The Innovation

High-level exposure to indoor air pollutants (IAPs), including volatile organic compounds (VOCs), has substantially contributed to the burden of disease in China over the past two decades. However, the source contributions to the indoor VOC-related health burden remain unknown. This study utilized a novel approach based on positive matrix factorization (PMF) of indoor multipollutant data to estimate the source-specific residential VOC concentrations and associated burden of disease. Indoor concentrations of 39 VOCs were collected repeatedly in different seasons from 2016 to 2017 in 249 residences across nine cities in China. In 2017, the disability-adjusted life years (DALYs) attributable to residential VOC exposure across nine provinces in China reached 134.2 (95% UI: 65.7 - 225.0) per 100,000, resulting in financial costs of 28.1 (13.8 - 47.1) billion CNY. Contributions to indoor VOC concentrations from six indoor sources and three outdoor sources were derived by PMF. The top three sources, i.e., wood building materials and furniture, outdoor vehicle exhaust, and cooking and indoor combustion, accounted for 42.7%, 25.9%, and 11.0% of the VOC-attributable DALYs, which suggests prioritizing controlling these sources in China. This approach can be extended to other IAPs and provide fundamental data for future cost-benefit analysis of source control interventions. TOC Art O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/25333590v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@1a69a9corg.highwire.dtl.DTLVardef@f07ec4org.highwire.dtl.DTLVardef@1129103org.highwire.dtl.DTLVardef@1ee68d1_HPS_FORMAT_FIGEXP M_FIG C_FIG SynopsisThis novel method leverages multi-seasonal and multi-room residential VOC measurements to identify emission sources, quantify source-specific exposure concentrations, and estimate source-specific health burden, thus prioritizing the sources needing control.

BackgroundEnvironmental factors, including air pollution, can strongly impact on spatio-temporal patterns of infectious diseases outbreak. In this study, we aimed to investigate the association and correlation between ambient air pollutants and case fatality rate (CFR) of the novel coronavirus disease (COVID-19) in China. MethodsPublicly accessible data on COVID-19 average CFR were utilized in the data analysis. The ambient daily air pollutants including fine particulate matter (PM2.5), inhalable particles (PM10) and nitrogen dioxide (NO2) during the period from December 25, 2019 to March 5, 2020 were obtained from National Air Quality Real-time Publishing System of China. Ecological analysis was performed to explore the association and correlation between the cumulative average exposure of ambient air pollutants at different lag days (14 and 28 days) and average CFR in China outside Hubei and cities in Hubei province via model fitting. ResultsThe average case fatality rate was highest in Wuhan city (4.53%) and the cumulative average exposure of ambient PM2.5, PM10 and NO2 at lag 28 days was 55.8{+/-}12.1g/m3, 66.8{+/-}9.2g/m3, 20.7{+/-}4.4g/m3, respectively in Hubei province during the study period. Ecological analysis showed that ambient PM2.5, PM10 and NO2 exposure at both lag 14 and 28 days was positively correlated with average CFR in China outside Hubei (province-level). For city-level analysis in Hubei, significant associations were only found between cumulative ambient NO2 exposure and average CFR(r=0.693 for Lag0-14, r=0.697 for Lag0-28, respectively) during the same period. ConclusionOur findings suggested ambient PM2.5, PM10 and NO2 exposure, especially at 28 lag days, positively associated with the case fatality rate of COVID-19 in China. These results could help provide guidance for identifying potential exposure window and preventing and controlling the epidemic.

OBJECTIVETo investigate the impact of temperature and absolute humidity on the coronavirus disease 2019 (COVID-19) outbreak. DESIGNEcological study. SETTING31 provincial-level regions in mainland China. MAIN OUTCOME MEASURESData on COVID-19 incidence and climate between Jan 20 and Feb 29, 2020. RESULTSThe number of new confirm COVID-19 cases in mainland China peaked on Feb 1, 2020. COVID-19 daily incidence were lowest at -10 {degrees}C and highest at 10 {degrees}C, while the maximum incidence was observed at the absolute humidity of approximately 7 g/m3. COVID-19 incidence changed with temperature as daily incidence decreased when the temperature rose. No significant association between COVID-19 incidence and absolute humidity was observed in distributed lag nonlinear models. Additionally, A modified susceptible-exposed-infectious-recovered (M-SEIR) model confirmed that transmission rate decreased with the increase of temperature, leading to further decrease of infection rate and outbreak scale. CONCLUSIONTemperature is an environmental driver of the COVID-19 outbreak in China. Lower and higher temperatures might be positive to decrease the COVID-19 incidence. M-SEIR models help to better evaluate environmental and social impacts on COVID-19. What is already known on this topicO_LIMany infectious diseases present an environmental pattern in their incidence. C_LIO_LIEnvironmental factors, such as climate and weather condition, could drive the space and time correlations of infectious diseases, including influenza. C_LIO_LISevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can be transmitted through aerosols, large droplets, or direct contact with secretions (or fomites) as influenza virus can. C_LIO_LILittle is known about environmental pattern in COVID-19 incidence. C_LI What this study addsO_LIThe significant association between COVID-19 daily incidence and temperature was confirmed, using 3 methods, based on the data on COVID-19 and weather from 31 provincial-level regions in mainland China. C_LIO_LIEnvironmental factors were considered on the basis of SEIR model, and a modified susceptible-exposed-infectious-recovered (M-SEIR) model was developed. C_LIO_LISimulations of the COVID-19 outbreak in Wuhan presented similar effects of temperature on incidence as the incidence decrease with the increase of temperature. C_LI

The novel coronavirus (SARS-CoV-2/2019-nCoV) identified in Wuhan, China, in December 2019 has caused great damage to public health and economy worldwide with over 140,000 infected cases up to date. Previous research has suggested an involvement of meteorological conditions in the spread of droplet-mediated viral diseases, such as influenza. However, as for the recent novel coronavirus, few studies have discussed systematically about the role of daily weather in the epidemic transmission of the virus. Here, we examine the relationships of meteorological variables with the severity of the outbreak on a worldwide scale. The confirmed case counts, which indicates the severity of COVID-19 spread, and four meteorological variables, i.e., air temperature, relative humidity, wind speed, and visibility, were collected daily between January 20 and March 11 (52 days) for 430 cities and districts all over China, 21 cities/provinces in Italy, 21 cities/provinces in Japan, and 51 other countries around the world. Four different time delays of weather (on the day, 3 days ago, 7 days ago, and 14 days ago) as to the epidemic situation were taken for modeling and we finally chose the weather two weeks ago to model against the daily epidemic situation as its correlated with the outbreak best. Taken Chinese cities as a discovery dataset, it was suggested that temperature, wind speed, and relative humidity combined together could best predict the epidemic situation. The meteorological model could well predict the outbreak around the world with a high correlation (r2>0.6) with the real data. Using this model, we further predicted the possible epidemic situation in the future 12 days in several high-latitude cities with potential outbreak. This model could provide more information for governments future decisions on COVID-19 outbreak control.

Since the first case of Novel Coronavirus (2019-nCov) was identified in December 2019 in Wuhan City, China, the number of cases continues to grow across China and multiple cases have been exported to other countries. The cumulative number of reported deaths is at 637 as of February 7, 2020. Here we statistically estimated the time-delay adjusted death risk for Wuhan as well as for China excluding Wuhan to interpret the current severity of the epidemic in China. We found that the latest estimates of the death risk in Wuhan could be as high as 20% in the epicenter of the epidemic whereas we estimate it [~]1% in the relatively mildly-affected areas. Because the elevated death risk estimates are likely associated with a breakdown of the medical/health system, enhanced public health interventions including social distancing and movement restrictions should be effectively implemented to bring the epidemic under control.

BackgroundThe infectious respiratory disease COVID-19, caused novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reached pandemic status during 2020. The primary statistic data are important to survey the actual circumstances of COVID-19. Here, we report the analysis of the primary data of COVID-19 in Japan during 2020. MethodsData were collected and released systematically under Japan domestic law. Machine learning was conducted to estimate the positive rate in Japan and four prefectures (Tokyo, Osaka, Chiba, and Fukuoka). ResultsPrimary data analysis revealed there were at least two peaks of infection in Japan; the first one was during April 2020 and the second one started from November 1, 2020. Estimating the positive rate in Japan as well as in the four prefectures reinforced the above observations. The positive rate in Japan during 2020 was estimated to be around 6% to 8%. We also estimated that 1.95 million people were possibly exposed to the novel virus on October 31, 2020. The numbers of related deaths were over 3,000 people at the end of 2020. ConclusionWe estimated the infection rate of SARS-CoV-2 in Japan to be 6-8% in 2020. We also concluded that Japan had at least two infection-spreading periods, the first one being from Jan 19, 2020 until May 2020, and the second one beginning from November 1, 2020. Importantly, our analysis supports the need for clear definition of the criteria for conducting confirmation tests before embarking on data analysis.

There are large differences in premature mortality in the USA by racial/ethnic, education, rurality, and social vulnerability index groups. Using existing concentration-response functions, particulate matter (PM2.5) air pollution, population estimates at the tract level, and county-level mortality data, we estimated the degree to which these mortality discrepancies can be attributed to differences in exposure and susceptibility to PM2.5. We show that differences in mortality attributable to PM2.5 were consistently more pronounced between racial/ethnic groups than by education, rurality, or social vulnerability index, with the Black American population having by far the highest proportion of deaths attributable to PM2.5 in all years from 1990 to 2016. Over half of the difference in age-adjusted all-cause mortality between the Black American and non-Hispanic White population was attributable to PM2.5 in the years 2000 to 2011.

This manuscript has been withdrawn as it was submitted without the full consent of all the authors. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

Vigorous non-pharmaceutical interventions have largely suppressed the COVID-19 outbreak in Wuhan, China. We developed a susceptible-exposed-infectious-recovered model to study the transmission dynamics and evaluate the impact of interventions using 32,583 laboratory-confirmed cases from December 8, 2019 till March 8, 2020, accounting for time-varying ascertainment rates, transmission rates, and population movements. The effective reproductive number R0 dropped from 3.89 (95% credible interval: 3.79-4.00) before intervention to 0.14 (0.11-0.28) after full-scale multi-8 pronged interventions. By projection, the interventions reduced the total infections in Wuhan by 96.5% till March 8. Furthermore, we estimated that 79% (lower bound: 60%) of the total infections were unascertained, potentially including asymptomatic and mild-symptomatic cases. The probability of resurgence was 0.22 and 0.10 based on models with 79% and 60% infections unascertained, respectively, assuming interventions were lifted after a 14-day period of no new ascertained infections. These results provide important implications for continuing surveillance and interventions to eventually contain the outbreak.

BackgroundSince late December 2019, novel coronavirus-infected pneumonia (NCP) emerged in Wuhan, Hubei province, China. Meanwhile, NCP rapidly spread from China to other countries, and several countries government rush to evacuate their citizens from Wuhan. We analyzed the infection rate of the evacuees and extrapolated the results in Wuhans NCP incidence estimation. MethodsWe collected the total number and confirmed cases of 2019-nCov infection in the evacuation of Korea, Japan, Germany, Singapore, and France and estimated the infection rate of the 2019 novel coronavirus (2019-nCov) among people who were evacuated from Wuhan with a meta-analysis. NCP incidence of Wuhan was indirectly estimated based on data of evacuation. ResultsFrom Jan 29 to Feb 2, 2020, 1916 people have been evacuated from Wuhan, among them 17 have been confirmed 2019-nCov infected. The infection rate is estimated to be 1.1% (95% CI 0.4%-3.1%) using one group meta-analysis method with random effect model. We then estimated that almost 110,000 (95% CI: 40,000-310,000) people were infected with 2019-nCov in Wuhan around Feb 2, 2020, assuming the infection risk of evacuees is close to Chinese citizens in Wuhan. ConclusionsAt the beginning of the outbreak, incidence of NCP may be vastly underestimated. Our result emphasizes that 2019-nCov has proposed a huge public health threats in Wuhan. We need to respond more rapidly, take large-scale public health interventions and draconian measures to limiting population mobility and control the epidemic.

BackgroundAs the coronavirus pandemic rages on, 692,000 (August 7, 2020) human lives and counting have been lost worldwide to COVID-19. Understanding the relationship between short- and long-term exposure to air pollution and adverse COVID-19 health outcomes is crucial for developing solutions to this global crisis. ObjectivesTo conduct a scoping review of epidemiologic research on the link between short- and long-term exposure to air pollution and COVID-19 health outcomes. MethodWe searched PubMed, Web of Science, Embase, Cochrane, MedRxiv, and BioRxiv for preliminary epidemiological studies of the association between air pollution and COVID-19 health outcomes. 28 papers were finally selected after applying our inclusion/exclusion criteria; we categorized these studies as long-term studies, short-term time-series studies, or short-term cross-sectional studies. One study included both short-term time-series and a cross-sectional study design. Results27 studies of the 28 reported evidence of statistically significant positive associations between air pollutant exposure and adverse COVID-19 health outcomes; 11 of 12 long-term studies and all 16 short-term studies reported statistically significant positive associations. The 28 identified studies included various confounders, spatial and temporal resolutions of pollution concentrations, and COVID-19 health outcomes. DiscussionWe discuss methodological challenges and highlight additional research areas based on our findings. Challenges include data quality issues, ecological study design limitations, improved adjustment for confounders, exposure errors related to spatial resolution, geographic variability in testing, mitigation measures and pandemic stage, clustering of health outcomes, and a lack of publicly available data and code.

BackgroundHow could we anticipate the progression of the ongoing epidemic of the coronavirus disease 2019 (COVID-19) in China? As a measure of transmissibility, we aimed to estimate concurrently the time-varying reproduction number over time during the COVID-19 epidemic in China. MethodsWe extracted the epidemic data from the "Tracking the Epidemic" website of the Chinese Center for Disease Control and Prevention for the duration of January 19, 2020 and March 14, 2020. Then, we specified two plausible distributions of serial interval to apply the novel estimation method implemented in the incidence and EpiEstim packages to the data of daily new confirmed cases for robustly estimating the time-varying reproduction number in the R software. ResultsThe epidemic curve of daily new confirmed cases in China peaked around February 4-6, 2020, and then declined gradually, except the very high peak on February 12, 2020 owing to the added clinically diagnosed cases of the Hubei Province. Under two specified plausible scenarios for the distribution of serial interval, both curves of the estimated time-varying reproduction numbers fell below 1.0 around February 17-18, 2020. Finally, the COVID-19 epidemic in China abated around March 7-8, 2020, indicating that the prompt and aggressive control measures of China were effective. ConclusionSeeing the estimated time-varying reproduction number going downhill speedily was more informative than looking for the drops in the daily number of new confirmed cases during an ongoing epidemic of infectious disease. We urged public health authorities and scientists to estimate time-varying reproduction numbers routinely during an epidemic of infectious diseases and to report them daily to the public until the end of the epidemic.

Pneumonia patients of 2019-ncov in latent period are not easy to be effectively quarantined, but there is evidence that they have strong infectious ability. Here, the infectious ability of patients during the latent period is slightly less than that of the infected patients was assumed. We established a new SEIR propagation dynamics model, that considered the weak transmission ability of the incubation period, the variation of the incubation period length, and the government intervention measures to track and isolate comprehensively. Based on the raw epidemic data of China from January 23, 2020 to February 10, 2020, the dynamic parameters of the new present SEIR model are fitted. Through the Euler integration algorithm to solve the model, the effect of infectious ability of incubation patients on the theoretical estimation of the present SEIR model was analyzed, and the occurrence time of peak number in China was predicted.

Recently, a novel coronavirus (COVID-19) has caused viral pneumonia worldwide, spreading to more than 200 countries, posing a major threat to international health. To prevent the spread of COVID-19, in this study, we report that the city lockdown measure was an effective way to reduce the number of new cases, and the nitrogen dioxide (NO2) concentration can be adopted as an environmental lockdown indicator. In China, after strict city lockdown, the average NO2 concentration decreased 55.7% (95% confidence interval (CI): 51.5-59.6%) and the total number of newly confirmed cases decreased significantly. Our results also indicate that the global airborne NO2 concentration steeply decreased over the vast majority of COVID-19-hit areas based on satellite measurements. We found that the total number of newly confirmed cases reached an inflection point about two weeks after the lockdown. The total number of newly confirmed cases can be reduced by about 50% within 30 days of the lockdown. The stricter lockdown will help newly confirmed cases to decline earlier and more rapidly. Italy, Germany and France are good examples. Our results suggest that NO2 satellite measurement can help decision makers effectively monitor control regulations to reduce the spread of COVID-19.

BackgroundIn recent years, the impact of air pollution on the emergency departments of medical institutions has become increasingly evident. Emergency Ambulance Calls (EACs), compared to traditional health indicators such as mortality and hospitalization rates, provide a more direct reflection of the short-term effects of air pollution on public health. This study aims to explore the short-term association between the daily average concentrations of six major pollutants (PM2.5, PM10, SO2, NO2, CO, O3) and EACs related to respiratory diseases in the central urban areas of Shanghai. Methods: The Generalized Additive Model (GAM) was used to estimate the excess relative risk (ERR) of each pollutant on EACs at different lag times (0-7 days). Stratified analyses were also conducted based on age, time of day, and season. Results: 122,037 respiratory diseases related EACs were recorded during the study period. In different lag-day models, each interquartile range increase in pollutant concentration was associated with the highest single-day lag excess risk of EACs on the 6th day for all six pollutants, except for O3, which peaked on the 3rd day. The study found that individuals aged 65 and above are a vulnerable population to exposure. Specifically, in spring, PM2.5 on the 6th day of single-day lag was associated with a 3.19% (95% CI, 1.48-4.93%) increase in all-day EACs risk; PM10 on the 7th day of cumulative lag was associated with a 4.98% (95% CI, 1.35-8.74%) increase in daytime EACs risk; and O3 on the 3rd day of single-day lag was associated with a 3.60% increase in daytime EACs risk among the elderly (95% CI, 1.19-6.06%). Conclusion: This study indicates that even under the national ambient air pollutant concentration limits, air pollution could still serve as significant triggers for acute respiratory disease exacerbations. It is recommended that stricter air pollution control and early warning policies be implemented to reduce the occurrence of respiratory disease-related emergencies.

UV (ultraviolet) light is an important factor should be considered to predict coronavirus epidemic growth pace. UV is different from weather temperature since UV is electromagnetic wavelength from 10 nm to 400 nm in size, shorter than of visible lights. For some people, UV light can lead to cancer from unprotected sun exposure, however, for tropical people, which have been used to live in such condition, have resisted from negative effect high UV index. Moreover, UV has the capability to inactivate virus. This conclusion has been discussed deeply with biological experts. Although UV light has the ability to inactivate viruses, it may be meaningless in areas with high air pollution where UV light turns into heat. The data visualization code is available here https://github.com/cbasemaster/uvcorona

Despite the substantial evidence on the health effects of short-term exposure to ambient fine particles (PM2.5), including increasing studies focusing on those from wildland fire smoke, the impacts of long-term wildland fire smoke PM2.5 exposure remain unclear. We investigated the association between long-term exposure to wildland fire smoke PM2.5 and non-accidental mortality and mortality from a wide range of specific causes in all 3,108 counties in the contiguous U.S., 2007-2020. Controlling for non-smoke PM2.5, air temperature, and unmeasured spatial and temporal confounders, we found a non-linear association between 12-month moving average concentration of smoke PM2.5 and monthly non-accidental mortality rate. Relative to a month with the long-term smoke PM2.5 exposure below 0.1 g/m3, non-accidental mortality increased by 0.16-0.63 and 2.11 deaths per 100,000 people per month when the 12-month moving average of PM2.5 concentration was of 0.1-5 and 5+ g/m3, respectively. Cardiovascular, ischemic heart disease, digestive, endocrine, diabetes, mental, and chronic kidney disease mortality were all found to be associated with long-term wildland fire smoke PM2.5 exposure. Smoke PM2.5 contributed to approximately 11,415 non-accidental deaths/year (95% CI: 6,754, 16,075) in the contiguous U.S. Higher smoke PM2.5-related increases in mortality rates were found for people aged 65 above. Positive interaction effects with extreme heat (monthly number of days with daily mean air temperature higher than the countys 90th percentile warm season air temperature) were also observed. Our study identified the detrimental effects of long-term exposure to wildland fire smoke PM2.5 on a wide range of mortality outcomes, underscoring the need for public health actions and communications that span the health risks of both short- and long-term exposure. Significance StatementThe area burned by wildland fire has greatly increased in the U.S. in recent decades. Short-term exposure to smoke pollutants emitted by wildland fires, particularly PM2.5, is associated with numerous adverse health effects. However, the impacts of long-term exposure to wildland fire smoke PM2.5 on health and specifically mortality remain unclear. Utilizing wildland fire smoke PM2.5 and mortality data in the contiguous U.S. during 2007-2020, we found positive associations between long-term smoke PM2.5 exposure and increased non-accidental, cardiovascular, ischemic heart disease, digestive, endocrine, diabetes, mental, and chronic kidney disease mortality rates. Each year, in addition to the well-recognized mortality burden from non-smoke PM2.5, smoke PM2.5 contributed to an estimated over 10 thousand non-accidental deaths in the U.S. This study demonstrates the detrimental effects of wildland fire smoke PM2.5 on a wide range of health outcomes, and calls for more effective public health actions and communications that span the health risks of both short- and long-term exposure.

Millions of outdoor workers cannot avoid wildfire smoke, likely leading to inequalities in exposure and health risk. We characterized work-related exposure to wildfire PM2.5 for 3,108 contiguous US counties during 2006-2019. Despite experiencing less ambient exposure to wildfire PM2.5, counties with higher portions of non-Hispanic Black and Hispanic Americans experienced higher work-related exposure. We also find suggestive evidence that the effect of ambient smoke fine particulate matter (PM2.5) concentrations on all-cause mortality may differ by workplace exposure. These findings suggest that workplace exposures should be considered in wildfire smoke adaptation measures.

Individual-level studies on the coronavirus disease 2019 (COVID-19) have proliferated; however, research on neighborhood-level factors associated with COVID-19 is limited. We gathered the geographic data of all publically released COVID-19 cases in China and used a case-control (1:4 ratio) design to investigate the association between having COVID-19 cases in a neighborhood and number and types of public facilities nearby. Having more restaurants, shopping centers, hotels, living facilities, recreational facilities, public transits, educational institutions, and health service facilities was associated with significantly higher odds of having COVID-19 cases in a neighborhood. The associations for restaurants, hotels, reactional and education facilities were more pronounced in cities with fewer than six million people than those in larger cities. Our results have implications for designing targeted prevention strategies at the neighborhood level to reduce the burden of COVID-19.

Since COVID-19 emerged in early December, 2019 in Wuhan and swept across China Mainland, a series of large-scale public health interventions, especially Wuhan lock-down combined with nationwide traffic restrictions and Stay At Home Movement, have been taken by the government to control the epidemic. Based on Baidu Migration data and the confirmed cases data, we identified two key factors affecting the later (e.g February 27, 2020) cumulative confirmed cases in non-Wuhan region (y). One is the sum travelers from Wuhan during January 20 to January 26 (x1), which had higher infected probability but lower transmission ability because the human-to-human transmission risk of COVID-19 was confirmed and announced on January 20. The other is the "seed cases" from Wuhan before January 19, which had higher transmission ability and could be represented with the confirmed cases before January 29 (x2) due to a mean 10-day delay between infection and detection. A simple yet effective regression model then was established as follow: y= 70.0916+0.0054xx1+2.3455xx2 (n = 44, R2 = 0.9330, P<10-7). Even the lock-down date only delay or in advance 3 days, the estimated confirmed cases by February 27 in non-Wuhan region will increase 35.21% or reduce 30.74% - 48.59%. Although the above interventions greatly reduced the human mobility, Wuhan lock-down combined with nationwide traffic restrictions and Stay At Home Movement do have a determining effect on the ongoing spread of COVID-19 across China Mainland. The strategy adopted by China has changed the fast-rising curve of newly diagnosed cases, the international community should learn from lessons of Wuhan and experience from China. Efforts of 29 Provinces and 44 prefecture-level cities against COVID-19 were also assessed preliminarily according to the interpretive model. Big data has played and will continue playing an important role in public health.