tetano
Editor, Senior Moderator
PLoS One
. 2024 Feb 23;19(2):e0269515.
doi: 10.1371/journal.pone.0269515. eCollection 2024. Predicting COVID-19 cases in Belo Horizonte-Brazil taking into account mobility and vaccination issues
Eder Dias[SUP] 1 [/SUP], Alexandre M A Diniz[SUP] 2 [/SUP], Giovanna R Souto[SUP] 3 [/SUP], Henrique L Guerra[SUP] 4 [/SUP], Humberto Torres Marques-Neto[SUP] 1 [/SUP], Simon Malinowski[SUP] 5 [/SUP], Silvio Jamil F Guimarães[SUP] 1 [/SUP]
Affiliations
The pandemic caused millions of deaths around the world and forced governments to take drastic measures to reduce the spread of Coronavirus. Understanding the impact of social distancing measures on urban mobility and the number of COVID-19 cases allows governments to change public policies according to the evolution of the pandemic and plan ahead. Given the increasing rates of vaccination worldwide, immunization data may also represent an important predictor of COVID-19 cases. This study investigates the impact of urban mobility and vaccination upon COVID-19 cases in Belo Horizonte, Brazil using Prophet and ARIMA models to predict future outcomes. The developed models generated projections fairly close to real numbers, and some inferences were drawn through experimentation. Brazil became the epicenter of the COVID-19 epidemic shortly after the first case was officially registered on February 25th, 2020. In response, several municipalities adopted lockdown (total or partial) measures to minimize the risk of new infections. Here, we propose prediction models which take into account mobility and vaccination data to predict new COVID-19 cases.
. 2024 Feb 23;19(2):e0269515.
doi: 10.1371/journal.pone.0269515. eCollection 2024. Predicting COVID-19 cases in Belo Horizonte-Brazil taking into account mobility and vaccination issues
Eder Dias[SUP] 1 [/SUP], Alexandre M A Diniz[SUP] 2 [/SUP], Giovanna R Souto[SUP] 3 [/SUP], Henrique L Guerra[SUP] 4 [/SUP], Humberto Torres Marques-Neto[SUP] 1 [/SUP], Simon Malinowski[SUP] 5 [/SUP], Silvio Jamil F Guimarães[SUP] 1 [/SUP]
Affiliations
- PMID: 38394233
- PMCID: PMC10889860
- DOI: 10.1371/journal.pone.0269515
The pandemic caused millions of deaths around the world and forced governments to take drastic measures to reduce the spread of Coronavirus. Understanding the impact of social distancing measures on urban mobility and the number of COVID-19 cases allows governments to change public policies according to the evolution of the pandemic and plan ahead. Given the increasing rates of vaccination worldwide, immunization data may also represent an important predictor of COVID-19 cases. This study investigates the impact of urban mobility and vaccination upon COVID-19 cases in Belo Horizonte, Brazil using Prophet and ARIMA models to predict future outcomes. The developed models generated projections fairly close to real numbers, and some inferences were drawn through experimentation. Brazil became the epicenter of the COVID-19 epidemic shortly after the first case was officially registered on February 25th, 2020. In response, several municipalities adopted lockdown (total or partial) measures to minimize the risk of new infections. Here, we propose prediction models which take into account mobility and vaccination data to predict new COVID-19 cases.