Open AccessAn investigation of spatial-temporal patterns and predictions of the coronavirus 2019 pandemic in Colombia, 2020–2021
Author(s) -
Amna Tariq,
Tsira Chakhaia,
Sushma Dahal,
Alexander C. Ewing,
Xinyi Hua,
Sylvia K. Ofori,
Olaseni Prince,
Argita D. Salindri,
Ayotomiwa Ezekiel Adeniyi,
Juan M. Banda,
Pavel Skums,
Ruiyan Luo,
Leidy Yissedt Lara-Díaz,
Raimund Bürger,
Isaac Chun-Hai Fung,
Eunha Shim,
Alexander Kirpich,
Anuj Srivastava,
Gerardo Chowell
Publication year - 2022
Publication title -
plos neglected tropical diseases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.99
H-Index - 135
eISSN - 1935-2735
pISSN - 1935-2727
DOI - 10.1371/journal.pntd.0010228
Subject(s) - pandemic , transmission (telecommunications) , incidence (geometry) , demography , covid-19 , geography , basic reproduction number , public health , disease , medicine , computer science , infectious disease (medical specialty) , mathematics , population , telecommunications , geometry , nursing , pathology , sociology
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.