Population density and basic reproductive number of COVID-19 across United States counties

Reconsidering the role of place in health and welfare services: lessons from the COVID-19 pandemic in the United States and Canada

Places-the meaningful locations of daily life-have been central to the wellbeing of humans since they first formed social groups, providing a stable base for individuals, families, and communities. In the United States and Canada, as elsewhere, place also plays a foundational role in the provision of critical social and health services and resources. Yet the globally destabilizing events of the COVID-19 pandemic have dramatically challenged the concept, experience, and meaning of place. Place-centered public health measures such as lockdowns and stay-at-home orders have disrupted and transformed homes, neighborhoods, workplaces, and schools. These measures stressed families and communities, particularly among marginalized groups, and made the delivery of vital resources and services more difficult. At the same time, the pandemic has stimulated a range of creative and resilient responses. Building from an overview of these effects and drawing conceptually on theories of people-place relationships, this paper argues for critical attention to reconsidering and re-envisioning prevailing assumptions about place-centric policies, services, and practices. Such reappraisal is vital to ensuring that, going forward, scholars, policymakers, and practitioners can effectively design and deliver services capable of maintaining social connections, safety, and wellbeing in contexts of uncertainty, inequality, and flux.

Structural modeling of COVID-19 spread in relation to human mobility

Human mobility is considered as one of the prominent non-pharmaceutical interventions to control the spread of the pandemic (positive effect from mobility to infection). Conversely, the spread of the pandemic triggered massive changes to people's daily schedules by limiting their movement (negative effect from infection to mobility). The purpose of this study is to investigate this bi-directional relationship between human mobility and COVID-19 spread across U.S. counties during the early phase of the pandemic when infection rates were stabilizing and activity-travel behavior reflected a fairly steady return to normal following the drastic changes observed during the pandemic's initial shock. In particular, we applied Structural Regression (SR) model to investigate a bi-directional relationship between COVID-19 infection rate and the degree of human mobility in a county in association with socio-demographic and location characteristics of that county, and state-wide COVID-19 policies. Combining U.S. county-level cross-sectional data from multiple sources, our model results suggested that during the study period, human mobility and infection rate in a county both influenced each other, but in an opposite direction. Metropolitan counties experienced higher infection and lower mobility than non-metropolitan counties in the early stage of the pandemic. Counties with highly infected neighboring counties and more external trips had a higher infection rate. During the study period, community mitigation strategies, such as stay at home order, emergency declaration, and non-essential business closure significantly reduced mobility whereas public mask mandate significantly reduced infection rates. The findings of this study will provide important insights to policy makers in understanding the two-way relationship between human mobility and COVID-19 spread and to derive mobility-driven policy actions accordingly.

Corona virus optimization (CVO): a novel optimization algorithm inspired from the Corona virus pandemic

This research introduces a new probabilistic and meta-heuristic optimization approach inspired by the Corona virus pandemic. Corona is an infection that originates from an unknown animal virus, which is of three known types and COVID-19 has been rapidly spreading since late 2019. Based on the SIR model, the virus can easily transmit from one person to several, causing an epidemic over time. Considering the characteristics and behavior of this virus, the current paper presents an optimization algorithm called Corona virus optimization (CVO) which is feasible, effective, and applicable. A set of benchmark functions evaluates the performance of this algorithm for discrete and continuous problems by comparing the results with those of other well-known optimization algorithms. The CVO algorithm aims to find suitable solutions to application problems by solving several continuous mathematical functions as well as three continuous and discrete applications. Experimental results denote that the proposed optimization method has a credible, reasonable, and acceptable performance.

The importance of saturating density dependence for population-level predictions of SARS-CoV-2 resurgence compared with density-independent or linearly density-dependent models, England, 23 March to 31 July 2020

BackgroundPopulation-level mathematical models of outbreaks typically assume that disease transmission is not impacted by population density ('frequency-dependent') or that it increases linearly with density ('density-dependent').AimWe sought evidence for the role of population density in SARS-CoV-2 transmission.MethodsUsing COVID-19-associated mortality data from England, we fitted multiple functional forms linking density with transmission. We projected forwards beyond lockdown to ascertain the consequences of different functional forms on infection resurgence.ResultsCOVID-19-associated mortality data from England show evidence of increasing with population density until a saturating level, after adjusting for local age distribution, deprivation, proportion of ethnic minority population and proportion of key workers among the working population. Projections from a mathematical model that accounts for this observation deviate markedly from the current status quo for SARS-CoV-2 models which either assume linearity between density and transmission (30% of models) or no relationship at all (70%). Respectively, these classical model structures over- and underestimate the delay in infection resurgence following the release of lockdown.ConclusionIdentifying saturation points for given populations and including transmission terms that account for this feature will improve model accuracy and utility for the current and future pandemics.

Economic Role of Population Density during Pandemics-A Comparative Analysis of Saudi Arabia and China

As a novel infection with relatively high contagiousness, the coronavirus disease emerged as the most pertinent threat to the global community in the twenty-first century. Due to Covid-19's severe economic impacts, the establishment of reliable determining factors can help to alleviate future pandemics. While a population density is often cited as a major determinant of infectious cases and mortality rates, there are both proponents and opponents to this claim. In this framework, the study seeks to assess the role of population density as a predictor of Covid-19 cases and deaths in Saudi Arabia and China during the Covid-19 pandemic. With high infectivity and mortality being a definitive characteristic of overpopulated regions, the authors propose that Henry Kissinger's population reduction theory can be applied as a control measure to control future pandemics and alleviate social concerns. If high-density Chinese regions are more susceptible to Covid-19 than low-density Saudi cities, the authors argue that Neo-Malthusian models can be used as a basis for reducing the impacts of the coronavirus disease on the economic growth in countries with low population density. However, the performed correlation analysis and simple linear regression produced controversial results with no clear connection between the three studied variables. By assessing population density as a determinant of health crises associated with multiple socio-economic threats and epidemiological concerns, the authors seek to reinvigorate the scholarly interest in Neo-Malthusian models as a long-term solution intended to mitigate future disasters. The authors recommend that future studies should explore additional confounding factors influencing the course and severity of infectious diseases in states with different population densities.

Adaptive Susceptible-Infectious-Removed Model for Continuous Estimation of the COVID-19 Infection Rate and Reproduction Number in the United States: Modeling Study

BACKGROUND

The dynamics of the COVID-19 pandemic vary owing to local population density and policy measures. During decision-making, policymakers consider an estimate of the effective reproduction number Rt, which is the expected number of secondary infections spread by a single infected individual.

OBJECTIVE

We propose a simple method for estimating the time-varying infection rate and the Rt.

METHODS

We used a sliding window approach with a Susceptible-Infectious-Removed (SIR) model. We estimated the infection rate from the reported cases over a 7-day window to obtain a continuous estimation of Rt. A proposed adaptive SIR (aSIR) model was applied to analyze the data at the state and county levels.

RESULTS

The aSIR model showed an excellent fit for the number of reported COVID-19 cases, and the 1-day forecast mean absolute prediction error was <2.6% across all states. However, the 7-day forecast mean absolute prediction error approached 16.2% and strongly overestimated the number of cases when the Rt was rapidly decreasing. The maximal Rt displayed a wide range of 2.0 to 4.5 across all states, with the highest values for New York (4.4) and Michigan (4.5). We found that the aSIR model can rapidly adapt to an increase in the number of tests and an associated increase in the reported cases of infection. Our results also suggest that intensive testing may be an effective method of reducing Rt.

CONCLUSIONS

The aSIR model provides a simple and accurate computational tool for continuous Rt estimation and evaluation of the efficacy of mitigation measures.

Hesitancy Toward a COVID-19 Vaccine

The scientific community has come together in a mass mobilization to combat the public health risks of COVID-19, including efforts to develop a vaccine. However, the success of any vaccine depends on the share of the population that gets vaccinated. We designed a survey experiment in which a nationally representative sample of 3,133 adults in the USA stated their intentions to vaccinate themselves and their children for COVID-19. The factors that we varied across treatments were: the stated severity and infectiousness of COVID-19 and the stated source of the risk information (White House or the Centers for Disease Control). We find that 20% of people in the USA intend to decline the vaccine. We find no statistically significant effect on vaccine intentions from the severity of COVID-19. In contrast, we find that the degree of infectiousness of the coronavirus influences vaccine intentions and that inconsistent risk messages from public health experts and elected officials may reduce vaccine uptake. However, the most important determinants of COVID-19 vaccine hesitancy seem to be distrust of the vaccine safety (including uncertainty due to vaccine novelty), as well as general vaccine avoidance, as implied by not having had a flu shot in the last two years.

Mask Wearing and Control of SARS-CoV-2 Transmission in the United States

INTRODUCTION

Cloth face coverings and surgical masks have become commonplace across the United States in response to the SARS-CoV-2 epidemic. While evidence suggests masks help curb the spread of respiratory pathogens, population level, empirical research remains limited. Face masks have quickly become a topic of public debate as government mandates have started requiring their use. Here we investigate the association between self-reported mask wearing, social distancing and community SARS-CoV-2 transmission in the United States, as well as the effect of statewide mandates on mask uptake.

METHODS

Serial cross-sectional surveys were administered June 3 through July 27, 2020 via a web platform. Surveys queried individuals' likelihood to wear a face mask to the grocery store or with family and friends. Responses (N = 378,207) were aggregated by week and state and combined with measures of the instantaneous reproductive number (R t ), social distancing proxies, respondent demographics and other potential sources of confounding. We fit multivariate logistic regression models to estimate the association between mask wearing and community transmission control (R t <1) for each state and week. Multiple sensitivity analyses were considered to corroborate findings across mask wearing definitions, R t estimators and data sources. Additionally, mask wearing in 12 states was evaluated two weeks before and after statewide mandates.

RESULTS

We find an increasing trend in mask usage across the U.S., although uptake varies by geography and demographic groups. A multivariate logistic model controlling for social distancing and other variables found a 10% increase in mask wearing was associated with a 3.53 (95% CI: 2.03, 6.43) odds of transmission control (R t <1). We also find that communities with high mask wearing and social distancing have the highest predicted probability of a controlled epidemic. These positive associations were maintained across sensitivity analyses. Following state mandates, mask wearing did not show significant statistical changes in uptake, however the positive trend of increased mask wearing over time was preserved.

CONCLUSION

Widespread utilization of face masks combined with social distancing increases the odds of SARS-CoV-2 transmission control. Mask wearing rose separately from government mask mandates, suggesting supplemental public health interventions are needed to maximize mask adoption and disrupt the spread of SARS-CoV-2, especially as social distancing measures are relaxed.