Studying Evolutionary Adaptation of MERS-CoV

Letko, Michael; Munster, Vincent J.

doi:10.1007/978-1-0716-0211-9_1

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…Coronaviruses are a large family of respiratory viruses. Several viral epidemics have occurred in the previous two decades, including the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002-2003 and H1N1 influenza in 2009. In 2012, the Middle East respiratory syndrome coronavirus (MERS-CoV) was detected for the first time in Saudi Arabia (Letko and Munster, 2020). Originally, these viruses were passed from animals to humans.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Mobility Trends in Southeast Asia during the Coronavirus (Covid-19) Pandemic by Machine Learning Approaches

Jitpattanakul

2021

IJG

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The novel coronavirus (COVID-19) was declared as the 2019-20 coronavirus pandemic by the World Health Organization (WHO) in March 2020. The COVID-19 virus has rapidly spread nationwide and internationally and caused 188 countries to report more than ten million cases of individuals contracting COVID-19. Typically, the virus is conveyed from person to person via respiratory droplets produced by coughing and sneezing. The time period between exposure and onset of symptoms is typically between two and fourteen days, and on average five days. The COVID-19 pandemic has affected many businesses relating to transportation including tourism, import-export commerce, the aviation business, and so forth. Governmental intervention in each country has had an impact on mobility trends depending on the degree of restriction such as social distancing, sharing mobility, and public transport. A COVID-19 surveillance system is one of the principal methods used for detecting COVID-19 epidemics, using short-period monitoring. However, while these networks present information on the activities of COVID-19, acquiring completed surveillance data from every medical station is profusely difficult due to many factors. This research aims to propose a performance model of machine learning approaches for COVID-19 pandemic forecasting of mobility trends in each country in Southeast Asia. Spatial data and non-spatial data are used to build the machine learning models. The experiments conducted showed that the model gave a forecasting accuracy in walking and driving mobility of 94.40% and 92.00%, respectively. The proposed forecasting model was developed to be of benefits to health authorities in the planning and administration of a suitable strategy to make decisions concerning transportation planning in each country.

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Section: Introductionmentioning

confidence: 99%

Forecasting Mobility Trends in Southeast Asia during the Coronavirus (Covid-19) Pandemic by Machine Learning Approaches

Jitpattanakul

2021

IJG

View full text Add to dashboard Cite

show abstract

Studying Evolutionary Adaptation of MERS-CoV

Cited by 1 publication

References 18 publications

Forecasting Mobility Trends in Southeast Asia during the Coronavirus (Covid-19) Pandemic by Machine Learning Approaches

Forecasting Mobility Trends in Southeast Asia during the Coronavirus (Covid-19) Pandemic by Machine Learning Approaches

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