COVID-19 spread prediction using socio-demographic and mobility-related data

Qiao, Mengling; Huang, Bo

doi:10.1016/j.cities.2023.104360

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…Geography has an essential contribution to COVID-19 epidemic research [23], which is reflected in four aspects-the spread of the pandemic, social management, public behavior, and impacts of the pandemic. Some geographical models have been widely used in the prediction of COVID-19, such as the GWR model [24][25][26], the GTWR model [27][28][29], and the MGWR model [24]. In addition, geographic models have long been used to predict infectious diseases, such as the GWR model in Schistosoma haematobium [30], human leptospirosis [31], Dengue Fever [32], leptospirosis [33], GTWR in Porcine Reproductive and Respiratory Syndrome [34], MGWR in tropical parasitic diseases [35].…”

Section: Of 19mentioning

confidence: 99%

Quantitative Study on American COVID-19 Epidemic Predictions and Scenario Simulations

Sun,

Qi,

Yan

et al. 2024

IJGI

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The COVID-19 pandemic has had a profound impact on people’s lives, making accurate prediction of epidemic trends a central focus in COVID-19 research. This study innovatively utilizes a spatiotemporal heterogeneity analysis (GTNNWR) model to predict COVID-19 deaths, simulate pandemic prevention scenarios, and quantitatively assess their preventive effects. The results show that the GTNNWR model exhibits superior predictive capacity to the conventional infectious disease dynamics model (SEIR model), which is approximately 9% higher, and reflects the spatial and temporal heterogeneity well. In scenario simulations, this study established five scenarios for epidemic prevention measures, and the results indicate that masks are the most influential single preventive measure, reducing deaths by 5.38%, followed by vaccination at 3.59%, and social distancing mandates at 2.69%. However, implementing single stringent preventive measures does not guarantee effectiveness across all states and months, such as California in January 2025, Florida in August 2024, and March–April 2024 in the continental U.S. On the other hand, the combined implementation of preventive measures proves 5 to-10-fold more effective than any single stringent measure, reducing deaths by 27.2%. The deaths under combined implementation measures never exceed that of standard preventive measures in any month. The research found that the combined implementation of measures in mask wearing, vaccination, and social distancing during winter can reduce the deaths by approximately 45%, which is approximately 1.5–3-fold higher than in the other seasons. This study provides valuable insights for COVID-19 epidemic prevention and control in America.

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Section: Of 19mentioning

confidence: 99%

Quantitative Study on American COVID-19 Epidemic Predictions and Scenario Simulations

Sun,

Qi,

Yan

et al. 2024

IJGI

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“…It is really hard to make an overall comprehensive literature review of related studies, as the number of related studies has been huge since the pandemic’s outbreak and many latest studies keep emerging 2 – 4 . In our study of the spread of COVID-19, we are particularly interested in the environmental factors, which we divide into two.…”

Section: Related Literaturementioning

confidence: 99%

Study on the spatial decomposition of the infection probability of COVID-19

Liu

2023

Sci Rep

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In the course of our observations of the transmission of COVID-19 around the world, we perceived substantial concern about imported cases versus cases of local transmission. This study, therefore, tries to isolate cases due to local transmission (also called community spread) from those due to externally introduced COVID-19 infection, which can be key to understanding the spread pattern of the pandemic. In particular, we offer a probabilistic perspective to estimate the scale of the outbreak at the epicenter of the COVID-19 epidemic with an environmental focus. First, this study proposes a novel explanation of the probability of COVID-19 cases in the local population of the target city, in which the chain of probability is based on the assumption of independent distribution. Then it conducts a spatial statistical analysis on the spread of COVID-19, using two model specifications to identify the spatial dependence, more commonly known as the spillover effect. The results are found to have strong spatial dependence. Finally, it confirms the significance of residential waste in the transmission of COVID-19, which indicates that the fight against COVID-19 requires us to pay close attention to environmental factors. The method shown in this study is critical and has high practical value, because it can be easily applied elsewhere and to other future pandemics.

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