Inapparent and Vertically Transmitted Infections in Two Host-Virus Systems

Despite the advances made since the advent of germ theory, infectious diseases still wreak havoc on human societies, not only affecting us directly but impacting the crops and livestock upon which we rely. Infectious diseases also have dramatic effects on wildlife ecology. Therefore research into infectious diseases could not only directly lead to the improvement and saving of human lives, but aid in food security and the conservation of many wildlife species. Of vital importance in understanding the ecology of infectious diseases are the mechanisms by which they persist in host populations. One possible mechanism is vertical transmission: the transmission of a pathogen from a parent to its offspring as a result of the process of host reproduction. Another possible mechanism is inapparant infections, where an infected host does not display symptoms. Focusing on dengue fever and the Plodia interpunctella granulovirus laboratory system, this PhD thesis looks at the role these two mechanisms play on the persistence of two viral infections and their ecology. Regarding the Plodia interpunctella granulovirus (PiGV) low host food quality led to greater detection of vertically transmitted inapparant PiGV, but did not lead to its activation to an apparent form. Host inbreeding did not lead to vertically transmitted inapparant PiGV’s activation, nor had an effect on its vertical transmission. The vertical infection rate of PiGV was very low. I would therefore suggest that it may be better to use an insect virus system with a higher rate of vertical infection in future research into vertically transmitting inapparent infections. Regarding dengue virus I conclude that vertical transmission is not likely to play a role in the persistence of this virus. However modelling work found that inapparent infections could provide dengue viruses with a means of persistence and should be subject to further research

Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves

Technological advancements in phylodynamic modeling coupled with the accessibility of real-time pathogen genetic data are increasingly important for understanding the infectious disease transmission dynamics. In this study, we compare the transmission potentials of North American influenza A(H1N1)pdm09 derived from sequence data to that derived from surveillance data. The impact of the choice of tree-priors, informative epidemiological priors, and evolutionary parameters on the transmission potential estimation is evaluated. North American Influenza A(H1N1)pdm09 hemagglutinin (HA) gene sequences are analyzed using the coalescent and birth-death tree prior models to estimate the basic reproduction number (R0). Epidemiological priors gathered from published literature are used to simulate the birth-death skyline models. Path-sampling marginal likelihood estimation is conducted to assess model fit. A bibliographic search to gather surveillance-based R0 values were consistently lower (mean ≤ 1.2) when estimated by coalescent models than by the birth-death models with informative priors on the duration of infectiousness (mean ≥ 1.3 to ≤2.88 days). The user-defined informative priors for use in the birth-death model shift the directionality of epidemiological and evolutionary parameters compared to non-informative estimates. While there was no certain impact of clock rate and tree height on the R0 estimation, an opposite relationship was observed between coalescent and birth-death tree priors. There was no significant difference (p = 0.46) between the birth-death model and surveillance R0 estimates. This study concludes that tree-prior methodological differences may have a substantial impact on the transmission potential estimation as well as the evolutionary parameters. The study also reports a consensus between the sequence-based R0 estimation and surveillance-based R0 estimates. Altogether, these outcomes shed light on the potential role of phylodynamic modeling to augment existing surveillance and epidemiological activities to better assess and respond to emerging infectious diseases

Estimating social contacts in mass gatherings for disease outbreak prevention and management: case of Hajj pilgrimage

Abstract Background Most mass gathering events have been suspended due to the SARS-CoV-2 pandemic. However, with vaccination rollout, whether and how to organize some of these mass gathering events arises as part of the pandemic recovery discussions, and this calls for decision support tools. The Hajj, one of the world's largest religious gatherings, was substantively scaled down in 2020 and 2021 and it is still unclear how it will take place in 2022 and subsequent years. Simulating disease transmission dynamics during the Hajj season under different conditions can provide some insights for better decision-making. Most disease risk assessment models require data on the number and nature of possible close contacts between individuals. Methods We sought to use integrated agent-based modeling and discrete events simulation techniques to capture risky contacts among the pilgrims and assess different scenarios in one of the Hajj major sites, namely Masjid-Al-Haram. Results The simulation results showed that a plethora of risky contacts may occur during the rituals. Also, as the total number of pilgrims increases at each site, the number of risky contacts increases, and physical distancing measures may be challenging to maintain beyond a certain number of pilgrims in the site. Conclusions This study presented a simulation tool that can be relevant for the risk assessment of a variety of (respiratory) infectious diseases, in addition to COVID-19 in the Hajj season. This tool can be expanded to include other contributing elements of disease transmission to quantify the risk of the mass gathering events

Effect of different Test Regimes on infections and hospitalisations as measured by % Relative Difference to simulations with no testing regime.

A: Boxplots Total Infections and Hospitalisation in simulations made with no testing regime. B and C: Boxplots of a Testing Regimes % Relative Differences in Total infections and Hospitalisation. For every parameter set produced under LHS the % relative difference in outputs simulated under a testing regime, Fig 3B and 3C, was calculated against the corresponding output from the “No Testing” regime simulations, depicted in Fig 3A, as a control (see Eq 4). The white dots are the means. The array of samples used in simulation was generated from Latin Hypercube sampling drawing upon the distributions outlined in Tables 2, 3 and 5. Details of testing regimes can be found in Table 7.</p

Comparison of a policy ensuring all visitors must be effectively vaccinated but not having testing “effective visitor vaccination”) against other policies.

A: Boxplots of Total Infections and Hospitalisation under “effective visitor vaccination” (vA = vB = 1). B Boxplots of % relative differences in Total Infections and Hospitalisation seen under various testing regimes at differing levels of effective vaccination for visitors compared to “effective visitor vaccination” as a control. In B % relative differences are calculated between simulations made with the same Latin Hypercbe (LH) sample, see Eq 4. Testing regimes used in comparisons are “No Testing”, “Pre-Travel RT-PCR”, “Pre-Match RT-PCR”, “Pre-Match RA” or “RT-PCR then RA” testing regimes (see Table 7). Levels of effective vaccination for visitors in the comparisons are vA = vB = 0, vA = vB = 0.25, vA = vB = 0.5 and vA = vB = 0.75. The white dots on the boxplots represent mean values. All parameters other than those relating to effective vaccination for visitors (vA and vB) are drawn using LH sampling from distributions outlined in Tables 2, 3 and 5.</p

Starting Values of Variables used for Simulating a FIFA 2022 World Cup Match.

Starting Values of Variables used for Simulating a FIFA 2022 World Cup Match.</p

Deviation of the Basic Reproductive Number, <i>R</i>₀, and its relationship to the transmission term, <i>β</i>.

Deviation of the Basic Reproductive Number, R0, and its relationship to the transmission term, β.</p

In depth examinations of % relative differences and PRCCs.

In depth examinations of % relative differences and PRCCs.</p