Dataset Information

Keeping Low Reproductive Number Despite the Rebound Population Mobility in Korea, a Country Never under Lockdown during the COVID-19 Pandemic.

ABSTRACT: Nonpharmaceutical intervention has been one of the most important strategies to prevent the spread of the SARS-CoV-2 in the communities during the COVID-19 pandemic. Korea has a unique experience that we had the first large outbreak during the early pandemic and could flatten the epidemic curve without lockdown. In this study, the effective reproductive numbers were calculated for the entire nation and Seoul (the capital city) Metropolitan Area from February 16-15 July, where 60% of the population reside. We compared the changes in population mobility data and reproductive number trends according to the changes in the government's nonpharmaceutical intervention strategy. The total daily mobility decreased when Korea had the first wave of a large outbreak in February-March 2020, which was mainly caused by the decrease of daily noncommuting mobility. However, daily commuting mobility from 16 February to 30 June 2020 was maintained at a similar level since there was no national lockdown for workers who commute between home and work. During the first half-year of 2020, Korea could control the outbreak to a manageable level without a significant decrease in daily public mobility. However, it may be only possible when the public follows personal hygiene principles and social distancing without crisis fatigue or reduced compliance.

SUBMITTER: Kim S

PROVIDER: S-EPMC7765860 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Keeping Low Reproductive Number Despite the Rebound Population Mobility in Korea, a Country Never under Lockdown during the COVID-19 Pandemic.

Kim Soyoung S Kim Yae-Jean YJ Peck Kyong Ran KR Ko Youngsuk Y Lee Jonggul J Jung Eunok E

International journal of environmental research and public health 20201220 24

Nonpharmaceutical intervention has been one of the most important strategies to prevent the spread of the SARS-CoV-2 in the communities during the COVID-19 pandemic. Korea has a unique experience that we had the first large outbreak during the early pandemic and could flatten the epidemic curve without lockdown. In this study, the effective reproductive numbers were calculated for the entire nation and Seoul (the capital city) Metropolitan Area from February 16-15 July, where 60% of the populati ...[more]

PMID: 33419347

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Project description:The effective reproduction number (ℜt) is a theoretical indicator of the course of an infectious disease that allows policymakers to evaluate whether current or previous control efforts have been successful or whether additional interventions are necessary. This metric, however, cannot be directly observed and must be inferred from available data. One approach to obtaining such estimates is fitting compartmental models to incidence data. We can envision these dynamic models as the ensemble of structures that describe the disease's natural history and individuals' behavioural patterns. In the context of the response to the COVID-19 pandemic, the assumption of a constant transmission rate is rendered unrealistic, and it is critical to identify a mathematical formulation that accounts for changes in contact patterns. In this work, we leverage existing approaches to propose three complementary formulations that yield similar estimates for ℜt based on data from Ireland's first COVID-19 wave. We describe these Data Generating Processes (DGP) in terms of State-Space models. Two (DGP1 and DGP2) correspond to stochastic process models whose transmission rate is modelled as Brownian motion processes (Geometric and Cox-Ingersoll-Ross). These DGPs share a measurement model that accounts for incidence and transmission rates, where mobility data is assumed as a proxy of the transmission rate. We perform inference on these structures using Iterated Filtering and the Particle Filter. The final DGP (DGP3) is built from a pool of deterministic models that describe the transmission rate as information delays. We calibrate this pool of models to incidence reports using Hamiltonian Monte Carlo. By following this complementary approach, we assess the tradeoffs associated with each formulation and reflect on the benefits/risks of incorporating proxy data into the inference process. We anticipate this work will help evaluate the implications of choosing a particular formulation for the dynamics and observation of the time-varying transmission rate.

Project description:IntroductionSARS-CoV-2 was declared a pandemic by the WHO on March 11th, 2020. Public protective measures were enforced in every country to limit the diffusion of SARS-CoV-2. Its transmission, mainly by droplets, has been measured by the effective reproduction number (Rt) that counts the number of secondary cases caused in a population by an average infectious individual at time t. Current strategies to calculate Rt reflect the number of secondary cases after several days, due to a delay from symptoms onset to reporting. We propose a complementary Rt estimation using supervised machine learning techniques to predict short term variations with more timely results.Material and methodsOur primary goal was to predict Rt of the current day in the twelve provinces of Lombardy with the highest possible accuracy, and with no influence of the local testing strategies. We gathered data about mobility, weather, and pollution from different public sources as a proxy of human behavior and public health measures. We built four supervised machine learning algorithms with different strategies: the outcome variable was the daily median Rt values per province obtained from officially adopted algorithms.ResultsData from 243 days for every province were presented to our four models (from February 15th, 2020, to October 14th, 2020). Two models using differential calculation of Rt instead of the raw values showed the highest mean coefficient of determination (0.93 for both) and residuals reported the lowest mean error (-0.03 and 0.01) and standard deviation (0.13 for both) as well. The one with access to the value of Rt of the day before heavily relied on that feature for prediction, while the other one had more distributed weights.DiscussionThe model that had not access to the Rt value of the previous day and used Rt differential value as outcome (FDRt) was considered the most robust according to the metrics. Its forecasts were able to predict the trend that Rt values would have developed over different weeks, but it was not particularly accurate in predicting the precise value of Rt. A correlation among mobility, atmospheric, features, pollution and Rt values is plausible, but further testing should be performed.

Dataset Information

Keeping Low Reproductive Number Despite the Rebound Population Mobility in Korea, a Country Never under Lockdown during the COVID-19 Pandemic.

Publications

Keeping Low Reproductive Number Despite the Rebound Population Mobility in Korea, a Country Never under Lockdown during the COVID-19 Pandemic.

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