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      An investigation of spatial-temporal patterns and predictions of the coronavirus 2019 pandemic in Colombia, 2020–2021

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          Abstract

          Colombia announced the first case of severe acute respiratory syndrome coronavirus 2 on March 6, 2020. Since then, the country has reported a total of 5,002,387 cases and 127,258 deaths as of October 31, 2021. The aggressive transmission dynamics of SARS-CoV-2 motivate an investigation of COVID-19 at the national and regional levels in Colombia. We utilize the case incidence and mortality data to estimate the transmission potential and generate short-term forecasts of the COVID-19 pandemic to inform the public health policies using previously validated mathematical models. The analysis is augmented by the examination of geographic heterogeneity of COVID-19 at the departmental level along with the investigation of mobility and social media trends. Overall, the national and regional reproduction numbers show sustained disease transmission during the early phase of the pandemic, exhibiting sub-exponential growth dynamics. Whereas the most recent estimates of reproduction number indicate disease containment, with R t<1.0 as of October 31, 2021. On the forecasting front, the sub-epidemic model performs best at capturing the 30-day ahead COVID-19 trajectory compared to the Richards and generalized logistic growth model. Nevertheless, the spatial variability in the incidence rate patterns across different departments can be grouped into four distinct clusters. As the case incidence surged in July 2020, an increase in mobility patterns was also observed. On the contrary, a spike in the number of tweets indicating the stay-at-home orders was observed in November 2020 when the case incidence had already plateaued, indicating the pandemic fatigue in the country.

          Author summary

          As the COVID-19 pandemic continues to spread across Colombia, studies highlighting the intensity of the pandemic have become imperative for appropriate resource allocation and informing public health policies. In this study we utilize mathematical models to infer the transmission dynamics of SARS-CoV-2 at the regional and national level as well as short-term forecasting of the COVID-19 epidemic trajectory. Moreover, we examine the geographic heterogeneity of the COVID-19 case incidence in Colombia along with the analysis of mobility and social media trends in relation to the observed COVID-19 case incidence in the country. The estimates of reproduction numbers at the national and regional level show a decline in disease transmission as of October 31, 2021. Moreover, the 30-day ahead short-term forecasts for the most recent time-period (October 2-October 31, 2021) generated using the mathematical models needs to be interpreted with caution as the single peak models point towards a sustained decline in case incidence contrary to the sub-epidemic wave model. Nevertheless, the spatial analysis in Colombia shows distinct variations in incidence rate patterns across different departments which can be grouped into four distinct clusters. Moreover, the social media and mobility trends also explain the occurrence of case resurgences over time.

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          Most cited references65

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          MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

          We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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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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              MUSCLE: multiple sequence alignment with high accuracy and high throughput.

              We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Funding acquisitionRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: Funding acquisitionRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Funding acquisitionRole: Writing – review & editing
                Role: Funding acquisitionRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Funding acquisitionRole: Writing – review & editing
                Role: Writing – review & editing
                Role: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Negl Trop Dis
                PLoS Negl Trop Dis
                plos
                PLoS Neglected Tropical Diseases
                Public Library of Science (San Francisco, CA USA )
                1935-2727
                1935-2735
                4 March 2022
                March 2022
                : 16
                : 3
                : e0010228
                Affiliations
                [1 ] Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America
                [2 ] Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia, United States of America
                [3 ] Department of Computer Science, College of Arts and Sciences, Georgia State University, Atlanta, Georgia, United States of America
                [4 ] Centro de Investigación en Ingeniería Matemática (CI 2MA) and Departamento de Ingeniería Matemática, Universidad de Concepción, Concepción, Chile
                [5 ] Department of Mathematics and Integrative Institute of Basic Sciences, Soongsil University, Seoul, Republic of Korea
                [6 ] Department of Statistics, Florida State University, Tallahassee, Florida, United States of America
                University of Hong Kong, HONG KONG
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0003-2344-5398
                https://orcid.org/0000-0002-8210-3032
                https://orcid.org/0000-0001-9368-1228
                https://orcid.org/0000-0002-9961-9975
                https://orcid.org/0000-0001-6714-6782
                https://orcid.org/0000-0003-0962-0348
                https://orcid.org/0000-0002-0673-0026
                https://orcid.org/0000-0001-8499-824X
                https://orcid.org/0000-0003-4007-5624
                https://orcid.org/0000-0003-4365-1148
                https://orcid.org/0000-0003-1849-518X
                https://orcid.org/0000-0001-9298-8981
                https://orcid.org/0000-0001-5496-2529
                https://orcid.org/0000-0003-3135-6360
                Article
                PNTD-D-21-01087
                10.1371/journal.pntd.0010228
                8926206
                35245285
                dcdf063c-3ae1-434d-9817-835a7dee7983
                © 2022 Tariq et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 July 2021
                : 1 February 2022
                Page count
                Figures: 14, Tables: 5, Pages: 33
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: 1610429
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: 1633381
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01 GM 130900
                Award Recipient :
                Funded by: ANID/MEC
                Award ID: ANID/MEC/80170119
                Award Recipient :
                Funded by: Georgia State University
                Award ID: 2CI fellowship
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: 1R01EB025022
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: 2047828
                Award Recipient :
                Funded by: ANID (Chile)
                Award ID: Fondecyt project 1210610
                Award Recipient :
                Funded by: Centro de Modelamiento Matemático (CMM)
                Award ID: ACE210010
                Award Recipient :
                Funded by: BASAL funds for Centers of Excellence
                Award ID: FB210005
                Award Recipient :
                Funded by: CRHIAM
                Award ID: project ANID/FONDAP/15130015
                Award Recipient :
                Funded by: ANID
                Award ID: scholarship ANID-PCHA/Doctorado Nacional/2019-21190640 and Anillo project ANID/PIA/ACT210030
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003725, National Research Foundation of Korea;
                Award ID: 2018R1C1B6001723
                Award Recipient :
                Funded by: National Research Foundation of Korea, Ministry of Education
                Award ID: 2021R1A6A1A10044154
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100013669, Ionis Pharmaceuticals;
                Award Recipient :
                Funded by: National Institute of Aging
                Award ID: 3P30AG059307-02S1
                Award Recipient :
                G.C. is partially supported from NSF grants 1610429 and 1633381, R01 GM 130900 and project ANID/MEC/80170119. A.T. is supported by a 2CI fellowship from Georgia State University. P.S. is supported by the National Institutes of Health grant 1R01EB025022 and by the National Science Foundation grant 2047828. R.B. is supported by ANID (Chile) through Fondecyt project 1210610; Centro de Modelamiento Matemático (CMM) projects ACE210010 and FB210005 of BASAL funds for Centers of Excellence; and CRHIAM, project ANID/FONDAP/15130015. L.Y.L-D is supported by ANID scholarship ANID-PCHA/Doctorado Nacional/2019-21190640 and Anillo project ANID/PIA/ACT210030. E.S. is supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT, No. 2018R1C1B6001723) and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1A6A1A10044154). S.K.O. is supported by Ionis Pharmaceuticals. J.M.B was supported by a grant by the National Institute of Aging (3P30AG059307-02S1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                Custom metadata
                vor-update-to-uncorrected-proof
                2022-03-16
                Case incidence data by the dates of symptom onset and mortality data are available from the Colombian Ministry of Health. url: https://coronaviruscolombia.gov.co/Covid19/index.html. Genomic data is available from the "global initiative on sharing avian influenza data” (GISAID) repository. url: https://www.gisaid.org/ Apple mobility data is available from the Apple's mobility trends reports. url: https://covid19.apple.com/mobility. Google mobility data is available from the Google mobility page (COVID-19 Community Mobility Reports Google 2020): https://www.google.com/covid19/mobility/ Twitter data is available from the Twitter data set of the COVID-19 chatter: https://github.com/thepanacealab/covid19_twitter.
                COVID-19

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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