Can the Content of Social Networks Explain Epidemic Outbreaks?

People share and seek information online that reflects a variety of social phenomena, including concerns about health conditions. We analyze how the contents of social networks provide real-time information to monitor and anticipate policies aimed at controlling or mitigating public health outbreaks. In November 2020, we collected tweets on the COVID-19 pandemic with content ranging from safety measures, vaccination, health, to politics. We then tested different specifications of spatial econometrics models to relate the frequency of selected keywords with administrative data on COVID-19 cases and deaths. Our results highlight how mentions of selected keywords can significantly explain future COVID-19 cases and deaths in one locality. We discuss two main mechanisms potentially explaining the links we find between Twitter contents and COVID-19 diffusion: risk perception and health behavior.

Connected in health: Place-to-place commuting networks and COVID-19 spillovers.

Biweekly county COVID-19 data were linked with Longitudinal Employer-Household Dynamics data to analyze population risk exposures enabled by pre-pandemic, country-wide commuter networks. Results from fixed-effects, spatial, and computational statistical approaches showed that commuting network exposure to COVID-19 predicted an area's COVID-19 cases and deaths, indicating spillovers. Commuting spillovers between counties were independent from geographic contiguity, pandemic-time mobility, or social media ties. Results suggest that commuting connections form enduring social linkages with effects on health that can withstand mobility disruptions. Findings contribute to a growing relational view of health and place, with implications for neighborhood effects research and place-based policies.

SPATIAL MODELLING OF KEY REGIONAL-LEVEL FACTORS OF COVID-19 MORTALITY IN RUSSIA

Intensive socio-economic interactions are a prerequisite for the innovative development of the economy, but at the same time, they may lead to increased epidemiological risks. Persistent migration patterns, the socio-demographic composition of the population, income level, and employment structure by type of economic activity determine the intensity of socio-economic interactions and, therefore, the spread of COVID-19. We used the excess mortality (mortality from April 2020 to February 2021 compared to the five-year mean) as an indicator of deaths caused directly and indirectly by COVID-19. Similar to some other countries, due to irregularities and discrepancies in the reported infection rates, excess mortality is currently the only available and reliable indicator of the impact of the COVID-19 pandemic in Russia. We used the regional level data and fit regression models to identify the socio-economic factors that determined the impact of the pandemic. We used ordinary least squares as a baseline model and a selection of spatial models to account for spatial autocorrelation of dependent and independent variables as well as the error terms. Based on the comparison of AICc (corrected Akaike information criterion) and standard error values, it was found that SEM (spatial error model) is the best option with reliably significant coefficients. Our results show that the most critical factors that increase the excess mortality are the share of the elderly population and the employment structure represented by the share of employees in manufacturing (C economic activity according to European Skills, Competences, and Occupations (ESCO) v1 classification). High humidity as a proxy for temperature and a high number of retail locations per capita reduce the excess mortality. Except for the share of the elderly, most identified factors influence the opportunities and necessities of human interaction and the associated excess mortality. © 2022, Russian Geographical Society. All rights reserved.

Examining the status of forest fire emission in 2020 and its connection to COVID-19 incidents in West Coast regions of the United States.

Forest fires impact on soil, water, and biota resources. The current forest fires in the West Coast of the United States (US) profoundly impacted the atmosphere and air quality across the ecosystems and have caused severe environmental and public health burdens. Forest fire led emissions could significantly exacerbate the air pollution level and, therefore, would play a critical role if the same occurs together with any epidemic and pandemic health crisis. Limited research is done so far to examine its impact in connection to the current pandemic. As of October 21, nearly 8.2 million acres of forest area were burned, with more than 25 casualties reported so far. In-situ air pollution data were utilized to examine the effects of the 2020 forest fire on atmosphere and coronavirus (COVID-19) casualties. The spatial-temporal concentrations of particulate matter (PM2.5 and PM10) and Nitrogen Dioxide (NO2) were collected from August 1 to October 30 for 2020 (the fire year) and 2019 (the reference year). Both spatial (Multiscale Geographically Weighted Regression) and non-spatial (Negative Binomial Regression) analyses were performed to assess the adverse effects of fire emission on human health. The in-situ data-led measurements showed that the maximum increases in PM2.5, PM10, and NO2 concentrations (µg/m3) were clustered in the West Coastal fire-prone states during August 1 - October 30, 2020. The average concentration (µg/m3) of particulate matter (PM2.5 and PM10) and NO2 was increased in all the fire states severely affected by forest fires. The average PM2.5 concentrations (µg/m3) over the period were recorded as 7.9, 6.3, 5.5, and 5.2 for California, Colorado, Oregon, and Washington in 2019, increasing up to 24.9, 13.4, 25.0, and 17.0 in 2020. Both spatial and non-spatial regression models exhibited a statistically significant association between fire emission and COVID-19 incidents. Such association has been demonstrated robust and stable by a total of 30 models developed for analyzing the spatial non-stationary and local association. More in-depth research is needed to better understand the complex relationship between forest fire emission and human health.

Public efforts to reduce disease transmission implied from a spatial game.

One approach to understand people's efforts to reduce disease transmission, is to consider the effect of behaviour on case rates. In this paper we present a spatial infection-reducing game model of public behaviour, formally equivalent to a Hopfield neural network coupled to SIRS disease dynamics. Behavioural game parameters can be precisely calibrated to geographical time series of Covid-19 active case numbers, giving an implied spatial history of behaviour. This is used to investigate the effects of government intervention, quantify behaviour area by area, and measure the effect of wealth on behaviour. We also demonstrate how a delay in people's perception of risk levels can induce behavioural instability, and oscillations in infection rates.

Characterizing human mobility patterns in rural settings of sub-Saharan Africa.

Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.

Are regions equal in adversity? A spatial analysis of spread and dynamics of COVID-19 in Europe.

Often presented as a global pandemic spreading all over the world, COVID-19, however, hit not only countries but also regions differently. The objective of this paper is to focus on the spatial heterogeneity in the spread of the COVID-19 pandemic and to contribute to an understanding of the channels by which it spread, focusing on the regional socioeconomic dimension. For this, we use a dataset covering 125 European regions in 12 countries. Considering that the impact of the COVID-19 crisis differed sharply not only across countries but also across regions within the same country, the empirical strategy is based, on the one hand, on an exploratory analysis of spatial autocorrelations, which makes it possible to identify regional clusters of the disease. On the other hand, we use spatial regression models to capture the diffusion effect and the role of different families of regional factors in this process. We find that the share of older people in the population, GDP per capita, distance from achieving EU objectives, and the unemployment rate are correlated with high COVID-19 death rates. In contrast, the number of medical practitioners and hospital beds and the level of social trust are correlated with low COVID-19 death rates.

Detecting space-time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities.

The first case of COVID-19 in South America occurred in Brazil on February 25, 2020. By July 20, 2020, there were 2,118,646 confirmed cases and 80,120 confirmed deaths. To assist with the development of preventive measures and targeted interventions to combat the pandemic in Brazil, we present a geographic study to detect "active" and "emerging" space-time clusters of COVID-19. We document the relationship between relative risk of COVID-19 and mortality, inequality, socioeconomic vulnerability variables. We used the prospective space-time scan statistic to detect daily COVID-19 clusters and examine the relative risk between February 25-June 7, 2020, and February 25-July 20, 2020, in 5570 Brazilian municipalities. We apply a Generalized Linear Model (GLM) to assess whether mortality rate, GINI index, and social inequality are predictors for the relative risk of each cluster. We detected 7 "active" clusters in the first time period, being one in the north, two in the northeast, two in the southeast, one in the south, and one in the capital of Brazil. In the second period, we found 9 clusters with RR > 1 located in all Brazilian regions. The results obtained through the GLM showed that there is a significant positive correlation between the predictor variables in relation to the relative risk of COVID-19. Given the presence of spatial autocorrelation in the GLM residuals, a spatial lag model was conducted that revealed that spatial effects, and both GINI index and mortality rate were strong predictors in the increase in COVID-19 relative risk in Brazil. Our research can be utilized to improve COVID-19 response and planning in all Brazilian states. The results from this study are particularly salient to public health, as they can guide targeted intervention measures, lowering the magnitude and spread of COVID-19. They can also improve resource allocation such as tests and vaccines (when available) by informing key public health officials about the highest risk areas of COVID-19.

Long-term exposure to air-pollution and COVID-19 mortality in England: A hierarchical spatial analysis.

Recent studies suggested a link between long-term exposure to air-pollution and COVID-19 mortality. However, due to their ecological design based on large spatial units, they neglect the strong localised air-pollution patterns, and potentially lead to inadequate confounding adjustment. We investigated the effect of long-term exposure to NO2 and PM2.5 on COVID-19 mortality in England using high geographical resolution. In this nationwide cross-sectional study in England, we included 38,573 COVID-19 deaths up to June 30, 2020 at the Lower Layer Super Output Area level (n = 32,844 small areas). We retrieved averaged NO2 and PM2.5 concentration during 2014-2018 from the Pollution Climate Mapping. We used Bayesian hierarchical models to quantify the effect of air-pollution while adjusting for a series of confounding and spatial autocorrelation. We find a 0.5% (95% credible interval: -0.2%, 1.2%) and 1.4% (95% CrI: -2.1%, 5.1%) increase in COVID-19 mortality risk for every 1 µg/m3 increase in NO2 and PM2.5 respectively, after adjusting for confounding and spatial autocorrelation. This corresponds to a posterior probability of a positive effect equal to 0.93 and 0.78 respectively. The spatial relative risk at LSOA level revealed strong patterns, similar for the different pollutants. This potentially captures the spread of the disease during the first wave of the epidemic. Our study provides some evidence of an effect of long-term NO2 exposure on COVID-19 mortality, while the effect of PM2.5 remains more uncertain.