Possible environmental effects on the spread of COVID-19 in China.
ABSTRACT: At the end of 2019, a novel coronavirus, designated as SARS-CoV-2, emerged in Wuhan, China and was identified as the causal pathogen of COVID-19. The epidemic scale of COVID-19 has increased dramatically, with confirmed cases increasing across China and globally. Understanding the potential affecting factors involved in COVID-19 transmission will be of great significance in containing the spread of the epidemic. Environmental and meteorological factors might impact the occurrence of COVID-19, as these have been linked to various diseases, including severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), whose causative pathogens belong to the same virus family as SARS-CoV-2. We collected daily data of COVID-19 confirmed cases, air quality and meteorological variables of 33 locations in China for the outbreak period of 29 January 2020 to 15 February 2020. The association between air quality index (AQI) and confirmed cases was estimated through a Poisson regression model, and the effects of temperature and humidity on the AQI-confirmed cases association were analyzed. The results show that the effect of AQI on confirmed cases associated with an increase in each unit of AQI was statistically significant in several cities. The lag effect of AQI on the confirmed cases was statistically significant on lag day 1 (relative risk (RR) = 1.0009, 95% confidence interval (CI): 1.0004, 1.0013), day 2 (RR = 1.0007, 95% CI: 1.0003, 1.0012) and day 3 (RR = 1.0008, 95% CI: 1.0003, 1.0012). The AQI effect on the confirmed cases might be stronger in the temperature range of 10 °C ? T < 20 °C than in other temperature ranges, while the RR of COVID-19 transmission associated with AQI was higher in the relative humidity (RH) range of 10% ? RH < 20%. Results may suggest an enhanced impact of AQI on the COVID-19 spread under low RH.
Project description:This study aims to explore the state-wise assessment of SARS-CoV-2 (COVID-19) pandemic spread in Malaysia with focus on influence of meteorological parameters and air quality. In this study, state-wise COVID-19 data, meteorological parameters and air quality index (AQI) were collected from March 13 to April 30, 2020, which encompass three movement control order (MCO) periods in the country. Overall, total infected cases were observed to be higher in MCO phase 1 and 2 and significantly reduced in MCO phase 3. Due to the variation in the spatial interval of population density and individual immunity, the relationship of these parameters to pandemic spread could not be achieved. The study infers that temperature (T) between 23 and 25 °C and relative humidity (RH) (70-80%) triggered the pandemic spread by increase in the infected cases in northern and central Peninsular Malaysia. Selangor, WP Kuala Lumpur and WP Putrajaya show significantly high infected cases and a definite trend was not observed with respect to a particular meteorological factor. It is identified that high precipitation (PPT), RH and good air quality have reduced the spread in East Malaysia. A negative correlation of T and AQI and positive correlation of RH with total infected cases were found during MCO phase 3. Principal component analysis (PCA) indicated that T, RH, PPT, dew point (DP) and AQI are the main controlling factors for the spread across the country apart from social distancing. Vulnerability zones were identified based on the spatial analysis of T, RH, PPT and AQI with reference to total infected cases. Based on time series analysis, it was determined that higher RH and T in Peninsular Malaysia and high amount of PPT, RH and good air quality in East Malaysia have controlled the spreading during MCO phase 3. The predominance of D614 mutant was observed prior to March and decreases at the end of March, coinciding with the fluctuation of meteorological factors and air quality. The outcome of this study gives a general awareness to the public on COVID-19 and the influence of meteorological factors. It will also help the policymakers to enhance the management plans against the pandemic spreading apart from social distancing in the next wave of COVID-19.<h4>Supplementary information</h4>The online version contains supplementary material available at 10.1007/s10668-021-01719-z.
Project description:<h4>Background</h4>In December 2019, a novel coronavirus disease (COVID-19) broke out in Wuhan, China; however, the factors affecting the mortality of COVID-19 remain unclear.<h4>Methods</h4>Thirty-two days of data (the growth rate/mortality of COVID-19 cases) that were shared by Chinese National Health Commission and Chinese Weather Net were collected by two authors independently. Student's t-test or Mann-Whitney U test was used to test the difference in the mortality of confirmed/severe cases before and after the use of "Fangcang, Huoshenshan, and Leishenshan" makeshift hospitals (MSHs). We also studied whether the above outcomes of COVID-19 cases were related to air temperature (AT), relative humidity (RH), or air quality index (AQI) by performing Pearson's analysis or Spearman's analysis.<h4>Results</h4>Eight days after the use of MSHs, the mortality of confirmed cases was significantly decreased both in Wuhan (<i>t</i> = 4.5, <i>P</i> < 0.001) and Hubei (<i>U</i> = 0, <i>P</i> < 0.001), (t and U are the test statistic used to test the significance of the difference). In contrast, the mortality of confirmed cases remained unchanged in non-Hubei regions (<i>U</i> = 76, <i>P</i> = 0.106). While on day 12 and day 16 after the use of MSHs, the reduce in mortality was still significant both in Wuhan and Hubei; but in non-Hubei regions, the reduce also became significant this time (<i>U</i> = 123, <i>P</i> = 0.036; <i>U</i> = 171, <i>P</i> = 0.015, respectively). Mortality of confirmed cases was found to be negatively correlated with AT both in Wuhan (<i>r</i> = - 0.441, <i>P</i> = 0.012) and Hubei (<i>r</i> = - 0.440, <i>P</i> = 0.012). Also, both the growth rate and the mortality of COVID-19 cases were found to be significantly correlated with AQI in Wuhan and Hubei. However, no significant correlation between RH and the growth rate/mortality of COVID-19 cases was found in our study.<h4>Conclusions</h4>Our findings indicated that both the use of MSHs, the rise of AT, and the improvement of air quality were beneficial to the survival of COVID-19 patients.
Project description:Brazil presented a very high number of maternal deaths and evident delays in healthcare. We aimed at evaluating the characteristics of SARS-CoV-2 infection and associated outcomes in the obstetric population. We conducted a prospective cohort study in 15 Brazilian centers including symptomatic pregnant or postpartum women with suspected COVID-19 from Feb/2020 to Feb/2021. Women were followed from suspected infection until the end of pregnancy. We analyzed maternal characteristics and pregnancy outcomes associated with confirmed COVID-19 infection and SARS, determining unadjusted risk ratios. In total, 729 symptomatic women with suspected COVID-19 were initially included. Among those investigated for COVID-19, 51.3% (n = 289) were confirmed COVID-19 and 48% (n = 270) were negative. Initially (before May 15th), only 52.9% of the suspected cases were tested and it was the period with the highest proportion of ICU admission and maternal deaths. Non-white ethnicity (RR 1.78 [1.04–3.04]), primary schooling or less (RR 2.16 [1.21–3.87]), being overweight (RR 4.34 [1.04–19.01]) or obese (RR 6.55 [1.57–27.37]), having public prenatal care (RR 2.16 [1.01–4.68]), planned pregnancies (RR 2.09 [1.15–3.78]), onset of infection in postpartum period (RR 6.00 [1.37–26.26]), chronic hypertension (RR 2.15 [1.37–4.10]), pre-existing diabetes (RR 3.20 [1.37–7.46]), asthma (RR 2.22 [1.14–4.34]), and anaemia (RR 3.15 [1.14–8.71]) were associated with higher risk for SARS. The availability of tests and maternal outcomes varied throughout the pandemic period of the study; the beginning was the most challenging period, with worse outcomes. Socially vulnerable, postpartum and previously ill women were more likely to present SARS related to COVID-19.
Project description:<b>Objectives:</b> To evaluate the long- and short-term effects of air pollution on COVID-19 transmission simultaneously, especially in high air pollution level countries. <b>Methods:</b> Quasi-Poisson regression was applied to estimate the association between exposure to air pollution and daily new confirmed cases of COVID-19, with mutual adjustment for long- and short-term air quality index (AQI). The independent effects were also estimated and compared. We further assessed the modification effect of within-city migration (WM) index to the associations. <b>Results:</b> We found a significant 1.61% (95%CI: 0.51%, 2.72%) and 0.35% (95%CI: 0.24%, 0.46%) increase in daily confirmed cases per 1 unit increase in long- and short-term AQI. Higher estimates were observed for long-term impact. The stratifying result showed that the association was significant when the within-city migration index was low. A 1.25% (95%CI: 0.0.04%, 2.47%) and 0.41% (95%CI: 0.30%, 0.52%) increase for long- and short-term effect respectively in low within-city migration index was observed. <b>Conclusions:</b> There existed positive associations between long- and short-term AQI and COVID-19 transmission, and within-city migration index modified the association. Our findings will be of strategic significance for long-run COVID-19 control.
Project description:The survival of COVID-19 in different environments may be affected by a variety of weather, pollution, and seasonal parameters. Therefore, the present study aims to conduct an ecological investigation on COVID-19 average growth rate of daily cases and deaths influenced by environmental factors (temperature, humidity, and air pollution) using a sample size of adjusted cumulative incidence of daily cases and deaths based on five 60-day periods. Research data was gathered on official websites, including information on COVID-19, meteorological data, and air pollution indicators from December 31, 2019, to October 12, 2020, from 210 countries. Spearman correlation and generalized additive model (GAM) were used to analyze the data. During the observed period, the COVID-19 average growth rate of daily cases (r = -0.08, P =0.151) and deaths (r= -0.09, P = 0.207) were not correlated with humidity. Also, there was a negative relationship between the COVID-19 average growth rate of new cases and deaths with the Air Quality Index (AQI) and wind (new cases and wind: r=-0.25, P= 0.04). Furthermore, the data related to the first and second 60 day of the adjusted cumulative incidence of COVID-19 daily cases and deaths were not associated with humidity and Air Quality Index (AQI). The result of GAM showed the effect of AQI on the average growth rate of COVID-19 new cases and deaths. This study provides evidence for a positive relationship between COVID-19 daily cases, deaths, and AQI.
Project description:The causative organism, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), exhibits a wide spectrum of clinical manifestations in disease-ridden patients. Differences in the severity of COVID-19 ranges from asymptomatic infections and mild cases to the severe form, leading to acute respiratory distress syndrome (ARDS) and multiorgan failure with poor survival. MiRNAs can regulate various cellular processes, including proliferation, apoptosis, and differentiation, by binding to the 3′UTR of target mRNAs inducing their degradation, thus serving a fundamental role in post-transcriptional repression. Alterations of miRNA levels in the blood have been described in multiple inflammatory and infectious diseases, including SARS-related coronaviruses. We used microarrays to delineate the miRNAs and snoRNAs signature in the peripheral blood of severe COVID-19 cases (n=9), as compared to mild (n=10) and asymptomatic (n=10) patients, and identified differentially expressed transcripts in severe versus asymptomatic, and others in severe versus mild COVID-19 cases. A cohort of 29 male age-matched patients were selected. All patients were previously diagnosed with COVID-19 using TaqPath COVID-19 Combo Kit (Thermo Fisher Scientific, Waltham, Massachusetts), or Cobas SARS-CoV-2 Test (Roche Diagnostics, Rotkreuz, Switzerland), with a CT value < 30. Additional criterion for selection was age between 35 and 75 years. Participants were grouped into severe, mild and asymptomatic. Classifying severe cases was based on requirement of high-flow oxygen support and ICU admission (n=9). Whereas mild patients were identified based on symptoms and positive radiographic findings with pulmonary involvement (n=10). Patients with no clinical presentation were labelled as asymptomatic cases (n=10).
Project description:<h4>Background</h4>Both coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome (SARS) are caused by coronaviruses and have infected people in China and worldwide. We aimed to investigate whether COVID-19 and SARS exhibited similar spatial and temporal features at provincial level in mainland China.<h4>Methods</h4>The number of people infected by COVID-19 and SARS were extracted from daily briefings on newly confirmed cases during the epidemics, as of Mar. 4, 2020 and Aug. 3, 2003, respectively. We depicted spatiotemporal patterns of the COVID-19 and SARS epidemics using spatial statistics such as Moran's I and the local indicators of spatial association (LISA).<h4>Results</h4>Compared to SARS, COVID-19 had a higher overall incidence. We identified 3 clusters (predominantly located in south-central China; the highest RR = 135.08, 95% CI: 128.36-142.08) for COVID-19 and 4 clusters (mainly in Northern China; the highest RR = 423.51, 95% CI: 240.96-722.32) for SARS. Fewer secondary clusters were identified after the "Wuhan lockdown". The LISA cluster map detected a significantly high-low (Hubei) and low-high spatial clustering (Anhui, Hunan, and Jiangxi, in Central China) for COVID-19. Two significant high-high (Beijing and Tianjin) and low-high (Hebei) clusters were detected for SARS.<h4>Conclusions</h4>COVID-19 and SARS outbreaks exhibited distinct spatiotemporal clustering patterns at the provincial levels in mainland China, which may be attributable to changes in social and demographic factors, local government containment strategies or differences in transmission mechanisms.
Project description:As of July 27, 2020, COVID-19 has caused 640,000 deaths worldwide and has had a major impact on people's productivity and lives. Analyzing the spatial distribution characteristics of COVID-19 cases and their relationships with meteorological and environmental factors might help enrich our knowledge of virus transmission and formulate reasonable epidemic prevention strategies. Taking the cumulative confirmed cases in Hubei province from January 23, 2020, to April 8, 2020, as an example, this study analyzed the spatial evolution characteristics of confirmed COVID-19 cases in Hubei province using exploratory spatial data analysis and explored the spatial relationship between the main environmental and meteorological factors and confirmed COVID-19 cases using a geographically weighted regression (GWR) model. Results show that there was no obvious spatial clustering of confirmed COVID-19 cases in Hubei province, while the decline and end of the newly confirmed cases revealed relatively obvious negative spatial correlations. Due to the lockdown in Hubei province, the main air quality indexes (e.g., AQI and PM<sub>2.5</sub>) decreased significantly and environmental quality was better than historical contemporaneous levels. Meanwhile, the results of the GWR model suggest that the impacts of environmental and meteorological factors on the development of COVID-19 were not significant. These findings indicate that measures such as social distancing and isolation played the primary role in controlling the development of the COVID-19 epidemic.
Project description:<h4>Importance</h4>The impact of the SARS-CoV-2 pandemic on children remains unclear. Better understanding of the burden of COVID-19 among children and their protection against re-infection is crucial as they will be among the last groups vaccinated.<h4>Objective</h4>To characterize the burden of COVID-19 and assess how protection from symptomatic re-infection among children may vary by age.<h4>Design</h4>A prospective, community-based pediatric cohort study conducted from March 1, 2020 through October 15, 2021.<h4>Setting</h4>The Nicaraguan Pediatric Influenza Cohort is a community-based cohort in District 2 of Managua, Nicaragua.<h4>Participants</h4>A total of 1964 children aged 0-14 years participated in the cohort. Non-immunocompromised children were enrolled by random selection from a previous pediatric influenza cohort. Additional newborn infants aged ≤4 weeks were randomly selected and enrolled monthly, via home visits.<h4>Exposures</h4>Prior COVID-19 infection as confirmed by positive anti SARS-CoV-2 antibodies (receptor binding domain [RBD] and spike protein) or real time RT-PCR confirmed COVID-19 infection ≥60 days prior to current COVID-19.<h4>Main outcomes and measures</h4>Symptomatic COVID-19 cases confirmed by real time RT-PCR and hospitalization within 28 days of symptom onset of confirmed COVID-19 case.<h4>Results</h4>Overall, 49.8% of children tested were seropositive over the course of the study. There were also 207 PCR-confirmed COVID-19 cases, 12 (6.4%) of which were severe enough to require hospitalization. Incidence of COVID-19 was highest among children aged <2 years-16.1 per 100 person-years (95% Confidence Interval [CI]: 12.5, 20.5)-approximately three times that of children in any other age group assessed. Additionally, 41 (19.8%) symptomatic SARS-CoV-2 episodes were re-infections, with younger children slightly more protected against symptomatic reinfection. Among children aged 6-59 months, protection was 61% (Rate Ratio [RR]:0.39, 95% CI:0.2,0.8), while protection among children aged 5-9 and 10-14 years was 64% (RR:0.36,0.2,0.7), and 49% (RR:0.51,0.3-0.9), respectively.<h4>Conclusions and relevance</h4>In this prospective community-based pediatric cohort rates of symptomatic and severe COVID-19 were highest among the youngest participants, with rates stabilizing around age 5. Reinfections represent a large proportion of PCR-positive cases, with children <10 years displaying greater protection from symptomatic reinfection. A vaccine for children <5 years is urgently needed.<h4>Key points</h4><b>Question:</b> What is the burden of COVID-19 among young children and how does protection from re-infection vary with age?<b>Findings:</b> In this study of 1964 children aged 0-14 years children <5 years had the highest rates of symptomatic and severe COVID-19 while also displaying greater protection against re-infection compared to children ≥10 years.<b>Meaning:</b> Given their greater risk of infection and severe disease compared to older children, effective vaccines against COVID-19 are urgently needed for children under 5.
Project description:Blood transcriptomes were determined in COVID-19 cases and healthy controls using the Clariom S RNA microarray, Affymetrix assay. Overall design: COVID-19 cases with a positive respiratory samples SARS-CoV-2 and healthy Controls cases were recruited. Blood transcriptomes were analysed using Clariom S RNA Microarray, Affymetrix Inc.