A Comparative Study of Compartmental Models for COVID-19 Transmission in Ontario, Canada (preprint)

The number of confirmed COVID-19 cases reached over 1.3 million in Ontario, Canada by June 4, 2022. The continued spread of the virus underlying COVID-19 has been spurred by the emergence of variants since the initial outbreak in December, 2019. Much attention has thus been devoted to tracking and modelling the transmission of COVID-19. Compartmental models are commonly used to mimic epidemic transmission mechanisms and are easy to understand. Their performance in real-world settings, however, needs to be more thoroughly assessed. In this comparative study, we examine five compartmental models -- four existing ones and an extended model that we propose -- and analyze their ability to describe COVID-19 transmission in Ontario from January 2022 to June 2022.

Monte Carlo sampling of flexible protein structures: an application to the SARS-CoV-2 omicron variant (preprint)

Proteins can exhibit dynamic structural flexibility as they carry out their functions, especially in binding regions that interact with other molecules. For the key SARS-CoV-2 spike protein that facilitates COVID-19 infection, studies have previously identified several such highly flexible regions with therapeutic importance. However, protein structures available from the Protein Data Bank are presented as static snapshots that may not adequately depict this flexibility, and furthermore these cannot keep pace with new mutations and variants. In this paper we present a sequential Monte Carlo method for broadly sampling the 3-D conformational space of protein structure, according to the Boltzmann distribution of a given energy function. Our approach is distinct from previous sampling methods that focus on finding the lowest-energy conformation for predicting a single stable structure. We exemplify our method on the SARS-CoV-2 omicron variant as an application of timely interest. Our results identify sequence positions 495-508 as a key region where omicron mutations have the most impact on the space of possible conformations, which coincides with the findings of other preliminary studies on the binding properties of the omicron variant.

Comparing regional and provincial-wide COVID-19 models with physical distancing in British Columbia (preprint)

We study the effects of physical distancing measures for the spread of COVID-19 in regional areas within British Columbia, using the reported cases of the five provincial Health Authorities. Building on the Bayesian epidemiological model of Anderson et al. (2020), we propose a hierarchical regional Bayesian model with time-varying regional parameters between March to December of 2020. In the absence of COVID-19 variants and vaccinations during this period, we examine the regionalized basic reproduction number, modelled prevalence, relative reduction in contact due to physical distancing, and proportion of anticipated cases that have been tested and reported. We observe significant differences between the regional and provincial-wide models and demonstrate the hierarchical regional model can better estimate regional prevalence, especially in rural regions. These results indicate that it can be useful to apply similar regional models to other parts of Canada or other countries.

SIR Simulation of COVID-19 Pandemic in Malaysia: Will the Vaccination Program be Effective? (preprint)

Since the end of 2019, COVID-19 has significantly affected the lives of people around the world. Towards the end of 2020, several COVID-19 vaccine candidates with relatively high efficacy have been reported in the final phase of clinical trials. Vaccines have been considered as critical tools for opening up social and economic activities, thereby lessening the impact of this disease on the society. This paper presents a simulation of COVID-19 spread using modified Susceptible-Infected-Removed (SIR) model under vaccine intervention in several localities of Malaysia, i.e. those cities or states with high relatively COVID-19 cases such as Kuala Lumpur, Penang, Sabah, and Sarawak. The results show that at different vaccine efficacy levels (0.75, 0.85, and 0.95), the curves of active infection vary slightly, indicating that vaccines with efficacy above 0.75 would produce the herd immunity required to level the curves. In addition, disparity is significant between implementing or not implementing a vaccination program. Simulation results also show that lowering the reproduction number, R0 is necessary to keep the infection curve flat despite vaccination. This is due to the assumption that vaccination is mostly carried out gradually at the assumed fixed rate. The statement is based on our simulation results with two values of R0: 1.1 and 1.2, indicative of reduction of R0 by social distancing. The lower R0 shows a smaller peak amplitude about half the value simulated with R0=1.2. In conclusion, the simulation model suggests a two-pronged strategy to combat the COVID-19 pandemic in Malaysia: vaccination and compliance with standard operating procedure issued by the World Health Organization (e.g. social distancing).

Statistical challenges in the analysis of sequence and structure data for the COVID-19 spike protein (preprint)

As the major target of many vaccines and neutralizing antibodies against SARS-CoV-2, the spike (S) protein is observed to mutate over time. In this paper, we present statistical approaches to tackle some challenges associated with the analysis of S-protein data. We build a Bayesian hierarchical model to study the temporal and spatial evolution of S-protein sequences, after grouping the sequences into representative clusters. We then apply sampling methods to investigate possible changes to the S-protein's 3-D structure as a result of commonly observed mutations. While the increasing spread of D614G variants has been noted in other research, our results also show that the co-occurring mutations of D614G together with S477N or A222V may spread even more rapidly, as quantified by our model estimates.

Assessing the impacts of mutations to the structure of COVID-19 spike protein via sequential Monte Carlo (preprint)

Proteins play a key role in facilitating the infectiousness of the 2019 novel coronavirus. A specific spike protein enables this virus to bind to human cells, and a thorough understanding of its 3-dimensional structure is therefore critical for developing effective therapeutic interventions. However, its structure may continue to evolve over time as a result of mutations. In this paper, we use a data science perspective to study the potential structural impacts due to ongoing mutations in its amino acid sequence. To do so, we identify a key segment of the protein and apply a sequential Monte Carlo sampling method to detect possible changes to the space of low-energy conformations for different amino acid sequences. Such computational approaches can further our understanding of this protein structure and complement laboratory efforts.