Bernard Cazelles

Sorbonne University | UPMC · Ecologie et évolution - UMR 7625

Anthropogenic land-use change is an important driver of global biodiversity loss and threatens public health through biological interactions. Understanding these landscape–ecological effects at local scales will help achieve the United Nations Sustainable Development Goals by balancing urbanization, biodiversity and the spread of infectious disease...

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...

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...

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...

Spatial synchrony occurs when geographically separated time series exhibit correlated temporal variability. Studies of synchrony between different environmental variables within marine ecosystems worldwide have highlighted the extent of system responses to exogenous large-scale forcing. However, these spatial connections remain largely unstudied in...

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 Ch...

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 incid...

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

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 Her...

Sea surface temperature (SST) can influence the phytoplankton biomass, measured as sea surface chlorophyll concentration (SSCC), by affecting the physical and chemical properties of the seawater, living environment, and the consumption of zooplankton in a complex way. Yet, the quantitative assessment of the spatiotemporal variation of the inherent...

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...

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 testi...

Using 19 years of remotely sensed Enhanced Vegetation Index (EVI), we examined the effects of climatic variability on terrestrial vegetation of six protected areas along southwestern South America, from the semiarid edge of the Atacama desert to southern Patagonia (30°S-51°S). The relationship between satellite phenology and climate indices, namely...

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 deterministic...

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 testi...

Recent literature strongly supports the idea that mobility reduction and social distancing play a crucial role in transmission of SARS-Cov-2 infections. It was shown during the first wave that mobility restrictions reduce significantly infection transmission. Here we document another relationship and show that, in the period between the first two C...

Background: In Ireland and across the European Union, cases of COVID-19 continue to rise with recent increases in reported cases following a period of stability. Public health interventions continue in their attempts to control the epidemic in spite of a lack of information on the scale of silent transmission. Methods: To tackle this challenge and...

Recent literature strongly supports the idea that mobility reduction and social distancing play a crucial role in transmission of SARS-Cov-2 infections. It was shown during the first wave that mobility restrictions reduce significantly infection transmission. Here we document the reverse relationship by showing, between the first two Covid-19 waves...

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...

Introduction Since the emergence of SARS-CoV-2, governments have implemented a combination of public health responses based on non-pharmaceutical interventions (NPIs), with significant social and economic consequences. Quantifying the efficiency of different NPIs implemented by European countries to overcome the first epidemic wave could inform pre...

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 deterministic...

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 si...

Long-term ecological surveys (LTES) often exhibit strong variability among sampling dates. The use and interpretation of such interannual variability is challenging due to the combination of multiple processes involved and sampling uncertainty. Here, we analysed the interannual variability in ∼30 years of 150 species density (fish and invertebrate)...

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 us...

Over the last century climate change has impacted the physiology, distribution and phenology of marine and terrestrial primary producers worldwide. The study of these fluctuations has been hindered due to the complex response of plants to environmental forcing over large spatial and temporal scales. To bridge this gap, we investigated the synchrony...

Time series measured from real spatially extended 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 spatial coherent patterns in non- stationary multivariate time-series. In contrast with classic...

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...

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 ha...

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. Considering that the climatic and phytoplankton concentration time series are highly non-stationary, we applied the advanced time-frequency analysis - Ensemb...

Aim: We assess the spatial distribution of a suite of coastal biophysical characteristics and how their variability is related to the distribution and geographic range of a diverse assemblage of coastal benthic species with different larval dispersal strategies. Location: South-eastern Pacific (SEP) coast between 18°20’S and 42°35’S. Methods: Bioph...

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 Yun...

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,...

Test of the MCMC chains: Geweke diagnosis [67] that tests for the non-stationarity of the chains, the parameter means computed using the first 10% and the last 50% of the chain are compared through a Z-score (stationarity is not rejected if the Z-scores are below the critical values at 5%, NS (non-significant) in the Table). The test was implemente...

Test of the MCMC chains: Heidelberger and Welch’s diagnosis [68] that tests for the non-stationarity of the chains (NS (non-significant) in the Table meaning stationarity is not rejected at the 5% level). The test was implemented with the Coda package in R [66]. (PDF)

Reconstruction of both the incidence (A) and the time evolution β(t) (B) for the SIRS model as in Fig 1 but only 5 parameters have been inferred, β(0) and ρ were fixed. Model parameters as in Fig 1 and S6 Fig. (PDF)

As in S6 Fig but the logarithm transformation of the Brownian process of β(t) is not used: dθ(t) = σ.dB(t). (PDF)

Prior and posterior distributions for the parameters of the SIRS flu model. I(0), S(0) initial values expressed in percentage of the population N, β(0) initial value of β(t), i imported infectious, 1/α is the average duration of immunity, γ is the recovery rate and σ is the volatility of the Brownian process of β(t). The blue distributions are the...

Prior and posterior distributions for the SIRS model inferences displayed on Fig 3 when the initial conditions are near the attractor of the dynamics. A/ Observed data generated with a SIRS model and a sinusoidal β with 1 periodic component (5). B/ Observed data generated with a SIRS model and a sinusoidal β with 2 periodic components. In A/ and B/...

The trace of the MCMC chain and the prior and posterior distributions for the SIRS model inferences of S7 Fig when σ is the volatility of the Brownian process of β(t) is the only parameter inferred. (PDF)

Reconstruction of both the incidence (A) and the time evolution β(t) (B) for the SIRS model as in S4 Fig but the logarithm transformation of the Brownian process of β(t) is not used: dθ(t) = σ.dB(t). Model parameters as in Fig 1 and S4 Fig. (PDF)

Reconstruction of both the incidence (A) and the time evolution β(t) (B) with the true SIRS model. In (A) the black points are observations generated with a Poisson process with a mean equal to the incidence simulated by the model. In (B) the black points are the true values of β(t) = β0.(1 + β1 sin(2πt/365+2πϕ)). The blue lines are the median of t...

Simulation of the true SIRS model: (A) Susceptibles; (B) Infectious; (C) Time evolution of both Reff and β(t). In (A) and (B) the black lines are the true values, the blue lines are the median of the posterior, the mauve areas are the 50% CI and the light blue areas the 95% CI. In (C) the black line is the true values of Reff, the blue line is the...

Prior and posterior distributions for the SIRS model inferences of Fig 4. I(0), S(0) initial values, 1/α is the average duration of immunity, γ is the recovery rate and σ is the volatility of the Brownian process of εS(t). The blue distributions are the priors and the discrete histograms are the posteriors. The medians of the prior distributions fo...

Association between dengue transmission rate and monthly average maximum temperature recorded at the Phnom Penh International Airport (Cambodia). (A) Time evolution of the normalized β(t) (blue line) and normalized average temperature (red line). (B) and (C) Wavelet Power Spectrum (WPS) [48,70] of the two time series. The graph on the right shows t...

Prior and posterior distributions for the SIRS model inferences of Fig 1. I(0), S(0) initial values, β(0) initial value of β(t), 1/α is the average duration of immunity, γ is the recovery rate, ρ is the reporting rate and σ is the volatility of the Brownian process of β(t). The blue distributions are the priors and the discrete histograms are the p...

Simulation of the SIRS model when the initial conditions are near the attractor of the dynamics and just 5 parameters inferred: (A) Susceptibles; (B) Infectious; (C) Time evolution of both Reff and β(t). In (A) and (B) the black lines are the true values, the blue lines are the median of the posterior, the mauve areas are the 50% CI and the light b...

Prior and posterior distributions for the SIRS model inferences of S4 Fig. I(0), S(0) initial values, 1/α is the average duration of immunity, γ is the recovery rate and σ is the volatility of the Brownian process of β(t). The blue distributions are the priors and the discrete histograms are the posteriors. The medians of the prior distributions fo...

Reconstruction of both the incidence (A) and the time evolution β(t) (B) for the SIRS model as in Fig 1 but σ the volatility of the Brownian process of β(t) is the only parameter inferred. Model parameters as in Fig 1 and S8 Fig. (PDF)

Simulation of the SIRS model: (A) Susceptibles; (B) Infectious; (C) Time evolution of both Reff and εS(t). In (A) and (B) the black lines are the true values, the blue lines are the median of the posterior, the mauve areas are the 50% CI and the light blue areas the 95% CI. In (C) the black line is the true values of Reff, the blue line is the medi...

Prior and posterior distributions for the parameters of the SEIR dengue model. E(0), S(0) expressed in percentage of the population N, β(0) the initial value of β(t), γ is the recovery rate, i imported infectious, 1/α is the average duration of immunity, σ is the volatility of the Brownian process of β(t) and ρ the reporting rate. The blue distribu...

Association between dengue transmission rate and the Dipole Mode Index (DMI), a proxy of Ocean Indian Dipole (see [72] and http://www.jamstec.go.jp/frcgc/research/d1/iod/HTML/Dipole%20Mode%20Index.html). (A) Time evolution of the normalized β(t) (blue line) and normalized DMI (red line). (B) and (C) Wavelet Power Spectrum (WPS) [48,70] of the two t...

Examples of model files and code for using SSM. (ZIP)

The traces of the MCMC chain for the SIRS model inferences of Fig 1. I(0), S(0) initial values, β(0) initial value of β(t), 1/α is the average duration of immunity, γ is the recovery rate, ρ is the reporting rate and σ is the volatility of the Brownian process of β(t). (PDF)

Prior and posterior distributions for the true SIRS model inferences of S11 Fig. I(0), S(0) initial values, β0, β1, ϕ, the parameters of the sinusoidal β, 1/α is the average duration of immunity, γ is the recovery rate and ρ is the reporting rate. The blue distributions are the priors and the discrete histograms are the posteriors. The medians of t...

Association between dengue transmission rate and monthly average minimum temperature recorded at the Phnom Penh International Airport (Cambodia). (A) Time evolution of the normalized β(t) (blue line) and normalized average temperature (red line). (B) and (C) Wavelet Power Spectrum (WPS) [48,70] of the two time series. The graph on the right shows t...

Association between dengue transmission rate and monthly rainfall recorded at the Phnom Penh International Airport (Cambodia). (A) Time evolution of the normalized β(t) (blue line) and normalized monthly rainfall (red line). (B) and (C) Wavelet Power Spectrum (WPS) [48,70] of the two time series. The graph on the right shows the average WPS. (D) Wa...

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 fact...

Western Patagonia harbors unique and sparsely studied terrestrial ecosystems that are threatened by land use changes and exposure to basin-scale climatic variability. We assessed the performance of two satellite vegetation indices derived from MODIS — Terra, EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index), over the...

The 20th century has been a century where inequalities among countries, concerning health, have increased. Several factors can explain this pattern, such as immunization and massive antibiotherapy, but nutrition, housing, and hygiene are also key parameters for health improvement. This heterogeneity among countries is well illustrated by malaria, w...

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. The quantitative assessment of the sp...

The GBM# model (excluding spatial coordinates). (DOCX)

Land use types of study area in 2015. (TIF)

Distribution of HFRS incidence in the study area during the period 2005–2016. Population in 2015 was used to standardize the HFRS cases in each city. The unit of HFRS incidence is cases/100,000 population. (TIF)

Empirical and fitted theoretical covariance model of climate-HFRS association. A Composite space-time empirical and fitted theoretical covariance; B empirical and fitted theoretical covariance when T-lag equals to 0; C empirical and fitted theoretical covariance when S-lag equals to 0. (TIF)

Strength of the climate-HFRS association in 2015. (TIF)

Hotspot map of climate-HFRS association in 2005. (TIF)

Hotspot map of climate-HFRS association in 2006. (TIF)

Hotspot map of climate-HFRS association in 2007. (TIF)

Hotspot map of climate-HFRS association in 2009. (TIF)

Hotspot map of climate-HFRS association in 2014. (TIF)

Hotspot map of climate-HFRS association in 2016. (TIF)

Hotspot of the climate-HFRS association in various months during the period 2005–2016. (TIF)

Wavelet coherency spectra between global climate dynamics and HFRS infections at 127 cities in the study area. Purple line represents the cone of influence that delimits the region that is not influenced by edge effects; black line shows a = 5% significance level computed based on 500 bootstrap. (GIF)

Strength of the climate-HFRS association in 2005. (TIF)

Strength of the climate-HFRS association in 2007. (TIF)