ABSTRACT
The outbreak of the severe acute respiratory syndrome coronavirus 2 started in Wuhan, China, towards the end of 2019 and spread worldwide. The rapid spread of the disease can be attributed to many factors including its high infectiousness and the high rate of human mobility around the world. Although travel/movement restrictions and other non-pharmaceutical interventions aimed at controlling the disease spread were put in place during the early stages of the pandemic, these interventions did not stop COVID-19 spread. To better understand the impact of human mobility on the spread of COVID-19 between regions, we propose a hybrid gravity-metapopulation model of COVID-19. Our model explicitly incorporates time-dependent human mobility into the disease transmission rate, and has the potential to incorporate other factors that affect disease transmission such as facemasks, physical distancing, contact rates, etc. An important feature of this modeling framework is its ability to independently assess the contribution of each factor to disease transmission. Using a Bayesian hierarchical modeling framework, we calibrate our model to the weekly reported cases of COVID-19 in thirteen local health areas in metro Vancouver, British Columbia (BC), Canada, from July 2020 to January 2021. We consider two main scenarios in our model calibration: using a fixed distance matrix and time-dependent weekly mobility matrices. We found that the distance matrix provides a better fit to the data, whilst the mobility matrices have the ability to explain the variance in transmission between regions. This result shows that the mobility data provides more information in terms of disease transmission than the distances between the regions.
Subject(s)
COVID-19 , Respiratory InsufficiencyABSTRACT
The pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) took the world by surprise. Following the first outbreak of COVID-19 in December 2019, several models have been developed to study and understand its transmission dynamics. Although the spread of COVID-19 is being slowed down by vaccination and other interventions, there is still a need to have a clear understanding of the evolution of the pandemic across countries, states and communities. To this end, there is a need to have a clearer picture of the initial spread of the disease in different regions. In this project, we used a simple SEIR model and a Bayesian inference framework to estimate the basic reproduction number of COVID-19 across Africa. Our estimates vary between 1.98 (Sudan) and 9.66 (Mauritius), with a median of 3.67 (90% CrI: 3.31 - 4.12). The estimates provided in this paper will help to inform COVID-19 modeling in the respective countries/regions.
Subject(s)
COVID-19 , Coronavirus InfectionsABSTRACT
Following the emergence of COVID-19 at the end of 2019, several mathematical models have been developed to study the transmission dynamics of this disease. Many of these models assume homogeneous mixing in the underlying population. However, contact rates and mixing patterns can vary dramatically among individuals depending on their age and activity level. Variation in contact rates among age groups and over time can significantly impact how well a model captures observed trends. To properly model the age-dependent dynamics of COVID-19 and understand the impacts of interventions, it is essential to consider heterogeneity arising from contact rates and mixing patterns. We developed an age-structured model that incorporates time-varying contact rates and population mixing computed from the ongoing BC Mix COVID-19 survey to study transmission dynamics of COVID-19 in British Columbia (BC), Canada. Using a Bayesian inference framework, we fit four versions of our model to weekly reported cases of COVID-19 in BC, with each version allowing different assumptions of contact rates. We show that in addition to incorporating age-specific contact rates and mixing patterns, time-dependent (weekly) contact rates are needed to adequately capture the observed transmission dynamics of COVID-19. Our approach provides a framework for explicitly including empirical contact rates in a transmission model, which removes the need to otherwise model the impact of many non-pharmaceutical interventions. Further, this approach allows projection of future cases based on clear assumptions of age-specific contact rates, as opposed to less tractable assumptions regarding transmission rates.
Subject(s)
COVID-19ABSTRACT
Adherence to public health policies such as the non-pharmaceutical interventions implemented against COVID-19 plays a major role in reducing infections and controlling the spread of the diseases. In addition, understanding the transmission dynamics of the disease is also important in order to make and implement efficient public health policies. In this paper, we developed an SEIR-type compartmental model to assess the impact of adherence to COVID-19 non-pharmaceutical interventions and indirect transmission on the dynamics of the disease. Our model considers both direct and indirect transmission routes and stratifies the population into two groups: those that adhere to COVID-19 non-pharmaceutical interventions (NPIs) and those that do not adhere to the NPIs. We compute the control reproduction number and the final epidemic size relation for our model and study the effect of different parameters of the model on these quantities. Our results show that direct transmission has more effect on the reproduction number and final epidemic size, relative to indirect transmission. In addition, we showed that there is a significant benefit in adhering to the COVID-19 NPIs.
Subject(s)
COVID-19ABSTRACT
The spread of SARS-CoV-2 through direct transmission (person-to-person) has been the focus of most studies on the dynamics of COVID-19. The efficacy of social distancing and mask usage at reducing the risk of direct transmission of COVID-19 has been studied by many researchers. Little or no attention is given to indirect transmission of the virus through shared items, commonly touch surfaces and door handles. The impact of the persistence of SARS-CoV-2 on hard surfaces and in the environment, on the dynamics of COVID-19 remain largely unknown. Also, the current increase in the number of cases despite the strict non-pharmaceutical interventions suggests a need to study the indirect transmission of COVID-19 while incorporating testing of infected individuals as a preventive measure. Assessing the impact of indirect transmission of the virus may improve our understanding of the overall dynamics of COVID-19. We developed a novel deterministic susceptible-exposedinfected- removed-virus-death compartmental model to study the impact of indirect transmission pathway on the spread of COVID-19, the sources of infection, and prevention/control. We fitted the model to the cumulative number of confirmed cases at episode date in Toronto, Canada using a Markov Chain Monte Carlo optimization algorithm. We studied the effect of indirect transmission on the epidemic peak, peak time, epidemic final size and the effective reproduction number, based on different initial conditions and at different stages. Our findings revealed an increase in cases with indirect transmission. Our work highlights the importance of implementing additional preventive and control measures involving cleaning of surfaces, fumigation, and disinfection to lower the spread of COVID-19, especially in public areas like the grocery stores, malls and so on. We conclude that indirect transmission of SARS-CoV- 2 has a significant effect on the dynamics of COVID-19, and there is need to consider this transmission route for effective mitigation, prevention and control of COVID-19 epidemic.