Cohort-based approach to understanding the roles of generation and serial intervals in shaping epidemiological dynamics
Abstract Generation intervals and serial intervals are critical quantities for characterizing outbreak dynamics. Generation intervals characterize the time between infection and transmission, while serial intervals characterize the time between the onset of symptoms in a chain of transmission. They are often used interchangeably, leading to misunderstanding of how these intervals link the epidemic growth raterand the reproduction numberℛ. Generation intervals provide a mechanistic link betweenrandℛbut are harder to measure via contact tracing. While serial intervals are easier to measure from contact tracing, recent studies suggest that the two intervals give different estimates ofℛfromr. We present a general framework for characterizing epidemiological delays based on cohorts (i.e., a group of individuals that share the same event time, such as symptom onset) and show thatforward-lookingserial intervals, which correctly linkℛwithr, are not the same as “intrinsic” serial intervals, but instead change withr. We provide a heuristic method for addressing potential biases that can arise from not accounting for changes in serial intervals across cohorts and apply the method to estimatingℛfor the COVID-19 outbreak in China using serial-interval data — our analysis shows that using incorrectly defined serial intervals can severely bias estimates. This study demonstrates the importance of early epidemiological investigation through contact tracing and provides a rationale for reassessing generation intervals, serial intervals, andℛestimates, for COVID-19.Significance Statement The generation- and serial-interval distributions are key, but different, quantities in outbreak analyses. Recent theoretical studies suggest that two distributions give different estimates of the reproduction numberℛfrom the exponential growth rater; however, both intervals, by definition, describe disease transmission at the individual level. Here, we show that the serial-interval distribution, defined from the correct reference time and cohort, gives the same estimate ofℛas the generation-interval distribution. We then apply our framework to serial-interval data from the COVID-19 outbreak in China. While our study supports the use of serial-interval distributions in estimatingℛ, it also reveals necessary changes to the current understanding and applications of serial-interval distribution..
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Preprint| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
bioRxiv.org - (2022) vom: 26. Okt. Zur Gesamtaufnahme - year:2022 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Park, Sang Woo [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2020.06.04.20122713 |
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| PPN (Katalog-ID): |
XBI018078281 |
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| 520 | |a Abstract Generation intervals and serial intervals are critical quantities for characterizing outbreak dynamics. Generation intervals characterize the time between infection and transmission, while serial intervals characterize the time between the onset of symptoms in a chain of transmission. They are often used interchangeably, leading to misunderstanding of how these intervals link the epidemic growth raterand the reproduction numberℛ. Generation intervals provide a mechanistic link betweenrandℛbut are harder to measure via contact tracing. While serial intervals are easier to measure from contact tracing, recent studies suggest that the two intervals give different estimates ofℛfromr. We present a general framework for characterizing epidemiological delays based on cohorts (i.e., a group of individuals that share the same event time, such as symptom onset) and show thatforward-lookingserial intervals, which correctly linkℛwithr, are not the same as “intrinsic” serial intervals, but instead change withr. We provide a heuristic method for addressing potential biases that can arise from not accounting for changes in serial intervals across cohorts and apply the method to estimatingℛfor the COVID-19 outbreak in China using serial-interval data — our analysis shows that using incorrectly defined serial intervals can severely bias estimates. This study demonstrates the importance of early epidemiological investigation through contact tracing and provides a rationale for reassessing generation intervals, serial intervals, andℛestimates, for COVID-19.Significance Statement The generation- and serial-interval distributions are key, but different, quantities in outbreak analyses. Recent theoretical studies suggest that two distributions give different estimates of the reproduction numberℛfrom the exponential growth rater; however, both intervals, by definition, describe disease transmission at the individual level. Here, we show that the serial-interval distribution, defined from the correct reference time and cohort, gives the same estimate ofℛas the generation-interval distribution. We then apply our framework to serial-interval data from the COVID-19 outbreak in China. While our study supports the use of serial-interval distributions in estimatingℛ, it also reveals necessary changes to the current understanding and applications of serial-interval distribution. | ||
| 650 | 4 | |a Biology |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 570 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Sun, Kaiyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Champredon, David |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Bolker, Benjamin M. |e verfasserin |4 aut | |
| 700 | 1 | |a Earn, David J. D. |e verfasserin |4 aut | |
| 700 | 1 | |a Weitz, Joshua S. |e verfasserin |4 aut | |
| 700 | 1 | |a Grenfell, Bryan T. |e verfasserin |4 aut | |
| 700 | 1 | |a Dushoff, Jonathan |e verfasserin |4 aut | |
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