The COVID-19 pandemic highlighted the current global limitations in managing an ongoing pandemic. A virus is an invisible enemy that spreads exponentially, and what we observe today (new diagnoses, hospitalizations, and deaths) is the effect of infections that happened days or weeks ago. Like in a game of chess, Public Health Institutions provide effective actions if they can predict the moves of the virus instead of simply reacting to them.
Viruses spread over the population through direct or indirect person-to-person contacts in space and time. Therefore, to control viral pandemics, it is necessary to know the population’s demographic structure, social behaviours (like origin-destination matrices), and virus features (transmissibility and lethality). All this information is useful if appropriately combined in a mathematical model suitable to represent the epidemiology of the ongoing pandemic and its evolution through likely assumptions.
The pioneering article of Kermark and McKendrik (1927) modelled the epidemic evolution as a renewal process, introduced the basic reproduction number (with the threshold over which an epidemic explodes) and the Susceptible-Infected–Recovered model (able to predict the peak and duration of the pandemic). Many epidemiological studies on the COVID-19 pandemic are based on this work, but their prediction power failed due to several causes including:
1) The underlying assumptions of predictive models are inaccurate; for example, age strongly affects the lethality rate and person-to-person contact.
2) Parameter estimators are unreliable: detected cases are biased estimators of the incidence of infection because they strongly depend on the national system contact tracing and the number of asymptomatic infections.
The COVID-19 pandemic taught us that we need to improve our efforts in developing accurate mathematical models to help Public Health Institutions to take the right actions at the right moment to keep ongoing pandemics under control.
Research problem considered in this Special Issue:
Sharing the successes and failures of the applied models around the world as well as proposing new ones can help health institutions to better prepare for the future. This Research Topic aims to collect the pandemic experience gained by epidemiologists in the development of mathematical models on COVID-19. We encourage (without limiting to) the submissions of papers about:
a) Systematic literature Reviews: used models, highlighting weakness and strength points;
b) Descriptive models: estimating the incidence and lethality, asymptomatic cases, etc., and highlighting evaluations of the adopted public health measures;
c) Predictive models: peaks and duration of waves to plan future public health policies.
d) Analysis of the effects of policy decisions on the spread
e) Sensitivity analysis, to take into account how spreading increases or decreases according to various factors
f) Analysis of the impact of population and density on the spread
By sharing your valuable contribution with us, you will help to make us more prepared for future pandemics.
The COVID-19 pandemic highlighted the current global limitations in managing an ongoing pandemic. A virus is an invisible enemy that spreads exponentially, and what we observe today (new diagnoses, hospitalizations, and deaths) is the effect of infections that happened days or weeks ago. Like in a game of chess, Public Health Institutions provide effective actions if they can predict the moves of the virus instead of simply reacting to them.
Viruses spread over the population through direct or indirect person-to-person contacts in space and time. Therefore, to control viral pandemics, it is necessary to know the population’s demographic structure, social behaviours (like origin-destination matrices), and virus features (transmissibility and lethality). All this information is useful if appropriately combined in a mathematical model suitable to represent the epidemiology of the ongoing pandemic and its evolution through likely assumptions.
The pioneering article of Kermark and McKendrik (1927) modelled the epidemic evolution as a renewal process, introduced the basic reproduction number (with the threshold over which an epidemic explodes) and the Susceptible-Infected–Recovered model (able to predict the peak and duration of the pandemic). Many epidemiological studies on the COVID-19 pandemic are based on this work, but their prediction power failed due to several causes including:
1) The underlying assumptions of predictive models are inaccurate; for example, age strongly affects the lethality rate and person-to-person contact.
2) Parameter estimators are unreliable: detected cases are biased estimators of the incidence of infection because they strongly depend on the national system contact tracing and the number of asymptomatic infections.
The COVID-19 pandemic taught us that we need to improve our efforts in developing accurate mathematical models to help Public Health Institutions to take the right actions at the right moment to keep ongoing pandemics under control.
Research problem considered in this Special Issue:
Sharing the successes and failures of the applied models around the world as well as proposing new ones can help health institutions to better prepare for the future. This Research Topic aims to collect the pandemic experience gained by epidemiologists in the development of mathematical models on COVID-19. We encourage (without limiting to) the submissions of papers about:
a) Systematic literature Reviews: used models, highlighting weakness and strength points;
b) Descriptive models: estimating the incidence and lethality, asymptomatic cases, etc., and highlighting evaluations of the adopted public health measures;
c) Predictive models: peaks and duration of waves to plan future public health policies.
d) Analysis of the effects of policy decisions on the spread
e) Sensitivity analysis, to take into account how spreading increases or decreases according to various factors
f) Analysis of the impact of population and density on the spread
By sharing your valuable contribution with us, you will help to make us more prepared for future pandemics.
