Impact of doubling COVID-19 cases on health parameters
In a recent study published in PLoS ONE, researchers assessed the impact of doubling coronavirus disease 2019 (COVID-19) cases on health parameters.
In biosciences and epidemiology, the doubling of a significant 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 often used to estimate the characteristics of infectious disease outbreaks, particularly during the initial phase of an epidemic, and to measure the risk imposed by infectious disease epidemics.
This study presents a relatively simple but helpful measure to assess the public health threat caused by an epidemic, termed the "doubling effect," and expressed mathematically as relative risk.
About the study
In the present study, researchers demonstrated a novel method for quantifying the impact of doubling COVID-19 cases on hospitalizations and deaths.
The initial analysis modeled the association between novel confirmed COVID-19 infections and new hospitalizations. The team conducted the same assessment for the following situations: the impact of doubling the number of COVID-19-positive cases on death rates and the impact of doubling the number of hospitalizations on death rates.
In generating the base 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 number of hospitalizations was modeled. This model compensated for the overdispersion of count data typically observed in data on epidemic outbreaks.
The estimated number of hospitalizations rises by the multiplicative factor exp(beta) when the number of cases doubles, a phenomenon known as the "doubling effect." Given that numerous nations are included in the study, the latter perspective is particularly relevant for policymakers, who may use it to examine the current scenario in their nation and relate it to the progress in other countries, like neighboring countries. A changing coefficient model, also known as a locally parametric model, has linear regressors, but their coefficients are permitted to change gradually with other variables, also known as effect modifiers.
Among the countries in North-Western Europe, the UK had the largest relative risk, with a 70% rise in hospitalization beginning in September 2020 if the proportion of confirmed COVID-19 infections doubles. This risk reduced to 60% in May 2021 and after remained steady. Denmark and Norway had a reduced doubling risk, with hospitalizations increasing by around 50% when the number of infections was doubled. The trajectory of the relative risk over time is not as consistent as in the UK, presumably because the population is smaller and the number of hospitalizations is lower. In the first year of the COVID-19 pandemic, Netherlands and Belgium experienced a 50% to 60% rise in hospitalizations owing to a doubling number of infections, with increased relative risks.
Croatia had the greatest risk level in the group of Eastern European nations, with a rise from 50% to 70%. In comparison, Estonia's relative risk varied from 40% to 60%. In June 2021, the relative risk in the Czech Republic was reduced but increased in October 2021. The relative risk characteristics for North-Western European countries were comparable, with a higher risk in January 2021, a reduced risk in May to June 2021, and a minor rise in December 2021. The same is true for Estonia, Croatia, Latvia, and the Czech Republic. Suppose South Africa had a significantly higher relative risk in the cases-hospitalizations scenario than other European countries. In that case, South Africa's profile approaches that of the other nations in the cases-mortality scenario.
Lastly, the hospitalization-mortality analysis demonstrated the same pattern as the cases-hospitalizations analysis; however, the relative risks for all nations appear to have been less stable over time. In all the countries analyzed, the number of hospitalizations and mortalities is far 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 number of cases of COVID-19-related hospitalizations and mortality, as well as a characterization of the evolution of these measures for many nations across time.
- Petrof O, Fajgenblat M, Neyens T, Molenberghs G, Faes C. (2022). The doubling effect of COVID-19 cases on key health indicators. PLoS ONE. doi: https://doi.org/10.1371/journal.pone.0275523 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0275523
Posted in: Medical Science News | Medical Research News | Disease/Infection News
Tags: Cell, Coronavirus, Coronavirus Disease COVID-19, covid-19, Epidemiology, Evolution, Frequency, in vitro, Mortality, Pandemic, Public Health, Tumor
Bhavana Kunkalikar is a medical writer based in Goa, India. Her academic background is in Pharmaceutical sciences and she holds a Bachelor's degree in Pharmacy. Her educational background allowed her to foster an interest in anatomical and physiological sciences. Her college project work based on ‘The manifestations and causes of sickle cell anemia’ formed the stepping stone to a life-long fascination with human pathophysiology.
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