ABSTRACT
So far COVID-19 has resulted in mass deaths and huge economic losses across the world. Various measures such as quarantine and social distancing have been taken to prevent the spread of this disease. These prevention measures have changed the transmission dynamics of COVID-19 and introduced new challenges for epidemic modelling and prediction. In this paper, we study a novel disease spreading model with two important aspects. First, the proposed model takes the quarantine effect of confirmed cases on transmission dynamics into account, which can better resemble the real-world scenario. Second, our model incorporates two types of human mobility, where the intra-region human mobility is related to the internal transmission speed of the disease in the focal area and the inter-region human mobility reflects the scale of external infectious sources to a focal area. With the proposed model, we use the human mobility data from 24 cities in China and 8 states in the USA to analyse the disease spreading patterns. The results show that our model could well fit/predict the reported cases in both countries. The predictions and findings shed light on how to effectively control COVID-19 by managing human mobility behaviours.
ABSTRACT
OBJECTIVES: We aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status and human mobility status. DESIGN: A retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted. SETTING: We use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1005 US counties. PARTICIPANTS: A total of 69 498 cases in China and 740 843 cases in the USA are used for calculating the effective reproductive numbers. PRIMARY OUTCOME MEASURES: Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value). RESULTS: Statistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number (R value) in both China and the USA. CONCLUSIONS: Higher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1°C is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI (-0.0395 to -0.0125)) in China and by 0.020 (95% CI (-0.0311 to -0.0096)) in the USA; an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI (-0.0108 to -0.0045)) in China and by 0.0080 (95% CI (-0.0150 to -0.0010)) in the USA. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic.