Effects of ventilation on the indoor spread of COVID-19.
Ontology highlight
ABSTRACT:
SUBMITTER: Bhagat RK
PROVIDER: S-EPMC7520710 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Ventilation is of critical importance to containing COVID-19 contagion in indoor environments. Keeping the ventilation rate at high level is recommended by many guidelines to dilute virus-laden respiratory particles and mitigate airborne transmission risk. However, high ventilation rate will cause high energy use. Demand-controlled ventilation is a promising technology option for controlling indoor air quality in an energy-efficient manner. This paper proposes a novel CO2-based demand-controlled ventilation strategy to limit the spread of COVID-19 in indoor environments. First, the quantitative relationship is established between COVID-19 infection risk and average CO2 level. Then, a sufficient condition is proposed to ensure COVID-19 event reproduction number is less than 1 under a conservative consideration of the number of infectors. Finally, a ventilation control scheme is designed to make sure the above condition can be satisfied. Case studies of different indoor environments have been conducted on a testbed of a real ventilation system to validate the effectiveness of the proposed strategy. Results show that the proposed strategy can efficiently maintain the reproduction number less than 1 to limit COVID-19 contagion while saving about 30%-50% of energy compared with the fixed ventilation scheme. The proposed strategy offers more practical values compared with existing studies: it is applicable to scenarios where there are multiple infectors, and the number of infectors varies with time; it only requires CO2 sensors and does not require occupancy detection sensors. Since CO2 sensors are very mature and low-cost, the proposed strategy is suitable for mass deployment in most existing ventilation systems.
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Project description:The recent COVID-19 pandemic has made people acutely aware of the importance of indoor air quality (IAQ) and building ventilation systems, particularly in densely occupied places like offices and schools. As a result, governments and other public entities are increasingly investing in the installation, maintenance, and upgrades of ventilation systems in public buildings. However, little is known about the effect of building ventilation systems on actual IAQ and its impact on occupant behavior. This paper exploits exogenous closing and opening events of schools during the COVID-19 pandemic, combined with policy measures focusing on maximizing ventilation rates inside classrooms, to assess the effectiveness of building ventilation systems in primary schools. We use a unique sensor network implemented before the COVID-19 pandemic, consisting of measurement devices installed in 252 classrooms across 27 Dutch primary schools, continuously monitoring IAQ indicators such as CO2 levels and fine particle concentrations. Using high-frequency data from 2018 to 2022 school years, we compare the IAQ differences between natural and mechanical ventilation through a fixed-effect identification strategy. Our results show that mechanically ventilated classrooms perform better with respect to CO2 and fine particle levels. However, the post-COVID-19 ventilation measures implemented after school reopening had stronger effects on naturally ventilated (NV) classrooms, suggesting behavioral changes at the classroom level. We also investigate the longer term effects of these post-COVID-19 ventilation measures and show some evidence of decay in effectiveness, as well as a strong seasonal effect, particularly in NV classrooms, which seems the result of a trade-off between ventilation and thermal comfort.
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Project description:New COVID-19 ventilation guidelines have resulted in higher energy consumption to maintain indoor air quality (IAQ), and energy efficiency has become a secondary concern. Despite the significance of the studies conducted on COVID-19 ventilation requirements, a comprehensive investigation of the associated energy challenges has not been discussed. This study aims to present a critical systematic review of the Coronavirus viral spreading risk mitigation through ventilation systems (VS) and its relation to energy use. COVID-19 heating, ventilation and air conditioning (HVAC)-related countermeasures proposed by industry professionals have been reviewed and their influence on operating VS and energy consumption have also been discussed. A critical review analysis was then conducted on publications from 2020 to 2022. Four research questions (RQs) have been selected for this review concerning i) maturity of the existing literature, ii) building types and occupancy profile, iii) ventilation types and effective control strategies and iv) challenges and related causes. The results reveal that employing HVAC auxiliary equipment is mostly effective and increased fresh air supply is the most significant challenge associated with increased energy consumption due to maintaining IAQ. Future studies should focus on novel approaches toward solving the apparently conflicting objectives of minimizing energy consumption and maximizing IAQ. Also, effective ventilation control strategies should be assessed in various buildings with different occupancy densities. The implications of this study can be useful for future development of this topic not only to enhance the energy efficiency of the VS but also to enable more resiliency and health in buildings.
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Project description: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.
| S-EPMC7204718 | biostudies-literature
Project description:The outbreak of novel COVID-19 disease elicited a wide range of anti-contagion and economic policies like school closure, income support, contact tracing, and so forth, in the mitigation and suppression of the spread of the SARS-CoV-2 virus. However, a systematic evaluation of these policies has not been made. Here, 17 implemented policies from the Oxford COVID-19 Government Response Tracker dataset employed in 90 countries from December 31, 2019, to August 31, 2020, were analyzed. A Poisson regression model was applied to analyze the relationship between policies and daily confirmed cases using a generalized estimating equations approach. A lag is a fixed time displacement in time series data. With that, lagging (0, 3, 7, 10, and 14 days) was also considered during the analysis since the effects of policies implemented on a given day may affect the number of confirmed cases several days after implementation. The countries were divided into three groups depending on the number of waves of the pandemic observed in each country. Through subgroup analysis, we showed that with and without lagging, contact tracing and containment policies were significant for countries with two waves, while closing, economic, and health policies were significant for countries with three waves. Wave-specific analysis for each wave showed that significant health, economic, and containment policies varied across waves of the pandemic. Emergency investment in healthcare was consistently significant among the three groups of countries, while the Stringency index was significant among all waves of the pandemic. These findings may help in making informed decisions regarding whether, which, or when these policies should be intensified or lifted.
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Project description:This article proposes a study of the SARS-CoV-2 virus spread and the efficacy of public policies in Brazil. Using both aggregated (from large Internet companies) and fine-grained (from Departments of Motor Vehicles) mobility data sources, our work sheds light on the effect of mobility on the pandemic situation in the Brazilian territory. Our main contribution is to show how mobility data, particularly fine-grained ones, can offer valuable insights into virus propagation. For this, we propose a modification in the SENUR model to add mobility information, evaluating different data availability scenarios (different information granularities), and finally, we carry out simulations to evaluate possible public policies. In particular, we conduct a case study that shows, through simulations of hypothetical scenarios, that the contagion curve in several Brazilian cities could have been milder if the government had imposed mobility restrictions soon after reporting the first case. Our results also show that if the government had not taken any action and the only safety measure taken was the population's voluntary isolation (out of fear), the time until the contagion peak for the first wave would have been postponed, but its value would more than double.
| S-EPMC8651143 | biostudies-literature
Project description:ObjectivesAt the beginning of the coronavirus disease 2019 (COVID-19) pandemic, some countries imposed entry bans against Chinese visitors. We sought to identify the effects of border shutdowns on the spread of the COVID-19 outbreak.MethodsWe used the synthetic control method to measure the effects of entry bans against Chinese visitors on the cumulative number of confirmed cases using World Health Organization situation reports as the data source. The synthetic control method constructs a synthetic country that did not shut down its borders, but is similar in all other aspects.ResultsSix countries that shut down their borders were evaluated. For Australia, the effects of the policy began to appear 4 days after implementation, and the number of COVID-19 cases dropped by 94.4%. The border shutdown policy took around 13.2 days to show positive effects and lowered COVID-19 cases by 91.7% on average by the end of February.ConclusionsThe border shutdowns in early February significantly reduced the spread of the virus. Our findings are informative for future planning of public health policies.
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Project description:The Centers for Disease Control and Prevention (CDC) deem large indoor gatherings without social distancing the "highest risk" activity for COVID-19 contagion. On June 20, 2020, President Donald J. Trump held his first mass campaign rally following the US coronavirus outbreak at the indoor Bank of Oklahoma arena. In the weeks following the event, numerous high-profile national news outlets reported that the Trump rally was "more than likely" the cause of a coronavirus surge in Tulsa County based on time series data. This study is the first to rigorously explore the impacts of this event on social distancing and COVID-19 spread. First, using data from SafeGraph Inc, we show that while non-resident visits to census block groups hosting the Trump event grew by approximately 25 percent, there was no decline in net stay-at-home behavior in Tulsa County, reflecting important offsetting behavioral effects. Then, using data on COVID-19 cases from the CDC and a synthetic control design, we find little evidence that COVID-19 grew more rapidly in Tulsa County, its border counties, or in the state of Oklahoma than each's estimated counterfactual during the five-week post-treatment period we observe. Difference-in-differences estimates further provide no evidence that COVID-19 rates grew faster in counties that drew relatively larger shares of residents to the event. We conclude that offsetting risk-related behavioral responses to the rally-including voluntary closures of restaurants and bars in downtown Tulsa, increases in stay-at-home behavior, displacement of usual activities of weekend inflows, and smaller-than-expected crowd attendance-may be important mechanisms.Supplementary informationThe online version contains supplementary material available at 10.1007/s11166-021-09359-4.
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2023-07-26 | GSE227585 | GEO
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