Interstates of Infection: Preliminary Investigations of Human Mobility Patterns in the COVID-19 Pandemic

Exploring data sources and mathematical approaches for estimating human mobility rates and implications for understanding COVID-19 dynamics: a systematic literature review

Human mobility, which refers to the movement of people from one location to another, is believed to be one of the key factors shaping the dynamics of the COVID-19 pandemic. There are multiple reasons that can change human mobility patterns, such as fear of an infection, control measures restricting movement, economic opportunities, political instability, etc. Human mobility rates are complex to estimate as they can occur on various time scales, depending on the context and factors driving the movement. For example, short-term movements are influenced by the daily work schedule, whereas long-term trends can be due to seasonal employment opportunities. The goal of the study is to perform literature review to: (i) identify relevant data sources that can be used to estimate human mobility rates at different time scales, (ii) understand the utilization of variety of data to measure human movement trends under different contexts of mobility changes, and (iii) unraveling the associations between human mobility rates and social determinants of health affecting COVID-19 disease dynamics. The systematic review of literature was carried out to collect relevant articles on human mobility. Our study highlights the use of three major sources of mobility data: public transit, mobile phones, and social surveys. The results also provides analysis of the data to estimate mobility metrics from the diverse data sources. All major factors which directly and indirectly influenced human mobility during the COVID-19 spread are explored. Our study recommends that (a) a significant balance between primitive and new estimated mobility parameters need to be maintained, (b) the accuracy and applicability of mobility data sources should be improved, (c) encouraging broader interdisciplinary collaboration in movement-based research is crucial for advancing the study of COVID-19 dynamics among scholars from various disciplines.

A standardised differential privacy framework for epidemiological modeling with mobile phone data

During the COVID-19 pandemic, the use of mobile phone data for monitoring human mobility patterns has become increasingly common, both to study the impact of travel restrictions on population movement and epidemiological modeling. Despite the importance of these data, the use of location information to guide public policy can raise issues of privacy and ethical use. Studies have shown that simple aggregation does not protect the privacy of an individual, and there are no universal standards for aggregation that guarantee anonymity. Newer methods, such as differential privacy, can provide statistically verifiable protection against identifiability but have been largely untested as inputs for compartment models used in infectious disease epidemiology. Our study examines the application of differential privacy as an anonymisation tool in epidemiological models, studying the impact of adding quantifiable statistical noise to mobile phone-based location data on the bias of ten common epidemiological metrics. We find that many epidemiological metrics are preserved and remain close to their non-private values when the true noise state is less than 20, in a count transition matrix, which corresponds to a privacy-less parameter ϵ = 0.05 per release. We show that differential privacy offers a robust approach to preserving individual privacy in mobility data while providing useful population-level insights for public health. Importantly, we have built a modular software pipeline to facilitate the replication and expansion of our framework.

Risk Factors Associated with the Spatiotemporal Spread of the SARS-CoV-2 Omicron BA.2 Variant — Shanghai Municipality, China, 2022

What is already known about this topic?

Previous studies have explored the spatial transmission patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and have assessed the associated risk factors. However, none of these studies have quantitatively described the spatiotemporal transmission patterns and risk factors for Omicron BA.2 at the micro (within-city) scale.

What is added by this report?

This study highlights the heterogeneous spread of the 2022 Omicron BA.2 epidemic in Shanghai, and identifies associations between different metrics of spatial spread at the subdistrict level and demographic and socioeconomic characteristics of the population, human mobility patterns, and adopted interventions.

What are the implications for public health practice?

Disentangling different risk factors might contribute to a deeper understanding of the transmission dynamics and ecology of coronavirus disease 2019 and an effective design of monitoring and management strategies.

Rural infection preventionists' experiences during the COVID-19 pandemic: Findings from focus groups conducted with association of professionals in infection control & epidemiology (APIC) members

Background

SARS CoV-2, the virus that causes COVID-19, was identified and quickly developed into a pandemic in spring, 2020. This event posed immense difficulties for healthcare nationally, with rural areas experiencing different challenges than other regions.

Methods

The Association of Professionals in Infection Control & Epidemiology conducted focus groups with infection preventionist (IP) members in September and October, 2020. Zoom sessions were recorded and transcribed. Content analysis was used to identify themes.

Results

In all, 38 IPs who work at a critical access hospital or a healthcare facility in a rural location participated. Major challenges identified by IPs in this study included addressing the lack of access to personal protective equipment (PPE), overwhelming workloads caused by the pandemic and multiple roles/responsibilities, inaccurate social media messages, and generalized disbelief and disregard about the pandemic among rural community members.

Conclusions

Gaps in preparedness identified in this study, such as the lack of PPE, need to be addressed to prevent occupational illness. In addition, health disparities and inaccurate beliefs about COVID-19 heard by IPs in this study need to be addressed in order to increase compliance with public health safeguards among rural community members and minimize morbidity and mortality in these regions.

Interstates of Infection: Preliminary Investigations of Human Mobility Patterns in the COVID-19 Pandemic

Purpose

The COVID‐19 pandemic has illuminated various heterogeneities between urban and rural environments in public health. The SARS‐CoV‐2 virus initially spread into the United States from international ports of entry and into urban population centers, like New York City. Over the course of the pandemic, cases emerged in more rural areas, implicating issues of transportation and mobility. Additionally, many rural areas developed into national hotspots of prevalence and transmission. Our aim was to investigate the preliminary impacts of road travel on the spread of COVID‐19. This investigation has implications for future public health mitigation efforts and travel restrictions in the United States.

Methods

County‐level COVID‐19 data were analyzed for spatiotemporal patterns in time‐to‐event distributions using animated choropleth maps. Data were obtained from The New York Times and the Bureau of the Census. The arrival event was estimated by examining the number of days between the first reported national case (January 21, 2020) and the date that each county attained a given prevalence rate. Of the 3108 coterminous US counties, 2887 were included in the analyses. Data reflect cases accumulated between January 21, 2020, and May 17, 2020.

Findings

Animations revealed that COVID‐19 was transmitted along the path of interstates. Quantitative results indicated rural–urban differences in the estimated arrival time of COVID‐19. Counties that are intersected by interstates had an earlier arrival than non‐intersected counties. The arrival time difference was the greatest in the most rural counties and implicates road travel as a factor of transmission into rural communities.

Conclusion

Human mobility via road travel introduced COVID‐19 into more rural communities. Interstate travel restrictions and road travel restrictions would have supported stronger mitigation efforts during the earlier stages of the COVID‐19 pandemic and reduced transmission via network contact.