Mathematical modeling and investigation on the role of demography and contact patterns in social distancing measures effectiveness in COVID-19 dissemination.

In this article, we investigate the importance of demography and contact patterns in determining the spread of COVID-19 and to the effectiveness of social distancing policies. We investigate these questions proposing an augmented epidemiological model with an age-structured model, with the population divided into susceptible (S), exposed (E), asymptomatic infectious (A), hospitalized (H), symptomatic infectious (I) and recovered individuals (R), to simulate COVID-19 dissemination. The simulations were carried out using six combinations of four types of isolation policies (work restrictions, isolation of the elderly, community distancing and school closures) and four representative fictitious countries generated over alternative demographic transition stage patterns (aged developed, developed, developing and least developed countries). We concluded that the basic reproduction number depends on the age profile and the contact patterns. The aged developed country had the lowest basic reproduction number ($R0=1.74$) due to the low contact rate among individuals, followed by the least developed country ($R0=2.00$), the developing country ($R0=2.43$) and the developed country ($R0=2.64$). Because of these differences in the basic reproduction numbers, the same intervention policies had higher efficiencies in the aged and least developed countries. Of all intervention policies, the reduction in work contacts and community distancing were the ones that produced the highest decrease in the $R0$ value, prevalence, maximum hospitalization demand and fatality rate. The isolation of the elderly was more effective in the developed and aged developed countries. The school closure was the less effective intervention policy, though its effects were not negligible in the least developed and developing countries.

Analyzing the Effect of Social Distancing Policies on Traffic at Sinchon Station, South Korea, during the COVID-19 Pandemic in 2020 and 2021.

The COVID-19 pandemic is recognized as one of the most serious global health problems, and many countries implemented lockdown measures to mitigate the effects of the crisis caused by this respiratory infectious disease. In this study, we investigated the relationship between social distancing policies and changes in traffic volume in Sinchon Station, South Korea. We used an official COVID-19 report provided by the Korea Disease Control and Prevention Agency (KCDA) and Seoul Metropolitan Government (SMG) to review social distancing policies, and the changes in traffic patterns before and during the COVID-19 pandemic between January 2020 and November 2021 were analyzed. Our study reveals that the changes in the overall traffic patterns from acceleration phases to deceleration phases of COVID-19 were related to the alert levels of social distancing policies implemented to tackle the situation resulting from the COVID-19 pandemic. Herein, we found that a significant decline in traffic volume took place from August to September 2020 (13.5−19.7%, weekday; 19.4−31.7%, weekend), from December 2020 to January 2021 (20.0%−26.6%, weekday; 26.8−34.0%, weekend), and from July to September 2021 (3.2−13.1%, weekday; 38.3−44.7%, weekend) when compared to the corresponding periods in 2019 (paired t-test; p < 0.001). The results of this study provide strong support for the effectiveness of Seoul's preemptive measures, namely, the central government's intensive social distancing campaign, in managing and reducing the impact of the pandemic situation based on the precise analysis of 10 types of facilities.

Predicting COVID-19 county-level case number trend by combining demographic characteristics and social distancing policies.

Objective: Predicting daily trends in the Coronavirus Disease 2019 (COVID-19) case number is important to support individual decisions in taking preventative measures. This study aims to use COVID-19 case number history, demographic characteristics, and social distancing policies both independently/interdependently to predict the daily trend in the rise or fall of county-level cases. Materials and Methods: We extracted 2093 features (5 from the US COVID-19 case number history, 1824 from the demographic characteristics independently/interdependently, and 264 from the social distancing policies independently/interdependently) for 3142 US counties. Using the top selected 200 features, we built 4 machine learning models: Logistic Regression, Naïve Bayes, Multi-Layer Perceptron, and Random Forest, along with 4 Ensemble methods: Average, Product, Minimum, and Maximum, and compared their performances. Results: The Ensemble Average method had the highest area-under the receiver operator characteristic curve (AUC) of 0.692. The top ranked features were all interdependent features. Conclusion: The findings of this study suggest the predictive power of diverse features, especially when combined, in predicting county-level trends of COVID-19 cases and can be helpful to individuals in making their daily decisions. Our results may guide future studies to consider more features interdependently from conventionally distinct data sources in county-level predictive models. Our code is available at: https://doi.org/10.5281/zenodo.6332944.

Who Stays at Home? The Politics of Social Distancing in Brazil, Mexico, and the United States during the COVID-19 Pandemic.

CONTEXT: Reductions in population mobility can mitigate COVID-19 virus transmission and disease-related mortality. But do social distancing policies actually change population behavior and, if so, what factors condition policy effects? METHODS: We leverage subnational variation in the stringency and timing of state-issued social distancing policies to test their effects on mobility across 109 states in Brazil, Mexico, and the United States. We also explore how conventional predictors of compliance, including political trust, socioeconomic resources, health risks, and partisanship, modify these policy effects. FINDINGS: In Brazil and the United States, stay-at-home orders and workplace closures reduced mobility, especially early in the pandemic. In Mexico, where federal intervention created greater policy uniformity, workplace closures produced the most consistent mobility reductions. Conventional explanations of compliance perform well in the United States but not in Brazil or Mexico, apart from those emphasizing socioeconomic resources. CONCLUSIONS: In addition to new directions for research on the politics of compliance, the article offers insights for policy makers on which measures are likely to elicit compliance. Our finding that workplace closure effectiveness increases with socioeconomic development suggests that cash transfers, stimulus packages, and other policies that mitigate the financial burdens of the pandemic may help reduce population mobility.

The Impact of COVID-19 Management Policies Tailored to Airborne SARS-CoV-2 Transmission: Policy Analysis.

BACKGROUND: Daily new COVID-19 cases from January to April 2020 demonstrate varying patterns of SARS-CoV-2 transmission across different geographical regions. Constant infection rates were observed in some countries, whereas China and South Korea had a very low number of daily new cases. In fact, China and South Korea successfully and quickly flattened their COVID-19 curve. To understand why this was the case, this paper investigated possible aerosol-forming patterns in the atmosphere and their relationship to the policy measures adopted by select countries. OBJECTIVE: The main research objective was to compare the outcomes of policies adopted by countries between January and April 2020. Policies included physical distancing measures that in some cases were associated with mask use and city disinfection. We investigated whether the type of social distancing framework adopted by some countries (ie, without mask use and city disinfection) led to the continual dissemination of SARS-CoV-2 (daily new cases) in the community during the study period. METHODS: We examined the policies used as a preventive framework for virus community transmission in some countries and compared them to the policies adopted by China and South Korea. Countries that used a policy of social distancing by 1-2 m were divided into two groups. The first group consisted of countries that implemented social distancing (1-2 m) only, and the second comprised China and South Korea, which implemented distancing with additional transmission/isolation measures using masks and city disinfection. Global daily case maps from Johns Hopkins University were used to provide time-series data for the analysis. RESULTS: The results showed that virus transmission was reduced due to policies affecting SARS-CoV-2 propagation over time. Remarkably, China and South Korea obtained substantially better results than other countries at the beginning of the epidemic due to their adoption of social distancing (1-2 m) with the additional use of masks and sanitization (city disinfection). These measures proved to be effective due to the atmosphere carrier potential of SARS-CoV-2 transmission. CONCLUSIONS: Our findings confirm that social distancing by 1-2 m with mask use and city disinfection yields positive outcomes. These strategies should be incorporated into prevention and control policies and be adopted both globally and by individuals as a method to fight the COVID-19 pandemic.

How many could have been saved? Effects of social distancing on COVID-19 / Quantos poderiam ter sido salvos? Efeitos do distanciamento social na COVID-19 / ¿Cuántos podrían haberse salvado? Efectos del distanciamiento social en la COVID-19

Abstract What is the effect of social distancing policies on the spread of the new coronavirus? Social distancing policies rose to prominence as most capable of containing contagion and saving lives. Our purpose in this paper is to identify the causal effect of social distancing policies on the number of confirmed cases of COVID-19 and on contagion velocity. We align our main argument with the existing scientific consensus: social distancing policies negatively affect the number of cases. To test this hypothesis, we construct a dataset with daily information on 78 affected countries in the world. We compute several relevant measures from publicly available information on the number of cases and deaths to estimate causal effects for short-term and cumulative effects of social distancing policies. We use a time-series cross-sectional matching approach to match countries' observable histories. Causal effects (ATTs and ATEs) can be extracted via a dif-in-dif estimator. Results show that social distancing policies reduce the aggregated number of cases by 4,832 on average (or 17.5/100 thousand), but only when strict measures are adopted. This effect seems to manifest from the third week onwards.

Resumo Qual o efeito das políticas de distanciamento social na disseminação do novo coronavírus? As políticas de distanciamento social ganharam destaque como as mais capazes de conter contágio e salvar vidas. Nosso objetivo neste artigo é identificar o efeito causal das políticas de distanciamento social no número de casos confirmados da COVID-19 e na velocidade de contágio. Alinhamos nosso argumento principal com o consenso científico existente: políticas de distanciamento social afetam negativamente o número de casos de contaminação. Para testar esta hipótese, construímos um banco de dados com informações diárias sobre 78 países afetados no mundo. Calculamos várias medidas relevantes a partir de informações publicamente disponíveis sobre o número de casos de infectados e mortes, a fim de estimar efeitos causais para efeitos em curto prazo e cumulativos de políticas de distanciamento social. Usamos uma abordagem de time-series cross-sectional matching a fim de parear históricos observáveis dos países. Efeitos causais (ATTs e ATEs) podem ser extraídos através de um estimador dif-in-dif. Resultados mostram que as políticas de distanciamento social reduzem o número agregado de pessoas contaminadas em 4.832 em média (ou 17,5/100 mil), mas apenas quando medidas rigorosas são adotadas. Esse efeito parece se manifestar a partir da terceira semana.

Resumen ¿Cuál es el efecto de las políticas de distanciamiento social en la diseminación del nuevo coronavirus? Las políticas de distanciamiento social salieron a la fama como las más capaces de contener el contagio y salvar vidas. Nuestro objetivo en este artículo es identificar el efecto causal de las políticas de distanciamiento social en el número de casos confirmados de COVID-19 y en la velocidad de contagio. Alineamos nuestro argumento principal con el consenso científico existente: las políticas de distanciamiento social afectan negativamente el número de casos de contaminación. Para probar esta hipótesis, construimos un banco de datos con información diaria sobre 78 países afectados. Calculamos varias medidas relevantes a partir de la información disponible públicamente sobre el número de casos de infectados y muertes para estimar los efectos causales a corto plazo y acumulativos de las políticas de distanciamiento social. Utilizamos un enfoque de time-series cross-sectional matching para emparejar los historiales observables de los países. Los efectos causales (ATT y ATE) se pueden extraer a través de un estimador dif-in-dif. Los resultados muestran que las políticas de distanciamiento social reducen el número agregado de personas contaminadas en 4.832 en media (o 17,5/100 mil), pero solo cuando se adoptan medidas estrictas. Este efecto parece manifestarse desde la tercera semana.

Anticipating the Novel Coronavirus Disease (COVID-19) Pandemic.

The COVID-19 outbreak was first declared an international public health, and it was later deemed a pandemic. In most countries, the COVID-19 incidence curve rises sharply over a short period of time, suggesting a transition from a disease-free (or low-burden disease) equilibrium state to a sustained infected (or high-burden disease) state. Such a transition is often known to exhibit characteristics of "critical slowing down." Critical slowing down can be, in general, successfully detected using many statistical measures, such as variance, lag-1 autocorrelation, density ratio, and skewness. Here, we report an empirical test of this phenomena on the COVID-19 datasets of nine countries, including India, China, and the United States. For most of the datasets, increases in variance and autocorrelation predict the onset of a critical transition. Our analysis suggests two key features in predicting the COVID-19 incidence curve for a specific country: (a) the timing of strict social distancing and/or lockdown interventions implemented and (b) the fraction of a nation's population being affected by COVID-19 at that time. Furthermore, using satellite data of nitrogen dioxide as an indicator of lockdown efficacy, we found that countries where lockdown was implemented early and firmly have been successful in reducing COVID-19 spread. These results are essential for designing effective strategies to control the spread/resurgence of infectious pandemics.

Using implementation science to mitigate worsening health inequities in the United States during the COVID-19 pandemic.

With the threat of coronavirus disease 2019 (Covid-19) enduring in the United States, effectively and equitably implementing testing, tracing, and self-isolation as key prevention and detection strategies remain critical to safely re-opening communities. As testing and tracing capacities increase, frameworks are needed to inform design and delivery to ensure their effective implementation and equitable distribution, and to strengthen community engagement in slowing and eventually stopping Covid-19 transmission. In this commentary, we highlight opportunities for integrating implementation research into planned and employed strategies in the United States to accelerate reach and effectiveness of interventions to more safely relax social distancing policies and open economies, schools, and other institutions. Implementation strategies, such as adapting evidence-based interventions based on contextual factors, promoting community engagement, and providing data audit and feedback on implementation outcomes, can support the translation of policies on testing, tracing, social distancing, and public mask use into reality. These data can demonstrate how interventions are put into practice and where adaptation in policy or practice is needed to respond to the needs of specific communities and socially vulnerable populations. Incorporating implementation research into Covid-19 policy design and translation into practice is urgently needed to mitigate the worsening health inequities in the pandemic toll and response. Applying rigorous implementation research frameworks and evaluation systems to the implementation of evidence-based interventions which are adapted to contextual factors can promote effective and equitable pandemic response and accelerate learning both among local stakeholders as well as between states to further inform their varied experiences and responses to the pandemic.

Heterogeneidades das políticas estaduais de distanciamento social diante da COVID-19: aspectos políticos e técnico-administrativos / Heterogeneidades de las políticas estatales de distanciamiento social frente a la COVID-19: aspectos políticos y técnico-administrativos / Asymmetries of state government social distancing policies in the face of COVID-19: political and technical-administrative aspects

Resumo Este artigo tem o objetivo de analisar a liderança dos governos estaduais brasileiros na implementação de políticas de distanciamento social para o enfrentamento da disseminação da COVID-19. Pressupõe-se que as políticas de distanciamento social são heterogêneas, apesar da liderança dos governos, ou seja, apresentam certo grau de assimetria nas restrições do funcionamento do comércio e de atividades com potencial de aglomeração de pessoas. Diante disso, foram combinados os debates sobre processo de produção de políticas públicas e sobre autonomia e federalismo, para investigar a influência dos fatores políticos ou técnico-administrativos nas políticas estaduais. Os seguintes procedimentos metodológicos foram utilizados: análise de conteúdo de 134 normativas estaduais; mapeamento do alinhamento político-partidário dos governadores estaduais ao presidente da República; análise dos recursos médico-hospitalares de cada unidade da federação baseada no Cadastro Nacional de Estabelecimentos de Saúde (CNES). O contexto emergencial revelou baixa coordenação interfederativa pelo governo federal, provocando uma competição entre entes federativos e forte liderança estadual na gestão da crise no âmbito local. A pesquisa mostra, por um lado, que as decisões sobre as políticas de distanciamento social não podem ser explicadas por fatores políticos; e por outro, a correspondência entre a capacidade do sistema de saúde local e o nível de rigor das políticas de distanciamento social. Conclui-se que, na atual situação de transtorno social intenso, preferiu-se a racionalidade técnica a barganhas políticas.

Resumen Este artículo tiene como objetivo analizar el liderazgo de los gobiernos de los estados brasileños en la implementación de políticas de distanciamiento social para enfrentar la propagación de la COVID-19. Se parte del supuesto de que, a pesar de la existencia de este liderazgo, las políticas de aislamiento social son heterogéneas, con cierto grado de asimetría en las restricciones al funcionamiento del comercio y de actividades con potencial de aglomeración de personas. Se combinaron debates sobre el proceso de producción de políticas públicas, autonomía y federalismo para investigar si los factores políticos o técnico-administrativos influyen en la naturaleza de estas políticas a nivel estatal. Se utilizaron los siguientes procedimientos metodológicos: análisis de contenido de 134 regulaciones de estados; mapeo del alineamiento político-partidario de los gobernadores estatales con el presidente de la República; y análisis de los recursos médicos y hospitalarios de cada unidad de la federación con base en el Registro Nacional de Establecimientos de Salud. El contexto de emergencia reveló una baja coordinación interfederativa del gobierno federal, lo que provocó la competencia entre entidades federales y el fuerte liderazgo estatal en la gestión de la crisis en ámbito local. Por un lado, el artículo presenta evidencia de que las decisiones sobre políticas de distanciamiento social no pueden explicarse por factores políticos; y, por otro, existe una correspondencia entre la capacidad del sistema de salud local y el nivel de rigor de las políticas de distanciamiento social. Se concluye que, en la actual situación de intenso desorden social, se prefirió la racionalidad técnica a las negociaciones políticas.

Abstract This article aims to analyze the leadership of Brazilian state governments on lockdown and social distancing policies to keep COVID-19 from spreading. It is assumed that the states' policies on this matter are heterogeneous, and their implementation regarding how commercial activities - and others that potentially involve a large concentration of people - is asymmetric. Therefore, the study observed the debates on policy-making processes and on autonomy and federalism to investigate the influence of political or technical-administrative factors on policies adopted at the state level in Brazil. The methodology used content analysis of 134 state norms, mapping the political-party alignment of state governors to the president, analysis of medical and hospital resources of each federation unit based on the National Register of Health Establishments. The emergency context revealed low inter-federative coordination by the federal government, competition among states, and states leadership in crisis management at the local level. The article presents evidence that state governments' leadership cannot be justified by political party alignment with the president. However, there is a correspondence between both the local health system capacity and the rigor of lockdown and social distancing policies, which indicates that, in an intense social disorder situation, technical rationality was preferable to political bargaining.

Indirect effects of COVID-19 on the environment.

This research aims to show the positive and negative indirect effects of COVID-19 on the environment, particularly in the most affected countries such as China, USA, Italy, and Spain. Our research shows that there is a significant association between contingency measures and improvement in air quality, clean beaches and environmental noise reduction. On the other hand, there are also negative secondary aspects such as the reduction in recycling and the increase in waste, further endangering the contamination of physical spaces (water and land), in addition to air. Global economic activity is expected to return in the coming months in most countries (even if slowly), so decreasing GHG concentrations during a short period is not a sustainable way to clean up our environment.