Epidemiology and Transmission Dynamics of Infectious Diseases and Control Measures

Ryu, Sukhyun; Chun, June Young; Lee, Sun-Mi; Yoo, Dae-Sung; Kim, Yongdai; Ali, Sheikh Taslim; Chun, Byung Chul

doi:10.3390/v14112510

Cited by 6 publications

(2 citation statements)

References 42 publications

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“…Generation time is never negative as the secondary case’s infection time always occurs after the primary case’s infection time, but serial interval for a primary/secondary case pair can be negative if the secondary case has symptom onset earlier than the primary case. Understanding the serial interval is important for determining transmission characteristics of infectious diseases like COVID-19, including the reproduction number and secondary attack rates, which in turn could influence the design of control measures [ 10 – 14 ]. The serial interval depends on both biological and sociological factors.…”

Section: Introductionmentioning

confidence: 99%

Rapid review and meta-analysis of serial intervals for SARS-CoV-2 Delta and Omicron variants

et al. 2023

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Background The serial interval is the period of time between symptom onset in the primary case and symptom onset in the secondary case. Understanding the serial interval is important for determining transmission dynamics of infectious diseases like COVID-19, including the reproduction number and secondary attack rates, which could influence control measures. Early meta-analyses of COVID-19 reported serial intervals of 5.2 days (95% CI: 4.9–5.5) for the original wild-type variant and 5.2 days (95% CI: 4.87–5.47) for Alpha variant. The serial interval has been shown to decrease over the course of an epidemic for other respiratory diseases, which may be due to accumulating viral mutations and implementation of more effective nonpharmaceutical interventions. We therefore aggregated the literature to estimate serial intervals for Delta and Omicron variants. Methods This study followed Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A systematic literature search was conducted of PubMed, Scopus, Cochrane Library, ScienceDirect, and preprint server medRxiv for articles published from April 4, 2021, through May 23, 2023. Search terms were: (“serial interval” or “generation time”), (“Omicron” or “Delta”), and (“SARS-CoV-2” or “COVID-19”). Meta-analyses were done for Delta and Omicron variants using a restricted maximum-likelihood estimator model with a random effect for each study. Pooled average estimates and 95% confidence intervals (95% CI) are reported. Results There were 46,648 primary/secondary case pairs included for the meta-analysis of Delta and 18,324 for Omicron. Mean serial interval for included studies ranged from 2.3–5.8 days for Delta and 2.1–4.8 days for Omicron. The pooled mean serial interval for Delta was 3.9 days (95% CI: 3.4–4.3) (20 studies) and Omicron was 3.2 days (95% CI: 2.9–3.5) (20 studies). Mean estimated serial interval for BA.1 was 3.3 days (95% CI: 2.8–3.7) (11 studies), BA.2 was 2.9 days (95% CI: 2.7–3.1) (six studies), and BA.5 was 2.3 days (95% CI: 1.6–3.1) (three studies). Conclusions Serial interval estimates for Delta and Omicron were shorter than ancestral SARS-CoV-2 variants. More recent Omicron subvariants had even shorter serial intervals suggesting serial intervals may be shortening over time. This suggests more rapid transmission from one generation of cases to the next, consistent with the observed faster growth dynamic of these variants compared to their ancestors. Additional changes to the serial interval may occur as SARS-CoV-2 continues to circulate and evolve. Changes to population immunity (due to infection and/or vaccination) may further modify it.

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Section: Introductionmentioning

confidence: 99%

Rapid review and meta-analysis of serial intervals for SARS-CoV-2 Delta and Omicron variants

et al. 2023

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“…Transmission e ciency, de ned as the likelihood that a virus will move from one host to another during an exposure, and transmission intensity, de ned as the severity of an infection, are two key factors that in uence the epidemiological trajectories of disease transmission within a population (Hoverman et al 2010;Smith et al 2010;Reisen 2017). Animals infected by a pathogen with high transmission intensity may have high mortality and morbidity rates, which results in high population death tolls, physiological impacts, and their ability to transmit the diseases to new hosts (Leung 2021;Ryu et al 2022). For example, malaria research has linked transmission intensity with high rates of morbidity and mortality (Idro et al 2006;Smith et al 2010).…”

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confidence: 99%

Infection intensity and severity of Ranavirus transmission in juvenile wood frogs

Rayle,

Brunner,

Dahrouge

et al. 2024

Preprint

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Ranaviruses are responsible for mass die offs of wood frog (Lithobates sylvaticus) tadpoles. What happens in between epidemics is less clear, but juvenile (metamorphosed) stages are hypothesized to move Ranaviruses among wetlands and introduce or reintroduce these viruses into wetlands, initiating new outbreaks. A key question is under what circumstances can juvenile L. sylvaticus infect susceptible conspecifics. We examined Ranavirus transmission between juvenile L. sylvaticus in two settings: First, we measured transmission from a Ranavirus-infected frog to a co-housed susceptible frog via direct contact over a range of exposure periods. Second, we measured indirect transmission to susceptible frogs from a contaminated environment after a range of waiting times (i.e., from when the infected frog was removed to when the susceptible frog was exposed to the environment). We present evidence that juvenile frogs directly transmitted Ranavirus to susceptible frogs in all exposure periods (99.2% infected), with as little as one hour of co-housing resulting in 95.8% of susceptibles infected. Indirectly, 96.8% of susceptible frogs became infected after as long as 48-hour waiting times. Neither exposure period nor wait times influenced the probability of infection, because these probabilities are already high. In our linear regression models, viral load was best predicted by exposure period and cohort for the direct contact experiment, while wait time and the paired infective frog viral load best predicted viral load for the indirect contact experiment. Collectively, our results suggest that Ranavirus transmission readily occurs between recently metamorphosed wood frogs and via terrestrial environmental exposure.

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Precision epidemiology at the nexus of mathematics and nanotechnology: Unraveling the dance of viral dynamics

Aljabali,

Obeid,

El-Tanani

et al. 2024

Gene

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Epidemiology and Transmission Dynamics of Infectious Diseases and Control Measures

Cited by 6 publications

References 42 publications

Rapid review and meta-analysis of serial intervals for SARS-CoV-2 Delta and Omicron variants

Rapid review and meta-analysis of serial intervals for SARS-CoV-2 Delta and Omicron variants

Infection intensity and severity of Ranavirus transmission in juvenile wood frogs

Precision epidemiology at the nexus of mathematics and nanotechnology: Unraveling the dance of viral dynamics

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