A snapshot of a pandemic: The interplay between social isolation and COVID-19 dynamics in Brazil

Ferreira, Cláudia Pio; Marcondes, Diego; Melo, Mariana Pereira de; Oliva, Sergio; Peixoto, Cláudia; Peixoto, Pedro S.

doi:10.1016/j.patter.2021.100349

Cited by 6 publications

(5 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the evaluation of the SARS-CoV-2 pandemic several barriers to the free movement of the population were adopted, both internally and internationally. The objective was to promote social distancing and isolation ( 61 ), due to the existence of evidence that social distance resulting from the application of mobility restrictions reduces the virus transmission ( 62 ). Jia et al ( 63 ) were among the first to associate population movements with the spatiotemporal distribution in China.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal dynamics of epidemiology diseases: mobility based risk and short-term prediction modeling of COVID-19

Silva,

Viana,

Betco

et al. 2024

Front. Public Health

View full text Add to dashboard Cite

Nowadays, epidemiological modeling is applied to a wide range of diseases, communicable and non-communicable, namely AIDS, Ebola, influenza, Dengue, Malaria, Zika. More recently, in the context of the last pandemic declared by the World Health Organization (WHO), several studies applied these models to SARS-CoV-2. Despite the increasing number of researches using spatial analysis, some constraints persist that prevent more complex modeling such as capturing local epidemiological dynamics or capturing the real patterns and dynamics. For example, the unavailability of: (i) epidemiological information such as the frequency with which it is made available; (ii) sociodemographic and environmental factors (e.g., population density and population mobility) at a finer scale which influence the evolution patterns of infectious diseases; or (iii) the number of cases information that is also very dependent on the degree of testing performed, often with severe territorial disparities and influenced by context factors. Moreover, the delay in case reporting and the lack of quality control in epidemiological information is responsible for biases in the data that lead to many results obtained being subject to the ecological fallacy, making it difficult to identify causal relationships. Other important methodological limitations are the control of spatiotemporal dependence, management of non-linearity, ergodicy, among others, which can impute inconsistencies to the results. In addition to these issues, social contact, is still difficult to quantify in order to be incorporated into modeling processes. This study aims to explore a modeling framework that can overcome some of these modeling methodological limitations to allow more accurate modeling of epidemiological diseases. Based on Geographic Information Systems (GIS) and spatial analysis, our model is developed to identify group of municipalities where population density (vulnerability) has a stronger relationship with incidence (hazard) and commuting movements (exposure). Specifically, our framework shows how to operate a model over data with no clear trend or seasonal pattern which is suitable for a short-term predicting (i.e., forecasting) of cases based on few determinants. Our tested models provide a good alternative for when explanatory data is few and the time component is not available, once they have shown a good fit and good short-term forecast ability.

show abstract

Section: Discussionmentioning

confidence: 99%

Spatiotemporal dynamics of epidemiology diseases: mobility based risk and short-term prediction modeling of COVID-19

Silva,

Viana,

Betco

et al. 2024

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…This proportional value is used as a proxy for social isolation [6], recording the extent to which individuals travel outside their residence area. Higher or lower social isolation values indicate that fewer or more individuals are leaving their residence area, respectively [18]. The distribution of social isolation hexagon cells is presented in Figure 1.…”

Section: Data and Interpolationmentioning

confidence: 99%

“…Network-Based Analyses of COVID-19 One branch of literature on COVID-19 has focused on mobility networks to model the spread of the disease and assess the risks of cases and deaths. The data sources used to generate such networks range from domestic and international flight records [1,16], to cell phone mobility records and geo-located visits to places of interest [2,3,17,18]. The varying spatio-temporal granularity of the data sources used in these analyses have led to diverse outputs to identify regions at risk and explore how non-pharmaceutical interventions (NPIs) such as lockdowns impact mobility and vulnerability.…”

Section: Introductionmentioning

confidence: 99%

Impact of Network Centrality and Income on Slowing Infection Spread after Outbreaks

G.¹,

Pereira²,

Peixoto³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The COVID-19 pandemic has shed light on how the spread of infectious diseases worldwide are importantly shaped by both human mobility networks and socio-economic factors. Few studies, however, have examined the interaction of mobility networks with socio-spatial inequalities to understand the spread of infection. We introduce a novel methodology, called the Infection Delay Model, to calculate how the arrival time of an infection varies geographically, considering both effective distance-based metrics and differences in regions' capacity to isolate -a feature associated with socioeconomic inequalities. To illustrate an application of the Infection Delay Model, this paper integrates household travel survey data with cell phone mobility data from the São Paulo metropolitan region to assess the effectiveness of lockdowns to slow the spread of COVID-19. Rather than operating under the assumption that the next pandemic will begin in the same region as the last, the model estimates infection delays under every possible outbreak scenario, allowing for generalizable insights into the effectiveness of interventions to delay a region's first case. The model sheds light on how the effectiveness of lockdowns to slow the spread of disease is influenced by the interaction of mobility networks and socio-economic levels. We find that a negative relationship emerges between network centrality and the infection delay after lockdown, irrespective of income. Furthermore, for regions across all income and centrality levels, outbreaks starting in less central locations were more effectively slowed by a lockdown. Using the Infection Delay Model, this paper identifies and quantifies a new dimension of disease risk faced by those most central in a mobility network.

show abstract

“…Such inter- or intra-city movements and interactions promote economic circulation and cultural exchange between cities in urban agglomerations. 8 Consequently, understanding human mobility in urban agglomerations is important and has significant practical implications, such as controlling the spread of disease, 9 , 10 optimizing transportation hubs, 11 and assisting emergency management. 12 Over the past few decades, various models 13 , 14 , 15 , 16 , 17 , 18 , 19 have been proposed to explain the universal laws of human mobility.…”

Section: Introductionmentioning

confidence: 99%

A multi-scale unified model of human mobility in urban agglomerations

Chen,

Xu,

Chen

et al. 2023

Patterns

View full text Add to dashboard Cite

A snapshot of a pandemic: The interplay between social isolation and COVID-19 dynamics in Brazil

Cited by 6 publications

References 61 publications

Spatiotemporal dynamics of epidemiology diseases: mobility based risk and short-term prediction modeling of COVID-19

Spatiotemporal dynamics of epidemiology diseases: mobility based risk and short-term prediction modeling of COVID-19

Impact of Network Centrality and Income on Slowing Infection Spread after Outbreaks

A multi-scale unified model of human mobility in urban agglomerations

Contact Info

Product

Resources

About