Estimating the contribution of setting-specific contacts to SARS-CoV-2 transmission using digital contact tracing data.

While many countries employed digital contact tracing to contain the spread of SARS-CoV-2, the contribution of cospace-time interaction (i.e., individuals who shared the same space and time) to transmission and to super-spreading in the real world has seldom been systematically studied due to the lack of systematic sampling and testing of contacts. To address this issue, we utilized data from 2230 cases and 220,878 contacts with detailed epidemiological information during the Omicron outbreak in Beijing in 2022. We observed that contact number per day of tracing for individuals in dwelling, workplace, cospace-time interactions, and community settings could be described by gamma distribution with distinct parameters. Our findings revealed that 38% of traced transmissions occurred through cospace-time interactions whilst control measures were in place. However, using a mathematical model to incorporate contacts in different locations, we found that without control measures, cospace-time interactions contributed to only 11% (95%CI: 10%-12%) of transmissions and the super-spreading risk for this setting was 4% (95%CI: 3%-5%), both the lowest among all settings studied. These results suggest that public health measures should be optimized to achieve a balance between the benefits of digital contact tracing for cospace-time interactions and the challenges posed by contact tracing within the same setting.

Strong and consistent effects of waterbird composition on HPAI H5 occurrences across Europe.

Since 2014, highly pathogenic avian influenza (HPAI) H5 viruses of clade 2.3.4.4 have been dominating the outbreaks across Europe, causing massive deaths among poultry and wild birds. However, the factors shaping these broad-scale outbreak patterns, especially those related to waterbird community composition, remain unclear. In particular, we do not know whether these risk factors differ from those of other H5 clades. Addressing this knowledge gap is important for predicting and preventing future HPAI outbreaks. Using extensive waterbird survey datasets from about 6883 sites, we here explored the effect of waterbird community composition on HPAI H5Nx (clade 2.3.4.4) spatial patterns in the 2016/2017 and 2020/2021 epidemics in Europe, and compared it with the 2005/2006 HPAI H5N1 (clade 2.2) epidemic. We showed that HPAI H5 occurrences in wild birds in the three epidemics were strongly associated with very similar waterbird community attributes, which suggested that, in nature, similar interspecific transmission processes operate between the HPAI H5 subtypes or clades. Importantly, community phylogenetic diversity consistently showed a negative association with H5 occurrence in all three epidemics, suggesting a dilution effect of phylogenetic diversity. In contrast, waterbird community variables showed much weaker associations with HPAI H5Nx occurrence in poultry. Our results demonstrate that models based on previous epidemics can predict future HPAI H5 patterns in wild birds, implying that it is important to include waterbird community factors in future HPAI studies to predict outbreaks and improve surveillance activities.

An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere.

Corona virus disease 2019 (COVID-19) has exerted a profound adverse impact on human health. Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people. Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks. Monitoring of pathogenic microorganisms in the air, especially in densely populated areas, may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage. The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation, allocate health resources, and formulate epidemic response policies. This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission, which lays a theoretical foundation for the monitoring and prediction of epidemic development. Secondly, the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized. Subsequently, this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology, atmospheric sciences, environmental sciences, sociology, demography, etc. By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere, this review proposes suggestions for epidemic response, namely, the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

Indian Ocean temperature anomalies predict long-term global dengue trends.

Despite identifying El Niño events as a factor in dengue dynamics, predicting the oscillation of global dengue epidemics remains challenging. Here, we investigate climate indicators and worldwide dengue incidence from 1990 to 2019 using climate-driven mechanistic models. We identify a distinct indicator, the Indian Ocean basin-wide (IOBW) index, as representing the regional average of sea surface temperature anomalies in the tropical Indian Ocean. IOBW is closely associated with dengue epidemics for both the Northern and Southern hemispheres. The ability of IOBW to predict dengue incidence likely arises as a result of its effect on local temperature anomalies through teleconnections. These findings indicate that the IOBW index can potentially enhance the lead time for dengue forecasts, leading to better-planned and more impactful outbreak responses.

Synchrony of Bird Migration with Global Dispersal of Avian Influenza Reveals Exposed Bird Orders.

Highly pathogenic avian influenza virus (HPAIV) A H5, particularly clade 2.3.4.4, has caused worldwide outbreaks in domestic poultry, occasional spillover to humans, and increasing deaths of diverse species of wild birds since 2014. Wild bird migration is currently acknowledged as an important ecological process contributing to the global dispersal of HPAIV H5. However, this mechanism has not been quantified using bird movement data from different species, and the timing and location of exposure of different species is unclear. We sought to explore these questions through phylodynamic analyses based on empirical data of bird movement tracking and virus genome sequences of clade 2.3.4.4 and 2.3.2.1. First, we demonstrate that seasonal bird migration can explain salient features of the global dispersal of clade 2.3.4.4. Second, we detect synchrony between the seasonality of bird annual cycle phases and virus lineage movements. We reveal the differing exposed bird orders at geographical origins and destinations of HPAIV H5 clade 2.3.4.4 lineage movements, including relatively under-discussed orders. Our study provides a phylodynamic framework that links the bird movement ecology and genomic epidemiology of avian influenza; it highlights the importance of integrating bird behavior and life history in avian influenza studies.

Disentangling local and global climate drivers in the population dynamics of mosquito-borne infections.

Identifying climate drivers is essential to understand and predict epidemics of mosquito-borne infections whose population dynamics typically exhibit seasonality and multiannual cycles. Which climate covariates to consider varies across studies, from local factors such as temperature to remote drivers such as the El Niño-Southern Oscillation. With partial wavelet coherence, we present a systematic investigation of nonstationary associations between mosquito-borne disease incidence and a given climate factor while controlling for another. Analysis of almost 200 time series of dengue and malaria around the globe at different geographical scales shows a systematic effect of global climate drivers on interannual variability and of local ones on seasonality. This clear separation of time scales of action enhances detection of climate drivers and indicates those best suited for building early-warning systems.

Association of vaccination, international travel, public health and social measures with lineage dynamics of SARS-CoV-2.

Continually emerging SARS-CoV-2 variants of concern that can evade immune defenses are driving recurrent epidemic waves of COVID-19 globally. However, the impact of measures to contain the virus and their effect on lineage diversity dynamics are poorly understood. Here, we jointly analyzed international travel, public health and social measures (PHSM), COVID-19 vaccine rollout, SARS-CoV-2 lineage diversity, and the case growth rate (GR) from March 2020 to September 2022 across 63 countries. We showed that despite worldwide vaccine rollout, PHSM are effective in mitigating epidemic waves and lineage diversity. An increase of 10,000 monthly travelers in a single country-to-country route between endemic countries corresponds to a 5.5% (95% CI: 2.9 to 8.2%) rise in local lineage diversity. After accounting for PHSM, natural immunity from previous infections, and waning immunity, we discovered a negative association between the GR of cases and adjusted vaccine coverage (AVC). We also observed a complex relationship between lineage diversity and vaccine rollout. Specifically, we found a significant negative association between lineage diversity and AVC at both low and high levels but not significant at the medium level. Our study deepens the understanding of population immunity and lineage dynamics for future pandemic preparedness and responsiveness.

Optimization of Population-Level Testing, Contact Tracing, and Isolation in Emerging COVID-19 Outbreaks: a Mathematical Modeling Study - Tonghua City and Beijing Municipality, China, 2021-2022.

Introduction: The transmissibility of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant poses challenges for the existing measures containing the virus in China. In response, this study investigates the effectiveness of population-level testing (PLT) and contact tracing (CT) to help curb coronavirus disease 2019 (COVID-19) resurgences in China. Methods: Two transmission dynamic models (i.e. with and without age structure) were developed to evaluate the effectiveness of PLT and CT. Extensive simulations were conducted to optimize PLT and CT strategies for COVID-19 control and surveillance. Results: Urban Omicron resurgences can be controlled by multiple rounds of PLT, supplemented by CT - as long as testing is frequent. This study also evaluated the time needed to detect COVID-19 cases for surveillance under different routine testing rates. The results show that there is a 90% probability of detecting COVID-19 cases within 3 days through daily testing. Otherwise, it takes around 7 days to detect COVID-19 cases at a 90% probability level if biweekly testing is used. Routine testing applied to the age group 21-60 for COVID-19 surveillance would achieve similar performance to that applied to all populations. Discussion: Our analysis evaluates potential PLT and CT strategies for COVID-19 control and surveillance.

High-Resolution Data on Human Behavior for Effective COVID-19 Policy-Making - Wuhan City, Hubei Province, China, January 1-February 29, 2020.

Introduction: High-resolution data is essential for understanding the complexity of the relationship between the spread of coronavirus disease 2019 (COVID-19), resident behavior, and interventions, which could be used to inform policy responses for future prevention and control. Methods: We obtained high-resolution human mobility data and epidemiological data at the community level. We propose a metapopulation Susceptible-Exposed-Presymptomatic-Infectious-Removal (SEPIR) compartment model to utilize the available data and explore the internal driving forces of COVID-19 transmission dynamics in the city of Wuhan. Additionally, we will assess the effectiveness of the interventions implemented in the smallest administrative units (subdistricts) during the lockdown. Results: In the Wuhan epidemic of March 2020, intra-subdistrict transmission caused 7.6 times more infections than inter-subdistrict transmission. After the city was closed, this ratio increased to 199 times. The main transmission path was dominated by population activity during peak evening hours. Discussion: Restricting the movement of people within cities is an essential measure for controlling the spread of COVID-19. However, it is difficult to contain intra-street transmission solely through city-wide mobility restriction policies. This can only be accomplished by quarantining communities or buildings with confirmed cases, and conducting mass nucleic acid testing and enforcing strict isolation protocols for close contacts.

Bidirectional Movement of Emerging H5N8 Avian Influenza Viruses Between Europe and Asia via Migratory Birds Since Early 2020.

Migratory birds play a critical role in the rapid spread of highly pathogenic avian influenza (HPAI) H5N8 virus clade 2.3.4.4 across Eurasia. Elucidating the timing and pattern of virus transmission is essential therefore for understanding the spatial dissemination of these viruses. In this study, we surveyed >27,000 wild birds in China, tracked the year-round migration patterns of 20 bird species across China since 2006, and generated new HPAI H5N8 virus genomic data. Using this new data set, we investigated the seasonal transmission dynamics of HPAI H5N8 viruses across Eurasia. We found that introductions of HPAI H5N8 viruses to different Eurasian regions were associated with the seasonal migration of wild birds. Moreover, we report a backflow of HPAI H5N8 virus lineages from Europe to Asia, suggesting that Europe acts as both a source and a sink in the global HPAI virus transmission network.

Evolutionary and genomic insights into the long-term colonization of Shigella flexneri in animals.

The enteroinvasive bacterium Shigella flexneri is known as a highly host-adapted human pathogen. There had been no known other reservoirs reported until recently. Here 34 isolates obtained from animals (yaks, dairy cows and beef cattle) from 2016 to 2017 and 268 human S. flexneri isolates from China were sequenced to determine the relationships between animal and human isolates and infer the evolutionary history of animal-associated S. flexneri. The 18 animal isolates (15 yak and 3 beef cattle isolates) in PG1 were separated into 4 lineages, and the 16 animal isolates (1 yak, 5 beef cattle and 10 dairy cow isolates) in PG3 were clustered in 8 lineages. The most recent human isolates from China belonged to PG3 whereas Chinese isolates from the 1950s-1960s belonged to PG1. PG1 S. flexneri may has been transmitted to the yaks during PG1 circulation in the human population in China and has remained in the yak population since, while PG3 S. flexneri in animals were likely recent transmissions from the human population. Increased stability of the large virulence plasmid and acquisition of abundant antimicrobial resistance determinants may have enabled PG3 to expand globally and replaced PG1 in China. Our study confirms that animals may act as a reservoir for S. flexneri. Genomic analysis revealed the evolutionary history of multiple S. flexneri lineages in animals and humans in China. However, further studies are required to determine the public health threat of S. flexneri from animals.

Performance of TB-LAMP in the Diagnosis of Tuberculous Empyema Using Samples Obtained From Pleural Decortication.

Purpose: To evaluate the performance of TB-LAMP in the diagnosis of TB empyema using pleural tissue specimens obtained during pleural decortication. Methods: Using the clinical records and the different diagnostic test results of patients who underwent pleural decortication in a TB-designated hospital over 3.5 years, we calculated the sensitivity, specificity positive predictive, and negative predictive values of the pathology, MGIT 960 culture, and TB-LAMP obtained by using pleural tissue specimens against the etiologic diagnosis and composite clinical reference standard (CCRS) as the reference standards. Result: A total of 304 patients' records were extracted. All these patients had gone through pleural decortication. When the etiologic diagnosis was used as the reference, the sensitivity of TB-LAMP in identifying TB empyema was 77.8% (compared to 10.6% of MGIT 960 P < 0.05). The sensitivity of MGIT 960, pathology, and TB-LAMP was 8.2%, 77.7%, and 67.2% against CCRS as the reference; and the specificity of the three was 100.0, 100.0, and 96.2% against the same standard. A combination of pathology and TB-LAMP would increase the sensitivity and specificity to 84.7 and 96.0%. Using TB-LAMP to diagnose TB empyema using pleural tissue samples obtained from pleural decortication was faster with satisfactory performance. Conclusion: TB-LAMP has great potential in faster and more accurate diagnosis of TB empyema. Our findings provide insights for optimizing diagnostic algorithms for TB empyema.

Malaria elimination on Hainan Island despite climate change.

Background: Rigorous assessment of the effect of malaria control strategies on local malaria dynamics is a complex but vital step in informing future strategies to eliminate malaria. However, the interactions between climate forcing, mass drug administration, mosquito control and their effects on the incidence of malaria remain unclear. Methods: Here, we analyze the effects of interventions on the transmission dynamics of malaria (Plasmodium vivax and Plasmodium falciparum) on Hainan Island, China, controlling for environmental factors. Mathematical models were fitted to epidemiological data, including confirmed cases and population-wide blood examinations, collected between 1995 and 2010, a period when malaria control interventions were rolled out with positive outcomes. Results: Prior to the massive scale-up of interventions, malaria incidence shows both interannual variability and seasonality, as well as a strong correlation with climatic patterns linked to the El Nino Southern Oscillation. Based on our mechanistic model, we find that the reduction in malaria is likely due to the large scale rollout of insecticide-treated bed nets, which reduce the infections of P. vivax and P. falciparum malaria by 93.4% and 35.5%, respectively. Mass drug administration has a greater contribution in the control of P. falciparum (54.9%) than P. vivax (5.3%). In a comparison of interventions, indoor residual spraying makes a relatively minor contribution to malaria control (1.3%-9.6%). Conclusions: Although malaria transmission on Hainan Island has been exacerbated by El Nino Southern Oscillation, control methods have eliminated both P. falciparum and P. vivax malaria from this part of China.

Erratum: Author Correction: Malaria elimination on Hainan Island despite climate change.

[This corrects the article DOI: 10.1038/s43856-022-00073-z.].

The relationship between rising temperatures and malaria incidence in Hainan, China, from 1984 to 2010: a longitudinal cohort study.

BACKGROUND: The influence of rising global temperatures on malaria dynamics and distribution remains controversial, especially in central highland regions. We aimed to address this subject by studying the spatiotemporal heterogeneity of malaria and the effect of climate change on malaria transmission over 27 years in Hainan, an island province in China. METHODS: For this longitudinal cohort study, we used a decades-long dataset of malaria incidence reports from Hainan, China, to investigate the pattern of malaria transmission in Hainan relative to temperature and the incidence at increasing altitudes. Climatic data were obtained from the local meteorological stations in Hainan during 1984-2010 and the WorldClim dataset. A temperature-dependent R0 model and negative binomial generalised linear model were used to decipher the relationship between climate factors and malaria incidence in the tropical region. FINDINGS: Over the past few decades, the annual peak incidence has appeared earlier in the central highland regions but later in low-altitude regions in Hainan, China. Results from the temperature-dependent model showed that these long-term changes of incidence peak timing are linked to rising temperatures (of about 1·5°C). Further, a 1°C increase corresponds to a change in cases of malaria from -5·6% (95% CI -4·5 to -6·6) to -9·2% (95% CI -7·6 to -10·9) from the northern plain regions to the central highland regions during the rainy season. In the dry season, the change in cases would be 4·6% (95% CI 3·7 to 5·5) to 11·9% (95% CI 9·8 to 14·2) from low-altitude areas to high-altitude areas. INTERPRETATION: Our study empirically supports the idea that increasing temperatures can generate opposing effects on malaria dynamics for lowland and highland regions. This should be further investigated and incorporated into future modelling, disease burden calculations, and malaria control, with attention for central highland regions under climate change. FUNDING: Scientific and Technological Innovation 2030: Major Project of New Generation Artificial Intelligence, National Natural Science Foundation of China, Beijing Natural Science Foundation, National Key Research and Development Program of China, Young Elite Scientist Sponsorship Program by CAST, Research on Key Technologies of Plague Prevention and Control in Inner Mongolia Autonomous Region, and Beijing Advanced Innovation Program for Land Surface Science.

Measuring the effects of COVID-19-related disruption on dengue transmission in southeast Asia and Latin America: a statistical modelling study.

BACKGROUND: The COVID-19 pandemic has resulted in unprecedented disruption to society, which indirectly affects infectious disease dynamics. We aimed to assess the effects of COVID-19-related disruption on dengue, a major expanding acute public health threat, in southeast Asia and Latin America. METHODS: We assembled data on monthly dengue incidence from WHO weekly reports, climatic data from ERA5, and population variables from WorldPop for 23 countries between January, 2014 and December, 2019 and fit a Bayesian regression model to explain and predict seasonal and multi-year dengue cycles. We compared model predictions with reported dengue data January to December, 2020, and assessed if deviations from projected incidence since March, 2020 are associated with specific public health and social measures (from the Oxford Coronavirus Government Response Tracer database) or human movement behaviours (as measured by Google mobility reports). FINDINGS: We found a consistent, prolonged decline in dengue incidence across many dengue-endemic regions that began in March, 2020 (2·28 million cases in 2020 vs 4·08 million cases in 2019; a 44·1% decrease). We found a strong association between COVID-19-related disruption (as measured independently by public health and social measures and human movement behaviours) and reduced dengue risk, even after taking into account other drivers of dengue cycles including climatic and host immunity (relative risk 0·01-0·17, p<0·01). Measures related to the closure of schools and reduced time spent in non-residential areas had the strongest evidence of association with reduced dengue risk, but high collinearity between covariates made specific attribution challenging. Overall, we estimate that 0·72 million (95% CI 0·12-1·47) fewer dengue cases occurred in 2020 potentially attributable to COVID-19-related disruption. INTERPRETATION: In most countries, COVID-19-related disruption led to historically low dengue incidence in 2020. Continuous monitoring of dengue incidence as COVID-19-related restrictions are relaxed will be important and could give new insights into transmission processes and intervention options. FUNDING: National Key Research and Development Program of China and the Medical Research Council.

Evaluating the effectiveness of control measures in multiple regions during the early phase of the COVID-19 pandemic in 2020.

The number of COVID-19 confirmed cases rapidly grew since the SARS-CoV-2 virus was identified in late 2019. Due to the high transmissibility of this virus, more countries are experiencing the repeated waves of the COVID-19 pandemic. However, with limited manufacturing and distribution of vaccines, control measures might still be the most critical measures to contain outbreaks worldwide. Therefore, evaluating the effectiveness of various control measures is necessary to inform policymakers and improve future preparedness. In addition, there is an ongoing need to enhance our understanding of the epidemiological parameters and the transmission patterns for a better response to the COVID-19 pandemic. This review focuses on how various models were applied to guide the COVID-19 response by estimating key epidemiologic parameters and evaluating the effectiveness of control measures. We also discuss the insights obtained from the prediction of COVID-19 trajectories under different control measures scenarios.