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Register a new userModelling transmission and control of the COVID-19 pandemic in Australia
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There is a continuing debate on relative benefits of various mitigation and suppression strategies aimed to control the spread of COVID-19. Here we report the results of agent-based modelling using a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia. This model is calibrated to match key characteristics of COVID-19 transmission. An important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults. We apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation, home quarantine, social distancing with varying levels of compliance, and school closures. School closures are not found to bring decisive benefits, unless coupled with high level of social distancing compliance. We report several trade-offs, and an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels, with compliance at the 90% level found to control the disease within 13--14 weeks, when coupled with effective case isolation and international travel restrictions.
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We develop an agent-based model of disease transmission in remote communities in Western Australia. Despite extreme isolation, we show that the movement of people amongst a large number of small but isolated communities has the effect of causing transmission to spread quickly. Significant movement between remote communities, and regional and urban centres allows for infection to quickly spread to and then among these remote communities. Our conclusions are based on two characteristic features of remote communities in Western Australia: (1) high mobility of people amongst these communities, and (2) relatively high proportion of travellers from very small communities to major population centres. In models of infection initiated in the state capital, Perth, these remote communities are collectively and uniquely vulnerable. Our model and analysis does not account for possibly heightened impact due to preexisting conditions, such additional assumptions would only make the projections of this model more dire. We advocate stringent monitoring and control of movement to prevent significant impact on the indigenous population of Western Australia.
In late 2019, a novel coronavirus, the SARS-CoV-2 outbreak was identified in Wuhan, China and later spread to every corner of the globe. Whilst the number of infection-induced deaths in Ghana, West Africa are minimal when compared with the rest of the world, the impact on the local health service is still significant. Compartmental models are a useful framework for investigating transmission of diseases in societies. To understand how the infection will spread and how to limit the outbreak. We have developed a modified SEIR compartmental model with nine compartments (CoVCom9) to describe the dynamics of SARS-CoV-2 transmission in Ghana. We have carried out a detailed mathematical analysis of the CoVCom9, including the derivation of the basic reproduction number, $mathcal{R}_{0}$. In particular, we have shown that the disease-free equilibrium is globally asymptotically stable when $mathcal{R}_{0}<1$ via a candidate Lyapunov function. Using the SARS-CoV-2 reported data for confirmed-positive cases and deaths from March 13 to August 10, 2020, we have parametrised the CoVCom9 model. The results of this fit show good agreement with data. We used Latin hypercube sampling-rank correlation coefficient (LHS-PRCC) to investigate the uncertainty and sensitivity of $mathcal{R}_{0}$ since the results derived are significant in controlling the spread of SARS-CoV-2. We estimate that over this five month period, the basic reproduction number is given by $mathcal{R}_{0} = 3.110$, with the 95% confidence interval being $2.042 leq mathcal{R}_0 leq 3.240$, and the mean value being $mathcal{R}_{0}=2.623$. Of the 32 parameters in the model, we find that just six have a significant influence on $mathcal{R}_{0}$, these include the rate of testing, where an increasing testing rate contributes to the reduction of $mathcal{R}_{0}$.
COVID-19--a viral infectious disease--has quickly emerged as a global pandemic infecting millions of people with a significant number of deaths across the globe. The symptoms of this disease vary widely. Depending on the symptoms an infected person is broadly classified into two categories namely, asymptomatic and symptomatic. Asymptomatic individuals display mild or no symptoms but continue to transmit the infection to otherwise healthy individuals. This particular aspect of asymptomatic infection poses a major obstacle in managing and controlling the transmission of the infectious disease. In this paper, we attempt to mathematically model the spread of COVID-19 in India under various intervention strategies. We consider SEIR type epidemiological models, incorporated with India specific social contact matrix representing contact structures among different age groups of the population. Impact of various factors such as presence of asymptotic individuals, lockdown strategies, social distancing practices, quarantine, and hospitalization on the disease transmission is extensively studied. Numerical simulation of our model is matched with the real COVID-19 data of India till May 15, 2020 for the purpose of estimating the model parameters. Our model with zone-wise lockdown is seen to give a decent prediction for July 20, 2020.
As of July 2021, there is a continuing outbreak of the B.1.617.2 (Delta) variant of SARS-CoV-2 in Sydney, Australia. The outbreak is of major concern as the Delta variant is estimated to have twice the reproductive number to previous variants that circulated in Australia in 2020, which is worsened by low levels of acquired immunity in the population. Using a re-calibrated agent-based model, we explored a feasible range of non-pharmaceutical interventions, in terms of both mitigation (case isolation, home quarantine) and suppression (school closures, social distancing). Our nowcasting modelling indicated that the level of social distancing currently attained in Sydney is inadequate for the outbreak control. A counter-factual analysis suggested that if 80% of agents comply with social distancing, then at least a month is needed for the new daily cases to reduce from their peak to below ten. A small reduction in social distancing compliance to 70% lengthens this period to 45 days.
In this study, we present a new epidemiological model, with contamination from confirmed and unreported. We also compute equilibria and study their stability without intervention strategies. Optimal control theory has proven to be a successful tool in understanding ways to curtail the spread of infectious diseases by devising the optimal disease intervention strategies. We investigate the impact of distancing, case finding, and case holding controls while at the same time, we minimize the number of infected and dead individuals. The method consists of minimizing the cost functional related to infectious, death, and controls through some strategies to reduce the spread of the COVID19 epidemic.
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