The first case of coronavirus disease (COVID-19) was detected in Wuhan, China, in December 2019. The subsequent rapid spread of the virus in that city was called an epidemic, and by March 2020 the virus has spread to all continents leading the World Health Organization (WHO) to declare COVID-19 a global pandemic.
Since then, scientists have worked to understand and represent the mechanisms underlying the spread of the pandemic, and forecast its future spread, and so contribute to mitigation strategies. Unfortunately, conflicting estimates and inaccurate results in models for this novel disease have to some extent undermined trust and confidence in models, and in science’s ability to predict the epidemic’s size, duration, and societal impact.
After almost two years living with the pandemic, scientists already know much more than in the outbreak’s early stages, but we still have a lot of open questions such as: 1) what have we learned so far?; 2) what do society and the scientific community learn from our own mistakes?; 3) are we prepared for new variants (or epidemics/pandemics) we are going to face in the future?
It is the purpose of this Research Topic to add to this discussion, and to review the ability to model the epidemic/pandemic outbreak, with a view to better its epidemiological characteristics, and its size and duration. Such information has already played a major role in guiding health authorities in the implementation of interventions to control the spread of SARS-CoV-2 virus.
We are looking forward to research papers on the following or related topics :
● Novel mathematical and statistical models
● Novel methods for expressing uncertainty in models
● Methods to include epidemiological information in models (as against data based techniques)
● Evaluating the success of alternative models
● Models from other disciplines, such as physics, economics and wildlife ecology
● Strategies for border control and traffic management
● Impacts on infrastructure (hospitals, airports, logistics and supply chain) and health care systems
● Impact of vaccination strategies
The focus will be on models based on, but not restricted to, the following themes:
● Forward and inverse uncertainty quantification
● Characterization of the disease
● Transmission vectors and environment
● Curve-fitting, and biological models
● Machine learning and artificial intelligence
● Spatio-temporal covid forecasting
● Bayesian epidemic modelling
● Population and population density
● Economic and social conditions of the population
● Health decision making aided by computer
Topic Editors have confirmed that they have no conflict of interest.
The first case of coronavirus disease (COVID-19) was detected in Wuhan, China, in December 2019. The subsequent rapid spread of the virus in that city was called an epidemic, and by March 2020 the virus has spread to all continents leading the World Health Organization (WHO) to declare COVID-19 a global pandemic.
Since then, scientists have worked to understand and represent the mechanisms underlying the spread of the pandemic, and forecast its future spread, and so contribute to mitigation strategies. Unfortunately, conflicting estimates and inaccurate results in models for this novel disease have to some extent undermined trust and confidence in models, and in science’s ability to predict the epidemic’s size, duration, and societal impact.
After almost two years living with the pandemic, scientists already know much more than in the outbreak’s early stages, but we still have a lot of open questions such as: 1) what have we learned so far?; 2) what do society and the scientific community learn from our own mistakes?; 3) are we prepared for new variants (or epidemics/pandemics) we are going to face in the future?
It is the purpose of this Research Topic to add to this discussion, and to review the ability to model the epidemic/pandemic outbreak, with a view to better its epidemiological characteristics, and its size and duration. Such information has already played a major role in guiding health authorities in the implementation of interventions to control the spread of SARS-CoV-2 virus.
We are looking forward to research papers on the following or related topics :
● Novel mathematical and statistical models
● Novel methods for expressing uncertainty in models
● Methods to include epidemiological information in models (as against data based techniques)
● Evaluating the success of alternative models
● Models from other disciplines, such as physics, economics and wildlife ecology
● Strategies for border control and traffic management
● Impacts on infrastructure (hospitals, airports, logistics and supply chain) and health care systems
● Impact of vaccination strategies
The focus will be on models based on, but not restricted to, the following themes:
● Forward and inverse uncertainty quantification
● Characterization of the disease
● Transmission vectors and environment
● Curve-fitting, and biological models
● Machine learning and artificial intelligence
● Spatio-temporal covid forecasting
● Bayesian epidemic modelling
● Population and population density
● Economic and social conditions of the population
● Health decision making aided by computer
Topic Editors have confirmed that they have no conflict of interest.