Dataset Information

Oscillatory Dynamics in Infectivity and Death Rates of COVID-19.

ABSTRACT: The analysis of systematically collected data for coronavirus disease 2019 (COVID-19) infectivity and death rates has revealed, in many countries around the world, a typical oscillatory pattern with a 7-day (circaseptan) period. Additionally, in some countries, 3.5-day (hemicircaseptan) and 14-day periodicities have also been observed. Interestingly, the 7-day infectivity and death rate oscillations are almost in phase, showing local maxima on Thursdays/Fridays and local minima on Sundays/Mondays. These observations are in stark contrast to a known pattern correlating the death rate with the reduced medical staff in hospitals on the weekends. While we cannot exclude the possibility that a significant portion of the observed oscillations is associated with the reporting of the individual cases, other reasons might contribute at least partly to these data. One possible hypothesis addressing these observations is that they reflect gradually increasing stress with the progressing week, which can trigger the higher death rates on Thursdays/Fridays. Moreover, assuming the weekends provide the likely time for new infections, the maximum number of new cases might fall, again, on Thursdays/Fridays. These observations deserve further study to provide a better understanding of COVID-19 dynamics.IMPORTANCE The infectivity and death rates for COVID-19 have been observed in many countries around the world as well as in the collective data of the whole world. These oscillations show distinct circaseptan periodicity, which could be associated with numerous biological reasons as well as with improper reporting of the data collected. Since very different results are observed in different countries and even continents, such as Sweden (very significant oscillations) or India (almost no oscillations), these data provide a very important message about different conditions under which the disease is spread or is reported, which, in turn, could serve as guidance tools in future epidemics. It is necessary that follow-up studies track the observed differences and fully reliably address their origins.

SUBMITTER: Pavlicek T

PROVIDER: S-EPMC7438025 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Oscillatory Dynamics in Infectivity and Death Rates of COVID-19.

Pavlíček Tomáš T Rehak Pavel P Král Petr P

mSystems 20200818 4

The analysis of systematically collected data for coronavirus disease 2019 (COVID-19) infectivity and death rates has revealed, in many countries around the world, a typical oscillatory pattern with a 7-day (circaseptan) period. Additionally, in some countries, 3.5-day (hemicircaseptan) and 14-day periodicities have also been observed. Interestingly, the 7-day infectivity and death rate oscillations are almost in phase, showing local maxima on Thursdays/Fridays and local minima on Sundays/Monday ...[more]

PMID: 32817387

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Project description:The Coronavirus Disease 2019 (COVID-19) pandemic has had a variable worldwide impact, likely related to country-level characteristics. In this ecological study, we explored the association of COVID-19 case rates (per 100,000 people) and death rates (per 100,000 people) with country-level population health characteristics, economic and human development indicators, and habitat-related variables. To calculate country-level COVID-19 case and death rates, the number of cases and deaths were extracted from the Johns Hopkins Coronavirus Resource Center through September 30, 2021. Country-level population health characteristics, economic, human development, and habitat-related indicators were extracted from several publicly available online sources of international organizations. Results were tabulated according to world zones and country economies. Unadjusted and adjusted multiple imputation linear regression analyses were performed to examine the association between country-level variables (per 1-standard deviation [SD] increase) and COVID-19 case and death rates. To satisfy the linear regression model assumptions of normality of residuals, we used the square root transformation of both outcomes. A total of 187 countries and territories were analyzed, with a median (25th, 75th percentiles) aggregate COVID-19 case rate of 3,605 (463, 8,228) per 100,000, a COVID-19 death rate of 45.9 (8.9, 137.1) per 100,000, and a case-fatality rate of 1.6% (1.2%, 2.6%). On multivariable analyses, each country-level 1-SD higher percentage of adults with obesity (β coefficient 13.7; 95% confidence interval [CI] 13.7; 8.9, 18.4), percentage of smokers (5.8; 95% CI 1.2, 10.5), percentage of adults with high blood pressure (4.9; 95% CI 0.3, 9.6), and gross national income (GNI) per capita (9.5; 95% CI 4.6, 14.5) was independently associated with higher square root of COVID-19 case rate, while average household size (-1.7; 95% CI -12.3, -3.2) was independently associated with lower square root of COVID-19 case rate. Similarly, each 1-SD higher percentage of adults with obesity (1.76; 95% CI 0.99, 2.52), percentage of adults with high blood pressure (1.11; 95% CI 0.48, 1.74), percentage of adults with physical inactivity (1.01; 95% CI 0.10, 1.191), and travel & tourism competitiveness index (1.05; 95% CI 0.06, 2.04) was independently associated with higher square root of COVID-19 death rate, whereas GNI per capita (-0.92; 95% CI -1.81, -0.03), and average household size (-1.07; 95% CI -1.87, -0.27) was independently associated with lower square root of COVID-19 death rate. This ecological study informs the need to develop country-specific public health interventions to better target populations at high risk for COVID-19, and test interventions to prevent transmission of SARS-CoV-2, taking into consideration cross-country differences in population health characteristics, and economic, human development and habitat-related factors.

Project description:BackgroundAfter claiming nearly five hundred thousand lives globally, the COVID-19 pandemic is showing no signs of slowing down. While the UK, USA, Brazil and parts of Asia are bracing themselves for the second wave-or the extension of the first wave-it is imperative to identify the primary social, economic, environmental, demographic, ethnic, cultural and health factors contributing towards COVID-19 infection and mortality numbers to facilitate mitigation and control measures.MethodsWe process several open-access datasets on US states to create an integrated dataset of potential factors leading to the pandemic spread. We then apply several supervised machine learning approaches to reach a consensus as well as rank the key factors. We carry out regression analysis to pinpoint the key pre-lockdown factors that affect post-lockdown infection and mortality, informing future lockdown-related policy making.FindingsPopulation density, testing numbers and airport traffic emerge as the most discriminatory factors, followed by higher age groups (above 40 and specifically 60+). Post-lockdown infected and death rates are highly influenced by their pre-lockdown counterparts, followed by population density and airport traffic. While healthcare index seems uncorrelated with mortality rate, principal component analysis on the key features show two groups: states (1) forming early epicenters and (2) experiencing strong second wave or peaking late in rate of infection and death. Finally, a small case study on New York City shows that days-to-peak for infection of neighboring boroughs correlate better with inter-zone mobility than the inter-zone distance.InterpretationStates forming the early hotspots are regions with high airport or road traffic resulting in human interaction. US states with high population density and testing tend to exhibit consistently high infected and death numbers. Mortality rate seems to be driven by individual physiology, preexisting condition, age etc., rather than gender, healthcare facility or ethnic predisposition. Finally, policymaking on the timing of lockdowns should primarily consider the pre-lockdown infected numbers along with population density and airport traffic.

Dataset Information

Oscillatory Dynamics in Infectivity and Death Rates of COVID-19.

Publications

Oscillatory Dynamics in Infectivity and Death Rates of COVID-19.

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