What determines city’s resilience against epidemic outbreak: evidence from China’s COVID-19 experience

Chen, Jie; Guo, Xiaoxin; Pan, Haozhi; Zhong, Shihu

doi:10.1016/j.scs.2021.102892

Cited by 96 publications

(59 citation statements)

References 71 publications

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“…It is clear that public mobility and meteorological factors are highly correlated with the pandemic in many regions worldwide [ 1 , 2 , 3 , 4 , 5 ]. Based on a recent study on data from China, high-density cities (with potentially more mobility factors) were found to be at a higher risk of SARS-CoV-2 infection [ 6 ]. However, how these factors determine the spread of the pandemic remains controversial and under discussion.…”

Section: Introductionmentioning

confidence: 99%

One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan

Rashed

Hirata

2021

IJERPH

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With the wide spread of COVID-19 and the corresponding negative impact on different life aspects, it becomes important to understand ways to deal with the pandemic as a part of daily routine. After a year of the COVID-19 pandemic, it has become obvious that different factors, including meteorological factors, influence the speed at which the disease is spread and the potential fatalities. However, the impact of each factor on the speed at which COVID-19 is spreading remains controversial. Accurate forecasting of potential positive cases may lead to better management of healthcare resources and provide guidelines for government policies in terms of the action required within an effective timeframe. Recently, Google Cloud has provided online COVID-19 forecasting data for the United States and Japan, which would help in predicting future situations on a state/prefecture scale and are updated on a day-by-day basis. In this study, we propose a deep learning architecture to predict the spread of COVID-19 considering various factors, such as meteorological data and public mobility estimates, and applied it to data collected in Japan to demonstrate its effectiveness. The proposed model was constructed using a neural network architecture based on a long short-term memory (LSTM) network. The model consists of multi-path LSTM layers that are trained using time-series meteorological data and public mobility data obtained from open-source data. The model was tested using different time frames, and the results were compared to Google Cloud forecasts. Public mobility is a dominant factor in estimating new positive cases, whereas meteorological data improve their accuracy. The average relative error of the proposed model ranged from 16.1% to 22.6% in major regions, which is a significant improvement compared with Google Cloud forecasting. This model can be used to provide public awareness regarding the morbidity risk of the COVID-19 pandemic in a feasible manner.

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Section: Introductionmentioning

confidence: 99%

One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan

Rashed

Hirata

2021

IJERPH

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“…The environment should be designed to prevent the spread of the virus and improve the protection of the residents in buildings and cities [ 3 ]. Large and high-density cities with high inter and intra-city mobility flows have more difficulties in containing the epidemic spread, but improving healthcare infrastructure adequacy and urban governance capacity can increase time efficacy of pandemic control [ 4 ]. Furthermore, improving the governance capacity and infrastructure in regards to residential community is the most effective way to control the epidemic, so as to improve the resilience of cities and communities against pandemic.…”

Section: Introductionmentioning

confidence: 99%

Development of a Healthy Assessment System For Residential Building Epidemic Prevention

Gong

Liu

et al. 2021

Building and Environment

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During the period of COVID-19, the number of residents infected in urban communities continued to rise, implying that most of the current building layouts can't effectively resist the spread of infectious diseases, and the outbreak of COVID-19 has led to the need of changes for the current building environment. Therefore, the epidemic prevention should be considered in the residential building design, and the health design of residential community should be carried out from the perspective of epidemic prevention. In order to improve the ability of epidemic prevention of residential buildings and deal with the sudden pandemic and influenza in the post-epidemic era, a Healthy Assessment System for Residential Building Epidemic Prevention ( HASRBEP ) was developed according to the epidemic impact on residential buildings, the design and measures of epidemic prevention for residential buildings and the Chinese Assessment standard for healthy building (T/ASC 02–2016) . Both entropy weight method and expert scoring method were used to determine the specific weight of the index. The HASRBEP includes control item assessment, preliminary assessment and extension assessment. The newly developed HASRBEP was used to assess the residential buildings of the Yulongzhuang Building Community located in Quanzhou, Fujian Province, China. The results show that the HASRBEP can be used to guide the health and epidemic prevention design of residential buildings.

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“…It has been found to be greater in big and high-density municipalities compared to smaller cities and rural areas [15,16]. Large cities with high inter-and intra-city mobility flows have more difficulties in containing epidemic spread [16]. In many studies, the mobility of the population has been indicated as a key mechanism of the accelerated infectious disease transmission [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…The epidemic intensity of COVID-19 is strongly shaped by crowding. It has been found to be greater in big and high-density municipalities compared to smaller cities and rural areas [ 15 , 16 ]. Large cities with high inter- and intra-city mobility flows have more difficulties in containing epidemic spread [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

The Use of GIS Technology to Optimize COVID-19 Vaccine Distribution: A Case Study of the City of Warsaw, Poland

Krzysztofowicz

Osińska-Skotak

2021

IJERPH

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The COVID-19 pandemic is a global challenge, and the key to tackling it is vaccinating a specified percentage of the population to acquire herd immunity. The observed problems with the efficiency of the vaccination campaigns in numerous countries around the world, as well as the approach used at the initial stage of the National Immunization Program in Poland, prompted us to analyse the possibility of using GIS technology to optimize the distribution of vaccines to vaccination sites so as to minimize the period needed to vaccinate individual population groups. The research work was carried out on the example of Warsaw, the capital of Poland and the city with the largest population in the country. The analyses were carried out for the 60–70 and 50–60 age groups, in various approaches and for vaccines of different companies (Moderna, BioNTech, AstraZeneca), used to vaccinate people in Poland. The proposed approach to optimize vaccine distribution uses Thiessen’s tessellation to obtain information on the number of people in a given population group living in the area of each vaccination site, and then to estimate the time needed to vaccinate that group. Compared to the originally used vaccination scenario with limited availability of vaccines, the proposed approach allows practitioners to design fast and efficient distribution scenarios. With the developed methodology, we demonstrated ways to achieve uniform vaccination coverage throughout the city. We anticipate that the proposed approach can be easily automated and broadly applied to various urban settings.

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What determines city’s resilience against epidemic outbreak: evidence from China’s COVID-19 experience

Cited by 96 publications

References 71 publications

One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan

One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan

Development of a Healthy Assessment System For Residential Building Epidemic Prevention

The Use of GIS Technology to Optimize COVID-19 Vaccine Distribution: A Case Study of the City of Warsaw, Poland

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