=Paper=
{{Paper
|id=Vol-2805/invited1
|storemode=property
|title=Risks of Data Inconsistency in Information Systems Used for Predicting the Pandemics Development
|pdfUrl=https://ceur-ws.org/Vol-2805/invited1.pdf
|volume=Vol-2805
|authors=Volodymyr Bakhrushin,Anna Bakurova,Mariia Pasichnyk,Elina Tereschenko
|dblpUrl=https://dblp.org/rec/conf/citrisk/BakhrushinBPT20
}}
==Risks of Data Inconsistency in Information Systems Used for Predicting the Pandemics Development==
https://ceur-ws.org/Vol-2805/invited1.pdf
Risks of Data Inconsistency in Information Systems Used
for Predicting the Pandemics Development
Volodymyr Bakhrushin1[0000-0003-3771-5256], Anna Bakurova2[0000-0001-6986-3769], Mariia
Pasichnyk3[0000-0002-5179-4272] and Elina Tereschenko4[0000-0001-6207-8071]
1,2,3,4National University «Zaporizhzhia Polytechnic», Zaporizhzhia, Ukraine
1vladimir.bakhrushin@gmail.com,2abaka111060@gmail.com,
3mary.pasechnik@gmail.com, 4elina_vt@ukr.net
Abstract. Predicting the pandemics development is based on mathematical mod-
els and empirical data. Prediction errors can lead to ineffective decisions, both in
terms of protecting human health and in terms of the economy. In this regard, it
is important to prevent the risks associated with the irrelevance and inaccuracy
of data contained in the information systems used for forecasting. Experience in
predicting the development of COVID-19 pandemic shows that primary data are
not always suitable for direct application in mathematical models. One of the
problems is the reliability of data on cases and deaths. Different countries have
different approaches to their detection and registration, which may also change
over time. Another problem is the deviation of real dynamics from the assump-
tions of the basic models, in particular, due to spatial heterogeneity, changes in
quarantine measures and different practices of their observance, and so on. This
can result in significant errors in predicting the number of new cases, the number
of deaths, the probability and expected parameters of the "second wave", and so
on. In this regard, some indicators of pandemic development and possible ap-
proaches to eliminate the risks caused with the specifics of the relevant data con-
tained in information systems were analyzed.
The proposed system of measures to identify and prevent the risks of data incon-
sistencies in information systems used to predict the development of pandemics
that could be useful in the development of The Risk-Informed Systems Analysis
(RISA).
Keywords. RISA, Information, COVID-19, prediction, risk, data, reliability, ac-
curacy, sources of errors.
1 Introduction
The COVID-19 pandemic was one of the most critical events of 2020, resulting in nu-
merous casualties, significant economic downturn in most countries and other negative
consequences. The choice of effective solutions for the response of public authorities
to the challenges of a pandemic requires reliable assessments of various risks, which
requires relevant models and data. Therefore, to reduce the risks of ineffective health
Copyright © 2020 for this paper by its authors. This volume and its papers are published under
the Creative Commons License Attribution 4.0 International (CC BY 4.0).
care solutions, the economy needs to implement evidence-based policies. This requires
reliable information on decision-making issues. For COVID-19, such data were almost
non-existent at the initial stage. But researches are gradually emerging that could sig-
nificantly change the prognosis of the pandemic and its aftermath in different scenarios
and different strategies to prevent pandemic.
One key issue whose solution is needed to develop effective measures to counteract
the spread of the COVID-19 pandemic is to make a substantial increase in the accuracy
of forecasts for future morbidity, mortality, social and economic consequences. At the
beginning of the pandemic, such predictions were based mainly on relatively simple
mathematical models and limited data sets. But over time, new pandemic data were
emerging in different countries that could be used to improve models, increase forecast
accuracy, and make better decisions.
The difference between this year's pandemic and previous ones is large-scale re-
search on the new SARS-CoV-2 coronavirus, testing of people who can be infected,
studying the impact of SARS-CoV-2 on various systems of the human body, as well as
social, economic and other consequences. One of the results of this research was the
creation of large-scale data collection systems (which also include RISA), which are
used to make strategic and operational decisions and recommendations at the level of
governments and international organizations. However, over time it becomes clear that
individual data are not reliable and relevant, similar data from different countries are
not always comparable, available data are not always suitable for direct application in
mathematical models used to build pandemic forecasts, and so on. This creates the risk
of making big mistakes in forecasts and decisions based on them. Analysis and imple-
mentation of measures to prevent risks arising in the current conditions of collection,
presentation and application of primary data on morbidity and mortality will signifi-
cantly increase the effectiveness of strategic and operational decisions to limit the
spread of the pandemic.
As part of the system risk analysis, The Risk-Informed Systems Analysis (RISA)
helps support decision-making in a pandemic related to economics, reliability and se-
curity, provides the use of RISA-tools to quantify projected differences by region, re-
duce costs by reducing risks.
2 Related Works
Prognostic mathematical models are the basis for understanding the development of the
pandemic and making effective decisions to prevent its spread [1]. The first solution to
the development of the COVID-19 pandemic was robbed based on the results of pre-
dictions based on simple SIR, SEIR models and their modifications [2], which may
have been prompted by the supervisors of the system and the extraordinary differential
equations. Thus, SEIR models include such groups of people: S - Susceptible (number
of people who has not been infected and has no immunity); E - Exposed (number of
people who are currently infected, but are not contagious); I - Infected (number of peo-
ple who are currently infected and are contagious); R - Recovered (number of recovered
people who have immunity).
According to these models, the dynamics of daily cases of the disease is described
by a symmetrical or asymmetric peak, and the dynamics of the total number of cases
has the shape of an S-shaped curve. Such models are still used for forecasting in many
countries. It was on their basis that strict quarantine measures were introduced at the
beginning of the pandemic [3]. However, they are oversimplified and are only suitable
for qualitative analysis under certain conditions. In particular, they do not take into
account the heterogeneity of the distribution of the active population, the impact of the
demographic structure of the population on key indicators and so on. In addition, such
models use a set of constants - basic reproduction number, effective contact rate, recov-
ery delay, as well as empirical data on the number of people who can be infected, the
number of infected and the number of those who recovered or died. However, these
constants can be estimated only by indirect methods and are in fact quantities that
change in time and space, and some empirical indicators are determined with large er-
rors. From the point of view of strategic decision-making, an important disadvantage
of these models is that they describe only the "first wave" condition, which is the only
one according to such models. But minimizing morbidity and mortality through strict
quarantine during the first wave does not answer the question of what will happen after
the quarantine is relaxed. And whether the decisions made will remain optimal, given
the longer period of time, as well as additional mortality due to stress, limited access to
health care and diagnosis, deteriorating quality of life, and so on.
Recently, more complex models [1, 4-6] have been increasingly used to predict the
development of a pandemic, which, in particular, can take into account a larger number
of parameters and their temporal and spatial changes in computer implementation.
However, the parameters of such models are determined by the quality of approxima-
tion of empirical data, which increases the impact of errors in these data on modeling
results and forecasts. Therefore, even for relatively short-term forecasts, they can pro-
vide a scatter of results in 1-2 orders of magnitude higher [7].
One of the main problems is the significant underestimation of real data in infor-
mation systems on the number of infected. Sample studies for the presence of IgG and
memory T cells conducted in different countries, show that the total number of people
who were infected and have antibodies to SARS-CoV-2 may be 1 - 2 orders of magni-
tude higher than the number officially registered cases: [10-13]. This problem is less
critical in terms of forecasting the dynamics, unless there is a significant change in
policy or scope of testing. But it is becoming very critical in choosing strategies to
counter the pandemic and assess the likelihood and scale of new outbreaks. The latter
significantly depend on the proportion of the population that is immune to infection.
For its realistic assessment it is necessary to know the proportion of people who have
already fallen ill and have immunity. It is also important for strategy selection to assess
and compare risks, in particular expected mortality from COVID-19, mortality from
other diseases, including side effects from pandemics and quarantine measures, and
expected social and economic consequences from different solutions. From this point
of view, the indicators of Case fatality rate (CFR) and Infection fatality rate (IFR) are
important. The first indicator provides estimates of infection mortality based on primary
data on the number of reported infections and deaths. Due to these problems, underes-
timation of the real number of infected CFR mortality estimates is significantly over-
estimated and for most countries is in the range of 1 - 15%. IFR estimates are more
realistic. They are usually obtained on the basis of model parameters identification and
sample surveys. According to the latest data, the most probable IFR values are in the
range of 0.1 - 1%, and according to some data, this value may be less than 0.1% [14-
16].
The Working Group on Mathematical Modeling of Problems Related to the SARS-
CoV-2 Coronavirus Epidemic in Ukraine of the National Academy of Sciences of
Ukraine, the National Academy of Medical Sciences of Ukraine and the Taras
Shevchenko National University of Kyiv has developed its own mathematical model to
the class of deterministic SEIR-models. It allows to take into account the presence of
asymptomatic infected persons, takes into account three levels of complexity of the
disease for patients with symptoms and allows for short-term prognosis [17]. However,
as with most other similar models, attempts to forecast for a longer period of time (more
than 1-2 weeks) lead to a significant increase in the range of uncertain-ty. The group of
researchers from the Operations Research Center of the Massachusetts Institute of
Technology [18] is also based on SEIR and takes into account the possibility of incom-
plete detection of infected people, the number of people in con-tact with the infected
person during the day, and possible government’s and societies actions. As in other
similar models, the forecast is based on official data, which makes it sensitive to the
relevance and reliability of this data.
Another area of research is the general risk analysis of information systems associ-
ated with the COVID-19 pandemic. In particular, [19] states that the main component
of risk is uncertainty. The lack and unreliability of information, in particular, has led to
inadequate risk assessment and uneffective solutions in China, resulting in the rapid
spread of the COVID-19 worldwide [20]. The authors [19] believe that solutions for
overcoming the pandemic are too complex and cannot be formalized in the form of
certain algorithms. Therefore, they propose to apply adaptive management approaches.
Many countries have started to develop their strategies "from scratch", not based on
existing knowledge about the development of pandemics, relevant models and experi-
ence of countries that have encountered a pandemic in the past [21].
The INFORM collaboration [22] developed the INFORM COVID-19 Risk Index to
support decision-making on the allocation of global and regional resources. It assesses
the risks of the COVID-19's impact on health and the humanitarian situation that may
lead to the need for international assistance. For a country-wide risk assessment, RIKA
India has proposed a four-factor model (health, behavior, impact, social policy) [23].
The analysis of the recent studies results shows that to assess the risks of decision-
making in a pandemic, the problem of the lack of common protocols for collecting
primary information about the global pandemic remains. Different strategies and ap-
proaches of different countries to testing, collection, registration of data in information
systems make it impossible to directly use primary information according to common
models for all countries. There are problems in assessing the effectiveness of quarantine
measures in different countries, including due to differences in approaches to collecting
primary information.
Thus, the inaccuracy and irrelevance of available pandemic data collected in infor-
mation systems is a significant risk factor for pandemic fore-casting and decision-mak-
ing. Therefore, the presented study is devoted to identifying the risks associated with
the available data, as well as developing approaches to reduce their impact on forecast-
ing results.
3 Proposed methodology
The research methodology provided that forecasting the dynamics of pandemic devel-
opment in individual countries/regions can be based not only on the use of classical or
modified models, but also on statistical analysis of data available in information sys-
tems (total and daily reported cases of infection and death, PCR and ELISA testing,
etc.) in those countries/regions where the outbreak started earlier. Despite the differ-
ences in the dynamics of indicators due to differences in testing and registration of
morbidity and mortality, demographic, social and other differences, etc., statistical
analysis of indicators allows to determine the expected range of values at a certain stage
of a country's pandemic data relating to countries or regions where these stages oc-
curred earlier. Such analysis can also establish the link between the factors influencing
the development of the pandemic and the dynamics of the studied indicators. For the
COVID-19 pandemic, the maximum of the first pandemic wave in China was in Janu-
ary, in South Korea in early March, in Spain, Italy, Luxembourg, New Zealand, Norway
and a number of other countries in the second half of March. In Ukraine, on the other
hand, the maximum of the first wave was in early May, and in many countries of Asia,
Africa and Latin America (Brazil, India, South Africa) it was reached in the first half
of July or even not reached yet. The short-term forecast of the time and height of the
maximum daily incidence for the first wave of the pandemic in Ukraine made in [24],
despite the limited range of reference countries available at that time, agreed well with
the actual data and forecast of NASU based on mathematical model [25].
To build such forecasts, it is necessary to use large arrays of data. Data on the
COVID-19 pandemic are now available in many databases and information systems. In
this study, the source data were taken from [26], where the European Center for Disease
Prevention and Control (registered cases of infection and death), Our World in Data
(official test reports), and the United Nations, World Bank, Global Burden of Disease,
Blavatnik School of Government (other data). Official government resources of Euro-
pean countries and the United States, in particular www.cdc.gov, www.cebm.net,
www.epicentro.iss.it and others, were used as additional sources.
Analysis of the daily morbidity and mortality dynamics shows that it usually be-
longs to several typical patterns: symmetrical or asymmetrical isolated peak, peak with
a "wide flat top" (plateau), a mixture of several normal peaks or peaks from the plateau.
Accordingly, the dynamics of total morbidity and mortality can usually be described as
a single S-shaped curve, or the sum of such curves. Based on this, an isolated peak can
be considered as the main element of the dynamics of daily cases (Fig. 1). This corre-
sponds to the basic SIR and SEIR models. As the main characteristics for the peak
description can be taken as its date (tm), height (h), half-width (t2 - t1) - the time interval
between the dates when the daily incidence was h/2 and asymmetry (t2 - tm)/(tm - t1).
Fig. 1. Daily morbidity peak (Ireland)
31 countries were taken for analysis, where as of July 20, 2020, clear peaks in daily
morbidity and mortality were identified. In many countries, these figures are signifi-
cantly weekly. Therefore, to determine the characteristics of the maxima, smoothing by
the moving average method with a 7-day smoothing interval was used. Data on the
dynamics of weekly indicators obtained by grouping daily primary data were also used
to clarify the maximum position. To ensure data’s comparability, all morbidity and
mortality rates were used per 1 million inhabitants. However, even with such an adjust-
ment, the use of data for forecasting needs further analysis, as the available indicators
relate to the country as a whole and do not take into account the regional distribution.
The importance of taking this into account is illustrated by the results for the US states.
Here, after the first maximum, which was reached in early April, and the two-month
plateau, the second peak of morbidity began. According to the analysis, this trend is
due to the non-simultaneous spread of infection in different states. In March-April, the
main contribution to the overall incidence was made by New York, New Jersey, Mas-
sachusetts and several other states, but in June-July the number of new cases here de-
creased by 5 - 15 times. Instead, the main contributors are California, Texas, and Flor-
ida, where the daily number of new cases has increased by more than an order of mag-
nitude since March. Data on the regional distribution of key indicators were analyzed
in Ukraine in the context of risk analysis associated with the further development of the
pandemic.
4 Results and Discussions
As noted, one of the key problems in forecasting the development of pandemics is the
incorrect data on the total number of infected and lethal.
The case fatality rate (CFR) commonly used for decision making is obtained by di-
viding the number of mortality by the number of registered patients, or by dividing the
number of mortality by the sum of mortality and the number of patients who have re-
covered (respectively, lower and upper grades). For countries where the number of ac-
tive cases is a small percentage of the total number of reported cases, these CFR esti-
mates are close to each other. For example, for China, where the share of active cases
on 25.07.2020 is 0.31% of the total, they are equal to 5.53% and 5.55%, respectively.
As of February 15, when the share of active cases was 83.8%, and the daily number of
new cases was close to the maximum, they differed significantly and were equal to
2.43% and 13.1%, respectively. For the United States, where the share of active cases
on April 26, 2020 was 82.1%, these CFR estimates were 5.65% and 31.5%, respec-
tively, and as of July 25, 2020, when the share of active cases decreased to 48.8%, they
are, respectively, 3.50% and 6.82%. Both estimates are significantly different for dif-
ferent countries due to differences in testing policies and different stages of develop-
ment of the COVID-19. Therefore, these CFR estimates can be used to short-term pre-
dict the development of a pandemic in a particular country in the absence of changes in
testing policies, or to compare countries with the same testing policies. But they are
unsuitable for decision-making based on estimates of the true proportion of fatal and
severe cases.
Table 1 shows the data on the share of infected people from the total population,
obtained from sample surveys of the population.
Table 1. The share of infected people from the total population according to sample surveys
Country The part of infected by the The share of The share of
results of sample surveys those informed ac- infected accord-
[27], % cording to official ing to official
data at the end of the data on
respective period, 18.07.2020, cal-
the calculation ac- culated accord-
cording to ing to
github.com, % github.com, %
Austria 4,7 (18 week) 0,18 0,22
Belgian 2,9 – 6% (mid-April) 0,29 0,55
Bulgaria 4,8 (13 – 17 weeks) 0,019 0,12
Spain 5,0 – 5,47 (17 – 19 weeks) 0,57 0,66
Luxembourg 1,97 (17 – 19 weeks) 0,62 0,88
Finland 1,0 – 4,3 (16 – 23 weeks) 0,13 0,13
The Czech Re- 0,0 – 4,0 (18 week) 0,073 0,13
public
As can be seen from the above data, the number of people with antibodies to SARS-
CoV-2 coronavirus is 3-54 times higher than the number of officially registered cases
of infection. According to the latest data on the study of memory T cells [8-10], the
actual number of infected may be 2-3 times higher. However, even with such an adjust-
ment, only in some regions the share of the population with immunity to COVID-19
today is approaching 50%. In most cases, it does not exceed 5-10%, which makes prob-
able new waves of disease. This assumption is confirmed by a significant increase in
morbidity in Bulgaria, Luxembourg, the Czech Republic and a number of other Euro-
pean countries in June-February.
Another approach to estimating the actual number of infected is based on IFR esti-
mates. According to the above data, taking the range of the most probable values of 0.3
- 0.6%, you can get lower and upper estimates of the actual number of cases of infection
on 20.07.2020, which are shown in Table 2.
Table 2. Lower and upper estimates of the share of infected in the total population, calculated
by IFR,%
Country Lower esti- Upper esti- Relation to the share of in-
mate mate fected, calculated by the number
of registered cases
Lower esti- Upper esti-
mate mate
USA 5,4 21,7 4,6 18,4
Brazil 4,7 18,7 4,7 18,9
India 0,3 1,0 3,1 12,3
Spain 7,6 30,4 11,6 46,2
UK 8,3 33,4 19,2 76,8
Italy 7,3 29,0 17,9 71,7
Germany 1,4 5,5 5,6 22,5
France 5,8 23,1 21,6 86,3
Sweden 7,0 27,8 9,1 36,3
Belgium 10,6 42,3 19,2 76,7
Ukraine 0,4 1,7 3,2 12,6
Netherlands 4,5 17,9 14,8 59,3
Poland 0,5 2,2 5,1 20,3
Armenia 2,7 11,0 2,3 9,3
Switzerland 2,8 11,4 7,3 29,3
Moldova 2,1 8,5 4,1 16,3
Serbia 0,7 2,7 2,8 11,3
Austria 1,0 4,0 4,5 18,1
Czechia 0,4 1,7 3,3 13,1
Denmark 1,3 5,3 5,8 23,1
Bulgaria 0,5 2,2 4,3 17,1
Finland 0,7 3,0 5,6 22,3
Luxembourg 2,2 8,9 2,5 9,9
Hungary 0,8 3,1 17,3 69,0
The given data generally correspond to the estimates given in Table 1 according to the
data of sample surveys. They also confirm the above conclusions about the significant
underestimation of official data on cases of infection, even during applying the lower
estimates. At the same time, even using the upper estimates, it can be concluded that
there is a high risk of new outbreaks in most of these countries. However, such a con-
clusion can be significantly adjusted in view of the following circumstances.
Firstly, according to [8], immunity to COVID-19 can have not only individuals who
have relapsed into the COVID-19 infection. It can also be found in people who have
previously had SARS or other coronavirus infections.
Secondly, from the data [28-29], it follows that collective immunity can be formed
at significantly lower than 50-70% of the infected population due to the heterogeneity
of the system.
Thirdly, the analysis of the available data in the information systems shows that in
recent months the CFR for the COVID-19 has decreased significantly. This may have
various explanations, some of which are related to the decrease in IFR. Also, as noted
above, available IFR estimates may be significantly overestimated. Then, even the
above estimates obtained using IFR, in some cases may be significantly lower than the
actual level of morbidity.
The IFR estimates, that mentioned above, are based on sample studies or identifica-
tion of pandemic spread patterns. Another approach to estimating IFR can be based on
the analysis of the distribution of CFR values. It can be assumed that the lowest CFR
values will be observed in the countries with the highest proportion of infected persons.
Therefore, they will be closest to the real IFR values. Analysis of the available data
shows that for different countries, the CFR can vary from a few hundredths of a percent
to more than 10%. Data for some countries are shown in Table 3.
Table 3. Indicators characterizing the development of the COVID-19 pandemic for some coun-
tries (as of 25.07.2020)
Country Number of Number of CFR Number of
cases per 1 deaths per 1 tests per 1 mil-
million inhab- million inhab- lion inhabit-
itants itants ants
USA 12830 448 3,50 158610
Great Britain 4387 673 15,3 210452
Italy 4062 581 14,3 106994
Qatar 38691 58 0,15 165494
Sweden 7819 564 7,21 74353
Oman 14430 70 0,49 56851
Ukraine 1437 36 2,51 21437
Singapore 8435 5 0,06 199896
Iceland 5399 29 0,54 353657
As can be seen, there is a large variation in CFR values, due primarily to different ap-
proaches to identifying infected individuals. In countries whose strategies provide for
the most complete identification of such individuals (Iceland, Singapore, etc.), the CFR
is in the range of 0.06 - 0.5%, which can be taken as an empirical upper estimate of
IFR. This estimate is also obviously somewhat inflated, as in no country does the test
cover all patients, but it confirms the conclusion of other studies that the IFR does not
exceed a few tenths of a percent. It should be noted that actual IFR values may change
over time due to improvements in treatment protocols, and may vary significantly be-
tween countries due to different demographics and different capabilities of health sys-
tems. These factors must also be taken into account when forecasting the development
of a pandemic, in particular, when applying data from one country to another.
In fig. 2 presents data on the distribution by Ukraine and United States regions of
officially registered cases per 1 million people.
Fig. 2. Distribution by Ukraine and USA regions of the number of officially registered cases
per 1 million people in relation to the average levels
These data indicate a significant heterogeneity in the regional distribution of morbidity
in both countries. For example, on July 18, 2020, the maximum value for Ukraine ex-
ceeds the minimum value by 119 times, and the third quartile exceeds the first by 6.2
times. For the United States, similar ratios are 94 and 2.2. This indicates the incorrect
use of averages to predict the further development of the pandemic based on models
such as SIR, SEIR. In addition, the average level of officially registered morbidity in
Ukraine as of July 18, 2020 is about 1.4, and in the United States - about 11.6 people
per 1 million inhabitants. Even taking into account the fact that the number of tests per
1 million inhabitants is 7.2 times higher than in Ukraine, this gives grounds to conclude
that there is a risk of a significant increase in morbidity in Ukraine, which may mainly
occur at the expense of regions where today the incidence rate is the lowest.
As noted above, the main element that can be used to describe an outbreak of a pan-
demic are the peaks in daily morbidity and mortality. Analysis of github.com data al-
lowed us to identify 31 countries where clear peaks can be identified. This work did not
take into account countries, in particular France, where data were repeatedly corrected
by formally attributing large numbers of unaccounted cases (or subtracting erroneously
credited cases) to certain dates, as well as some other countries for which there are
doubts about the reliability or the reliability of statistical data, in particular, due to the
low incidence rate as of 20.07.2020. Of these 31 countries, only cases were considered
for Liechtenstein, as all data on mortality in information systems are formally assigned
to one date.
The analysis shows that the data on peak heights and the total number of infected
and dead at the dates tm and t2 per 1 million inhabitants have a significant (within 3
orders of magnitude) variance. This is due to different testing policies and sometimes
the introduction of quarantine measures. In some countries, such as South Korea and
Thailand, small domestic sources of infection have been rapidly tracked and isolated.
Therefore, even on 26.07.2020 in these countries the number of officially registered
cases is about 0.028% and 0.0047%. In Qatar, on the other hand, the number of reported
cases exceeded 2% of the total population during the first outbreak.
However, the available data analysis in the information systems indicates a signifi-
cant correlation between the individual parameters of the peaks. For example, for reg-
istered cases, the coefficients of determination for the linear model are equal to: 0.99
for the relationship between the total number of cases on the date t2 and tm, 0.76 for
the relationship between the daily and the total number of cases on the date t2, 0,44 for
the relationship between the time of outbreak (the number of days between the date
when the total number of cases was 30 people per 1 million inhabitants, and the date
tm) and the half-width of the peak. For fatalities, the first two figures are 0.86 and 0.73,
respectively.
Instead, the link between similar peaks in morbidity and mortality is much weaker.
In particular, for the heights of the corresponding maxima, it is equal to 0.16, for the
total number of cases on the dates of the corresponding maxima - <0.01. This may be
due to the fact that the absolute data on the number of registered cases deviate signifi-
cantly from the actual number of infected. However, their understatement is signifi-
cantly different in different countries due to different approaches to testing and case
registration. Therefore, mortality data are more reliable for predicting the dynamics of
pandemic outbreaks than data from reported cases. To estimate the total number of in-
fected, which is important for estimating the likelihood and extent of new outbreaks,
more reliable estimates can be obtained using mortality data and IFR estimates based
on sample data than on the number of reported cases.
Table 4 shows the statistical characteristics of some more stable indicators of mor-
bidity and mortality.
Table 4. Distribution quarters of separate parameters of peaks characterizing daily numbers of
new cases and dead
Quarter Growth time Half-width Asymmetry The ratio of the
total number of
cases on the date
t2 and tm
New cases
min -3 13 0,5 1,37
0,25 14 20 1,0 1,65
0,50 18 24 1,42 1,82
0,75 26 31 2,0 2,23
max 82 62 4,8 3,20
Dead
Number of Half-width Asymmetry The ratio of the
days be- total number of
tween peaks cases on the date
of mortality t2 and tm
and infec-
tion
min -6 7 0,27 1,23
0,25 4 18 1,06 1,70
0,50 9 28,5 1,5 2,00
0,75 13 35 2,3 2,34
max 28 52 6,3 5,83
As can be seen from these data, the variance between the values of the given indicators
of peak morbidity and mortality is much (1-2 orders of magnitude) smaller than the
variance between the absolute values of the indicators. This makes it possible to use
them to more accurately predict the development of a pandemic.
5 Conclusions
The results indicate that there are significant risks associated with data contained in
information systems used to predict the development and decision-making of the spread
of the COVID-19 pandemic. This risk reasons are:
1. Systematic errors in the primary data concerning the registration of cases of infection
and mortality from corona-viral infection. Data on cases of infection are significantly
underestimated, which affects the risk assessments of new outbreaks of the pan-
demic. Mortality data can be both underestimated and overestimated. This affects
IFR estimates, but in any case they are more reliable than infection data. Because of
this, estimates of general morbidity obtained by indirect methods may be more rele-
vant.
2. Significant regional heterogeneity of cases of infection, which affects the possibility
of their direct application as parameters of mathematical models of pandemic devel-
opment, which leads to an increase in the risk of significant modeling errors. To
reduce this risk, it is necessary to use models that take into account the available
heterogeneities and empirical data, for individual regions, rather than the whole
country.
3. Wrong strategic and operational decisions that can either increase mortality from
coronavirus infection due to insufficient countermeasures, or increase the risk of
negative social and economic consequences, including increased mortality due to
pandemic and quarantine stress, complications of chronic diseases, limited access to
medical care, etc. To reduce such risks, it is necessary to develop special optimiza-
tion models that use more powerful information systems that contain verified data
not only on epidemiological indicators, but also other data needed to correctly assess
the socio-economic consequences.
To reduce these risks, it is necessary to adjust the data used in predictive models, in
particular through the use of more reliable data on lethal and severe cases to estimate
the number of infected, as well as estimates of the number of infected on the basis of
sample studies of immunity in the population. The second method to improve forecasts
and improve the efficiency of decisions made on their basis is the using of statistical
estimates based on the use of information systems data on similar indicators of coun-
tries where the pandemic is similar, but significantly ahead of the country for which it
is made forecast. It is important to increase the accuracy of forecasts to take into account
in mathematical models the heterogeneity of pandemic development by region and the
use of regional data in modified models.
The work was made by non-governmental organization "system research".
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