Nick W. Ruktanonchai’s research while affiliated with Virginia Tech and other places

Introduction Community-level changes in population mobility can dramatically change the trajectory of any directly-transmitted infectious disease, by modifying where and between whom contact occurs. This was highlighted throughout the COVID-19 pandemic, where community response and nonpharmaceutical interventions changed the trajectory of SARS-CoV-2 spread, sometimes in unpredictable ways. Population-level changes in mobility also occur seasonally and during other significant events, such as hurricanes or earthquakes. To effectively predict the spread of future emerging directly-transmitted diseases, we should better understand how the spatial spread of infectious disease changes seasonally, and when communities are actively responding to local disease outbreaks and travel restrictions. Methods Here, we use population mobility data from Virginia spanning Aug 2019-March 2023 to simulate the spread of a hypothetical directly-transmitted disease under the population mobility patterns from various months. By comparing the spread of disease based on where the outbreak begins and the mobility patterns used, we determine the highest-risk areas and periods, and elucidate how seasonal and pandemic-era mobility patterns could change the trajectory of disease transmission. Results and discussion Through this analysis, we determine that while urban areas were at highest risk pre-pandemic, the heterogeneous nature of community response induced by SARS-CoV-2 cases meant that when outbreaks were occurring across Virginia, rural areas became relatively higher risk. Further, the months of September and January led to counties with large student populations to become particularly at risk, as population flows in and out of these counties were greatly increased with students returning to school.

The metapopulation perspective is an important conceptual framework in ecology, biogeography, and evolutionary ecology. Metapopulations are spatially distributed populations linked by dispersal. Both metapopulation models and their community and ecosystem level analogues, metacommunity and meta-ecosystem models, tend to be more stable regionally than locally and display an enhancement in abundance because of the interplay of spatio-temporal heterogeneity and dispersal (an effect that has been called the "inflationary effect"). We highlight the essential role of spatio-temporal heterogeneity in metapopulation biology, sketch empirical demonstrations of the inflationary effect, and provide a mechanistic interpretation of how the inflationary effect arises and impacts population growth and abundance. The spread of infectious disease is used to illustrate how this effect, emerging from the interplay of spatiotemporal variability and dispersal, can have serious real-world consequences. Namely, failure to recognize the full possible effects of spatio-temporal heterogeneity likely enhanced the spread of COVID-19, and a comparable lack of understanding of emergent population processes at large scales may hamper the control and eradication of other infectious diseases. We finish by noting how the effects of spatio-temporal heterogeneity, including the inflationary effect, have implicitly played roles in many traditional themes in the history of ecology. The inflationary effect is implicit in processes explored in subdisciplines as far ranging as natural enemy-victim dynamics, species coexistence, and conservation biology. Seriously confronting the complexity of spatiotemporal heterogeneity has the potential to push many of these subdisciplines forward.

Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts.

The ever-increasing pandemic and natural disasters might spatial-temporal overlap to trigger compound disasters that disrupt urban life, including human movements. In this study, we proposed a framework for data-driven analyses on mobility resilience to uncover the compound effects of COVID-19 and extreme weather events on mobility recovery across cities with varied socioeconomic contexts. The concept of suppression risk (SR) is introduced to quantify the relative risk of mobility being reduced below the pre-pandemic baseline when certain variables deviate from their normal values. By analysing daily mobility data within and between 313 Chinese cities, we consistently observed that the highest SR under outbreaks occurred at high temperatures and abnormal precipitation levels, regardless of the type of travel, incidences, and time. Specifically, extremely high temperatures (at 35°C) increased SR during outbreaks by 12.5%-120% but shortened the time for mobility recovery. Increased rainfall (at 20mm/day) added SRs by 12.5%-300%, with delayed effects reflected in cross-city movements. These compound impacts, with varying lagged responses, were aggravated in cities with high population density and low GDP levels. Our findings provide quantitative evidence to inform the design of preparedness and response strategies for enhancing urban resilience in the face of future pandemics and compound disasters.

A novel coronavirus emerged in Wuhan in late 2019 and has caused the COVID-19 pandemic announced by the World Health Organization on March 12, 2020. This study was originally conducted in January 2020 to estimate the potential risk and geographic range of COVID-19 spread within and beyond China at the early stage of the pandemic. A series of connectivity and risk analyses based on domestic and international travel networks were conducted using historical aggregated mobile phone data and air passenger itinerary data. We found that the cordon sanitaire of Wuhan was likely to have occurred during the latter stages of peak population numbers leaving the city, with travellers departing into neighbouring cities and other megacities in China. We estimated that 59,912 air passengers, of which 834 (95% uncertainty interval: 478–1349) had COVID-19 infection, travelled from Wuhan to 382 cities outside of mainland China during the two weeks prior to the city’s lockdown. Most of these destinations were located in Asia, but major hubs in Europe, the US and Australia were also prominent, with a strong correlation seen between the predicted risks of importation and the number of imported cases found. Given the limited understanding of emerging infectious diseases in the very early stages of outbreaks, our approaches and findings in assessing travel patterns and risk of transmission can help guide public health preparedness and intervention design for new COVID-19 waves caused by variants of concern and future pandemics to effectively limit transmission beyond its initial extent.

Non-pharmaceutical interventions (NPIs) and vaccination are two fundamental approaches for mitigating the coronavirus disease 2019 (COVID-19) pandemic. However, the real-world impact of NPIs versus vaccination, or a combination of both, on COVID-19 remains uncertain. To address this, we built a Bayesian inference model to assess the changing effect of NPIs and vaccination on reducing COVID-19 transmission, based on a large-scale dataset including epidemiological parameters, virus variants, vaccines, and climate factors in Europe from August 2020 to October 2021. We found that (1) the combined effect of NPIs and vaccination resulted in a 53% (95% confidence interval: 42–62%) reduction in reproduction number by October 2021, whereas NPIs and vaccination reduced the transmission by 35% and 38%, respectively; (2) compared with vaccination, the change of NPI effect was less sensitive to emerging variants; (3) the relative effect of NPIs declined 12% from May 2021 due to a lower stringency and the introduction of vaccination strategies. Our results demonstrate that NPIs were complementary to vaccination in an effort to reduce COVID-19 transmission, and the relaxation of NPIs might depend on vaccination rates, control targets, and vaccine effectiveness concerning extant and emerging variants. Non-pharmaceutical interventions (NPIs) and COVID-19 vaccination have been implemented concurrently, making their relative effects difficult to measure. Here, the authors show that effects of NPIs reduced as vaccine coverage increased, but that NPIs could still be important in the context of more transmissible variants.

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.