A pair formation model with recovery: Application to mpox.

The current global outbreaks of mpox is a unique infectious disease in the way it seems to be transmitting: it has been observed to be highly concentrated in communities of men who have sex with men (MSM) through pair formation, and also provides long lasting immunity. This framework of mostly close, prolonged contact spreading a disease that admits immunity after infection is unlike similar infections which either offer little to no immunity post-infection or are lifelong infections. This creates the need for a new model framework that incorporates pair formation structure with recovery. While seemingly a straight forward model, we show how new dynamics arise from the combination of pair formation and recovery that are not present in a standard model with recovery and also not present in a pair formation model without recovery. We see that the combination of these two properties allows for waves of infection that are not seen in a standard SIR model. These dynamics suggest that outbreaks of mpox around the world may require special attention from public health. We also derive a reproduction number for this model and estimate the reproduction number of human mpox to be ≈2.3 using global and Canadian data. The expression derived for R0 can help estimate key parameters for diseases transmission and public health interventions and compare to equivalent models without pair formation.

Multiple cohort study of hospitalized SARS-CoV-2 in-host infection dynamics: Parameter estimates, identifiability, sensitivity and the eclipse phase profile.

Within-host SARS-CoV-2 modelling studies have been published throughout the COVID-19 pandemic. These studies contain highly variable numbers of individuals and capture varying timescales of pathogen dynamics; some studies capture the time of disease onset, the peak viral load and subsequent heterogeneity in clearance dynamics across individuals, while others capture late-time post-peak dynamics. In this study, we curate multiple previously published SARS-CoV-2 viral load data sets, fit these data with a consistent modelling approach, and estimate the variability of in-host parameters including the basic reproduction number, R0, as well as the best-fit eclipse phase profile. We find that fitted dynamics can be highly variable across data sets, and highly variable within data sets, particularly when key components of the dynamic trajectories (e.g. peak viral load) are not represented in the data. Further, we investigated the role of the eclipse phase time distribution in fitting SARS-CoV-2 viral load data. By varying the shape parameter of an Erlang distribution, we demonstrate that models with either no eclipse phase, or with an exponentially-distributed eclipse phase, offer significantly worse fits to these data, whereas models with less dispersion around the mean eclipse time (shape parameter two or more) offered the best fits to the available data across all data sets used in this work. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

COVID-19 Vaccination and Healthcare Demand.

One of the driving concerns during any epidemic is the strain on the healthcare system. As we have seen many times over the globe with the COVID-19 pandemic, hospitals and ICUs can quickly become overwhelmed by cases. While strict periods of public health mitigation have certainly helped decrease incidence and thus healthcare demand, vaccination is the only clear long-term solution. In this paper, we develop a two-module model to forecast the effects of relaxation of non-pharmaceutical intervention and vaccine uptake on daily incidence, and the cascade effects on healthcare demand. The first module is a simple epidemiological model which incorporates non-pharmaceutical intervention, the relaxation of such measures and vaccination campaigns to predict caseloads into the Fall of 2021. This module is then fed into a healthcare module which can forecast the number of doctor visits, the number of occupied hospital beds, number of occupied ICU beds and any excess demand of these. From this module, we can also estimate the length of stay of individuals in ICU. For model verification and forecasting, we use the four most populous Canadian provinces as a case study.

A simple model for fitting mild, severe, and known cases during an epidemic with an application to the current SARS-CoV-2 pandemic.

One of the major difficulties with modelling an ongoing epidemic is that often data is limited or incomplete, making it hard to estimate key epidemic parameters and outcomes (e.g. attack rate, peak time, reporting rate, reproduction number). In the current study, we present a model for data-fitting limited infection case data which provides estimates for important epidemiological parameters and outcomes. The model can also provide reasonable short-term (one month) projections. We apply the model to the current and ongoing COVID-19 outbreak in Canada both at the national and provincial/territorial level.

The effects of diploid male production on honey bee colony evolution and survival.

The order Hymenoptera includes most of the eusocial species on the planet. Correlated is the fact that many of the social species within the order are haplodiploid and use complementary sex determination (CSD) to determine the sex of offspring. CSD is the mechanism by why single sex alleles within an organism result in male development (haploid) and mismatched sex alleles develop into females (diploids). Related to this is the production of diploid males: fertilized eggs with matched sex alleles which develop as male instead of female. Honey bees are no exception to this, and as their numbers continue to suffer globally and their genetic diversity lowers, the effects of diploid male production (DMP) may pose an increased risk to the survival of bee colonies. In the present study, we develop a model for diploid male production in a honey bee colony and show that with ample resources, this phenomena has little effect on a colony's health, but there is a limit to the sustainability of a colony suffering from diploid male production. We use our model to show that there were likely no great evolutionary pressures against CSD and DMP in wild honey bees as its effects on colony health in the wild would have been negligible but increased environmental hazards such as pesticides and monoculture crops increase the effects of DMP on colony health.

Evolutionary stability of the lysis-lysogeny decision: Why be virulent?

Lytic viruses infect and kill host cells, producing a large number of viral copies. Temperate viruses, in contrast, are able to integrate viral genetic material into the host cell DNA, leaving a viable host cell. The evolutionary advantage of this strategy, lysogeny, has been demonstrated in complex environments that include spatial structure, oscillating population dynamics, or periodic environmental collapse. Here, we examine the evolutionary stability of the lysis-lysogeny decision, that is, we predict the long-term outcome of the evolution of lysogeny rates. We demonstrate that viruses with high rates of lysogeny are stable against invasion by more virulent viral strains even in simple environments, as long as the pool of susceptible hosts is not unlimited. This mirrors well-known results in both r-K selection theory and virulence evolution: although virulent viruses have a faster potential growth rate, temperate strains are able to maintain positive growth on a lower density of the limiting resource, susceptible hosts. We then outline scenarios in which the rate of lysogeny is predicted to evolve either toward full lysogeny or full lysis. Finally, we demonstrate conditions under which intermediate rates of lysogeny, as observed in temperate viruses in nature, can be sustained long-term. In general, intermediate lysogeny rates persist when the coupling between susceptible host density and virus density is relaxed.