Cost and social distancing dynamics in a mathematical model of COVID-19 with application to Ontario, Canada.
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ABSTRACT:
SUBMITTER: Moyles IR
PROVIDER: S-EPMC8074800 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A mathematical model for estimating the risk of airborne transmission of a respiratory infection such as COVID-19 is presented. The model employs basic concepts from fluid dynamics and incorporates the known scope of factors involved in the airborne transmission of such diseases. Simplicity in the mathematical form of the model is by design so that it can serve not only as a common basis for scientific inquiry across disciplinary boundaries but it can also be understandable by a broad audience outside science and academia. The caveats and limitations of the model are discussed in detail. The model is used to assess the protection from transmission afforded by face coverings made from a variety of fabrics. The reduction in the transmission risk associated with increased physical distance between the host and susceptible is also quantified by coupling the model with available and new large eddy simulation data on scalar dispersion in canonical flows. Finally, the effect of the level of physical activity (or exercise intensity) of the host and the susceptible in enhancing the transmission risk is also assessed.
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Project description:In response to the pandemic in early 2020, cities implemented states of emergency and stay at home orders to reduce virus spread. Changes in social dynamics due to local restrictions impacted human behavior and led to a shift in crime dynamics. We analyze shifts in crime types by comparing crimes before the implementation of stay at home orders and the time period shortly after these orders were put in place across three cities. We find consistent changes across Chicago, Baltimore, and Baton Rouge with significant declines in total crimes during the time period immediately following stay at home orders. The starkest differences occurred in Chicago, but in all three cities the crime types contributing to these declines were related to property crime and statutory crime rather than interpersonal crimes.
| S-EPMC8016314 | biostudies-literature
Project description:ObjectivesThe unprecedented worldwide social distancing response to COVID-19 resulted in a quick reversal of escalating case numbers. Recently, local governments globally have begun to relax social distancing regulations. Using the situation in Manitoba, Canada as an example, we estimated the impact that social distancing relaxation may have on the pandemic.MethodsWe fit a mathematical model to empirically estimated numbers of people infected, recovered, and died from COVID-19 in Manitoba. We then explored the impact of social distancing relaxation on: (a) time until near elimination of COVID-19 (< one case per million), (b) time until peak prevalence, (c) proportion of the population infected within one year, (d) peak prevalence, and (e) deaths within one year.ResultsAssuming a closed population, near elimination of COVID-19 in Manitoba could have been achieved in 4-6 months (by July or August) if there were no relaxation of social distancing. Relaxing to 15% of pre-COVID effective contacts may extend the local epidemic for more than two years (median 2.1). Relaxation to 50% of pre-COVID effective contacts may result in a peak prevalence of 31-38% of the population, within 3-4 months of initial relaxation.ConclusionSlight relaxation of social distancing may immensely impact the pandemic duration and expected peak prevalence. Only holding the course with respect to social distancing may have resulted in near elimination before Fall of 2020; relaxing social distancing to 15% of pre-COVID-19 contacts will flatten the epidemic curve but greatly extend the duration of the pandemic.
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Project description:ObjectivesDuring the 6 months since the first coronavirus disease 2019 (COVID-19) patient was diagnosed in Korea on January 20, 2020, various prevention and control measures have been implemented according to the COVID-19 epidemic pattern. Therefore, this study aimed to estimate the reproductive numbers (R) for each epidemic stage to analyze the effects of the preventive measures and to predict the COVID-19 transmission trends.MethodsWe estimated the transmission rates for each epidemic stage by fitting a COVID-19 transmission model, based on a deterministic mathematical model, to the data on confirmed cases. The effects of preventive measures such as social distancing by time period were analyzed, and the size and trends of future COVID-19 outbreaks were estimated.ResultsThe value of R was 3.53 from February18, 2020 to February 28, 2020, and the mean R reduced to 0.45 from March 14, 2020 to April 29, 2020, but it significantly increased to 2.69 from April 30, 2020 to May13, 2020 and it was maintained at 1.03 from May 14, 2020 to July 23, 2020.ConclusionsAccording to the estimated R, it had fallen to below 1 and was maintained at that level owing to the isolation of infected persons by the public health authorities and social distancing measures followed by the general public. Then, the estimated R increased rapidly as the contact among individuals increased during the long holiday period from April 30, 2020 to May 5, 2020. Thereafter, the value of R dropped, with the continued use of preventive measures but remained higher than 1.00, indicating that the COVID-19 outbreak can be prolonged and develop into a severe outbreak at any time.
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Project description:BackgroundThis study examined the social cognition determinants of social distancing behavior during the COVID-19 pandemic in samples from Australia and the US guided by the health action process approach (HAPA).MethodsParticipants (Australia: N = 495, 50.1% women; US: N = 701, 48.9% women) completed HAPA social cognition constructs at an initial time-point (T1), and one week later (T2) self-reported their social distancing behavior.ResultsSingle-indicator structural equation models that excluded and included past behavior exhibited adequate fit with the data. Intention and action control were significant predictors of social distancing behavior in both samples, and intention predicted action and coping planning in the US sample. Self-efficacy and action control were significant predictors of intention in both samples, with attitudes predicting intention in the Australia sample and risk perceptions predicting intention in the US sample. Significant indirect effects of social cognition constructs through intentions were observed. Inclusion of past behavior attenuated model effects. Multigroup analysis revealed no differences in model fit across samples, suggesting that observed variations in the parameter estimates were relatively trivial.ConclusionResults indicate that social distancing is a function of motivational and volitional processes. This knowledge can be used to inform messaging regarding social distancing during COVID-19 and in future pandemics.
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Project description:Social distancing policies (SDPs) implemented in response to the COVID-19 pandemic have led to temporal and spatial shifts in water demand across cities. Water utilities need to understand these demand shifts to respond to potential operational and water-quality issues. Aided by a fixed-effects model of citywide water demand in Austin, Texas, we explore the impacts of various SDPs (e.g., time after the stay home-work safe order, reopening phases) using daily demand data gathered between 2013 and 2020. Our approach uses socio-technical determinants (e.g., climate, water conservation policy) with SDPs to model water demand, while accounting for spatial and temporal effects (e.g., geographic variations, weekday patterns). Results indicate shifts in behavior of residential and nonresidential demands that offset the change at the system scale, demonstrating a spatial redistribution of water demand after the stay home-work safe order. Our results show that some phases of Texas's reopening phases had statistically significant relationships to water demand. While this yielded only marginal net effects on overall demand, it underscores behavioral changes in demand at sub-system spatial scales. Our discussions shed light on SDPs' impacts on water demand. Equipped with our empirical findings, utilities can respond to potential vulnerabilities in their systems, such as water-quality problems that may be related to changes in water pressure in response to demand variations.
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Project description:In the absence of pharmaceutical interventions, social distancing is being used worldwide to curb the spread of COVID-19. The impact of these measures has been inconsistent, with some regions rapidly nearing disease elimination and others seeing delayed peaks or nearly flat epidemic curves. Here we build a stochastic epidemic model to examine the effects of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions. Our simulations show that long delays between the adoption of control measures and observed declines in cases, hospitalizations, and deaths occur in many scenarios. We find that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, the rate of decline, individual risks of infection, and the success of partial relaxation measures. The structure of residual external connections, driven by workforce participation and essential businesses, interacts to determine outcomes. We suggest limited conditions under which the formation of household "bubbles" can be safe. These findings can improve future predictions of the timescale and efficacy of interventions needed to control second waves of COVID-19 as well as other similar outbreaks, and highlight the need for better quantification and control of household transmission.
| S-EPMC7886148 | biostudies-literature
Project description:In the absence of pharmaceutical interventions, social distancing is being used worldwide to curb the spread of COVID-19. The impact of these measures has been inconsistent, with some regions rapidly nearing disease elimination and others seeing delayed peaks or nearly flat epidemic curves. Here we build a stochastic epidemic model to examine the effects of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions. Our simulations show that long delays between the adoption of control measures and observed declines in cases, hospitalizations, and deaths occur in many scenarios. We find that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, the rate of decline, individual risks of infection, and the success of partial relaxation measures. The structure of residual external connections, driven by workforce participation and essential businesses, interacts to determine outcomes. We suggest limited conditions under which the formation of household "bubbles" can be safe. These findings can improve future predictions of the timescale and efficacy of interventions needed to control second waves of COVID-19 as well as other similar outbreaks, and highlight the need for better quantification and control of household transmission.
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Project description:This article describes patterns of compliance with social distancing measures among the Spanish population during the coronavirus disease-2019 (COVID-19) pandemic. It identifies several factors associated with higher or lower compliance with recommended measures of social distancing. This research is part of a 67-country study, titled the International COVID-19 study on Social & Moral Psychology, in which we use a Spanish dataset. Participants were residents in Spain aged 18 or above. The sample comprises 1,090 respondents, weighted to be representative of the Spanish population. Frequencies, correlations, bivariate analysis, and six models based on hierarchical multiple regressions were applied. The main finding is that most Spaniards are compliant with established guidelines of social distance during the pandemic (State of Alarm, before May 2020). Variables associated more with lower levels of compliance with these standards were explored. Six hierarchical multiple regression models found that compliance with social distance measures has a multifactorial explanation (R 2 between 20.4 and 49.1%). Sociodemographic factors, personal hygiene patterns, and the interaction between personal hygiene patterns and the support for political measures related to the coronavirus brought significant effects on the regression models. Less compliance was also associated with beliefs in some specific conspiracy theories with regard to COVID-19 or general conspiracy mentality (Conspiracy Mentality Questionnaire, CMQ), consumption patterns of traditional mass media (television, paper newspapers, magazines, and radio) and modern means to get informed (online digital newspapers, blogs, and social networks), political ideology, vote, trust in institutions, and political identification. Among the future lines of action in preventing the possible outbreak of the virus, we suggest measures to reinforce trust in official information, mainly linked to reducing the influence of disinformation and conspiracy theories parallel to the pandemic.
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Project description:Non-compliance with social distancing (SD) measures clearly has negative effects on both public health and post-pandemic economic recovery. However, little is as yet known about people's views on and factors influencing their behavioral intentions toward SD measures. This study draws on moral disengagement theory and the norm-activation model to investigate mechanisms that promote or hinder compliance with SD measures. A longitudinal research approach was adopted to compare changes in the main factors over three periods of the COVID-19 pandemic in England (UK). The results reveal significant differences between the three periods regarding intentions to comply with SD measures, altruistic value, moral obligation and moral disengagement, with no significant change in ascription of responsibility. Residents showed the strongest intentions to comply with SD measures during the first national lockdown, with the highest moral obligation and lowest moral disengagement levels, compared with the lowest intention to comply during the first re-opening period. Altruistic value is important in promoting moral obligation and compliance with SD measures, whereas the predictive powers of ascription of responsibility and moral disengagement were weaker than expected. These findings offer guidance to policymakers and researchers in developing more effective policies and public communication strategies. The results suggest that communication is key to normalizing SD compliance, which can be achieved most effectively by fostering residents' altruistic value and moral considerations. Particular attention must be paid to re-opening periods between lockdowns, with clear messages to remind residents of prosocial aspects of SD compliance and public health. In addition to appropriate communication and education, technologies such as apps, QR codes and contactless shopping settings may also be used to facilitate compliance with SD measures.
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