Outdoor PM2.5 concentration and rate of change in COVID-19 infection in provincial capital cities in China
This article has been Reviewed by the following groups
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
- Evaluated articles (ScreenIT)
Abstract
This study investigates thoroughly whether acute exposure to outdoor PM 2.5 concentration, P, modifies the rate of change in the daily number of COVID-19 infections (R) across 18 high infection provincial capitals in China, including Wuhan. A best-fit multiple linear regression model was constructed to model the relationship between P and R, from 1 January to 20 March 2020, after accounting for meteorology, net move-in mobility (NM), time trend (T), co-morbidity (CM), and the time-lag effects. Regression analysis shows that P ( β = 0.4309, p < 0.001) is the most significant determinant of R. In addition, T ( β = −0.3870, p < 0.001), absolute humidity (AH) ( β = 0.2476, p = 0.002), P × AH ( β = −0.2237, p < 0.001), and NM ( β = 0.1383, p = 0.003) are more significant determinants of R, as compared to GDP per capita ( β = 0.1115, p = 0.015) and CM (Asthma) ( β = 0.1273, p = 0.005). A matching technique was adopted to demonstrate a possible causal relationship between P and R across 18 provincial capital cities. A 10 µg/m 3 increase in P gives a 1.5% increase in R ( p < 0.001). Interaction analysis also reveals that P × AH and R are negatively correlated (β = −0.2237, p < 0.001). Given that P exacerbates R, we recommend the installation of air purifiers and improved air ventilation to reduce the effect of P on R. Given the increasing observation that COVID-19 is airborne, measures that reduce P, plus mandatory masking that reduces the risks of COVID-19 associated with viral-particulate transmission, are strongly recommended. Our study is distinguished by the focus on the rate of change instead of the individual cases of COVID-19 when modelling the statistical relationship between R and P in China; causal instead of correlation analysis via the matching analysis, while taking into account the key confounders, and the individual plus the interaction effects of P and AH on R.
Article activity feed
-
-
SciScore for 10.1101/2020.05.19.20106484: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:A limitation of this study is that the statistical relationship between P and R in (1) (Coefficient = 0.0009, p < 0.05) was less strong as compared to (2) (Coefficient = 0.0261, p < 0.01). This might be due to the lack of datapoints at higher P and AH in (1) (see Figures 1 (c) – (d)), though the statistical relationship between P/AH and …
SciScore for 10.1101/2020.05.19.20106484: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:A limitation of this study is that the statistical relationship between P and R in (1) (Coefficient = 0.0009, p < 0.05) was less strong as compared to (2) (Coefficient = 0.0261, p < 0.01). This might be due to the lack of datapoints at higher P and AH in (1) (see Figures 1 (c) – (d)), though the statistical relationship between P/AH and R was significant (p < 0.05, see Table 1(a)). Our findings call for immediate medical and public health attention concerning the significance of P in exacerbating R. Controlling and reducing outdoor P, and reducing the possibility for outdoor P to be used as a carrier for COVID-19 viruses, have never been as urgent as they are now. Public health measures such as installing air purifiers, both indoors and outdoors, can help reduce P and alleviate the situation.27 Alternatively, improving air ventilation, both indoors and outdoors, presents another possibility.28 Despite previous claims that TEMP, UV, and WS will temper the COVID-19 infection,29,30 our result showed no statistical significance for these factors. Nevertheless, our study supports recent findings on AH.2 Given the result in China that AH < 8.6 g/m3 exacerbated the rate of change, dehumidifiers may help to reduce infection rates. Moreover, the possibility for airborne infection of COVID-19 is too high a cost to be ignored and proper public health measures, such as requiring citizens to wear face masks, should be established to reduce the possibility of COVID-19 infection through air...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
-
