Consistent pattern of epidemic slowing across many geographies led to longer, flatter initial waves of the COVID-19 pandemic
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Abstract
To define appropriate planning scenarios for future pandemics of respiratory pathogens, it is important to understand the initial transmission dynamics of COVID-19 during 2020. Here, we fit an age-stratified compartmental model with a flexible underlying transmission term to daily COVID-19 death data from states in the contiguous U.S. and to national and sub-national data from around the world. The daily death data of the first months of the COVID-19 pandemic was qualitatively categorized into one of four main profile types: “spring single-peak”, “summer single-peak”, “spring/summer two-peak” and “broad with shoulder”. We estimated a reproduction number R as a function of calendar time t c and as a function of time since the first death reported in that population (local pandemic time, t p ). Contrary to the diversity of categories and range of magnitudes in death incidence profiles, the R ( t p ) profiles were much more homogeneous. We found that in both the contiguous U.S. and globally, the initial value of both R ( t c ) and R ( t p ) was substantial: at or above two. However, during the early months, pandemic time R ( t p ) decreased exponentially to a value that hovered around one. This decrease was accompanied by a reduction in the variance of R ( t p ). For calendar time R ( t c ), the decrease in magnitude was slower and non-exponential, with a smaller reduction in variance. Intriguingly, similar trends of exponential decrease and reduced variance were not observed in raw death data. Our findings suggest that the combination of specific government responses and spontaneous changes in behaviour ensured that transmissibility dropped, rather than remaining constant, during the initial phases of a pandemic. Future pandemic planning scenarios should include models that assume similar decreases in transmissibility, which lead to longer epidemics with lower peaks when compared with models based on constant transmissibility.
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SciScore for 10.1101/2022.03.31.22273267: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study relies on a number of potentially important assumptions, approximations and limitations. First, we chose the daily deaths as the dataset to fit to the model, inferring it from cumulative confirmed deaths, as opposed to using other data such as cases, which have been associated with known and considerably larger biases. However, while the deaths are likely a more reliable measure of the pandemic than cases they are far from …
SciScore for 10.1101/2022.03.31.22273267: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study relies on a number of potentially important assumptions, approximations and limitations. First, we chose the daily deaths as the dataset to fit to the model, inferring it from cumulative confirmed deaths, as opposed to using other data such as cases, which have been associated with known and considerably larger biases. However, while the deaths are likely a more reliable measure of the pandemic than cases they are far from perfect. Different states and countries use different criteria when registering deaths (for example, some report only confirmed deaths while others report both probable and confirmed deaths) and have different delays in reporting. Additionally, the reported numbers have been shown to be lower than the true toll of the pandemic (see e.g [33, 34, 46–49]). Our age-stratified compartmental model treats each population separately and ignores travel and importation of cases from other locations. While both global and local travel were significantly disrupted in 2020, they were responsible for the initial spread of COVID-19, and continue to play a role during latter periods. In this work, we ignored the initial introduction of cases to a location and focused on the dynamics of the virus following its introduction to a population. Also, we assumed the same quality of care over time at all locations. In practical terms, in the model, we assume that the probability of death from COVID-19 depended only on age during this period prior to wide spread vaccinati...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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