 patients with 2019-nCoV pneumonia, 49 (49%) had a history of exposure to the Huanan seafood market. The average age of the patients was 55·5 years (SD 13·1), including 67 men and 32 women. 2019-nCoV was detected in all patients by real-time RT-PCR. 50 (51%) patients had chronic diseases. Patients had clinical manifestations of fever (82 [83%] patients), cough (81 [82%] patients), shortness of breath (31 [31%] patients), muscle ache (11 [11%] patients), confusion (nine [9%] patients), headache (eight [8%] patients), sore throat (five [5%] patients), rhinorrhoea (four [4%] patients), chest pain (two [2%] patients), diarrhoea (two [2%] patients), and nausea and vomiting (one [1%] patient). According to imaging examination, 74 (75%) patients showed bilateral pneumonia, 14 (14%) patients showed multiple mottling and ground-glass opacity, and one (1%) patient had pneumothorax. 17 (17%) patients developed acute respiratory distress syndrome and, among them, 11 (11%) patients worsened in a short period of time and died of multiple organ failure. Interpretation The 2019-nCoV infection was of clustering onset, is more likely to affect older males with comorbidities, and can result in severe and even fatal respiratory diseases such as acute respiratory distress syndrome. In general, characteristics of patients who died were in line with the MuLBSTA score, an early warning model for predicting mortality in viral pneumonia. Further investigation is needed to explore the applicability of the MuLBSTA score in predicting the risk of mortality in 2019-nCoV infection. Funding National Key R&D Program of China.
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            Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

            Abstract Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)
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              Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1

              To the Editor: A novel human coronavirus that is now named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (formerly called HCoV-19) emerged in Wuhan, China, in late 2019 and is now causing a pandemic. 1 We analyzed the aerosol and surface stability of SARS-CoV-2 and compared it with SARS-CoV-1, the most closely related human coronavirus. 2 We evaluated the stability of SARS-CoV-2 and SARS-CoV-1 in aerosols and on various surfaces and estimated their decay rates using a Bayesian regression model (see the Methods section in the Supplementary Appendix, available with the full text of this letter at NEJM.org). SARS-CoV-2 nCoV-WA1-2020 (MN985325.1) and SARS-CoV-1 Tor2 (AY274119.3) were the strains used. Aerosols (<5 μm) containing SARS-CoV-2 (105.25 50% tissue-culture infectious dose [TCID50] per milliliter) or SARS-CoV-1 (106.75-7.00 TCID50 per milliliter) were generated with the use of a three-jet Collison nebulizer and fed into a Goldberg drum to create an aerosolized environment. The inoculum resulted in cycle-threshold values between 20 and 22, similar to those observed in samples obtained from the upper and lower respiratory tract in humans. Our data consisted of 10 experimental conditions involving two viruses (SARS-CoV-2 and SARS-CoV-1) in five environmental conditions (aerosols, plastic, stainless steel, copper, and cardboard). All experimental measurements are reported as means across three replicates. SARS-CoV-2 remained viable in aerosols throughout the duration of our experiment (3 hours), with a reduction in infectious titer from 103.5 to 102.7 TCID50 per liter of air. This reduction was similar to that observed with SARS-CoV-1, from 104.3 to 103.5 TCID50 per milliliter (Figure 1A). SARS-CoV-2 was more stable on plastic and stainless steel than on copper and cardboard, and viable virus was detected up to 72 hours after application to these surfaces (Figure 1A), although the virus titer was greatly reduced (from 103.7 to 100.6 TCID50 per milliliter of medium after 72 hours on plastic and from 103.7 to 100.6 TCID50 per milliliter after 48 hours on stainless steel). The stability kinetics of SARS-CoV-1 were similar (from 103.4 to 100.7 TCID50 per milliliter after 72 hours on plastic and from 103.6 to 100.6 TCID50 per milliliter after 48 hours on stainless steel). On copper, no viable SARS-CoV-2 was measured after 4 hours and no viable SARS-CoV-1 was measured after 8 hours. On cardboard, no viable SARS-CoV-2 was measured after 24 hours and no viable SARS-CoV-1 was measured after 8 hours (Figure 1A). Both viruses had an exponential decay in virus titer across all experimental conditions, as indicated by a linear decrease in the log10TCID50 per liter of air or milliliter of medium over time (Figure 1B). The half-lives of SARS-CoV-2 and SARS-CoV-1 were similar in aerosols, with median estimates of approximately 1.1 to 1.2 hours and 95% credible intervals of 0.64 to 2.64 for SARS-CoV-2 and 0.78 to 2.43 for SARS-CoV-1 (Figure 1C, and Table S1 in the Supplementary Appendix). The half-lives of the two viruses were also similar on copper. On cardboard, the half-life of SARS-CoV-2 was longer than that of SARS-CoV-1. The longest viability of both viruses was on stainless steel and plastic; the estimated median half-life of SARS-CoV-2 was approximately 5.6 hours on stainless steel and 6.8 hours on plastic (Figure 1C). Estimated differences in the half-lives of the two viruses were small except for those on cardboard (Figure 1C). Individual replicate data were noticeably “noisier” (i.e., there was more variation in the experiment, resulting in a larger standard error) for cardboard than for other surfaces (Fig. S1 through S5), so we advise caution in interpreting this result. We found that the stability of SARS-CoV-2 was similar to that of SARS-CoV-1 under the experimental circumstances tested. This indicates that differences in the epidemiologic characteristics of these viruses probably arise from other factors, including high viral loads in the upper respiratory tract and the potential for persons infected with SARS-CoV-2 to shed and transmit the virus while asymptomatic. 3,4 Our results indicate that aerosol and fomite transmission of SARS-CoV-2 is plausible, since the virus can remain viable and infectious in aerosols for hours and on surfaces up to days (depending on the inoculum shed). These findings echo those with SARS-CoV-1, in which these forms of transmission were associated with nosocomial spread and super-spreading events, 5 and they provide information for pandemic mitigation efforts.

                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Biology (Basel)
                Biology (Basel)
                biology
                Biology
                MDPI
                2079-7737
                05 February 2021
                February 2021
                : 10
                : 2
                : 124
                Affiliations
                [1 ]Department of Mathematics, University of Dhaka, Dhaka 1000, Bangladesh
                [2 ]Department of Mathematics and Statistics, University of Calgary, Calgary, AB T2N 1N4, Canada
                [3 ]Department of Computer Science & Engineering, State University of Bangladesh, Dhaka 1205, Bangladesh; prism.shahriar@ 123456gmail.com
                [4 ]Department of Public Health, North South University, Dhaka 1229, Bangladesh; sahmedshaon@ 123456gmail.com
                [5 ]Institute of Epidemiology, Disease Control and Research, Dhaka 1212, Bangladesh; oqayum@ 123456yahoo.com
                [6 ]Health, Nutrition and Population Global Practice, The World Bank, Dhaka 1207, Bangladesh; mohammad.alam01@ 123456northsouth.edu
                [7 ]Department of Mathematics, Schreiner University, Kerrville, TX 78028, USA; mhassan@ 123456schreiner.edu
                [8 ]Department of Mathematics, Iowa State University, Ames, IA 50011, USA; rafiul@ 123456iastate.edu
                [9 ]Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, USA; kaniz.fatema.nipa@ 123456ttu.edu
                [10 ]Department of Mathematical, Physical, and Engineering Sciences, Texas A&M University San Antonio, San Antonio, TX 78224, USA; ubulut@ 123456tamusa.edu
                Author notes
                [* ]Correspondence: kamrujjaman@ 123456du.ac.bd ; Tel.: +880-1-553-458-910
                Author information
                https://orcid.org/0000-0002-4892-745X
                https://orcid.org/0000-0002-0547-5617
                https://orcid.org/0000-0003-3631-5832
                https://orcid.org/0000-0003-2765-4859
                Article
                biology-10-00124
                10.3390/biology10020124
                7914953
                33562509
                e1de3d79-0b5f-426f-8356-4cba7c541bc9
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 23 December 2020
                : 29 January 2021
                Categories
                Article

                covid-19,rohingya refugee camp,mathematical model,numerical results,basic reproduction number,92d25,92d30,97m60,97m99

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