Subgroup Contributed Talks

Samson Ogunlade

James Cook University

"Modeling the potential of wAu-Wolbachia strain invasion in mosquitoes to control Aedes-borne arboviral infections"

Arboviral infections such as dengue, Zika and chikungunya are fast spreading diseases that pose significant health problems globally. In order to control these infections, an intracellular bacterium called Wolbachia has been introduced into wild-type mosquito populations in the hopes of replacing the vector transmitting agent, Aedes aegypti with one that is incapable of transmission. In this study, we developed a Wolbachia transmission model for the novel wAu strain which possesses several favourable traits (e.g., enhanced viral blockage and maintenance at higher temperature) but not cyctoplasmic incompatibility (CI) - when a Wolbachia-infected male mosquito mates with an uninfected female mosquito, producing no viable offspring. This model describes the competitive dynamics between wAu-Wolbachia-infected and uninfected mosquitoes and the role of imperfect maternal transmission. By analysing the system via computing the basic reproduction number(s) and stability properties, the potential of the wAu strain as a viable strategy to control arboviral infections is established. The results of this work show that enhanced maintenance of Wolbachia infection at higher temperatures can overcome the lack of CI induction to support wAu-Wolbachia infected mosquito invasion. This study will support future arboviral control programs, that rely on the introduction of new Wolbachia variants.

Maryam Aliee

University of Warwick

"Estimating the distribution of extinction times of infectious diseases in deterministic models"

For many infectious diseases the eventual aim of control measures is eradication - completely removing the pathogen from host populations and the environment. Theoretical models can be used to predict the time to extinction under specific interventions. In general, this question requires the use of stochastic models which recognise the inherent individual-based, chance-driven nature of the dynamics; yet stochastic models are inherently computationally expensive, especially when considering parameter uncertainty. On the other side, deterministic models are practical and tractable, however, the endpoint of an infection is by definition ambiguous in these models since the populations are represented by continuous variables that never reach zero. We study the extinction problem in deterministic models with the help of an effective ``birth-death'' description of infection and recovery processes. We present a practical method to estimate the distribution, and therefore robust means and prediction intervals, of extinction times by calculating their different moments within the birth-death framework. We compare these predictions with the solutions of the corresponding forward Kolmogorov equations. We then extend this framework to estimate the extinction time of more complex and realistic infection dynamics, African sleeping sickness, gHAT, which is a vector-borne disease transmitted to humans by tsetse. This method will enable us to improve predictions of the timing of elimination of transmission for gHAT using our existing deterministic framework.

Sung-mok Jung

Hokkaido University

"Reconstruction and analysis of the transmission network of African swine fever in People’s Republic of China, August 2018–September 2019"

Introduction of African swine fever (ASF) to China in mid-2018 and subsequent transboundary spread across Asia devastated regional swine production, affecting live pig and pork product-related markets worldwide. In order to explore the spatiotemporal spread of ASF in China, we reconstructed possible ASF transmission networks using nearest neighbour, exponential function, equal probability, and spatiotemporal case-distribution algorithms. From these networks we estimated the reproduction numbers, serial intervals, and transmission distances of the outbreak. The mean serial interval between paired units was around days for all algorithms, while the mean transmission distance ranged from 332–456 kilometers. The reproduction numbers for each algorithm peaked during the first two weeks and steadily declined through the end of 2018 before hovering around the epidemic threshold value of one with sporadic increases during 2019. These results suggest that: 1) swine husbandry practices and production systems that lend themselves to long-range transmission drove ASF spread, and 2) outbreaks went undetected by the surveillance system. China and other affected countries have stepped up efforts to control ASF within their jurisdictions, and continued support for strict implementation of biosecurity standards and improvements to ASF surveillance are essential for halting transmission in China and further spread across Asia.

Usman Sanusi

Technical University of Munich

"Influence of quiescence on host-parasite coevolutionary dynamics"

Mathematical Modelling is widely being used as a tool to predict and understand the spread of infectious disease such as HIV, tuberculosis, Measles, Malaria, corona virus,. . . . However, most diseases have an intra-host quiescent stage defined sometimes as covert infection (malaria for example), while other parasites have dormant stages in the environment. The influence of these two life-history traits seems to be neglected by mathematicians when developing their models [1]. In this research, we develop a coevolutionary model similar to [2,3] to predict and understand the spread of disease considering the effect of intra-host quiescence. Analytical results are obtained for the stability of the system. We especially derived a stability conditions for a five by five system with quiescent stage. Numerical simulations were also performed and we show that the period of oscillations and the time to damping off is almost double under the quiescence compared to the classic epidemiological model. We finally extended the model to include the effect of stochasticity on disease transmission and study analytically the outcome using a Markov-Chain model. The model dynamics with stochasticity follow the deterministic one.