In a recent study published in PLoS ONE, researchers assessed the impact of doubling coronavirus disease 2019 (COVID-19) cases on health parameters.

Study: The doubling effect of COVID-19 cases on key health indicators. Image Credit: GEMINI PRO STUDIO/Shutterstock

Background

In biosciences and epidemiology, the doubling of a big statistic, the duration during which it happens, and its effect on other metrics are crucial concepts. The doubling time is one such factor that has been extensively investigated and used for various phenomena, including infectious disease epidemics, tumor growth, population size, and in vitro cell growth. The doubling time is commonly used to estimate the characteristics of infectious disease outbreaks, particularly in the course of the initial phase of an epidemic, and to measure the chance imposed by infectious disease epidemics.

This study presents a comparatively easy but helpful measure to evaluate the general public health threat brought on by an epidemic, termed the “doubling effect,” and expressed mathematically as relative risk.

Concerning the study

In the current study, researchers demonstrated a novel method for quantifying the impact of doubling COVID-19 cases on hospitalizations and deaths.

The initial evaluation modeled the association between novel confirmed COVID-19 infections and latest hospitalizations. The team conducted the identical assessment for the next situations: the impact of doubling the variety of COVID-19-positive cases on death rates and the impact of doubling the variety of hospitalizations on death rates.

In generating the bottom doubling model Yt, the weekly total of confirmed cases at times t − 6, t − 5 to t, and Ht was the weekly total of recently hospitalized patients at times t − 6, t − 5 to t. Utilizing a negative binomial regression, the variety of hospitalizations was modeled. This model compensated for the overdispersion of count data typically observed in data on epidemic outbreaks.

The estimated variety of hospitalizations rises by the multiplicative factor exp(beta) when the variety of cases doubles, a phenomenon often called the “doubling effect.” On condition that quite a few nations are included within the study, the latter perspective is especially relevant for policymakers, who may use it to look at the present scenario of their nation and relate it to the progress in other countries, like neighboring countries. A changing coefficient model, also often called a locally parametric model, has linear regressors, but their coefficients are permitted to alter regularly with other variables, also often called effect modifiers.

Results

Among the many countries in North-Western Europe, the UK had the biggest relative risk, with a 70% rise in hospitalization starting in September 2020 if the proportion of confirmed COVID-19 infections doubles. This risk reduced to 60% in May 2021 and after remained regular. Denmark and Norway had a reduced doubling risk, with hospitalizations increasing by around 50% when the variety of infections was doubled. The trajectory of the relative risk over time isn’t as consistent as within the UK, presumably since the population is smaller and the variety of hospitalizations is lower. In the primary 12 months of the COVID-19 pandemic, Netherlands and Belgium experienced a 50% to 60% rise in hospitalizations owing to a doubling variety of infections, with increased relative risks.

Croatia had the best risk level within the group of Eastern European nations, with an increase from 50% to 70%. As compared, Estonia’s relative risk varied from 40% to 60%. In June 2021, the relative risk within the Czech Republic was reduced but increased in October 2021. The relative risk characteristics for North-Western European countries were comparable, with a better risk in January 2021, a reduced risk in May to June 2021, and a minor rise in December 2021. The identical is true for Estonia, Croatia, Latvia, and the Czech Republic. Suppose South Africa had a significantly higher relative risk within the cases-hospitalizations scenario than other European countries. In that case, South Africa’s profile approaches that of the opposite nations within the cases-mortality scenario.

Lastly, the hospitalization-mortality evaluation demonstrated the identical pattern because the cases-hospitalizations evaluation; nonetheless, the relative risks for all nations appear to have been less stable over time. In all of the countries analyzed, the variety of hospitalizations and mortalities is much lower than the frequency of cases and hospitalizations.

Overall, the study proposed a modeling approach that facilitated the calculation of the epidemiological effect of doubling the variety of cases of COVID-19-related hospitalizations and mortality, in addition to a characterization of the evolution of those measures for many countries across time.