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.

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.

Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome.

Human microbiome research is helped by the characterization of microbial networks, as these may reveal key microbes that can be targeted for beneficial health effects. Prevailing methods of microbial network characterization are based on measures of association, often applied to limited sampling points in time. Here, we demonstrate the potential of wavelet clustering, a technique that clusters time series based on similarities in their spectral characteristics.

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

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

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 ( and ) on Hainan Island, China, controlling for environmental factors.

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.

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.

Dynamics of the COVID-19 epidemic in Ireland under mitigation.

Background: In Ireland and across the European Union the COVID-19 epidemic waves, driven mainly by the emergence of new variants of the SARS-CoV-2 have continued their course, despite various interventions from governments. Public health interventions continue in their attempts to control the spread as they wait for the planned significant effect of vaccination.

Methods: To tackle this challenge and the observed non-stationary aspect of the epidemic we used a modified SEIR stochastic model with time-varying parameters, following Brownian process.

A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic.

The effective reproduction number Reff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. However, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic.

Stochastic Epidemic Models inference and diagnosis with Poisson Random Measure Data Augmentation.

We present a new Bayesian inference method for compartmental models that takes into account the intrinsic stochasticity of the process. We show how to formulate a SIR-type Markov jump process as the solution of a stochastic differential equation with respect to a Poisson Random Measure (PRM), and how to simulate the process trajectory deterministically from a parameter value and a PRM realization. This forms the basis of our Data Augmented MCMC, which consists of augmenting parameter space with the unobserved PRM value.

Parallel trends in the transmission of SARS-CoV-2 and retail/recreation and public transport mobility during non-lockdown periods.

Recent literature strongly supports the hypothesis that mobility restriction and social distancing play a crucial role in limiting the transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). During the first wave of the coronavirus disease 2019 (COVID-19) pandemic, it was shown that mobility restriction reduced transmission significantly. This study found that, in the period between the first two waves of the COVID-19 pandemic, there was high positive correlation between trends in the transmission of SARS-CoV-2 and mobility.

Ecological Dynamics: Integrating Empirical, Statistical, and Analytical Methods.

Understanding ecological processes and predicting long-term dynamics are ongoing challenges in ecology. To address these challenges, we suggest an approach combining mathematical analyses and Bayesian hierarchical statistical modeling with diverse data sources. Novel mathematical analysis of ecological dynamics permits a process-based understanding of conditions under which systems approach equilibrium, experience large oscillations, or persist in transient states.

Coherence of dengue incidence and climate in the wet and dry zones of Sri Lanka.

We studied the dynamics of dengue disease in two epidemic regions in Sri Lanka, the densely populated Colombo district representing the wet zone and the relatively less populated Batticaloa district representing the dry zone. Regional differences in disease dynamics were analysed against regional weather factors. Wavelets, Granger causality and regression methods were used.

Long-term persistence of monotypic dengue transmission in small size isolated populations, French Polynesia, 1978-2014.

Understanding the transition of epidemic to endemic dengue transmission remains a challenge in regions where serotypes co-circulate and there is extensive human mobility. French Polynesia, an isolated group of 117 islands of which 72 are inhabited, distributed among five geographically separated subdivisions, has recorded mono-serotype epidemics since 1944, with long inter-epidemic periods of circulation. Laboratory confirmed cases have been recorded since 1978, enabling exploration of dengue epidemiology under monotypic conditions in an isolated, spatially structured geographical location.

Intrinsic and extrinsic drivers of transmission dynamics of hemorrhagic fever with renal syndrome caused by Seoul hantavirus.

Seoul hantavirus (SEOV) has recently raised concern by causing geographic range expansion of hemorrhagic fever with renal syndrome (HFRS). SEOV infections in humans are significantly underestimated worldwide and epidemic dynamics of SEOV-related HFRS are poorly understood because of a lack of field data and empirically validated models. Here, we use mathematical models to examine both intrinsic and extrinsic drivers of disease transmission from animal (the Norway rat) to humans in a SEOV-endemic area in China.

Detecting dynamic spatial correlation patterns with generalized wavelet coherence and non-stationary surrogate data.

Time series measured from real-world systems are generally noisy, complex and display statistical properties that evolve continuously over time. Here, we present a method that combines wavelet analysis and non-stationary surrogates to detect short-lived spatial coherent patterns from multivariate time-series. In contrast with standard methods, the surrogate data proposed here are realisations of a non-stationary stochastic process, preserving both the amplitude and time-frequency distributions of original data.

Aedes mosquitoes acquire and transmit Zika virus by breeding in contaminated aquatic environments.

Zika virus (ZIKV) is a mosquito-borne flavivirus that predominantly circulates between humans and Aedes mosquitoes. Clinical studies have shown that Zika viruria in patients persists for an extended period, and results in infectious virions being excreted. Here, we demonstrate that Aedes mosquitoes are permissive to ZIKV infection when breeding in urine or sewage containing low concentrations of ZIKV.

Teleconnection between phytoplankton dynamics in north temperate lakes and global climatic oscillation by time-frequency analysis.

We are still facing the knowledge gap of how the water-quality extremes (i.e. phytoplankton blooms), their causes, severity or occurrence could be directly related to the climatic oscillation.

Comparison of stochastic and deterministic frameworks in dengue modelling.

We perform estimations of compartment models for dengue transmission in rural Cambodia with increasing complexity regarding both model structure and the account for stochasticity. On the one hand, we successively account for three embedded sources of stochasticity: observation noise, demographic variability and environmental hazard. On the other hand, complexity in the model structure is increased by introducing vector-borne transmission, explicit asymptomatic infections and interacting virus serotypes.

Transmission dynamics of re-emerging rabies in domestic dogs of rural China.

Despite ongoing efforts to control transmission, rabies prevention remains a challenge in many developing countries, especially in rural areas of China where re-emerging rabies is under-reported due to a lack of sustained animal surveillance. By taking advantage of detailed genomic and epidemiological data for the re-emerging rabies outbreak in Yunnan Province, China, collected between 1999 and 2015, we reconstruct the demographic and dispersal history of domestic dog rabies virus (RABV) as well as the dynamics of dog-to-dog and dog-to-human transmission. Phylogeographic analyses reveal a lower diffusion coefficient than previously estimated for dog RABV dissemination in northern Africa.

Dengue modeling in rural Cambodia: Statistical performance versus epidemiological relevance.

Dengue dynamics are shaped by the complex interplay between several factors, including vector seasonality, interaction between four virus serotypes, and inapparent infections. However, paucity or quality of data do not allow for all of these to be taken into account in mathematical models. In order to explore separately the importance of these factors in models, we combined surveillance data with a local-scale cluster study in the rural province of Kampong Cham (Cambodia), in which serotypes and asymptomatic infections were documented.

Accounting for non-stationarity in epidemiology by embedding time-varying parameters in stochastic models.

The spread of disease through human populations is complex. The characteristics of disease propagation evolve with time, as a result of a multitude of environmental and anthropic factors, this non-stationarity is a key factor in this huge complexity. In the absence of appropriate external data sources, to correctly describe the disease propagation, we explore a flexible approach, based on stochastic models for the disease dynamics, and on diffusion processes for the parameter dynamics.

Spatiotemporal variation of the association between climate dynamics and HFRS outbreaks in Eastern China during 2005-2016 and its geographic determinants.

Background: Hemorrhagic fever with renal syndrome (HFRS) is a rodent-associated zoonosis caused by hantavirus. The HFRS was initially detected in northeast China in 1931, and since 1955 it has been detected in many regions of the country. Global climate dynamics influences HFRS spread in a complex nonlinear way.

Urbanization prolongs hantavirus epidemics in cities.

Urbanization and rural-urban migration are two factors driving global patterns of disease and mortality. There is significant concern about their potential impact on disease burden and the effectiveness of current control approaches. Few attempts have been made to increase our understanding of the relationship between urbanization and disease dynamics, although it is generally believed that urban living has contributed to reductions in communicable disease burden in industrialized countries.