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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">farmaec</journal-id><journal-title-group><journal-title xml:lang="en">FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology</journal-title><trans-title-group xml:lang="ru"><trans-title>ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2070-4909</issn><issn pub-type="epub">2070-4933</issn><publisher><publisher-name>IRBIS LLC</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.17749/2070-4909.2020.13.1.43-51</article-id><article-id custom-type="elpub" pub-id-type="custom">farmaec-341</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>Original Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>Оригинальные статьи</subject></subj-group></article-categories><title-group><article-title>COVID-19 in Moscow: prognoses and scenarios</article-title><trans-title-group xml:lang="ru"><trans-title>Коронавирусная инфекция в Москве: прогнозы и сценарии</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-3168-1307</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Тамм</surname><given-names>М. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Tamm</surname><given-names>M. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Тамм Михаил Владимирович – к. ф.-м. н., доцент</p><p>Researcher ID: G-6959-2016; Scopus Author ID: 7006098030</p><p>Ленинские горы, д. 1, Москва 119991; Таллинская ул., д. 34, Москва 123458</p></bio><bio xml:lang="en"><p>Mikhail V. Tamm  – PhD (Physico-Mathematical Sciences)</p><p> Researcher ID: G-6959-2016; Scopus Author ID: 7006098030</p><p>1 Leninskie gory, Moscow 119991; Tallinskaya Str., 34, Moscow 123458</p></bio><email xlink:type="simple">thumm.m@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru">Федеральное государственное бюджетное образовательное учреждение высшего образования «Московский государственный университет имени М.В. Ломоносова» ; Департамент прикладной математики МИЭМ НИУ ВШЭ<country>Россия</country></aff><aff xml:lang="en">Moscow State University ; HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM NRU HSE)<country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2020</year></pub-date><pub-date pub-type="epub"><day>23</day><month>04</month><year>2020</year></pub-date><volume>13</volume><issue>1</issue><fpage>43</fpage><lpage>51</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Tamm M.V., 2020</copyright-statement><copyright-year>2020</copyright-year><copyright-holder xml:lang="ru">Тамм М.В.</copyright-holder><copyright-holder xml:lang="en">Tamm M.V.</copyright-holder><license license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://www.pharmacoeconomics.ru/jour/article/view/341">https://www.pharmacoeconomics.ru/jour/article/view/341</self-uri><abstract><sec><title>Aim</title><p>Aim: to present a mathematical model of the development of COVID-19 in Moscow along with the analysis of some scenarios of epidemic control and possible epidemic consequences.</p></sec><sec><title>Materials and Methods</title><p>Materials and Methods. The modeling of the epidemics was based on the extended SEIR model proposed lately in the group of Prof. R. Neher and realized as a freely available software program. The authors based the choice of the parameters of modeling on published data on the epidemiological properties of the novel coronavirus SARS-CoV-2 and open access data on the registered cases of COVID-19 in Moscow for 8-27 March 2020.</p></sec><sec><title>Results</title><p>Results. Five potential scenarios of the development of COVID-19 epidemics are studied. The scenarios are differed by the levels of the control measures: Null Scenario corresponded to the lack of protective measures, Scenario A – mild measures of the epidemic control (closing of schools and universities, recommendations for senior citizens to stay inside), Scenario B – medium level of control (closing of all public places, recommendation for the citizens to stay inside), Scenarios C and D – complete lockdown (from the beginning of May 2020 within Scenario C and from the beginning of April 2020 within Scenario D). It was shown that within the Null Scenario, the lethality from the novel coronavirus in Moscow will exceed 100 thousand people, and the number of critically ill patients on the peak of the epidemics will exceed the capacities of the system of healthcare. Scenarios A and B did not provide for a radical decrease in the fatality rate, and the number of critically ill patients at the peak of epidemics will still exceed the capacities of the system of healthcare. Besides, within Scenario B, the epidemics will last for more than a year. Scenarios C and D will allow for the control of epidemics and a significant decrease in the rate of letha lity (by 30 and 400 times, respectively). At the same time, these two scenarios prevent the population from developing herd immunity, which would result in the population susceptibility to repeated epidemics outbreaks.</p></sec><sec><title>Conclusion</title><p> Conclusion. The scenarios intended for the slow development of herd immunity in the conditions of epidemic control would not bring sufficient results: the lethality would remain unacceptably high, the capacities of the system of healthcare would be overloaded, and the time of limiting measures would be unacceptably long. Such measures as complete lockdown would stop the present epidemics. The earlier they are introduced, the more efficient will be the results. To prevent further repeated outbreaks of the epidemics, it is necessary to establish a system of available, quick, and efficient testing in combination with point isolation of the infected patients and their contacts. </p></sec></abstract><trans-abstract xml:lang="ru"><p>Цель – математическое моделирование развития эпидемии COVID-19 в Москве с анализом ряда сценариев подавления эпидемии и их возможных последствий.</p><sec><title>Материалы и методы</title><p> Материалы и методы. Для моделирования эпидемии использована расширенная модель SEIR, предложенная в последние недели в группе Р. Нейера и реализованная в виде общедоступной компьютерной программы. При выборе параметров моделирования мы ориентировались на литературные данные об эпидемических свойствах нового коронавируса SARS-CoV-2 и открытые данные о зарегистрированных случаях вызываемого им заболевания COVID-19 в Москве 8-27 марта 2020 г.</p></sec><sec><title>Результаты</title><p>Результаты. Рассмотрены пять сценариев развития эпидемии COVID-19, отличающихся разным уровнем мер по ее подавлению: нулевой сценарий соответствует отсутствию защитных мер, сценарий А – мягким шагам подавления эпидемии (закрытие школ и университетов, рекомендации пожилым людям не выходить из дома), сценарий Б – среднему уровню подавления (закрытие всех публичных мест, рекомендация не выходить из дома), сценарии В и Г – полному локдауну, вводимому в сценарии В с начала мая, в сценарии Г – с начала апреля 2020 г. Показано, что в нулевом варианте число умерших от нового коронавируса в Москве превысит 100 тысяч человек, а число критически больных на пике эпидемии более чем на порядок превысит пропускную способность системы здравоохранения. Показано, что сценарии А и Б не позволяют радикально снизить число умерших, а число критически больных на пике эпидемии будет по-прежнему намного превышать возможности системы здравоохранения. Кроме того, сценарий Б предполагает растягивание эпидемии более чем на год. Сценарии В и Г позволяют подавить эпидемию и существенно (в 30 и 400 раз соответственно) снизить число умерших. При этом в результате этих обоих сценариев в популяции не вырабатывается групповой иммунитет, и популяция остается уязвимой для повторных вспышек эпидемии.</p></sec><sec><title>Заключение</title><p>Заключение. Сценарии, направленные на медленную выработку группового иммунитета в условиях снижения вреда от эпидемии, не приводят к должным результатам: смертность остается неприемлемо высокой, система здравоохранения существенно перегруженной, а ограничительные меры – недопустимо продолжительными. Меры типа жесткого локдауна позволяют остановить нынешнюю вспышку эпидемии, причем чем раньше они вводятся, тем эффективнее работают. Для предотвращения последующих вспышек необходима система легкодоступного, быстрого и качественного тестирования в сочетании с точечными мерами изоляции заболевших и их контактов.</p></sec></trans-abstract><kwd-group xml:lang="ru"><kwd>SARS-CoV-2</kwd><kwd>новая коронавирусная инфекция</kwd><kwd>эпидемия</kwd><kwd>COVID-19</kwd><kwd>модель SEIR</kwd><kwd>подавление эпидемии</kwd><kwd>групповой иммунитет</kwd></kwd-group><kwd-group xml:lang="en"><kwd>SARS-CoV-2</kwd><kwd>novel coronavirus infection</kwd><kwd>epidemics</kwd><kwd>COVID-19</kwd><kwd>SEIR model</kwd><kwd>epidemic control</kwd><kwd>herd immunity</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">В Москве ввели жесткие карантинные меры. Похоже, это правильно: математическая модель показывает, что иначе могли бы погибнуть больше 100 тысяч человек. 30.03.2020. [Электронный ресурс] URL: https://meduza.io/feature/2020/03/30/ v-moskve-vveli-zhestkie-karantinnye-mery-pohozhe-eto-pravilnomatematicheskaya-model-pokazyvaet-chto-inache-mogli-by-pogibnut-bolshe-100-tysyach-chelovek. Дата обращения: 30.03.2020.</mixed-citation><mixed-citation xml:lang="en">Tough quarantine measures have been introduced in Moscow. This seems to be correct: the mathematical model shows that otherwise more than 100 thousand people could have died. 30.03.2020. [Electronic resource] URL: https://meduza.io/ feature/2020/03/30/v-moskve-vveli-zhestkie-karantinnye-merypohozhe-eto-pravilno-matematicheskaya-model-pokazyvaet-chtoinache-mogli-by-pogibnut-bolshe-100-tysyach-chelovek. Accessed: 30.03.2020.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Kermack W.O., McKendrick A.G. A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A. Containing papers of a mathematical and physical character. 1927; 115 (772): 700-721.</mixed-citation><mixed-citation xml:lang="en">Kermack W.O., McKendrick A.G. A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A. Containing papers of a mathematical and physical character. 1927; 115 (772): 700-721.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Daley D.J., Gani J. Epidemic Modelling: An Introduction, Cambridge University Press, Cambridge, UK, 1999.</mixed-citation><mixed-citation xml:lang="en">Daley D.J., Gani J. Epidemic Modelling: An Introduction, Cambridge University Press, Cambridge, UK, 1999.</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Diekmann O., Heesterbeek J.A.P. Mathematical epidemiology of infectious diseases: model building, analysis and interpretation, John Wiley&amp; Sons, Chichester, UK, 2000.</mixed-citation><mixed-citation xml:lang="en">Diekmann O., Heesterbeek J.A.P. Mathematical epidemiology of infectious diseases: model building, analysis and interpretation, John Wiley&amp; Sons, Chichester, UK, 2000.</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Murray J.D., Mathematical Biology: I. An Introduction, Springer-Verlag, New York, NY, 2002.</mixed-citation><mixed-citation xml:lang="en">Murray J.D., Mathematical Biology: I. An Introduction, SpringerVerlag, New York, NY, 2002.</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Keeling M.J., Rohani P. Modelling Infectious Diseases in Humans and Animals, Princeton University Press, Princeton, NJ, 2008.</mixed-citation><mixed-citation xml:lang="en">Keeling M.J., Rohani P. Modelling Infectious Diseases in Humans and Animals, Princeton University Press, Princeton, NJ, 2008.</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Bjørnstad O. SEIR model. 2005. [Электронный ресурс] URL: https:// ms.mcmaster.ca/~bolker/eeid/sir.pdf. Дата обращения: 27.03.2020.</mixed-citation><mixed-citation xml:lang="en">Bjørnstad O. SEIR model. 2005; [Electronic resource] URL: https://ms.mcmaster.ca/~bolker/eeid/sir.pdf. Accessed: 27.03.2020.</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Rocklow J., Sjodin H., Wilder-Smith A. COVID-19 outbreak on the Diamond Princess cruise ship: estimating the epidemic potential and effectiveness of public health countermeasures. J. Travel Medicine. 2020; DOI: 10.1093/jtm/taaa030.</mixed-citation><mixed-citation xml:lang="en">Rocklow J., Sjodin H., Wilder-Smith A. COVID-19 outbreak on the Diamond Princess cruise ship: estimating the epidemic potential and effectiveness of public health countermeasures. J. Travel Medicine. 2020; DOI: 10.1093/jtm/taaa030.</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Peng L., Yang W., Zhang D., Zhuge C., Hong L. Epidemic analysis of COVID-19 in China by dynamical modeling. arXiv preprint. 2020; arXiv:2002.06563.</mixed-citation><mixed-citation xml:lang="en">Peng L., Yang W., Zhang D., Zhuge C., Hong L. Epidemic analysis of COVID-19 in China by dynamical modeling. arXiv preprint. 2020; arXiv:2002.06563.</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">COVID-19 reports of the MRC Centre for Global Infectious Disease Analysis, Imperial College London. [Электронный ресурс] URL: https:// www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/. Дата обращения: 27.03.2020.</mixed-citation><mixed-citation xml:lang="en">COVID-19 reports of the MRC Centre for Global Infectious Disease Analysis, Imperial College London. [Electronic resource] URL: https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/ covid-19/. Accessed: 27.03.2020.</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Maslov S., Goldenfeld N. Window of Opportunity for Mitigation to Prevent Overflow of ICU capacity in Chicago by COVID-19. arXiv preprint. 2020; arXiv:2003.09564.</mixed-citation><mixed-citation xml:lang="en">Maslov S., Goldenfeld N. Window of Opportunity for Mitigation to Prevent Overflow of ICU capacity in Chicago by COVID-19. arXiv preprint. 2020; arXiv:2003.09564.</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">COVID-19 Scenarios. [Электронный ресурс] URL: https://neherlab.org/covid19/. Дата обращения: 27.03.2020.</mixed-citation><mixed-citation xml:lang="en">COVID-19 Scenarios. [Electronic resource] URL: https:// neherlab.org/covid19/. Accessed: 27.03.2020.</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Lauer S.A., Grantz K.H., Bi Q., Jones F.K., Zheng Q. et al. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine, 2020; DOI: 10.1101/2020.02.02.20020016.</mixed-citation><mixed-citation xml:lang="en">Lauer S.A., Grantz K.H., Bi Q., Jones F.K., Zheng Q. et al., The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine, 2020; DOI: 10.1101/2020.02.02.20020016.</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Chan J. F.-W., Yuan S., Kok K.-H., To K. K.-W., Chu H. et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. The Lancet. 2020; 395: 514.</mixed-citation><mixed-citation xml:lang="en">Chan J. F.-W., Yuan S., Kok K.-H., To K. K.-W., Chu H. et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. The Lancet. 2020; 395: 514.</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Wu J.T., Leung K., Leung G.M. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet. 2020; 395: 689.</mixed-citation><mixed-citation xml:lang="en">Wu J.T., Leung K., Leung G.M. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet. 2020; 395: 689.</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Kucharski A.J., Russell T.W., Diamond C., Liu Y., Edmunds J. et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The Lancet. Infectious Diseases. 2020; DOI: 10.1016/S1473-3099(20)30144-4.</mixed-citation><mixed-citation xml:lang="en">Kucharski A.J., Russell T.W., Diamond C., Liu Y., Edmunds J. et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The Lancet. Infectious Diseases. 2020; DOI: 10.1016/S1473-3099(20)30144-4.</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Liu T., Hu J., Xiao J., He G., Kang M. et al. Time-varying transmission dynamics of Novel Coronavirus Pneumonia in China. bioRxiv.org preprint. DOI: 10.1101/2020.01.25.919787.</mixed-citation><mixed-citation xml:lang="en">Liu T., Hu J., Xiao J., He G., Kang M. et al. Time-varying transmission dynamics of Novel Coronavirus Pneumonia in China. bioRxiv.org preprint. DOI: 10.1101/2020.01.25.919787.</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Wu Z., McGoogan J.M. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China. J. Amer. Med. 2020; DOI: 10.1001/jama.2020.2648.</mixed-citation><mixed-citation xml:lang="en">Wu Z., McGoogan J.M. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China. J. Amer. Med. 2020; DOI: 10.1001/jama.2020.2648.</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Neher R.A., Dyrdack R., Druelle V., Hodcroft E.B., Albert J. Potential impact of seasonal forcing on a SARS-CoV-2 pandemic. Swiss Med. Weekly. 2020; 150: w20224. DOI: 10.4414/ smw.2020.20224.</mixed-citation><mixed-citation xml:lang="en">Neher R.A., Dyrdack R., Druelle V., Hodcroft E.B., Albert J. Potential impact of seasonal forcing on a SARS-CoV-2 pandemic. Swiss Med. Weekly. 2020; 150: w20224. DOI: 10.4414/ smw.2020.20224.</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Liu Y., Gayle A.A., Wilder-Smith A., Rocklow J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. J. Travel Med. 2020; DOI: 10.1093/jtm/taaa021.</mixed-citation><mixed-citation xml:lang="en">Liu Y., Gayle A.A., Wilder-Smith A., Rocklow J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. J. Travel Med. 2020; DOI: 10.1093/jtm/taaa021.</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Flaxman S., Mishra S., Gangy A., Unwin H.J.T., Coupland et al. Estimating the number of infections and the impact of nonpharmaceutical interventions on COVID-19 in 11 European countries. 13th report of the Imperial College COVID-19 Response Team. 2020; DOI: 10.25561/77731.</mixed-citation><mixed-citation xml:lang="en">Flaxman S., Mishra S., Gangy A., Unwin H.J.T., Coupland et al. Estimating the number of infections and the impact of nonpharmaceutical interventions on COVID-19 in 11 European countries. 13th report of the Imperial College COVID-19 Response Team. 30 March 2020; DOI: 10.25561/77731.</mixed-citation></citation-alternatives></ref><ref id="cit22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">Численность постоянного населения – мужчин по возрасту на 1 января. [Электронный ресурс] URL: https://www.fedstat.ru/ indicator/31548. Дата обращения: 27.03.2020.</mixed-citation><mixed-citation xml:lang="en">Number of resident population – men by age on January 1 [Electronic resource] URL: https://www.fedstat.ru/indicator/31548. Accessed: 27.03.2020.</mixed-citation></citation-alternatives></ref><ref id="cit23"><label>23</label><citation-alternatives><mixed-citation xml:lang="ru">Численность постоянного населения – женщин по возрасту на 1 января. [Электронный ресурс] URL: https://www.fedstat.ru/ indicator/33459. Дата обращения: 27.03.2020.</mixed-citation><mixed-citation xml:lang="en">The number of resident population – women by age on January 1. [Electronic resource] URL: https://www.fedstat.ru/indicator/33459. Accessed: 27.03.2020.</mixed-citation></citation-alternatives></ref><ref id="cit24"><label>24</label><citation-alternatives><mixed-citation xml:lang="ru">Wang C., Liu L., Hao X., Guo H., Wang Q. et al. Evolving Epidemiology and Impact of Non-pharmaceutical Interventions on the Outbreak of Coronavirus Disease 2019 in Wuhan, China. medRxiv.org preprint. DOI: 10.1101/2020.03.03.20030593.</mixed-citation><mixed-citation xml:lang="en">Wang C., Liu L., Hao X., Guo H., Wang Q. et al. Evolving Epidemiology and Impact of Non-pharmaceutical Interventions on the Outbreak of Coronavirus Disease 2019 in Wuhan, China. medRxiv.org preprint. DOI: 10.1101/2020.03.03.20030593</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
