Assessing the Effect of Global Travel and Contact Restrictions on Mitigating the COVID-19 Pandemic

Comparing lagged impacts of mobility changes and environmental factors on COVID-19 waves in rural and urban India: a Bayesian spatiotemporal modelling study

Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximate (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban, and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the R t of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.

Marginal effects of public health measures and COVID-19 disease burden in China: A large-scale modelling study

China had conducted some of the most stringent public health measures to control the spread of successive SARS-CoV-2 variants. However, the effectiveness of these measures and their impacts on the associated disease burden have rarely been quantitatively assessed at the national level. To address this gap, we developed a stochastic age-stratified metapopulation model that incorporates testing, contact tracing and isolation, based on 419 million travel movements among 366 Chinese cities. The study period for this model began from September 2022. The COVID-19 disease burden was evaluated, considering 8 types of underlying health conditions in the Chinese population. We identified the marginal effects between the testing speed and reduction in the epidemic duration. The findings suggest that assuming a vaccine coverage of 89%, the Omicron-like wave could be suppressed by 3-day interval population-level testing (PLT), while it would become endemic with 4-day interval PLT, and without testing, it would result in an epidemic. PLT conducted every 3 days would not only eliminate infections but also keep hospital bed occupancy at less than 29.46% (95% CI, 22.73-38.68%) of capacity for respiratory illness and ICU bed occupancy at less than 58.94% (95% CI, 45.70-76.90%) during an outbreak. Furthermore, the underlying health conditions would lead to an extra 2.35 (95% CI, 1.89-2.92) million hospital admissions and 0.16 (95% CI, 0.13-0.2) million ICU admissions. Our study provides insights into health preparedness to balance the disease burden and sustainability for a country with a population of billions.

Post COVID-19 pandemic recovery of intracity human mobility in Wuhan: Spatiotemporal characteristic and driving mechanism

After successfully inhibiting the first wave of COVID-19 transmission through a city lockdown, Wuhan implemented a series of policies to gradually lift restrictions and restore daily activities. Existing studies mainly focus on the intercity recovery under a macroscopic view. How does the intracity mobility return to normal? Is the recovery process consistent among different subareas, and what factor affects the post-pandemic recovery? To answer these questions, we sorted out policies adopted during the Wuhan resumption, and collected the long-time mobility big data in 1105 traffic analysis zones (TAZs) to construct an observation matrix (A). We then used the nonnegative matrix factorization (NMF) method to approximate A as the product of two condensed matrices (WH). The column vectors of W matrix were visualized as five typical recovery curves to reveal the temporal change. The row vectors of H matrix were visualized to identify the spatial distribution of each recovery type, and were analyzed with variables of population, GDP, land use, and key facility to explain the recovery driving mechanisms. We found that the "staggered time" policies implemented in Wuhan effectively staggered the peak mobility of several recovery types ("staggered peak"). Besides, different TAZs had heterogeneous response intensities to these policies ("staggered area") which were closely related to land uses and key facilities. The creative policies taken by Wuhan highlight the wisdom of public health crisis management, and could provide an empirical reference for the adjustment of post-pandemic intervention measures in other cities.

How to avoid a local epidemic becoming a global pandemic

Here, we combine international air travel passenger data with a standard epidemiological model of the initial 3 mo of the COVID-19 pandemic (January through March 2020; toward the end of which the entire world locked down). Using the information available during this initial phase of the pandemic, our model accurately describes the main features of the actual global development of the pandemic demonstrated by the high degree of coherence between the model and global data. The validated model allows for an exploration of alternative policy efficacies (reducing air travel and/or introducing different degrees of compulsory immigration quarantine upon arrival to a country) in delaying the global spread of SARS-CoV-2 and thus is suggestive of similar efficacy in anticipating the spread of future global disease outbreaks. We show that a lesson from the recent pandemic is that reducing air travel globally is more effective in reducing the global spread than adopting immigration quarantine. Reducing air travel out of a source country has the most important effect regarding the spreading of the disease to the rest of the world. Based upon our results, we propose a digital twin as a further developed tool to inform future pandemic decision-making to inform measures intended to control the spread of disease agents of potential future pandemics. We discuss the design criteria for such a digital twin model as well as the feasibility of obtaining access to the necessary online data on international air travel.

Quantifying COVID-19 recovery process from a human mobility perspective: An intra-city study in Wuhan

The COVID-19 pandemic has brought huge challenges to sustainable urban and community development. Although some recovery signals and patterns have been uncovered, the intra-city recovery process remains underexploited. This study proposes a comprehensive approach to quantify COVID-19 recovery leveraging fine-grained human mobility records. Taking Wuhan, a typical COVID-19 affected megacity in China, as the study area, we identify accurate recovery phases and select appropriate recovery functions in a data-driven manner. We observe that recovery characteristics regarding duration, amplitude, and velocity exhibit notable differences among urban blocks. We also notice that the recovery process under a one-wave outbreak lasts at least 84 days and has an S-shaped form best fitted with four-parameter Logistic functions. More than half of the recovery variance can be well explained and estimated by common variables from auxiliary data, including population, economic level, and built environments. Our study serves as a valuable reference that supports data-driven recovery quantification for COVID-19 and other crises.

Personality and Motivation to Comply With COVID-19 Protective Measures in Germany

The COVID-19 pandemic strains the healthcare systems, economy, education, and social life. Governments took several protective measures and formulated behavioral guidelines to prevent individual diseases and the collapse of healthcare systems. However, individual differences in the extent of compliance with the measures are apparent. To shed more light on this issue, the present correlational study examined the joint relation of several personal characteristics to people's motivation to comply with seven protective measures. Personal characteristics included age, gender, risk perception, the Big Five, the Dark Triad, conspiracy mentality, perceived locus of control, and general affect. Protective measures included social distancing, hygiene rules, wearing face masks, using a contact-tracing app, sharing one's infection status via the app, reducing physical contacts, and vaccinations. The study ran from 10 November 2020 to 29 December 2020. Based on a sample of 1,007 German-speaking participants, bivariate correlations and multiple regression analyses showed that personal characteristics are significantly linked to the motivation to comply with these measures. However, general affect, control beliefs, and basic personality traits play only a minor role. Age and gender showed some significant associations with protective measures. In contrast, protection motivation factors, in terms of perceived severity of and vulnerability to infection, and conspiracy mentality appear to be the major correlates of adopting protective behavior. The absolute motivation to comply with the measures also shows that hygiene rules and wearing face masks receive a higher average agreement than more personally intrusive measures such as physical contact restrictions and vaccinations. These results highlight that factors that are relevant to some measures may be irrelevant to other measures. Differences in people's personal characteristics should be considered in the design and communication of measures to support social acceptance and effectiveness. In this context, cognitive variables, which can be addressed by communication and education directly, seem to be more important than general affect and relatively time-invariant personality traits.

Impacts of worldwide individual non-pharmaceutical interventions on COVID-19 transmission across waves and space

Governments worldwide have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic. However, the effect of these individual NPI measures across space and time has yet to be sufficiently assessed, especially with the increase of policy fatigue and the urge for NPI relaxation in the vaccination era. Using the decay ratio in the suppression of COVID-19 infections and multi-source big data, we investigated the changing performance of different NPIs across waves from global and regional levels (in 133 countries) to national and subnational (in the United States of America [USA]) scales before the implementation of mass vaccination. The synergistic effectiveness of all NPIs for reducing COVID-19 infections declined along waves, from 95.4% in the first wave to 56.0% in the third wave recently at the global level and similarly from 83.3% to 58.7% at the USA national level, while it had fluctuating performance across waves on regional and subnational scales. Regardless of geographical scale, gathering restrictions and facial coverings played significant roles in epidemic mitigation before the vaccine rollout. Our findings have important implications for continued tailoring and implementation of NPI strategies, together with vaccination, to mitigate future COVID-19 waves, caused by new variants, and other emerging respiratory infectious diseases.

Effects of the Enhanced Public Health Intervention during the COVID‐19 Epidemic on Respiratory and Gastrointestinal Infectious Diseases in China

The public health interventions to mitigate coronavirus disease 2019 (COVID‐19) could also potentially reduce the global activity of influenza. However, this strategy's impact on other common infectious diseases is unknown. We collected data of 10 respiratory infectious (RI) diseases, influenza‐like illnesses (ILIs), and seven gastrointestinal infectious (GI) diseases during 2015–2020 in China and applied two proportional tests to check the differences in the yearly incidence and mortality, and case‐fatality rates (CFRs) over the years 2015–2020. The results showed that the overall RI activity decreased by 7.47%, from 181.64 in 2015–2019 to 168.08 per 100 000 in 2020 (p < 0.001); however, the incidence of influenza was seen to have a 16.08% escalation (p < 0.001). In contrast, the average weekly ILI percentage and positive influenza virus rate decreased by 6.25% and 61.94%, respectively, in 2020 compared to the previous 5 years (all p < 0.001). The overall incidence of GI decreased by 45.28%, from 253.73 in 2015–2019 to 138.84 in 2020 per 100 000 (p < 0.001), and with the greatest decline seen in hand, foot, and mouth disease (HFMD) (64.66%; p < 0.001). The mortality and CFRs from RI increased by 128.49% and 146.95%, respectively, in 2020, compared to 2015–2019 (p < 0.001). However, the mortality rates and CFRs of seven GI decreased by 70.56% and 46.12%, respectively (p < 0.001). In conclusion, China's COVID‐19 elimination/containment strategy is very effective in reducing the incidence rates of RI and GI, and ILI activity, as well as the mortality and CFRs of GI diseases.

COVID‐19 elimination strategy in China decreased the activity of respiratory infectious diseases (RI) and influenza‐like illnesses.

A 16.08% escalation was seen in influenza in 2020 compared to 2015–2019.

The mortality of RI increased slightly in 2020 compared to 2015–2019.

COVID‐19 elimination strategy in China decreased dramatically the activity and mortality of gastrointestinal infectious diseases.

Global holiday datasets for understanding seasonal human mobility and population dynamics

Public and school holidays have important impacts on population mobility and dynamics across multiple spatial and temporal scales, subsequently affecting the transmission dynamics of infectious diseases and many socioeconomic activities. However, worldwide data on public and school holidays for understanding their changes across regions and years have not been assembled into a single, open-source and multitemporal dataset. To address this gap, an open access archive of data on public and school holidays in 2010-2019 across the globe at daily, weekly, and monthly timescales was constructed. Airline passenger volumes across 90 countries from 2010 to 2018 were also assembled to illustrate the usage of the holiday data for understanding the changing spatiotemporal patterns of population movements.

Modeling international mobility using roaming cell phone traces during COVID-19 pandemic

Most of the studies related to human mobility are focused on intra-country mobility. However, there are many scenarios (e.g., spreading diseases, migration) in which timely data on international commuters are vital. Mobile phones represent a unique opportunity to monitor international mobility flows in a timely manner and with proper spatial aggregation. This work proposes using roaming data generated by mobile phones to model incoming and outgoing international mobility. We use the gravity and radiation models to capture mobility flows before and during the introduction of non-pharmaceutical interventions. However, traditional models have some limitations: for instance, mobility restrictions are not explicitly captured and may play a crucial role. To overtake such limitations, we propose the COVID Gravity Model (CGM), namely an extension of the traditional gravity model that is tailored for the pandemic scenario. This proposed approach overtakes, in terms of accuracy, the traditional models by 126.9% for incoming mobility and by 63.9% when modeling outgoing mobility flows.

The impact of COVID-19 on clinical research for Neglected Tropical Diseases (NTDs): A case study of bubonic plague

BACKGROUND

Among the many collaterals of the COVID-19 pandemic is the disruption of health services and vital clinical research. COVID-19 has magnified the challenges faced in research and threatens to slow research for urgently needed therapeutics for Neglected Tropical Diseases (NTDs) and diseases affecting the most vulnerable populations. Here we explore the impact of the pandemic on a clinical trial for plague therapeutics and strategies that have been considered to ensure research efforts continue.

METHODS

To understand the impact of the COVID-19 pandemic on the trial accrual rate, we documented changes in patterns of all-cause consultations that took place before and during the pandemic at health centres in two districts of the Amoron'I Mania region of Madagascar where the trial is underway. We also considered trends in plague reporting and other external factors that may have contributed to slow recruitment.

RESULTS

During the pandemic, we found a 27% decrease in consultations at the referral hospital, compared to an 11% increase at peripheral health centres, as well as an overall drop during the months of lockdown. We also found a nation-wide trend towards reduced number of reported plague cases.

DISCUSSION

COVID-19 outbreaks are unlikely to dissipate in the near future. Declining NTD case numbers recorded during the pandemic period should not be viewed in isolation or taken as a marker of things to come. It is vitally important that researchers are prepared for a rebound in cases and, most importantly, that research continues to avoid NTDs becoming even more neglected.

Practical geospatial and sociodemographic predictors of human mobility

Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.

Impact of COVID-19 outbreaks and interventions on influenza in China and the United States

Coronavirus disease 2019 (COVID-19) was detected in China during the 2019-2020 seasonal influenza epidemic. Non-pharmaceutical interventions (NPIs) and behavioral changes to mitigate COVID-19 could have affected transmission dynamics of influenza and other respiratory diseases. By comparing 2019-2020 seasonal influenza activity through March 29, 2020 with the 2011-2019 seasons, we found that COVID-19 outbreaks and related NPIs may have reduced influenza in Southern and Northern China and the United States by 79.2% (lower and upper bounds: 48.8%-87.2%), 79.4% (44.9%-87.4%) and 67.2% (11.5%-80.5%). Decreases in influenza virus infection were also associated with the timing of NPIs. Without COVID-19 NPIs, influenza activity in China and the United States would likely have remained high during the 2019-2020 season. Our findings provide evidence that NPIs can partially mitigate seasonal and, potentially, pandemic influenza.