Risk mapping for COVID-19 outbreaks in Australia using mobility data

Zachreson, Cameron; Mitchell, Lewis; Lydeamore, Michael J.; Rebuli, Nicolas; Tomko, Martin; Geard, Nicholas

doi:10.1098/rsif.2020.0657

Cited by 52 publications

(43 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also found other interesting results such as a significant similarity between the trend of mobility and our estimation of the transmission between the epidemic waves (see Fig. A2 and [36]), highlighting the importance of following the evolution of mobility when relaxing mitigation measures to anticipate the future evolution of the spread of the SARS-CoV-2 [37].…”

Section: Discussionsupporting

confidence: 71%

Dynamics of the COVID-19 epidemic in Ireland under mitigation

et al. 2021

View full text Add to dashboard Cite

Background In Ireland and across the European Union the COVID-19 epidemic waves, driven mainly by the emergence of new variants of the SARS-CoV-2 have continued their course, despite various interventions from governments. Public health interventions continue in their attempts to control the spread as they wait for the planned significant effect of vaccination. Methods To tackle this challenge and the observed non-stationary aspect of the epidemic we used a modified SEIR stochastic model with time-varying parameters, following Brownian process. This enabled us to reconstruct the temporal evolution of the transmission rate of COVID-19 with the non-specific hypothesis that it follows a basic stochastic process constrained by the available data. This model is coupled with Bayesian inference (particle Markov Chain Monte Carlo method) for parameter estimation and utilized mainly well-documented Irish hospital data. Results In Ireland, mitigation measures provided a 78–86% reduction in transmission during the first wave between March and May 2020. For the second wave in October 2020, our reduction estimation was around 20% while it was 70% for the third wave in January 2021. This third wave was partly due to the UK variant appearing in Ireland. In June 2020 we estimated that sero-prevalence was 2.0% (95% CI: 1.2–3.5%) in complete accordance with a sero-prevalence survey. By the end of April 2021, the sero-prevalence was greater than 17% due in part to the vaccination campaign. Finally we demonstrate that the available observed confirmed cases are not reliable for analysis owing to the fact that their reporting rate has as expected greatly evolved. Conclusion We provide the first estimations of the dynamics of the COVID-19 epidemic in Ireland and its key parameters. We also quantify the effects of mitigation measures on the virus transmission during and after mitigation for the three waves. Our results demonstrate that Ireland has significantly reduced transmission by employing mitigation measures, physical distancing and lockdown. This has to date avoided the saturation of healthcare infrastructures, flattened the epidemic curve and likely reduced mortality. However, as we await for a full roll out of a vaccination programme and as new variants potentially more transmissible and/or more infectious could continue to emerge and mitigation measures change silent transmission, challenges remain.

show abstract

Section: Discussionsupporting

confidence: 71%

Dynamics of the COVID-19 epidemic in Ireland under mitigation

et al. 2021

View full text Add to dashboard Cite

show abstract

“…COVID-19 transmits mainly through close contact with infected patients (2). Thus, human mobility flows play a crucial role in the spatial spread of the virus; the heterogeneity of mobility patterns and social distancing behavior can largely explain the geographic heterogeneity of transmission (3)(4)(5)(6)(7)(8)(9)(10)(11). In the absence of vaccines or pharmaceutical agents to reduce the transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes COVID-19, it is essential to understand the effects of nonpharmaceutical epidemic control and intervention measures.…”

mentioning

confidence: 99%

Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race

Hou

Gao

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

111

View full text Add to dashboard Cite

The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Metapopulation epidemic modeling studies in the susceptible–exposed–infectious–removed (SEIR) style have played important roles in informing public health policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic, and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What’s more, variation of intracounty environments creates spatial heterogeneity of transmission in different regions. To address this issue, we develop a human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behaviors. This modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and business foot traffic, race and ethnicity, and age structure are then investigated. The results reveal that, in a college town (Dane County), the most important heterogeneity is age structure, while, in a large city area (Milwaukee County), racial and ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate to various reopening policies, which suggests that policy makers may need to take these heterogeneities into account very carefully when designing policies for mitigating the ongoing spread of COVID-19 and reopening.

show abstract

“…Community mobility Mobility data have been shown to help the risk analysis of COVID-19 and enhance the predictions of the COVID-19 related deaths and cases 21 , 52 – 56 . In current article, we use Google mobility data 8 to incorporate mobility information in our model.…”

Section: Methodsmentioning

confidence: 99%

COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 prediction

Yang

Zhao

2021

Sci Rep

View full text Add to dashboard Cite

The global spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has casted a significant threat to mankind. As the COVID-19 situation continues to evolve, predicting localized disease severity is crucial for advanced resource allocation. This paper proposes a method named COURAGE (COUnty aggRegation mixup AuGmEntation) to generate a short-term prediction of 2-week-ahead COVID-19 related deaths for each county in the United States, leveraging modern deep learning techniques. Specifically, our method adopts a self-attention model from Natural Language Processing, known as the transformer model, to capture both short-term and long-term dependencies within the time series while enjoying computational efficiency. Our model solely utilizes publicly available information for COVID-19 related confirmed cases, deaths, community mobility trends and demographic information, and can produce state-level predictions as an aggregation of the corresponding county-level predictions. Our numerical experiments demonstrate that our model achieves the state-of-the-art performance among the publicly available benchmark models.

show abstract

Risk mapping for COVID-19 outbreaks in Australia using mobility data

Cited by 52 publications

References 36 publications

Dynamics of the COVID-19 epidemic in Ireland under mitigation

Dynamics of the COVID-19 epidemic in Ireland under mitigation

Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race

COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 prediction

Contact Info

Product

Resources

About