Dataset Information

Seasonality and uncertainty in global COVID-19 growth rates.

ABSTRACT: The virus causing COVID-19 has spread rapidly worldwide and threatens millions of lives. It remains unknown, as of April 2020, whether summer weather will reduce its spread, thereby alleviating strains on hospitals and providing time for vaccine development. Early insights from laboratory studies and research on related viruses predicted that COVID-19 would decline with higher temperatures, humidity, and ultraviolet (UV) light. Using current, fine-scaled weather data and global reports of infections, we develop a model that explains 36% of the variation in maximum COVID-19 growth rates based on weather and demography (17%) and country-specific effects (19%). UV light is most strongly associated with lower COVID-19 growth. Projections suggest that, without intervention, COVID-19 will decrease temporarily during summer, rebound by autumn, and peak next winter. Validation based on data from May and June 2020 confirms the generality of the climate signal detected. However, uncertainty remains high, and the probability of weekly doubling rates remains >20% throughout summer in the absence of social interventions. Consequently, aggressive interventions will likely be needed despite seasonal trends.

SUBMITTER: Merow C

PROVIDER: S-EPMC7959558 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Seasonality and uncertainty in global COVID-19 growth rates.

Merow Cory C Urban Mark C MC

Proceedings of the National Academy of Sciences of the United States of America 20201013 44

The virus causing COVID-19 has spread rapidly worldwide and threatens millions of lives. It remains unknown, as of April 2020, whether summer weather will reduce its spread, thereby alleviating strains on hospitals and providing time for vaccine development. Early insights from laboratory studies and research on related viruses predicted that COVID-19 would decline with higher temperatures, humidity, and ultraviolet (UV) light. Using current, fine-scaled weather data and global reports of infect ...[more]

PMID: 33051302

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Project description:Successive waves of infection by SARS-CoV-2 have left little doubt that this virus will transition to an endemic disease. Foreknowledge of when to expect seasonal surges is crucial for healthcare and public health decision-making. However, the future seasonality of COVID-19 remains uncertain. Evaluating its seasonality is complicated due to the limited years of SARS-CoV-2 circulation, pandemic dynamics, and varied interventions. In this study, we project the expected endemic seasonality by employing a phylogenetic ancestral and descendant state approach that leverages long-term data on the incidence of circulating HCoV coronaviruses. Our projections indicate asynchronous surges of SARS-CoV-2 across different locations in the northern hemisphere, occurring between October and January in New York and between January and March in Yamagata, Japan. This knowledge of spatiotemporal surges leads to medical preparedness and enables the implementation of targeted public health interventions to mitigate COVID-19 transmission.IMPORTANCEThe seasonality of COVID-19 is important for effective healthcare and public health decision-making. Previous waves of SARS-CoV-2 infections have indicated that the virus will likely persist as an endemic pathogen with distinct surges. However, the timing and patterns of potentially seasonal surges remain uncertain, rendering effective public health policies uninformed and in danger of poorly anticipating opportunities for intervention, such as well-timed booster vaccination drives. Applying an evolutionary approach to long-term data on closely related circulating coronaviruses, our research provides projections of seasonal surges that should be expected at major temperate population centers. These projections enable local public health efforts that are tailored to expected surges at specific locales or regions. This knowledge is crucial for enhancing medical preparedness and facilitating the implementation of targeted public health interventions.

Project description:IntroductionWhile the beneficial effect of vaccination, restrictive measures, and social distancing in reducing mortality due to SARS-CoV-2 is intuitive and taken for granted, seasonality (predictable fluctuation or pattern that recurs or repeats over a one-year period) is still poorly understood and insufficiently taken into consideration. We aimed to examine SARS-CoV-2 seasonality in countries with temperate climate.MethodsWe identified countries with temperate climate and extracted average country temperature data from the National Center for Environmental information and from the Climate Change Knowledge Portal. We obtained mortality and vaccination rates from an open access database. We used the stringency index derived from the Oxford COVID-19 Government Response Tracker to quantify restriction policies. We used Spearman's and rank-correlation non-parametric test coefficients to investigate the association between COVID-19 mortality and temperature values. We employed multivariate regression models to analyze how containment measures, vaccinations, and monthly temperatures affected COVID-19 mortality rates.ResultsThe time series for daily deaths per million inhabitants and average monthly temperatures of European countries and US states with a temperate climate had a negative correlation (p < 0.0001 for all countries, 0.40 < R < 0.86). When running multivariate regression models with country fixed effects, we noted that mortality rates were significantly lower when temperature were higher. Interestingly, when adding an interaction term between monthly temperatures and vaccination rates, we found that as monthly temperatures dropped, the effect of the vaccination campaign on mortality was larger than at higher temperatures.DiscussionDeaths attributed to SARS-CoV-2 decreased during the summer period in temperate countries. We found that the effect of vaccination rates on mortality was stronger when temperatures were lower. Stakeholders should consider seasonality in managing SARS-CoV-2 and future pandemics to minimize mortality, limit the pressure on hospitals and intensive care units while maintaining economic and social activities.

Project description:COVID-19 has been shown to have variable adverse effects on pregnancy. Reported data on stillbirth rates during the pandemic have, however, been inconsistent-some reporting a rise and others no change. Knowing the precise impact of COVID-19 on stillbirths should help with the planning and delivery of antenatal care. Our aim was, therefore, to undertake a meta-analysis to determine the impact of COVID-19 on the stillbirth rate. Databases searched included PubMed, Embase, Cochrane Library, ClinicalTrials.gov, and Web of Science, with no language restriction. Publications with stillbirth data on women with COVID-19, comparing stillbirth rates in COVID-19 and non-COVID-19 women, as well as comparisons before and during the pandemic, were included. Two independent reviewers extracted data separately and then compared them to ensure the accuracy of extraction and synthesis. Where data were incomplete, authors were contacted for additional information, which was included if provided. The main outcome measures were (1) stillbirth (SB) rate in pregnant women with COVID-19, (2) stillbirth rates in pregnant women with and without COVID-19 during the same period, and (3) population stillbirth rates in pre-pandemic and pandemic periods. A total of 29 studies were included in the meta-analysis; from 17 of these, the SB rate was 7 per 1000 in women with COVID-19. This rate was much higher (34/1000) in low- and middle-income countries. The odds ratio of stillbirth in COVID-19 compared to non-COVID-19 pregnant women was 1.89. However, there was no significant difference in population SB between the pre-pandemic and pandemic periods. Stillbirths are an ongoing global concern, and there is evidence that the rate has increased during the COVID-19 pandemic, but mostly in low- and middle-income countries. A major factor for this is possibly access to healthcare during the pandemic. Attention should be focused on education and the provision of high-quality maternity care, such as face-to-face consultation (taking all the preventative precautions) or remote appointments where appropriate.

Dataset Information

Seasonality and uncertainty in global COVID-19 growth rates.

Publications

Seasonality and uncertainty in global COVID-19 growth rates.

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