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.

Real-time digital data of international passengers will shine in the precaution of epidemics.

International movement plays an important role in spatial spread of infectious diseases. Here, we share two successful COVID-19 interventions based on real-time digital information collected from international passengers, which have been performed in Greece and China respectively. Both of the interventions demonstrated good performance and showed the potential of real-time digital data in containing the spread. However, several key points should not be ignored when we promote similar strategies.

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.].

Long-Lasting Virus-Specific T Cell Response with Divergent Features in Self-Resolved and Chronic Hepatitis C Virus Patients 35 Years Postinfection.

Although recognized as a curable disease, the persistence of hepatitis C virus (HCV) in chronically infected patients remains a great burden for public health. T cell immune responses serve a key role in anti-HCV infection; however, the features of T cell immunity in patients after a long-term infection are not well explored. We recruited a special cohort of patients with similar genetic background and natural developing progression of disease who were infected with HCV through blood donation 35 y ago. We found that self-resolved individuals had higher levels of cytokine-secreting T cells than individuals with chronic infections, indicating HCV-specific T cell immunity could be sustained for >35 y. Meanwhile, virus-specific CD8+ T cells in chronic patients were characterized by programmed cell death-1high, TIM-3high expression, which was related to liver injury characterized by aspartate transaminase/alanine aminotransferase levels and morphopathological changes. Unexpectedly, the expression of Lymphocyte-activation gene 3 on CD8+ T cells was lower in chronic patients and negatively correlated with alanine aminotransferase/aspartate transaminase. Our findings provided new insights into HCV-specific T cell responses and may shed light on a way to figure out novel effector targets and explore a way to reverse chronic infections.

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.

Assessing the impact of COVID-19 border restrictions on dengue transmission in Yunnan Province, China: an observational epidemiological and phylogenetic analysis.

BACKGROUND: In response to the COVID-19 pandemic, China implemented strict restrictions on cross-border travel to prevent disease importation. Yunnan, a Chinese province that borders dengue-endemic countries in Southeast Asia, experienced unprecedented reduction in dengue, from 6840 recorded cases in 2019 to 260 in 2020. METHODS: Using a combination of epidemiological and virus genomic data, collected from 2013 to 2020 in Yunnan and neighbouring countries, we conduct a series of analyses to characterise the role of virus importation in driving dengue dynamics in Yunnan and assess the association between recent international travel restrictions and the decline in dengue reported in Yunnan in 2020. FINDINGS: We find strong evidence that dengue incidence between 2013-2019 in Yunnan was closely linked with international importation of cases. A 0-2 month lag in incidence not explained by seasonal differences, absence of local transmission in the winter, effective reproductive numbers < 1 (as estimated independently using genetic data) and diverse cosmopolitan dengue virus phylogenies all suggest dengue is non-endemic in Yunnan. Using a multivariate statistical model we show that the substantial decline in dengue incidence observed in Yunnan in 2020 but not in neighbouring countries is closely associated with the timing of international travel restrictions, even after accounting for other environmental drivers of dengue incidence. INTERPRETATION: We conclude that Yunnan is a regional sink for DENV lineage movement and that border restrictions may have substantially reduced dengue burden in 2020, potentially averting thousands of cases. Targeted testing and surveillance of travelers returning from high-risk areas could help to inform public health strategies to minimise or even eliminate dengue outbreaks in non-endemic settings like southern China. FUNDING: Funding for this study was provided by National Key Research and Development Program of China, Beijing Science and Technology Planning Project (Z201100005420010); Beijing Natural Science Foundation (JQ18025); Beijing Advanced Innovation Program for Land Surface Science; National Natural Science Foundation of China (82073616); Young Elite Scientist Sponsorship Program by CAST (YESS) (2018QNRC001); H.T., O.P.G. and M.U.G.K. acknowledge support from the Oxford Martin School. O.J.B was supported by a Wellcome Trust Sir Henry Wellcome Fellowship (206471/Z/17/Z). Chinese translation of the abstract (Appendix 2).

Abnormal upregulation of cardiovascular disease biomarker PLA2G7 induced by proinflammatory macrophages in COVID-19 patients.

High rate of cardiovascular disease (CVD) has been reported among patients with coronavirus disease 2019 (COVID-19). Importantly, CVD, as one of the comorbidities, could also increase the risks of the severity of COVID-19. Here we identified phospholipase A2 group VII (PLA2G7), a well-studied CVD biomarker, as a hub gene in COVID-19 though an integrated hypothesis-free genomic analysis on nasal swabs (n = 486) from patients with COVID-19. PLA2G7 was further found to be predominantly expressed by proinflammatory macrophages in lungs emerging with progression of COVID-19. In the validation stage, RNA level of PLA2G7 was identified in nasal swabs from both COVID-19 and pneumonia patients, other than health individuals. The positive rate of PLA2G7 were correlated with not only viral loads but also severity of pneumonia in non-COVID-19 patients. Serum protein levels of PLA2G7 were found to be elevated and beyond the normal limit in COVID-19 patients, especially among those re-positive patients. We identified and validated PLA2G7, a biomarker for CVD, was abnormally enhanced in COVID-19 at both nucleotide and protein aspects. These findings provided indications into the prevalence of cardiovascular involvements seen in patients with COVID-19. PLA2G7 could be a potential prognostic and therapeutic target in COVID-19.

Genetic diversity and evolution of Hantaan virus in China and its neighbors.

BACKGROUND: Hantaan virus (HTNV; family Hantaviridae, order Bunyavirales) causes hemorrhagic fever with renal syndrome (HFRS), which has raised serious concerns in Eurasia, especially in China, Russia, and South Korea. Previous studies reported genetic diversity and phylogenetic features of HTNV in different parts of China, but the analyses from the holistic perspective are rare. METHODOLOGY AND PRINCIPAL FINDINGS: To better understand HTNV genetic diversity and gene evolution, we analyzed all available complete sequences derived from the small (S) and medium (M) segments with bioinformatic tools. Eleven phylogenetic groups were defined and showed geographic clustering; 42 significant amino acid variant sites were found, and 19 of them were located in immune epitopes; nine recombinant events and eight reassortments with highly divergent sequences were found and analyzed. We found that sequences from Guizhou showed high genetic divergence, contributing to multiple lineages of the phylogenetic tree and also to the recombination and reassortment events. Bayesian stochastic search variable selection analysis revealed that Heilongjiang, Shaanxi, and Guizhou played important roles in HTNV evolution and migration; the virus may originate from Zhejiang Province in the eastern part of China; and the virus population size expanded from the 1980s to 1990s. CONCLUSIONS/SIGNIFICANCE: These findings revealed the original and evolutionary features of HTNV, which will help to illustrate hantavirus epidemic trends, thus aiding in disease control and prevention.

OASL as a Diagnostic Marker for Influenza Infection Revealed by Integrative Bioinformatics Analysis With XGBoost.

Host response biomarkers offer a promising alternative diagnostic solution for identifying acute respiratory infection (ARI) cases involving influenza infection. However, most of the published panels involve multiple genes, which is problematic in clinical settings because polymerase chain reaction (PCR)-based technology is the most widely used genomic technology in these settings, and it can only be used to measure a small number of targets. This study aimed to identify a single-gene biomarker with a high diagnostic accuracy by using integrated bioinformatics analysis with XGBoost. The gene expression profiles in dataset GSE68310 were used to construct a co-expression network using weighted correlation network analysis (WGCNA). Fourteen hub genes related to influenza infection (blue module) that were common to both the co-expression network and the protein-protein interaction network were identified. Thereafter, a single hub gene was selected using XGBoost, with feature selection conducted using recursive feature elimination with cross-validation (RFECV). The identified biomarker was oligoadenylate synthetases-like (OASL). The robustness of this biomarker was further examined using three external datasets. OASL expression profiling triggered by various infections was different enough to discriminate between influenza and non-influenza ARI infections. Thus, this study presented a workflow to identify a single-gene classifier across multiple datasets. Moreover, OASL was revealed as a biomarker that could identify influenza patients from among those with flu-like ARI. OASL has great potential for improving influenza diagnosis accuracy in ARI patients in the clinical setting.