Seasonal dynamics of bacterial meningitis: a time-series analysis

Background Bacterial meningitis, which is caused mainly by Neisseria meningitidis, Haemophilus infl uenzae, and Streptococcus pneumoniae, infl icts a substantial burden of disease worldwide. Yet, the temporal dynamics of this disease are poorly characterised and many questions remain about the ecology of the disease. We aimed to comprehensively assess seasonal trends in bacterial meningitis on a global scale. Methods We developed the fi rst bacterial meningitis global database by compiling monthly incidence data as reported by country-level surveillance systems. Using country-level wavelet analysis, we identifi ed whether a 12 month periodic component (annual seasonality) was detected in time-series that had at least 5 years of data with at least 40 cases reported per year. We estimated the mean timing of disease activity by computing the centre of gravity of the distribution of cases and investigated whether synchrony exists between the three pathogens responsible for most cases of bacterial meningitis. Findings We used country-level data from 66 countries, including from 47 countries outside the meningitis belt in sub-Saharan Africa. A persistent seasonality was detected in 49 (96%) of the 51 time-series from 38 countries eligible for inclusion in the wavelet analyses. The mean timing of disease activity had a latitudinal trend, with bacterial meningitis seasons peaking during the winter months in countries in both the northern and southern hemispheres. The three pathogens shared similar seasonality, but time-shifts diff ered slightly by country. Interpretation Our fi ndings provide key insight into the seasonal dynamics of bacterial meningitis and add to knowledge about the global epidemiology of meningitis and the host, environment, and pathogen characteristics driving these patterns. Comprehensive understanding of global seasonal trends in meningitis could be used to design more eff ective prevention and control strategies

Numerical Observation of Disorder-Induced Anomalous Kinetics in the A + A -> 0 Reaction

We address via numerical simulation the two-dimensional bimolecular annihilation reaction $A + A \to \emptyset$ in the presence of quenched, random impurities. Renormalization group calculations have suggested that this reaction displays anomalous kinetics at long times, $c_{A}(t) \sim at^{\delta -1}$, for certain types of topological or charged reactants and impurities. Both the exponent and the prefactor depend on the strength of disorder. The decay exponents determined from our simulations agree well with the values predicted by theory. The observed renormalization of the prefactor also agrees well with the values predicted by theory.Comment: 16 pages, 5 figures, uses Elsevier style elsart. To appear in Physica

Reactive Turbulent Flow in Low-Dimensional, Disordered Media

We analyze the reactions $A+A \to \emptyset$ and $A + B \to \emptyset$ occurring in a model of turbulent flow in two dimensions. We find the reactant concentrations at long times, using a field-theoretic renormalization group analysis. We find a variety of interesting behavior, including, in the presence of potential disorder, decay rates faster than that for well-mixed reactions.Comment: 6 pages, 4 figures. To appear in Phys. Rev.

Evaluating the impact of curfews and other measures on SARS-CoV-2 transmission in French Guiana.

While general lockdowns have proven effective to control SARS-CoV-2 epidemics, they come with enormous costs for society. It is therefore essential to identify control strategies with lower social and economic impact. Here, we report and evaluate the control strategy implemented during a large SARS-CoV-2 epidemic in June-July 2020 in French Guiana that relied on curfews, targeted lockdowns, and other measures. We find that the combination of these interventions coincided with a reduction in the basic reproduction number of SARS-CoV-2 from 1.7 to 1.1, which was sufficient to avoid hospital saturation. We estimate that thanks to the young demographics, the risk of hospitalisation following infection was 0.3 times that of metropolitan France and that about 20% of the population was infected by July. Our model projections are consistent with a recent seroprevalence study. The study showcases how mathematical modelling can be used to support healthcare planning in a context of high uncertainty

Monitoring the proportion of the population infected by SARS-CoV-2 using age-stratified hospitalisation and serological data: a modelling study.

BACKGROUND: Regional monitoring of the proportion of the population who have been infected by SARS-CoV-2 is important to guide local management of the epidemic, but is difficult in the absence of regular nationwide serosurveys. We aimed to estimate in near real time the proportion of adults who have been infected by SARS-CoV-2. METHODS: In this modelling study, we developed a method to reconstruct the proportion of adults who have been infected by SARS-CoV-2 and the proportion of infections being detected, using the joint analysis of age-stratified seroprevalence, hospitalisation, and case data, with deconvolution methods. We developed our method on a dataset consisting of seroprevalence estimates from 9782 participants (aged ≥20 years) in the two worst affected regions of France in May, 2020, and applied our approach to the 13 French metropolitan regions over the period March, 2020, to January, 2021. We validated our method externally using data from a national seroprevalence study done between May and June, 2020. FINDINGS: We estimate that 5·7% (95% CI 5·1-6·4) of adults in metropolitan France had been infected with SARS-CoV-2 by May 11, 2020. This proportion remained stable until August, 2020, and increased to 14·9% (13·2-16·9) by Jan 15, 2021. With 26·5% (23·4-29·8) of adult residents having been infected in Île-de-France (Paris region) compared with 5·1% (4·5-5·8) in Brittany by January, 2021, regional variations remained large (coefficient of variation [CV] 0·50) although less so than in May, 2020 (CV 0·74). The proportion infected was twice as high (20·4%, 15·6-26·3) in 20-49-year-olds than in individuals aged 50 years or older (9·7%, 6·9-14·1). 40·2% (34·3-46·3) of infections in adults were detected in June to August, 2020, compared with 49·3% (42·9-55·9) in November, 2020, to January, 2021. Our regional estimates of seroprevalence were strongly correlated with the external validation dataset (coefficient of correlation 0·89). INTERPRETATION: Our simple approach to estimate the proportion of adults that have been infected with SARS-CoV-2 can help to characterise the burden of SARS-CoV-2 infection, epidemic dynamics, and the performance of surveillance in different regions. FUNDING: EU RECOVER, Agence Nationale de la Recherche, Fondation pour la Recherche Médicale, Institut National de la Santé et de la Recherche Médicale (Inserm)

Retrieving the global distribution of the threshold of wind erosion from satellite data and implementing it into the Geophysical Fluid Dynamics Laboratory land–atmosphere model (GFDL AM4.0/LM4.0)

Dust emission is initiated when surface wind velocities exceed the threshold of wind erosion. Many dust models used constant threshold values globally. Here we use satellite products to characterize the frequency of dust events and land surface properties. By matching this frequency derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products with surface winds, we are able to retrieve a climatological monthly global distribution of the wind erosion threshold (Vthreshold) over dry and sparsely vegetated surfaces. This monthly two-dimensional threshold velocity is then implemented into the Geophysical Fluid Dynamics Laboratory coupled land–atmosphere model (AM4.0/LM4.0). It is found that the climatology of dust optical depth (DOD) and total aerosol optical depth, surface PM10 dust concentrations, and the seasonal cycle of DOD are better captured over the “dust belt” (i.e., northern Africa and the Middle East) by simulations with the new wind erosion threshold than those using the default globally constant threshold. The most significant improvement is the frequency distribution of dust events, which is generally ignored in model evaluation. By using monthly rather than annual mean Vthreshold, all comparisons with observations are further improved. The monthly global threshold of wind erosion can be retrieved under different spatial resolutions to match the resolution of dust models and thus can help improve the simulations of dust climatology and seasonal cycles as well as dust forecasting

Impact of Zika Virus Emergence in French Guiana: A Large General Population Seroprevalence Survey.

BACKGROUND: Since the identification of Zika virus (ZIKV) in Brazil in May 2015, the virus has spread throughout the Americas. However, ZIKV burden in the general population in affected countries remains unknown. METHODS: We conducted a general population survey in the different communities of French Guiana through individual interviews and serologic survey during June-October 2017. All serum samples were tested for anti-ZIKV immunoglobulin G antibodies using a recombinant antigen-based SGERPAxMap microsphere immunoassay, and some of them were further evaluated through anti-ZIKV microneutralization tests. RESULTS: The overall seroprevalence was estimated at 23.3% (95% confidence interval [CI], 20.9%-25.9%) among 2697 participants, varying from 0% to 45.6% according to municipalities. ZIKV circulated in a large majority of French Guiana but not in the most isolated forest areas. The proportion of reported symptomatic Zika infection was estimated at 25.5% (95% CI, 20.3%-31.4%) in individuals who tested positive for ZIKV. CONCLUSIONS: This study described a large-scale representative ZIKV seroprevalence study in South America from the recent 2015-2016 Zika epidemic. Our findings reveal that the majority of the population remains susceptible to ZIKV, which could potentially allow future reintroductions of the virus

Analysing Spatio-Temporal Clustering of Meningococcal Meningitis Outbreaks in Niger Reveals Opportunities for Improved Disease Control

Meningococcal meningitis (MM) is an infection of the meninges caused by a bacterium, Neisseria meningitidis, transmitted through respiratory and throat secretions. It can cause brain damage and results in death in 5–15% of cases. Large epidemics of MM occur almost every year in sub-Saharan Africa during the hot, dry season. Understanding how epidemics emerge and spread in time and space would help public health authorities to develop more efficient strategies for the prevention and the control of meningitis. We studied the spatio-temporal distribution of MM cases in Niger from 2002 to 2009 at the scale of the health centre catchment areas (HCCAs). We found that spatial clusters of cases most frequently occurred within nine districts out of 42, which can assist public health authorities to better adjust allocation of resources such as antibiotics or rapid diagnostic tests. We also showed that the epidemics break out in different HCCAs from year to year and did not follow a systematic geographical direction. Finally, this analysis showed that surveillance at a finer spatial scale (health centre catchment area rather than district) would be more efficient for public health response: outbreaks would be detected earlier and reactive vaccination would be better targeted

Comparing the Performance of Three Models Incorporating Weather Data to Forecast Dengue Epidemics in Reunion Island, 2018-2019

We developed mathematical models to analyze a large dengue virus (DENV) epidemic in Reunion Island in 2018-2019. Our models captured major drivers of uncertainty including the complex relationship between climate and DENV transmission, temperature trends, and underreporting. Early assessment correctly concluded that persistence of DENV transmission during the austral winter 2018 was likely and that the second epidemic wave would be larger than the first one. From November 2018, the detection probability was estimated at 10%-20% and, for this range of values, our projections were found to be remarkably accurate. Overall, we estimated that 8% and 18% of the population were infected during the first and second wave, respectively. Out of the 3 models considered, the best-fitting one was calibrated to laboratory entomological data, and accounted for temperature but not precipitation. This study showcases the contribution of modeling to strengthen risk assessments and planning of national and local authorities