Meteorological conditions and nonpharmaceutical interventions jointly determined local transmissibility of COVID-19 in 41 Chinese cities: A retrospective observational study

Abstract

Background: Before effective vaccines become widely available, sufficient understanding of the impacts of climate, human movement and non-pharmaceutical interventions on the transmissibility of COVID-19 is needed but still lacking.

Methods: We collected by crowdsourcing a database of 11 003 COVID-19 cases from 305 cities outside Hubei Province from December 31, 2019 to April 27, 2020. We estimated the daily effective reproduction numbers (R_t ) of COVID-19 in 41 cities where the crowdsourced case data are comparable to the official surveillance data. The impacts of meteorological variables, human movement indices and nonpharmaceutical emergency responses on R_t were evaluated with generalized estimation equation models.

Findings: The median R_t was 0•46 (IQR: 0•37-0•87) in the northern cities, higher than 0•20 (IQR: 0•09-0•52) in the southern cities (p=0•004). A higher local transmissibility of COVID-19 was associated with a low temperature, a relative humidity near 70-75%, and higher intracity and intercity human movement. An increase in temperature from 0℃ to 20℃ would reduce R_t by 30% (95 CI 10-46%). A further increase to 30℃ would result in another 17% (95% CI 5-27%) reduction. An increase in relative humidity from 40% to 75% would raise the transmissibility by 47% (95% CI 9-97%), but a further increase to 90% would reduce the transmissibility by 12% (95% CI 4-19%). The decrease in intracity human movement as a part of the highest-level emergency response in China reduced the transmissibility by 36% (95% CI 27-44%), compared to 5% (95% CI 1-9%) for restricting intercity transport. Other nonpharmaceutical interventions further reduced R_t by 39% (95% CI 31-47%).

Interpretation: Climate can affect the transmission of COVID-19 where effective interventions are implemented. Restrictions on intracity human movement may be needed in places where other nonpharmaceutical interventions are unable to mitigate local transmission.

Funding: China Mega-Project on Infectious Disease Prevention; U.S. National Institutes of Health and National Science Foundation.

Fig. 1

Temporal and spatial distributions of COVID-19 cases in the crowdsourced contact-tracing data for the 41 cities of mainland China from January 1 to February 29, 2020. (A) Daily frequencies of emigrants departing Wuhan and symptom onsets of cases imported from Wuhan. (B) Daily numbers of symptom onsets among cases in each city. (C) Spatial distribution of the 41 cities and decomposition by case type in each city: imported primary, imported secondary, local primary and local secondary.

Fig. 2

Epidemic curves and estimated effective reproduction numbers (R_t) for (A) northern, (B) central and (C) southern China based on crowdsourced COVID-19 cases in 41 cities of mainland China from January 1 to February 29, 2020. Cases are classified into imported primary, imported secondary, local primary and local secondary and shaded correspondingly. R_t was estimated under two assumptions separately: imported secondary cases are considered as primary cases (infectors) in their clusters (red), and imported secondary cases are considered as secondary cases (infectees) in their clusters (green).

Fig. 3

Estimated risk-ratio curves (red) and observed frequencies (histogram) for (A) temperature, (B) relative humidity, (C) immigration index, and (D) urban traffic index on the effective reproduction number R_t based on a multivariable general estimation equation model fitted to daily R_t values in 41 cities of mainland China. Imported secondary cases were considered infectors for calculating R_t. The gray curves are the results of 100 times of parametric bootstrapping.

Fig. 4

Model-predicted weekly average R_t for 2020 under different assumptions about intervention policy: (A) level-1 emergency response is lifted, and human movement recovers to normal level, i.e., neither restricted nor within the spring festival commute period (immigration index and urban traffic index are set to the average level during March, 2019); (B) level-1 emergency response is lifted, but human movement is restricted (immigration index and urban traffic index are set to the average level during February, 2020); (C) level-1 emergency response is in place, but human movement recovers to normal level; and (D) Both level-1 emergency response and human movement restriction are in place.