Estimating COVID Case Fatality Rate in Bulgaria for 2020–2021

Томов, Л; Batselova, Hristiana; Velikova, Tsvetelina

doi:10.1007/978-3-031-17292-2_9

Cited by 3 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Still, it cannot be conclusively shown-the standard error of the relevant parameters, variants L1 and variants L2, is not small enough for that purpose (it should be at most half of the absolute value of the coefficient). This is possibly due to the only delta variant being significantly deadlier for that period[ 38 ].…”

Section: Resultsmentioning

confidence: 99%

Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic

Tomov,

Chervenkov,

Miteva

et al. 2023

World J Clin Cases

Self Cite

View full text Add to dashboard Cite

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

show abstract

Section: Resultsmentioning

confidence: 99%

Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic

Tomov,

Chervenkov,

Miteva

et al. 2023

World J Clin Cases

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is not small enough to accept the value of the coefficient but enough to give us some confidence about the direction of the influence. The current vaccination policy in Bulgaria, which is not focused on risk groups, does more harm than good since the mortality risk grows exponentially with age[ 16 ]. What is the reason?…”

Section: Discussionmentioning

confidence: 99%

“…In our previous research[ 16 ], we used ARIMA models to predict deaths weekly from new cases among different age groups. The approach is suitable for time series analysis with time-varying effective reproductive number Rt.…”

Section: Methodsmentioning

confidence: 99%

Role of children in the Bulgarian COVID-19 epidemic: A mathematical model study

Томов¹,

Batselova²,

Lazova³

et al. 2023

World J Exp Med

View full text Add to dashboard Cite

BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic affects all aspects of our lives, including children. With the advancement of the pandemic, children under five years old are at increased risk of hospitalization relative to other age groups. This makes it paramount that we develop tools to address the two critical aspects of preserving children's health – new treatment protocols and new predictive models. For those purposes, we need to understand better the effects of COVID-19 on children, and we need to be able to predict the number of affected children as a proportion of the number of infected children. This is why our research focuses on clinical and epidemiological pictures of children with heart damage post-COVID, as a part of the general picture of post-COVID among this age group. AIM To demonstrate the role of children in the COVID-19 spread in Bulgaria and to test the hypothesis that there are no secondary transmissions in schools and from children to adults. METHODS Our modeling and data show with high probability that in Bulgaria, with our current measures, vaccination strategy and contact structure, the pandemic is driven by the children and their contacts in school. RESULTS This makes it paramount that we develop tools to address the two critical aspects of preserving children's health – new treatment protocols and new predictive models. For those purposes, we need to understand better the effects of COVID-19 on children, and we need to be able to predict the number of affected children as a proportion of the number of infected children. This is why our research focuses on clinical and epidemiological pictures of children with heart damage post-COVID, as a part of the general picture of post-Covid among this age group. CONCLUSION Our modeling rejects that hypothesis, and the epidemiological data supports that. We used epidemiological data to support the validity of our modeling. The first summer wave in 2020 from the listed here school proms endorse the idea of transmissions from students to teachers.

show abstract