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An investigation of spatial-temporal patterns and predictions of the coronavirus 2019 pandemic in Colombia, 2020-2021.
Tariq, Amna; Chakhaia, Tsira; Dahal, Sushma; Ewing, Alexander; Hua, Xinyi; Ofori, Sylvia K; Prince, Olaseni; Salindri, Argita D; Adeniyi, Ayotomiwa Ezekiel; Banda, Juan M; Skums, Pavel; Luo, Ruiyan; Lara-Díaz, Leidy Y; Bürger, Raimund; Fung, Isaac Chun-Hai; Shim, Eunha; Kirpich, Alexander; Srivastava, Anuj; Chowell, Gerardo.
  • Tariq A; Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America.
  • Chakhaia T; Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America.
  • Dahal S; Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America.
  • Ewing A; Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America.
  • Hua X; Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia, United States of America.
  • Ofori SK; Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia, United States of America.
  • Prince O; Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America.
  • Salindri AD; Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America.
  • Adeniyi AE; Department of Computer Science, College of Arts and Sciences, Georgia State University, Atlanta, Georgia, United States of America.
  • Banda JM; Department of Computer Science, College of Arts and Sciences, Georgia State University, Atlanta, Georgia, United States of America.
  • Skums P; Department of Computer Science, College of Arts and Sciences, Georgia State University, Atlanta, Georgia, United States of America.
  • Luo R; Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America.
  • Lara-Díaz LY; Centro de Investigación en Ingeniería Matemática (CI2MA) and Departamento de Ingeniería Matemática, Universidad de Concepción, Concepción, Chile.
  • Bürger R; Centro de Investigación en Ingeniería Matemática (CI2MA) and Departamento de Ingeniería Matemática, Universidad de Concepción, Concepción, Chile.
  • Fung IC; Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia, United States of America.
  • Shim E; Department of Mathematics and Integrative Institute of Basic Sciences, Soongsil University, Seoul, Republic of Korea.
  • Kirpich A; Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America.
  • Srivastava A; Department of Statistics, Florida State University, Tallahassee, Florida, United States of America.
  • Chowell G; Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America.
PLoS Negl Trop Dis ; 16(3): e0010228, 2022 03.
Article in English | MEDLINE | ID: covidwho-1731580
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 Rt<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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: South America / Colombia Language: English Journal: PLoS Negl Trop Dis Journal subject: Tropical Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pntd.0010228

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: South America / Colombia Language: English Journal: PLoS Negl Trop Dis Journal subject: Tropical Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pntd.0010228