Global COVID-19 pandemic demands joint interventions for the suppression of future waves.

Emerging evidence suggests a resurgence of COVID-19 in the coming years. It is thus critical to optimize emergency response planning from a broad, integrated perspective. We developed a mathematical model incorporating climate-driven variation in community transmissions and movement-modulated spatial diffusions of COVID-19 into various intervention scenarios. We find that an intensive 8-wk intervention targeting the reduction of local transmissibility and international travel is efficient and effective. Practically, we suggest a tiered implementation of this strategy where interventions are first implemented at locations in what we call the Global Intervention Hub, followed by timely interventions in secondary high-risk locations. We argue that thinking globally, categorizing locations in a hub-and-spoke intervention network, and acting locally, applying interventions at high-risk areas, is a functional strategy to avert the tremendous burden that would otherwise be placed on public health and society.

A maximum curvature method for estimating epidemic onset of seasonal influenza in Japan.

BACKGROUND: Detecting the onset of influenza epidemic is important for epidemiological surveillance and for investigating the factors driving spatiotemporal transmission patterns. Most approaches define the epidemic onset based on thresholds, which use subjective criteria and are specific to individual surveillance systems. METHODS: We applied the empirical threshold method (ETM), together with two non-thresholding methods, including the maximum curvature method (MCM) that we proposed and the segmented regression method (SRM), to determine onsets of influenza epidemics in each prefecture of Japan, using sentinel surveillance data of influenza-like illness (ILI) from 2012/2013 through 2017/2018. Performance of the MCM and SRM was evaluated, in terms of epidemic onset, end, and duration, with those derived from the ETM using the nationwide epidemic onset indicator of 1.0 ILI case per sentinel per week. RESULTS: The MCM and SRM yielded complete estimates for each of Japan's 47 prefectures. In contrast, ETM estimates for Kagoshima during 2012/2013 and for Okinawa during all six influenza seasons, except 2013/2014, were invalid. The MCM showed better agreement in all estimates with the ETM than the SRM (R2 = 0.82, p < 0.001 vs. R2 = 0.34, p < 0.001 for epidemic onset; R2 = 0.18, p < 0.001 vs. R2 = 0.05, p < 0.001 for epidemic end; R2 = 0.28, p < 0.001 vs. R2 < 0.01, p = 0.35 for epidemic duration). Prefecture-specific thresholds for epidemic onset and end were established using the MCM. CONCLUSIONS: The Japanese national epidemic onset threshold is not applicable to all prefectures, particularly Okinawa. The MCM could be used to establish prefecture-specific epidemic thresholds that faithfully characterize influenza activity, serving as useful complements to the influenza surveillance system in Japan.

Genetic evidence for avian influenza H5N1 viral transmission along the Black Sea-Mediterranean Flyway.

The current epidemic of highly pathogenic avian influenza H5N1 virus is considered to pose a significant threat to the health of wild and domestic avian species, and even to human beings. The Black Sea-Mediterranean Flyway is one of the most important epidemic areas of H5N1. However, the epidemic along this flyway has not been fully explored. To better understand the role of hosts in the spread and evolution of H5N1 virus along the flyway, a phylogeographic study was conducted using haemagglutinin (HA) gene sequences obtained during 2005-2013. To infer phylodynamic spread in time and space, we used a flexible Bayesian statistical framework and modelled viral spatial diffusion as a continuous-time Markov-chain process along time-measured genealogies. Our results revealed that H5N1 virus isolated from wild birds showed an increase in genetic variation of HA gene from 2005-2007. The mean genetic distance of viruses isolated from poultry reached its peak in 2010, and dropped in 2011, increasing again in 2012-2013. The reconstruction of virus circulation revealed a different viral-migration network of H5N1 virus by different hosts. Western Russia constituted a link in viral migration from Russia to Europe and Africa. Cross-species transmission of H5N1 viruses predominated in the migration network of the Black Sea-Mediterranean Flyway. This might be due to the migration of birds across long distances and interaction between local poultry and migratory birds. Additionally, the short-distance spread of H5N1 viruses among poultry followed local transportation networks. Such findings will aid in developing effective disease control and prevention strategies.

Spatial, Temporal and Genetic Dynamics Characteristics of Influenza B Viruses in China, 1973-2018.

Annual influenza B virus epidemics and outbreaks cause severe influenza diseases in humans and pose a threat to public health. China is an important epidemic area of influenza B viruses. However, the spatial, temporal transmission pathways and the demography history of influenza B viruses in China remain unknown. We collected the haemagglutinin gene sequences sampled of influenza B virus in China between 1973 and 2018. A Bayesian Markov chain Monte Carlo phylogeographic discrete approach was used to infer the spatial and temporal phylodynamics of influenza B virus. The Bayesian phylogeographic analysis of influenza B viruses showed that the North subtropical and South subtropical zones are the origins of the Victoria and Yamagata lineage viruses, respectively. Furthermore, the South temperate and North subtropical zones acted as transition nodes in the Victoria lineage virus dispersion network and that the North subtropical and Mid subtropical zones acted as transition nodes in the Yamagata lineage virus dispersion network. Our findings contribute to the knowledge regarding the spatial and temporal patterns of influenza B virus outbreaks in China.

A spatialized digital database for all bird species in China.

The lack of refined spatial detail on bird distribution in China has hindered further research due to the large geographic unit (provincial level) in existing national bird distribution data. Based on multi-source bird distribution data, we built a more spatially detailed distribution database for every bird species (1,371 species) in China, covering 2,908 counties. The sources on bird distribution are grouped into six categories: Handbooks, Literature; Fauna, Avifauna; Paper; Citizen Science data by ornithologists or birders; GPS tracing; and Website data. The database contains the following records: taxonomy, distribution data, suspicious species information, and data sources. Our database recorded 835 species (61%) appearing outside the distribution range previously known. The use of provincial boundaries as the smallest geographical unit has created misleading distribution results due to geographic aggregation for most species. The new database was built based on increased observational frequency and individuals observed in previously undetected areas particularly in Western China and towards higher altitudes and latitudes. They coincided with the discovery of the range expansion of some waterfowls into Xinjiang. The dataset provides a new base for Chinese and international ornithology studies, especially for those requiring more detailed distribution information for many taxa and large-scale regional research.

Roles of Different Transport Modes in the Spatial Spread of the 2009 Influenza A(H1N1) Pandemic in Mainland China.

There is increasing concern about another influenza pandemic in China. However, the understanding of the roles of transport modes in the 2009 influenza A(H1N1) pandemic spread across mainland China is limited. Herein, we collected 127,797 laboratory-confirmed cases of influenza A(H1N1)pdm09 in mainland China from May 2009 to April 2010. Arrival days and peak days were calculated for all 340 prefectures to characterize the dissemination patterns of the pandemic. We first evaluated the effects of airports and railway stations on arrival days and peak days, and then we applied quantile regressions to quantify the relationships between arrival days and air, rail, and road travel. Our results showed that early arrival of the virus was not associated with an early incidence peak. Airports and railway stations in prefectures significantly advanced arrival days but had no significant impact on peak days. The pandemic spread across mainland China from the southeast to the northwest in two phases that were split at approximately 1 August 2009. Both air and road travel played a significant role in accelerating the spread during phases I and II, but rail travel was only significant during phase II. In conclusion, in addition to air and road travel, rail travel also played a significant role in accelerating influenza A(H1N1)pdm09 spread between prefectures. Establishing a multiscale mobility network that considers the competitive advantage of rail travel for mid to long distances is essential for understanding the influenza pandemic transmission in China.