Community vulnerability and mobility: What matters most in spatio-temporal modeling of the COVID-19 pandemic?

Abstract

Community vulnerability is widely viewed as an important aspect to consider when modeling disease. Although COVID-19 does disproportionately impact vulnerable populations, human behavior as measured by community mobility is equally influential in understanding disease spread. In this research, we seek to understand which of four composite measures perform best in explaining disease spread and mortality, and we explore the extent to which mobility account for variance in the outcomes of interest. We compare two community mobility measures, three composite measures of community vulnerability, and one composite measure that combines vulnerability and human behavior to assess their relative feasibility in modeling the US COVID-19 pandemic. Extensions – via temporally dependent fixed effect coefficients – of the commonly used Bayesian spatio-temporal Poisson disease mapping models are implemented and compared in terms of goodness of fit as well as estimate precision and viability. A comparison of goodness of fit measures nearly unanimously suggests the human behavior-based models are superior. The duration at residence mobility measure indicates two unique and seemingly inverse relationships between mobility and the COVID-19 pandemic: the findings indicate decreased COVID-19 presence with decreased mobility early in the pandemic and increased COVID-19 presence with decreased mobility later in the pandemic. The early indication is likely influenced by a large presence of state-issued stay at home orders and self-quarantine, while the later indication likely emerges as a consequence of holiday gatherings in a country under limited restrictions. This study implements innovative statistical methods and furnishes results that challenge the generally accepted notion that vulnerability and deprivation are key to understanding disparities in health outcomes. We show that human behavior is equally, if not more important to understanding disease spread. We encourage researchers to build upon the work we start here and continue to explore how other behaviors influence the spread of COVID-19.

Spatial–temporal trends of COVID-19 infection and mortality in Sudan

“…Studies similar to this type of spatial presentation could become a guide for vaccination strategies 21 . As hypothesized, the population behaviors such as mobilization served as a superior measure for justifying the spreading of COVID-19 over time and space 12 , our study was lacking this property of studying the mobility. This study was not without limitations; all the data were obtained from the records of the Sudan federal ministry of health which contained the number of cases tested in governmental laboratories only.…”

“…The awareness of the epidemiological and geographical characteristics of COVID-19 had become particularly crucial in controlling the spread of the pandemic. The selection of proper measures for understanding and explaining the spread of this disease in order to model the spread of COVID-19 are yet to be identified 12 . A significant number of statistical models were applied for the prediction of the infection rate and the spread of cases during the pandemic 13 .…”

Exploring Health Impacts of Occupational Exposure to Carbon Monoxide in the Labour Community of Hattar Industrial Estate

“…Findings from statistical modelling in the current study indicated that dermatosis and respiratory diseases were more prevalent with a frequency and percentage of 35 and 23.3% and 25 and 16.7%, respectively, and with statistical modelling-attributable proportions-of 65.77% and 61.12%, respectively, in laborers of the Hattar industrial units who were exposed for a longer time to high concentrations of carbon monoxide as compared to the control site. The attributable proportion or health risk factor (AR) is a statistical finding which assures the association of disease with the presence of specific pollutant [37,38].…”

Population flow based spatial-temporal eigenvector filtering modeling for exploring effects of health risk factors on COVID-19

“…This might be because during the first stage of the epidemic, the total weekly increase in confirmed cases remained below 200,000, and the population under 14 was less infective and they might be less exposed to potential infected cases compared with working people ( Paul et al, 2020 ) . Additionally, during the initial stage of the pandemic, the probability of exposure to high-risk areas (e.g., workplaces and schools) might be relatively low for populations over 65 years old, as most of them are retired, which reduces their probability of gathering( Carroll & Prentice, 2021 ). However, over time, the total number of confirmed cases within 48 states was more than 2,000,000, and the number of confirmed cases increased rapidly, making more places even residential area at higher risk.…”