[medRxiv, 30 July 2023]

Two different epidemiological models of vaccination are commonly used in dynamical modeling studies. The leaky vaccination model assumes that all vaccinated individuals experience a reduced force of infection by the same amount. The polarized vaccination model assumes that some fraction of vaccinated individuals are completely protected, while the remaining fraction remains completely susceptible; this seemingly extreme assumption causes the polarized model to always predict lower final epidemic size than the leaky model under the same vaccine efficacy. However, the leaky model also makes an implicit, unrealistic assumption: vaccinated individuals who are exposed to infection but not infected remain just as susceptible as they were prior to exposures (i.e., independent of previous exposures). To resolve the independence assumption, we introduce an immune boosting mechanism, through which vaccinated, yet susceptible, individuals can gain protection without developing a transmissible infection. The boosting model further predicts identical epidemic dynamics as the polarized vaccination model, thereby bridging the differences between two models. We further develop a generalized vaccination model to explore how the assumptions of immunity affect epidemic dynamics and estimates of vaccine effectiveness.

[arXiv, 22 September 2022]

Maruotti et al. 2022 used a mark-recapture approach to estimate bounds on the true number of monkeypox infections in various countries. These approaches are fundamentally flawed; it is impossible to estimate undercounting based solely on a single stream of reported cases. Simulations based on a Richards curve for cumulative incidence show that, for reasonable epidemic parameters, the proposed methods estimate bounds on the ascertainment ratio of ≈0.2−0.5 roughly independently of the true ascertainment ratio. These methods should not be used.

[Epidemics, 25 January 2023]

Asymptomatic and symptomatic SARS-CoV-2 infections can have different characteristic time scales of transmission. These time-scale differences can shape outbreak dynamics as well as bias population-level estimates of epidemic strength, speed, and controllability. For example, prior work focusing on the initial exponential growth phase of an outbreak found that larger time scales for asymptomatic vs. symptomatic transmission can lead to under-estimates of the basic reproduction number as inferred from epidemic case data. Building upon this work, we use a series of nonlinear epidemic models to explore how differences in asymptomatic and symptomatic transmission time scales can lead to changes in the realized proportion of asymptomatic transmission throughout an epidemic. First, we find that when asymptomatic transmission time scales are longer than symptomatic transmission time scales, then the effective proportion of asymptomatic transmission increases as total incidence decreases. Moreover, these time-scale-driven impacts on epidemic dynamics are enhanced when infection status is correlated between infector and infectee pairs (e.g., due to dose-dependent impacts on symptoms). Next we apply these findings to understand the impact of time-scale differences on populations with age-dependent assortative mixing and in which the probability of having a symptomatic infection increases with age. We show that if asymptomatic generation intervals are longer than corresponding symptomatic generation intervals, then correlations between age and symptoms lead to a decrease in the age of infection during periods of epidemic decline (whether due to susceptible depletion or intervention). Altogether, these results demonstrate the need to explore the role of time-scale differences in transmission dynamics alongside behavioral changes to explain outbreak features both at early stages (e.g., in estimating the basic reproduction number) and throughout an epidemic (e.g., in connecting shifts in the age of infection to periods of changing incidence).

Citerank: (10) 679875Sarah OttoProfessor in Zoology. Theoretical biologist, Canada Research Chair in Theoretical and Experimental Evolution, and Killam Professor at the University of British Columbia.10019D3ABAB, 685445Michael WZ LiMichael Li is Senior Scientist in the Public Health Risk Science Division (PHRS) of the Public Health Agency of Canada (PHAC) and a Research Associate at the South African Centre for Epidemiological Modelling and Analysis (SACEMA).10019D3ABAB, 701020CANMOD – PublicationsPublications by CANMOD Members144B5ACA0, 701023GenomicsWhile virus genomes can describe the global context of introductions and origins of local clusters of cases, CANMOD will focus on building methods for characterizing and modelling local transmission once it is established, and for surveillance for viral determinants of increased fitness and of enhanced risk of spillover, virulence and transmission.859FDEF6, 701023GenomicsWhile virus genomes can describe the global context of introductions and origins of local clusters of cases, CANMOD will focus on building methods for characterizing and modelling local transmission once it is established, and for surveillance for viral determinants of increased fitness and of enhanced risk of spillover, virulence and transmission.859FDEF6, 701037MfPH – Publications144B5ACA0, 707634Gary Van DomselaarDr. Gary Van Domselaar, PhD (University of Alberta, 2003) is the Chief of the Bioinformatics Laboratory at the National Microbiology Laboratory in Winnipeg Canada, and Adjunct Professor in the Department of Medical Microbiology at the University of Manitoba.10019D3ABAB, 708734Genomics859FDEF6, 715277Covid-19Covid-19 » Relevance » Genomics10000FFFACD, 715329Nick OgdenNicholas Ogden is a senior research scientist and Director of the Public Health Risk Sciences Division within the National Microbiology Laboratory at the Public Health Agency of Canada.10019D3ABAB

[Canada Communicable Disease Report, 6 April 2022]

Genomic surveillance during the coronavirus disease 2019 (COVID-19) pandemic has been key to the timely identification of virus variants with important public health consequences, such as variants that can transmit among and cause severe disease in both vaccinated or recovered individuals. The rapid emergence of the Omicron variant highlighted the speed with which the extent of a threat must be assessed. Rapid sequencing and public health institutions’ openness to sharing sequence data internationally give an unprecedented opportunity to do this; however, assessing the epidemiological and clinical properties of any new variant remains challenging. Here we highlight a “band of four” key data sources that can help to detect viral variants that threaten COVID-19 management: 1) genetic (virus sequence) data; 2) epidemiological and geographic data; 3) clinical and demographic data; and 4) immunization data. We emphasize the benefits that can be achieved by linking data from these sources and by combining data from these sources with virus sequence data. The considerable challenges of making genomic data available and linked with virus and patient attributes must be balanced against major consequences of not doing so, especially if new variants of concern emerge and spread without timely detection and action.