Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data

A generalized multinomial probabilistic model for SARS-COV-2 infection prediction and public health intervention assessment in an indoor environment

SARS-CoV-2 Omicron and its sub-lineages have become the predominant variants globally since early 2022. As of January 2023, over 664 million confirmed cases and over 6.7 million deaths had been reported globally. Current infection models are limited by the need for large datasets or calibration to specific contexts, making them difficult to apply to different settings. This study aims to develop a generalized multinomial probabilistic model of airborne infection to assist public health decision-makers in evaluating the effectiveness of public health interventions (PHIs) across a broad spectrum of scenarios. The proposed model systematically incorporates group characteristics, epidemiology, viral loads, social activities, environmental conditions, and PHIs. Assumptions about social distance and contact duration that estimate infectivity during short-term group gatherings have been made. The study is differentiated from earlier works on probabilistic infection modeling in the following ways: (1) predicting new cases arising from more than one infectious person in a gathering, (2) incorporating additional key infection factors, and (3) evaluating the effectiveness of multiple PHIs on SARS-CoV-2 infection simultaneously. Although the results show that limiting group size has an impact on infection, improving ventilation has a much greater positive health impact. The proposed model is versatile and can flexibly accommodate other scenarios or airborne diseases by modifying the parameters allowing new factors to be added.

Does behavior mediate the effect of weather on SARS-CoV-2 transmission? evidence from cell-phone data

There is growing evidence that weather alters SARS-CoV-2 transmission, but it remains unclear what drives the phenomenon. One prevailing hypothesis is that people spend more time indoors in cooler weather, leading to increased spread of SARS-CoV-2 related to time spent in confined spaces and close contact with others. However, the evidence in support of that hypothesis is limited and, at times, conflicting. We use a mediation framework, and combine daily weather, COVID-19 hospital surveillance, cellphone-based mobility data and building footprints to estimate the relationship between daily indoor and outdoor weather conditions, mobility, and COVID-19 hospitalizations. We quantify the direct health impacts of weather on COVID-19 hospitalizations and the indirect effects of weather via time spent indoors away-from-home on COVID-19 hospitalizations within five Colorado counties between March 4th 2020 and January 31st 2021. We also evaluated the evidence for seasonal effect modification by comparing the results of all-season (using season as a covariate) to season-stratified models. Four weather conditions were associated with both time spent indoors away-from-home and 12-day lagged COVID-19 hospital admissions in one or more season: high minimum temperature (all-season), low maximum temperature (spring), low minimum absolute humidity (winter), and high solar radiation (all-season & winter). In our mediation analyses, we found evidence that changes in 12-day lagged hospital admissions were primarily via the direct effects of weather conditions, rather than via indirect effects by which weather changes time spent indoors away-from-home. Our findings do not support the hypothesis that weather impacted SARS-CoV-2 transmission via changes in mobility patterns during the first year of the pandemic. Rather, weather appears to have impacted SARS-CoV-2 transmission primarily via mechanisms other than human movement. We recommend further analysis of this phenomenon to determine whether these findings generalize to current SARS-CoV-2 transmission dynamics, as well as other seasonal respiratory pathogens.

The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modeling

Large-scale GPS location datasets hold immense potential for measuring human mobility and interpersonal contact, both of which are essential for data-driven epidemiology. However, despite their potential and widespread adoption during the COVID-19 pandemic, there are several challenges with these data that raise concerns regarding the validity and robustness of its applications. Here we outline two types of challenges-some related to accessing and processing these data, and some related to data quality-and propose several research directions to address them moving forward.

Impacts of human mobility on the citywide transmission dynamics of 18 respiratory viruses in pre- and post-COVID-19 pandemic years

Many studies have used mobile device location data to model SARS-CoV-2 dynamics, yet relationships between mobility behavior and endemic respiratory pathogens are less understood. We studied the effects of population mobility on the transmission of 17 endemic viruses and SARS-CoV-2 in Seattle over a 4-year period, 2018-2022. Before 2020, visits to schools and daycares, within-city mixing, and visitor inflow preceded or coincided with seasonal outbreaks of endemic viruses. Pathogen circulation dropped substantially after the initiation of COVID-19 stay-at-home orders in March 2020. During this period, mobility was a positive, leading indicator of transmission of all endemic viruses and lagging and negatively correlated with SARS-CoV-2 activity. Mobility was briefly predictive of SARS-CoV-2 transmission when restrictions relaxed but associations weakened in subsequent waves. The rebound of endemic viruses was heterogeneously timed but exhibited stronger, longer-lasting relationships with mobility than SARS-CoV-2. Overall, mobility is most predictive of respiratory virus transmission during periods of dramatic behavioral change and at the beginning of epidemic waves.

Does behavior mediate the effect of weather on SARS-CoV-2 transmission? Evidence from cell-phone data

Background

There is growing evidence that weather alters SARS-CoV-2 transmission, but it remains unclear what drives the phenomenon. One prevailing hypothesis is that people spend more time indoors in cooler weather, leading to increased spread of SARS-CoV-2 related to time spent in confined spaces and close contact with others. However, the evidence in support of that hypothesis is limited and, at times, conflicting.

Objectives

We aim to evaluate the extent to which weather impacts COVID-19 via time spent away-from-home in indoor spaces, as compared to a direct effect of weather on COVID-19 hospitalization, independent of mobility.

Methods

We use a mediation framework, and combine daily weather, COVID-19 hospital surveillance, cellphone-based mobility data and building footprints to estimate the relationship between daily indoor and outdoor weather conditions, mobility, and COVID-19 hospitalizations. We quantify the direct health impacts of weather on COVID-19 hospitalizations and the indirect effects of weather via time spent indoors away-from-home on COVID-19 hospitalizations within five Colorado counties between March 4th 2020 and January 31st 2021.

Results

We found evidence that changes in 12-day lagged hospital admissions were primarily via the direct effects of weather conditions, rather than via indirect effects by which weather changes time spent indoors away-from-home. Sensitivity analyses evaluating time at home as a mediator were consistent with these conclusions.

Discussion

Our findings do not support the hypothesis that weather impacted SARS-CoV-2 transmission via changes in mobility patterns during the first year of the pandemic. Rather, weather appears to have impacted SARS-CoV-2 transmission primarily via mechanisms other than human movement. We recommend further analysis of this phenomenon to determine whether these findings generalize to current SARS-CoV-2 transmission dynamics and other seasonal respiratory pathogens.

Implementing a provisional overarching intervention for COVID-19 monitoring and control in the Brazil-Colombia-Peru frontier

Introduction

he challenge was to provide comprehensive health resources to a remote and underserved population living in the Brazil-Colombia-Peru border, amid the most disruptive global crisis of the century.

Methods

In August 2021, Fundação Oswaldo Cruz Amazonia (FIOCRUZ Amazônia) and partner collaborators implemented an overarching provisional program for SARS-CoV-2 detection and lineages characterization, training of laboratory personnel and healthcare providers, donation of diagnostic supplies and personal protective equipment, and COVID-19 vaccination. The expedition was conducted at the Port of Tabatinga, a busy terminal with an intense flux of people arriving and departing in boats of all sizes, located in the Amazon River basin. Local government, non-profit organizations, private companies, and other stakeholders supported the intervention.

Results

The expedition was accomplished in a convergence point, where migrant workers, traders, army personnel, people living in urban areas, and people from small villages living in riversides and indigenous territories are in close and frequent contact, with widespread cross-border movement. Using a boat as a provisional lab and storage facility, the intervention provided clinical and laboratory monitoring for 891 participants; vaccination for 536 individuals; personal protective equipment for 200 healthcare providers; diagnostic supplies for 1,000 COVID-19 rapid tests; training for 42 community health agents on personal protection, rapid test execution, and pulse oximeter management; and hands-on training for four lab technicians on molecular diagnosis.

Discussion

Our experience demonstrates that multilateral initiatives can counterweigh the scarcity of health resources in underserved regions. Moreover, provisional programs can have a long-lasting effect if investments are also provided for local capacity building.

Heterogeneous changes in mobility in response to the SARS-CoV-2 Omicron BA.2 outbreak in Shanghai

The coronavirus disease 2019 (COVID-19) pandemic and the measures taken by authorities to control its spread have altered human behavior and mobility patterns in an unprecedented way. However, it remains unclear whether the population response to a COVID-19 outbreak varies within a city or among demographic groups. Here, we utilized passively recorded cellular signaling data at a spatial resolution of 1 km × 1 km for over 5 million users and epidemiological surveillance data collected during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BA.2 outbreak from February to June 2022 in Shanghai, China, to investigate the heterogeneous response of different segments of the population at the within-city level and examine its relationship with the actual risk of infection. Changes in behavior were spatially heterogenous within the city and population groups and associated with both the infection incidence and adopted interventions. We also found that males and individuals aged 30 to 59 y old traveled more frequently, traveled longer distances, and their communities were more connected; the same groups were also associated with the highest SARS-CoV-2 incidence. Our results highlight the heterogeneous behavioral change of the Shanghai population to the SARS-CoV-2 Omicron BA.2 outbreak and the effect of heterogenous behavior on the spread of COVID-19, both spatially and demographically. These findings could be instrumental for the design of targeted interventions for the control and mitigation of future outbreaks of COVID-19, and, more broadly, of respiratory pathogens.

The limits of human mobility traces to predict the spread of COVID-19: A transfer entropy approach

Mobile phone data have been widely used to model the spread of COVID-19; however, quantifying and comparing their predictive value across different settings is challenging. Their quality is affected by various factors and their relationship with epidemiological indicators varies over time. Here, we adopt a model-free approach based on transfer entropy to quantify the relationship between mobile phone-derived mobility metrics and COVID-19 cases and deaths in more than 200 European subnational regions. Using multiple data sources over a one-year period, we found that past knowledge of mobility does not systematically provide statistically significant information on COVID-19 spread. Our approach allows us to determine the best metric for predicting disease incidence in a particular location, at different spatial scales. Additionally, we identify geographic and demographic factors, such as users' coverage and commuting patterns, that explain the (non)observed relationship between mobility and epidemic patterns. Our work provides epidemiologists and public health officials with a general-not limited to COVID-19-framework to evaluate the usefulness of human mobility data in responding to epidemics.

Mass gatherings for political expression had no discernible association with the local course of the COVID-19 pandemic in the USA in 2020 and 2021

Epidemic disease can spread during mass gatherings. We assessed the impact of a type of mass gathering about which comprehensive data were available on the local-area trajectory of the COVID-19 epidemic. Here we examined five types of political event in 2020 and 2021: the US primary elections, the US Senate special election in Georgia, the gubernatorial elections in New Jersey and Virginia, Donald Trump's political rallies and the Black Lives Matter protests. Our study period encompassed over 700 such mass gatherings during multiple phases of the pandemic. We used data from the 48 contiguous states, representing 3,108 counties, and we implemented a novel extension of a recently developed non-parametric, generalized difference-in-difference estimator with a (high-quality) matching procedure for panel data to estimate the average effect of the gatherings on local mortality and other outcomes. There were no statistically significant increases in cases, deaths or a measure of epidemic transmissibility (Rt) in a 40-day period following large-scale political activities. We estimated small and statistically non-significant effects, corresponding to an average difference of -0.0567 deaths (95% CI = -0.319, 0.162) and 8.275 cases (95% CI = -1.383, 20.7) on each day for counties that held mass gatherings for political expression compared to matched control counties. In sum, there is no statistical evidence of a material increase in local COVID-19 deaths, cases or transmissibility after mass gatherings for political expression during the first 2 years of the pandemic in the USA. This may relate to the specific manner in which such activities are typically conducted.

Association of close-range contact patterns with SARS-CoV-2: a household transmission study

Background

Households are an important location for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, especially during periods when travel and work was restricted to essential services. We aimed to assess the association of close-range contact patterns with SARS-CoV-2 transmission.

Methods

We deployed proximity sensors for two weeks to measure face-to-face interactions between household members after SARS-CoV-2 was identified in the household, in South Africa, 2020-2021. We calculated the duration, frequency, and average duration of close-range proximity events with SARS-CoV-2 index cases. We assessed the association of contact parameters with SARS-CoV-2 transmission using mixed effects logistic regression accounting for index and household member characteristics.

Results

We included 340 individuals (88 SARS-CoV-2 index cases and 252 household members). On multivariable analysis, factors associated with SARS-CoV-2 acquisition were index cases with minimum C_t value <30 (aOR 16.8 95% CI 3.1-93.1) vs >35, and female contacts (aOR 2.5 95% CI 1.3-5.0). No contact parameters were associated with acquisition (aOR 1.0-1.1) for any of the duration, frequency, cumulative time in contact, or average duration parameters.

Conclusions

We did not find an association between close-range proximity events and SARS-CoV-2 household transmission. Our findings may be due to study limitations, that droplet-mediated transmission during close-proximity contacts plays a smaller role than airborne transmission of SARS-CoV-2 in the household, or due to high contact rates in households.

Funding

Wellcome Trust (Grant number 221003/Z/20/Z) in collaboration with the Foreign, Commonwealth, and Development Office, United Kingdom.

Direct indicators of social distancing effectiveness in COVID-19 outbreak stages: a correlational analysis of case contacts and population mobility in Korea

OBJECTIVES

The effectiveness of social distancing during the coronavirus disease 2019 (COVID-19) pandemic has been evaluated using the magnitude of changes in population mobility. This study aimed to investigate a direct indicator-namely, the number of close contacts per patient with confirmed COVID-19.

METHODS

From week 7, 2020 to week 43, 2021, population movement changes were calculated from the data of two Korean telecommunication companies and Google in accordance with social distancing stringency levels. Data on confirmed cases and their close contacts among residents of Gyeonggi Province, Korea were combined at each stage. Pearson correlation analysis was conducted to compare the movement data with the change in the number of contacts for each confirmed case calculated by stratification according to age group. The reference value of the population movement data was set using the value before mid-February 2020, considering each data's characteristics.

RESULTS

In the age group of 18 or younger, the number of close contacts per confirmed case decreased or increased when the stringency level was strengthened or relaxed, respectively. In adults, the correlation was relatively low, with no correlation between the change in the number of close contacts per confirmed case and the change in population movement after the commencement of vaccination for adults.

CONCLUSIONS

The effectiveness of governmental social distancing policies against COVID-19 can be evaluated using the number of close contacts per confirmed case as a direct indicator, especially for each age group. Such an analysis can facilitate policy changes for specific groups.

Future directions in human mobility science

We provide a brief review of human mobility science and present three key areas where we expect to see substantial advancements. We start from the mind and discuss the need to better understand how spatial cognition shapes mobility patterns. We then move to societies and argue the importance of better understanding new forms of transportation. We conclude by discussing how algorithms shape mobility behavior and provide useful tools for modelers. Finally, we discuss how progress on these research directions may help us address some of the challenges our society faces today.

Health disparity in the spread of COVID-19: Evidence from social distancing, risk of interactions, and access to testing

OBJECTIVE

- To identify and assess whether three major risk factors that due to differential access to flexible resources might help explain disparities in the spread of COVID-19 across communities with different socioeconomic status, including socioeconomic inequalities in social distancing, the potential risk of interpersonal interactions, and access to testing.

METHODS

Analysis uses ZIP code level weekly COVID-19 new cases, weekly population movement flows, weekly close-contact index, and weekly COVID-19 testing sites in Southern California from March 2020 to April 2021, merged with the U.S. census data to measure ZIP code level socioeconomic status and cofounders. This study first develops the measures for social distancing, the potential risk of interactions, and access to testing. Then we employ a spatial lag regression model to quantify the contributions of those factors to weekly COVID-19 case growth.

RESULTS

Results identify that, during the first COVID-19 wave, new case growth of the low-income group is two times higher than that of the high-income group. The COVID-19 case disparity widens to four times in the second COVID-19 wave. We also observed significant disparities in social distancing, the potential risk of interactions, and access to testing among communities with different socioeconomic status. In addition, all of them contribute to the disparities of COVID-19 incidences. Among them, the potential risk of interactions is the most important contributor, whereas testing accessibility contributes least. We also found that close-contact is a more effective measure of social distancing than population movements in examining the spread of COVID-19.

CONCLUSION

- This study answers critically unaddressed questions about health disparities in the spread of COVID-19 by assessing factors that might explain why the spread is different in different groups.

Behind closed doors: Protective social behavior during the COVID-19 pandemic

The success of personal non-pharmaceutical interventions as a public health strategy requires a high level of compliance from individuals in private social settings. Strategies to increase compliance in these hard-to-reach settings depend upon a comprehensive understanding of the patterns and predictors of protective social behavior. Social cognitive models of protective behavior emphasize the contribution of individual-level factors while social-ecological models emphasize the contribution of environmental factors. This study draws on 28 waves of survey data from the Understanding Coronavirus in America survey to measure patterns of adherence to two protective social behaviors-private social-distancing behavior and private masking behavior-during the COVID-19 pandemic and to assess the role individual and environmental factors play in predicting adherence. Results show that patterns of adherence fall into three categories marked by high, moderate, and low levels of adherence, with just under half of respondents exhibiting a high level of adherence. Health beliefs emerge as the single strongest predictor of adherence. All other environmental and individual-level predictors have relatively poor predictive power or primarily indirect effects.

Patterns of Infectious Disease Associated With Injection Drug Use in Massachusetts

BACKGROUND

Since 2014, multiple outbreaks of human immunodeficiency virus (HIV) among people who inject drugs have occurred across the United States along with hepatitis C virus (HCV), skin and soft tissue infections (SSTIs), and infective endocarditis (IE), creating a converging public health crisis.

METHODS

We analyzed the temporal patterns of infectious disease and overdose using a hierarchical Bayesian distributed lag logistic regression model examining the probability that a given geographic area experienced at least 1 HIV case in a given month as a function of the counts/rates of overdose, HCV, SSTI, and IE and associated medical procedures at different lagged time periods.

RESULTS

Current-month HIV is associated with increasing HCV cases, abscess incision and drainage, and SSTI cases, in distinct temporal patterns. For example, 1 additional HCV case occurring 5 and 7 months previously is associated with a 4% increase in the odds of observing at least 1 current-month HIV case in a given locale (odds ratios, 1.04 [90% credible interval {CrI}: 1.01-1.10] and 1.04 [90% CrI: 1.00-1.09]). No such associations were observed for echocardiograms, IE, or overdose.

CONCLUSIONS

Lagged associations in other infections preceding rises in current-month HIV counts cannot be described as predictive of HIV outbreaks but may point toward newly discovered epidemics of injection drug use and associated clinical sequalae, prompting clinicians to screen patients more carefully for substance use disorder and associated infections.

Close Contacts, Infected Cases, and the Trends of SARS-CoV-2 Omicron Epidemic in Shenzhen, China

(1) The overall trends of the number of daily close contacts and infected cases as well as their association during an epidemic of Omicron Variant of SARS-CoV-2 have been poorly described. (2) Methods: This study was to describe the trends during the epidemic of the Omicron variant of SARS-CoV-2 in Shenzhen, China, including the number of close contacts and infected cases as well as their ratios by days and stages (five stages). (3) Results: A total of 1128 infected cases and 80,288 close contacts were identified in Shenzhen from 13 February 2022 to 1 April 2022. Before the citywide lockdown (14 March), the number of daily close contacts and infected cases gradually increased. However, the numbers showed a decrease after the lockdown was imposed. The ratio of daily close contacts to daily infected cases ranged from 20.2:1 to 63.4:1 and reached the lowest during the lockdown period. The growth rate of daily close contacts was consistent with those of infected cases observed 6 days later to some extent. (4) Conclusions: The Omicron variant epidemic was promptly contained by tracing close contacts and taking subsequent quarantine measures.

Evidence of pandemic fatigue associated with stricter tiered COVID-19 restrictions

Despite the availability of effective vaccines against SARS-CoV-2, non-pharmaceutical interventions remain an important part of the effort to reduce viral circulation caused by emerging variants with the capability of evading vaccine-induced immunity. With the aim of striking a balance between effective mitigation and long-term sustainability, several governments worldwide have adopted systems of tiered interventions, of increasing stringency, that are calibrated according to periodic risk assessments. A key challenge remains in quantifying temporal changes in adherence to interventions, which can decrease over time due to pandemic fatigue, under such kind of multilevel strategies. Here, we examine whether there was a reduction in adherence to tiered restrictions that were imposed in Italy from November 2020 through May 2021, and in particular we assess whether temporal trends in adherence depended on the intensity of the restrictions adopted. We analyzed daily changes in movements and in residential time, combining mobility data with the restriction tier enforced in the Italian regions. Through mixed-effects regression models, we identified a general trend of reduction in adherence and an additional effect of faster waning associated with the most stringent tier. We estimated both effects being of the same order of magnitude, suggesting that adherence decreased twice as fast during the strictest tier as in the least stringent one. Our results provide a quantitative measure of behavioral responses to tiered interventions-a metric of pandemic fatigue-that can be integrated into mathematical models to evaluate future epidemic scenarios.