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Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis.

ABSTRACT: The novel coronavirus COVID-19 arrived on Australian shores around 25 January 2020. This paper presents a novel method of dynamically modeling and forecasting the COVID-19 pandemic in Australia with a high degree of accuracy and in a timely manner using limited data; a valuable resource that can be used to guide government decision-making on societal restrictions on a daily and/or weekly basis. The "partially-observable stochastic process" used in this study predicts not only the future actual values with extremely low error, but also the percentage of unobserved COVID-19 cases in the population. The model can further assist policy makers to assess the effectiveness of several possible alternative scenarios in their decision-making processes.

SUBMITTER: Eshragh A

PROVIDER: S-EPMC7531857 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis.

Eshragh Ali A Alizamir Saed S Howley Peter P Stojanovski Elizabeth E

PloS one 20201002 10

The novel coronavirus COVID-19 arrived on Australian shores around 25 January 2020. This paper presents a novel method of dynamically modeling and forecasting the COVID-19 pandemic in Australia with a high degree of accuracy and in a timely manner using limited data; a valuable resource that can be used to guide government decision-making on societal restrictions on a daily and/or weekly basis. The "partially-observable stochastic process" used in this study predicts not only the future actual v ...[more]

PMID: 33007054

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Project description:BackgroundCOVID-19, caused by SARS-CoV-2, has spread globally, presenting a significant public health challenge. Vaccination has played a critical role in reducing severe disease and deaths. However, the waning of immunity after vaccination and the emergence of immune-escape variants require the continuation of vaccination efforts, including booster doses, to maintain population immunity. This study models the dynamics of COVID-19 in the Basque Country, Spain, aiming to characterize the population's immunity profile and assess its impact on the severity of outbreaks from 2020 to 2022.MethodsA SIR/DS model was developed to analyze the interplay of virus-specific and vaccine-induced immunity. The model includes three levels of immunity, with boosting effects from reinfection and/or vaccination. It was validated using empirical daily case data from the Basque Country. The model tracks shifts in immunity status and their effects on disease dynamics over time.ResultsThe COVID-19 epidemic in the Basque Country progressed through three distinct phases, each shaped by dynamic interactions between virus transmission, public health interventions, and vaccination efforts. The initial phase was marked by a rapid surge in cases, followed by a decline due to strict public health measures, with a seroprevalence of 1.3% . In the intermediate phase, multiple smaller outbreaks emerged as restrictions were relaxed and new variants, such as Alpha and Delta, appeared. During this period, reinfection rates reached 20% , and seroprevalence increased to 32% . The final phase, dominated by the Omicron variant, saw a significant rise in cases driven by waning immunity and the variant's high transmissibility. Notably, 34% of infections during this phase occurred in the naive population, with seroprevalence peaking at 43% . Across all phases, the infection of naive and unvaccinated individuals contributed significantly to the severity of outbreaks, emphasizing the critical role of vaccination in mitigating disease impact.ConclusionThe findings underscore the importance of continuous monitoring and adaptive public health strategies to mitigate the evolving epidemiological and immunological landscape of COVID-19. Dynamic interactions between immunity levels, reinfections, and vaccinations are critical in shaping outbreak severity and guiding evidence-based interventions.

Project description:Originating from Wuhan, China, COVID-19 is spreading rapidly throughout the world. The transmission rate is reported to be high for this novel strain of coronavirus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as compared to its predecessors. Major strategies in terms of clinical trials of medicines and vaccines, social distancing, use of personal protective equipment (PPE), and so on are being implemented in order to control the spread. The current study concentrates on lockdown and social distancing policy followed by the Indian Government and evaluates its effectiveness using Bayesian probability model (BPM). The change point analysis (CPA) done through the above approach suggests that the states which implemented the lockdown before the exponential rise of cases are able to control the spread of the disease in a much better and efficient way. The analysis has been done for states of Maharashtra, Gujarat, Madhya Pradesh, Rajasthan, Tamil Nadu, West Bengal, Uttar Pradesh, and Delhi as union territory. The highest value of Δ (delta) is reported for Gujarat and Madhya Pradesh with a value of 9.6 weeks, while the lowest value is 4.7, evidently for Maharashtra which is the worst affected. All of the states indicate a significant correlation (p < 0.05, tstat > tcritical) for Δ, i.e., the difference in the time period of CPA and lockdown with cases per population (CPP) and cases per unit area (CPUA), while weak correlation (p < 0.1 and tstat < tcritical) is exhibited by delta and cases per unit population density (CPD). For both CPP and CPUA, tstat > tcritical indicating a significant correlation, while Pearson's correlation indicates the direction to be negative. Further analysis in terms of identification of high-risk areas has been studied from the Voronoi approach of GIS based on the inputs from BPM. All the states follow the above pattern of high population, high case scenario, and the boundaries of risk zones can be identified by Thiessen polygon (TP) constructed therein. The findings of the study help draw strategic and policy-driven response for India, toward tackling COVID-19 pandemic.

Dataset Information

Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis.

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Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis.

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