A mechanism-based parameterisation scheme to investigate the association between transmission rate of COVID-19 and meteorological factors on plains in China

Abstract

The novel coronavirus disease 2019 (COVID-19), which first emerged in Hubei province, China, has become a pandemic. However, data regarding the effects of meteorological factors on its transmission are limited and inconsistent. A mechanism-based parameterisation scheme was developed to investigate the association between the scaled transmission rate (STR) of COVID-19 and the meteorological parameters in 20 provinces/municipalities located on the plains in China. We obtained information on the scale of population migrated from Wuhan, the world epicentre of the COVID-19 outbreak, into the study provinces/municipalities using mobile-phone positioning system and big data techniques. The highest STRs were found in densely populated metropolitan areas and in cold provinces located in north-eastern China. Population density had a non-linear relationship with disease spread (linearity index, 0.9). Among various meteorological factors, only temperature was significantly associated with the STR after controlling for the effect of population density. A negative and exponential relationship was identified between the transmission rate and the temperature (correlation coefficient, -0.56; 99% confidence level). The STR increased substantially as the temperature in north-eastern China decreased below 0 °C (the STR ranged from 3.5 to 12.3 when the temperature was between -9.41 °C and -13.87 °C), whilst the STR showed less temperature dependence in the study areas with temperate weather conditions (the STR was 1.21 ± 0.57 when the temperature was above 0 °C). Therefore, a higher population density was linearly whereas a lower temperature (<0 °C) was exponentially associated with an increased transmission rate of COVID-19. These findings suggest that the mitigation of COVID-19 spread in densely populated and/or cold regions will be a great challenge.

Keywords: COVID-19; Imported scale; Meteorology; Population density; Temperature.

Graphical abstract

Fig. 1

(a) Spatial distribution of the imported scale index (ISI, I) for each province located on plains in China from 19 to 23 January 2020. (b) Spatial distribution of the cumulative number of confirmed cases (N) of COVID-19 for each province located on plains in China on 29 February 2020.

Fig. 2

Spatial distribution of (a) the scaled transmission rate (STR, K′ = N/I) and (b) the adjusted STR (${K_{\rho }}^{\prime }=K^{\prime }/\rho$, further controlling for population density) for each province located on plains in China.

Fig. 3

Spatial association between the adjusted STR (${K_{\rho }}^{\prime }$) and temperature for provinces located on plains in China. The blue curve shows the association between the adjusted STR and temperature for the 16 studied provinces (blue dots) and the four municipalities (blue squares) in China. The green curve shows the association between the adjusted STR and temperature for all 191 cities with a population larger than one million in the 16 provinces.