Influence of population density, temperature, and absolute humidity on spread and decay durations of COVID-19: A comparative study of scenarios in China, England, Germany, and Japan

Diao, Yinliang; Kodera, Sachiko; Anzai, Daisuke; Gómez-Tames, José; Rashed, Essam A.; Hirata, Akimasa

doi:10.1016/j.onehlt.2020.100203

Cited by 113 publications

(95 citation statements)

References 26 publications

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“…The Google model is based on a machine learning framework that considers mobility reports as a reference for potential social contact that is known to influence the spread of the infection. In our earlier studies, we confirmed that meteorological factors such as temperature and humidity are also correlated with the spread/decay of pandemics in Japan [23,24], as well as other countries [25].…”

Section: Introductionsupporting

confidence: 80%

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: Introductionsupporting

confidence: 80%

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

Rashed

Hirata

2021

IJERPH

Self Cite

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“…Population density is another possible contributing factor to a high SARS-CoV-2 transmission rate ( Diao et al, 2021 ; Li et al, 2018 ; Randolph and Barreiro, 2020 ; Sil and Kumar, 2020 ; Tarwater and Martin, 2001 ). A positive correlation between the population density of an area and the transmission rate has also been observed for viruses other than SARS-CoV-2, emphasizing the importance of taking population density into account when studying the seasonality of SARS-CoV-2 ( Vanden Broecke et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Is coronavirus disease (COVID-19) seasonal? A critical analysis of empirical and epidemiological studies at global and local scales

Byun

Heo

et al. 2021

Environmental Research

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Coronavirus disease (COVID-19) has infected more than 50 million people and killed more than one million, worldwide, during less than a year course. COVID-19, which has already become the worst pandemic in the last 100 years, is still spreading worldwide. Since the beginning of the outbreak, it has been of particular interest to understand whether COVID-19 is seasonal; the finding might help for better planning and preparation for the fight against the disease. Over the past 12 months, numerous empirical and epidemiological studies have been performed to define the distinct diffusion patterns of COVID-19. Thereby, a wealth of data has accumulated on the relationship between various seasonal meteorological factors and COVID-19 transmissibility at global and local scales. In this review, we aimed to discuss whether COVID-19 exhibits any seasonal features in a global and local perspective by collecting and providing summaries of the findings from empirical and epidemiological studies on the COVID-19 pandemic during its first seasonal cycle.

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“…The relationship between COVID-19 and environmental factors is complex [ 78 , 115 ]. Recent studies have demonstrated a positive correlation between spread and decay of SARS-CoV-2 and temperature, absolute humidity and population density [ 116 ] and a positive correlation between the transmissibility of the SARS-CoV-2 and lower UV radiation, lower latitudes, lower elevation, and smaller populations with historical air pollution exposure [ 117 ]. No association was detected between deaths and country temperature or precipitation [ 118 ].…”

Section: Evidence Of the Syndemic Nature Of The Sars-cov-2 Pandemicmentioning

confidence: 99%

The SARS-CoV-2 pandemic: A syndemic perspective

et al. 2021

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The SARS-CoV-2 pandemic has affected communities, populations, and countries throughout the world. As the SARS-CoV-2 pandemic developed, the extent to which the disease interacted with already existing endemic, non-communicable and infectious diseases became evident, hence deeply influencing health outcomes. Additionally, a synergistic effect has been demonstrated also with socio-economic, cultural, and contextual determinants of health which seem to contribute to poorer health and accumulating social disadvantages. In this essay, using as a starting point the syndemic theory that translates the cumulative and intertwined factors between different epidemics, we argue that the SARS-CoV-2 is a one health issue of a syndemic nature and that the failure to acknowledge this contributes to weakened policy-making processes and public health responses and ineffective health policies and programs.

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Influence of population density, temperature, and absolute humidity on spread and decay durations of COVID-19: A comparative study of scenarios in China, England, Germany, and Japan

Cited by 113 publications

References 26 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

Is coronavirus disease (COVID-19) seasonal? A critical analysis of empirical and epidemiological studies at global and local scales

The SARS-CoV-2 pandemic: A syndemic perspective

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