Introduction
The COVID-19 pandemic poses a great threat due to millions of infected people,
high mortality, and a very negative impact on the economy. Its detailed investigations are still ahead, but the public is already interested in the duration of the
pandemic, the expected number of patients, estimations of quarantine measures,
the scale of recurrences, etc. The threats of the COVID-19 pandemic require the
mobilization of scientists, including mathematicians familiar with methods of
infectious disease simulation.
The more complex the mathematical model, the more unknown parameters it
contains, the values of which must be determined using a limited number of
observations of the disease over time. Even long-term monitoring of the epidemic
may not provide reliable estimates of its parameters due to the constant change in
quarantine and testing conditions, in algorithms of isolation of infected persons, in
pathogen activity, etc.
Any mathematical modeling of the epidemic dynamics will be of particular value
if we make an accurate long-term forecast of its duration and number of diseases
using statistics data sets obtained immediately after the outbreak. That is why many
authors were trying to predict the COVID-19 pandemic dynamics in many countries
and regions. We will not dwell on a detailed analysis of these studies and only note
that the correct mathematical simulation of the COVID-19 pandemic is very difficult for at least two reasons.
First, data on the number of cases are clearly incomplete immediately after onset,
there are quite long hidden periods. The reason is the large number of asymptomatic
patients and the lack of skills to detect a new disease. It must be noted that a large
discrepancy between the registered and actual number of cases occurred even for
the later periods of the COVID-19 pandemic. Adequate modeling is further complicated by the fact that we do not know when the number of reported cases is
approaching the actual number. The second reason for the limited accuracy of
long-term forecasts is the constant changes in the conditions of the pandemic
(changing quarantine measures, social behavior, virulence of the pathogen, etc.).
Therefore, a prediction made using statistics for a certain time period is not suitable
for other periods of time.
vii
The COVID-19 pandemic poses a great threat due to millions of infected people,
high mortality, and a very negative impact on the economy. Its detailed investigations are still ahead, but the public is already interested in the duration of the
pandemic, the expected number of patients, estimations of quarantine measures,
the scale of recurrences, etc. The threats of the COVID-19 pandemic require the
mobilization of scientists, including mathematicians familiar with methods of
infectious disease simulation.
The more complex the mathematical model, the more unknown parameters it
contains, the values of which must be determined using a limited number of
observations of the disease over time. Even long-term monitoring of the epidemic
may not provide reliable estimates of its parameters due to the constant change in
quarantine and testing conditions, in algorithms of isolation of infected persons, in
pathogen activity, etc.
Any mathematical modeling of the epidemic dynamics will be of particular value
if we make an accurate long-term forecast of its duration and number of diseases
using statistics data sets obtained immediately after the outbreak. That is why many
authors were trying to predict the COVID-19 pandemic dynamics in many countries
and regions. We will not dwell on a detailed analysis of these studies and only note
that the correct mathematical simulation of the COVID-19 pandemic is very difficult for at least two reasons.
First, data on the number of cases are clearly incomplete immediately after onset,
there are quite long hidden periods. The reason is the large number of asymptomatic
patients and the lack of skills to detect a new disease. It must be noted that a large
discrepancy between the registered and actual number of cases occurred even for
the later periods of the COVID-19 pandemic. Adequate modeling is further complicated by the fact that we do not know when the number of reported cases is
approaching the actual number. The second reason for the limited accuracy of
long-term forecasts is the constant changes in the conditions of the pandemic
(changing quarantine measures, social behavior, virulence of the pathogen, etc.).
Therefore, a prediction made using statistics for a certain time period is not suitable
for other periods of time.
vii