A Modeling Study on the Effect of Interstate Mobility Restrictions on the SARS-CoV-2 Pandemic.

Mobility is a crucial element in comprehending the possible expansion of the transmission chain in an epidemic. In the initial phases, strategies for containing cases can be directly linked to population mobility restrictions, especially when only non-pharmaceutical measures are available. During the pandemic of COVID-19 in Brazil, mobility limitation measures were strongly opposed by a large portion of the population. Hypothetically, if the population had supported such measures, the sharp rise in the number of cases could have been suppressed. In this context, computational modeling offers systematic methods for analyzing scenarios about the development of the epidemiological situation taking into account specific conditions. In this study, we examine the impacts of interstate mobility in Brazil. To do so, we develop a metapopulational model that considers both intra and intercompartmental dynamics, utilizing graph theory. We use a parameter estimation technique that allows us to infer the effective reproduction number in each state and estimate the time-varying transmission rate. This makes it possible to investigate scenarios related to mobility and quantify the effect of people moving between states and how certain measures to limit movement might reduce the impact of the pandemic. Our results demonstrate a clear association between the number of cases and mobility, which is heightened when states are closer to each other. This serves as a proof of concept and shows how reducing mobility in more heavily trafficked areas can be more effective.

Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting.

The long duration of the COVID-19 pandemic allowed for multiple bursts in the infection and death rates, the so-called epidemic waves. This complex behavior is no longer tractable by simple compartmental model and requires more sophisticated mathematical techniques for analyzing epidemic data and generating reliable forecasts. In this work, we propose a framework for analyzing complex dynamical systems by dividing the data in consecutive time-windows to be separately analyzed. We fit parameters for each time-window through an approximate Bayesian computation (ABC) algorithm, and the posterior distribution of parameters obtained for one window is used as the prior distribution for the next window. This Bayesian learning approach is tested with data on COVID-19 cases in multiple countries and is shown to improve ABC performance and to produce good short-term forecasting. Supplementary Information: The online version contains supplementary material available at 10.1007/s11071-022-07865-x.

Framework for enhancing the estimation of model parameters for data with a high level of uncertainty.

Reliable data are essential to obtain adequate simulations for forecasting the dynamics of epidemics. In this context, several political, economic, and social factors may cause inconsistencies in the reported data, which reflect the capacity for realistic simulations and predictions. In the case of COVID-19, for example, such uncertainties are mainly motivated by large-scale underreporting of cases due to reduced testing capacity in some locations. In order to mitigate the effects of noise in the data used to estimate parameters of models, we propose strategies capable of improving the ability to predict the spread of the diseases. Using a compartmental model in a COVID-19 study case, we show that the regularization of data by means of Gaussian process regression can reduce the variability of successive forecasts, improving predictive ability. We also present the advantages of adopting parameters of compartmental models that vary over time, in detriment to the usual approach with constant values.

Impacts of a delayed and slow-paced vaccination on cases and deaths during the COVID-19 pandemic: a modelling study.

In Brazil, vaccination has always cut across party political and ideological lines, which has delayed its start and brought the whole process into disrepute. Such divergences put the immunization of the population in the background and create additional hurdles beyond the pandemic, mistrust and scepticism over vaccines. We conduct a mathematical modelling study to analyse the impacts of late vaccination along with slowly increasing coverage, as well as how harmful it would be if part of the population refused to get vaccinated or missed the second dose. We analyse data from confirmed cases, deaths and vaccination in the state of Rio de Janeiro in the period between 10 March 2020 and 27 October 2021. We estimate that if the start of vaccination had been 30 days earlier, combined with efforts to drive vaccination rates up, about 31 657 deaths could have been avoided. In addition, the slow pace of vaccination and the low demand for the second dose could cause a resurgence of cases as early as 2022. Even when reaching the expected vaccination coverage for the first dose, it is still challenging to increase adherence to the second dose and maintain a high vaccination rate to avoid new outbreaks.

Assessing social distancing release strategies in Brazil and Rio de Janeiro state / Evaluación de estrategias de relajación de la distancia social para Brasil y el estado de Río de Janeiro / Avaliação de estratégias de relaxamento do distanciamento social para o Brasil e estado do Rio de Janeiro

In this technical note we discuss some measures to release social distancing and their impacts in the epidemiological sense in order to assess the effects on the projections of the COVID-19 epidemic in Brazil and, in particular, in the state of Rio de Janeiro. The analysis of possible relaxation scenarios for social distancing is a topic of great relevance to aid the estimation of the most appropriate moment for the return to normality in daily life. In this context, we discuss the importance - and possible consequences - of relaxing social distancing at an appropriate time. The results suggest that the adoption of gradual release policies of social distancing is feasible when epidemiological control is assured. On the other hand, in the absence of verification of epidemiological control, both gradual and abrupt relaxation strategies generate a substantial increase in the number of confirmed cases and deaths, in addition to evidence of a considerable increase in the time required to eradicate the disease. Therefore, in the scenario where it is not possible to guarantee epidemiological control, social distancing release policies analyzed in this research are not recommended.

En esta nota técnica discutimos algunas medidas para relajar el distanciamiento social y sus impactos en el sentido epidemiológico para evaluar los efectos en las proyecciones de la epidemia de COVID-19 en Brasil y, en particular, en el estado de Rio de Janeiro. El análisis de posibles escenarios de relajación para el distanciamiento social es un tema de gran relevancia para ayudar a estimar el momento más apropiado para el retorno a la normalidad en la vida diaria. En este contexto, discutimos la importancia, y las posibles consecuencias, de relajar el distanciamiento social en el momento adecuado. Los resultados indican que la adopción de medidas de relajación gradual de el distanciamiento social, en una situación de control epidemiológico, es factible. Por otro lado, en ausencia de verificación del control epidemiológico, las medidas de relajación gradual y abrupta generan un aumento sustancial en el número de casos confirmados y muertes, además de evidencia de un aumento considerable en el tiempo requerido para erradicar la enfermedad. Por lo tanto, en el escenario donde no es posible asegurar el control epidemiológico, no se recomiendan las medidas de relajación de el distanciamiento social estudiadas en esta investigación.

Nesta nota técnica discutimos algumas medidas de relaxamento do distanciamento social e seus impactos no sentido epidemiológico com o objetivo de avaliar os efeitos nas projeções da epidemia da COVID-19 no Brasil e, em particular, no estado do Rio de Janeiro. A análise de possíveis cenários de relaxamento do distanciamento social é tema de grande relevância para auxiliar a estimar o momento mais apropriado para o retorno à normalidade do cotidiano. Neste contexto, discutimos a importância -- e as possíveis consequências -- de realizar o relaxamento do distanciamento social em um momento adequado. Os resultados indicam que a adoção de medidas de relaxamento gradual do distanciamento social, quando em situação de controle epidemiológico, são viáveis. Por outro lado, na ausência de verificação de controle epidemiológico, tanto medidas de relaxamento gradual quanto abruptas geram substancial aumento no número de casos confirmados e óbitos, além de evidências de considerável aumento no tempo necessário para a erradicação da doença. Portanto, no cenário em que não é possível aferir o controle epidemiológico, as medidas de relaxamento do distanciamento social estudadas nesta pesquisa não são recomendadas.