Predictors of indoor absolute humidity and estimated effects on influenza virus survival in grade schools

Background: Low absolute humidity (AH) has been associated with increased influenza virus survival and transmissibility and the onset of seasonal influenza outbreaks. Humidification of indoor environments may mitigate viral transmission and may be an important control strategy, particularly in schools where viral transmission is common and contributes to the spread of influenza in communities. However, the variability and predictors of AH in the indoor school environment and the feasibility of classroom humidification to levels that could decrease viral survival have not been studied. Methods: Automated sensors were used to measure temperature, humidity and CO2 levels in two Minnesota grade schools without central humidification during two successive winters. Outdoor AH measurements were derived from the North American Land Data Assimilation System. Variability in indoor AH within classrooms, between classrooms in the same school, and between schools was assessed using concordance correlation coefficients (CCC). Predictors of indoor AH were examined using time-series Auto-Regressive Conditional Heteroskedasticity models. Classroom humidifiers were used when school was not in session to assess the feasibility of increasing indoor AH to levels associated with decreased influenza virus survival, as projected from previously published animal experiments. Results: AH varied little within classrooms (CCC >0.90) but was more variable between classrooms in the same school (CCC 0.81 for School 1, 0.88 for School 2) and between schools (CCC 0.81). Indoor AH varied widely during the winter (range 2.60 to 10.34 millibars [mb]) and was strongly associated with changes in outdoor AH (p < 0.001). Changes in indoor AH on school weekdays were strongly associated with CO2 levels (p < 0.001). Over 4 hours, classroom humidifiers increased indoor AH by 4 mb, an increase sufficient to decrease projected 1-hour virus survival by an absolute value of 30% during winter months. Conclusions: During winter, indoor AH in non-humidified grade schools varies substantially and often to levels that are very low. Indoor results are predicted by outdoor AH over a season and CO2 levels (which likely reflects human activity) during individual school days. Classroom humidification may be a feasible approach to increase indoor AH to levels that may decrease influenza virus survival and transmission

Analysis of impacts of climate variability on malaria transmission in Sri Lanka and the development of an early warning system

This project set goals of bringing together a multi-disciplinary, multi-national and multi-institutional team to address the relationship between climate and malaria in aggregate terms for Sri Lanka, and in detail for the Uva Province to help develop early warning systems. The project was undertaken in partnership with Sri Lanka’s Anti-Malaria Campaign (AMC) and the Foundation for Environment, Climate and Technology, Sri Lanka (FECT). Overall, we have accomplished the projected outcomes in spite of several setbacks

Characterizing the Epidemiology of the 2009 Influenza A/H1N1 Pandemic in Mexico

Gerardo Chowell and colleagues address whether school closures and other social distancing strategies were successful in reducing pandemic flu transmission in Mexico by analyzing the age- and state-specific incidence of influenza morbidity and mortality in 32 Mexican states

Transmission Characteristics of the 2009 H1N1 Influenza Pandemic: Comparison of 8 Southern Hemisphere Countries

While in Northern hemisphere countries, the pandemic H1N1 virus (H1N1pdm) was introduced outside of the typical influenza season, Southern hemisphere countries experienced a single wave of transmission during their 2009 winter season. This provides a unique opportunity to compare the spread of a single virus in different countries and study the factors influencing its transmission. Here, we estimate and compare transmission characteristics of H1N1pdm for eight Southern hemisphere countries/states: Argentina, Australia, Bolivia, Brazil, Chile, New Zealand, South Africa and Victoria (Australia). Weekly incidence of cases and age-distribution of cumulative cases were extracted from public reports of countries' surveillance systems. Estimates of the reproduction numbers, R0, empirically derived from the country-epidemics' early exponential phase, were positively associated with the proportion of children in the populations (p = 0.004). To explore the role of demography in explaining differences in transmission intensity, we then fitted a dynamic age-structured model of influenza transmission to available incidence data for each country independently, and for all the countries simultaneously. Posterior median estimates of R0 ranged 1.2–1.8 for the country-specific fits, and 1.29–1.47 for the global fits. Corresponding estimates for overall attack-rate were in the range 20–50%. All model fits indicated a significant decrease in susceptibility to infection with age. These results confirm the transmissibility of the 2009 H1N1 pandemic virus was relatively low compared with past pandemics. The pattern of age-dependent susceptibility found confirms that older populations had substantial – though partial - pre-existing immunity, presumably due to exposure to heterologous influenza strains. Our analysis indicates that between-country-differences in transmission were at least partly due to differences in population demography

Local Spatial and Temporal Processes of Influenza in Pennsylvania, USA: 2003–2009

Background: Influenza is a contagious respiratory disease responsible for annual seasonal epidemics in temperate climates. An understanding of how influenza spreads geographically and temporally within regions could result in improved public health prevention programs. The purpose of this study was to summarize the spatial and temporal spread of influenza using data obtained from the Pennsylvania Department of Health's influenza surveillance system. Methodology and Findings: We evaluated the spatial and temporal patterns of laboratory-confirmed influenza cases in Pennsylvania, United States from six influenza seasons (2003-2009). Using a test of spatial autocorrelation, local clusters of elevated risk were identified in the South Central region of the state. Multivariable logistic regression indicated that lower monthly precipitation levels during the influenza season (OR = 0.52, 95% CI: 0.28, 0.94), fewer residents over age 64 (OR = 0.27, 95% CI: 0.10, 0.73) and fewer residents with more than a high school education (OR = 0.76, 95% CI: 0.61, 0.95) were significantly associated with membership in this cluster. In addition, time series analysis revealed a temporal lag in the peak timing of the influenza B epidemic compared to the influenza A epidemic. Conclusions: These findings illustrate a distinct spatial cluster of cases in the South Central region of Pennsylvania. Further examination of the regional transmission dynamics within these clusters may be useful in planning public health influenza prevention programs. © 2012 Stark et al

Prevalence of Acanthosis Nigricans in an urban population in Sri Lanka and its utility to detect metabolic syndrome

<p>Abstract</p> <p>Background</p> <p>Insulin resistance (IR) plays a major role in the pathogenesis of metabolic syndrome. Acanthosis nigricans (AN) is an easily detectable skin condition that is strongly associated with IR. The aims of this study were, firstly, to investigate the prevalence of AN among adults in an urban Sri Lankan community and secondly, to describe its utility to detect metabolic syndrome.</p> <p>Findings</p> <p>In a community based investigation, 35-64 year adults who were selected using stratified random sampling, underwent interview, clinical examination, liver ultrasound scanning, and biochemical and serological tests. Metabolic syndrome was diagnosed on revised ATP III criteria for Asian populations. AN was identified by the presence of dark, thick, velvety skin in the neck.</p> <p>2957 subjects were included in this analysis. The prevalence of AN, metabolic syndrome and type 2 diabetes mellitus were 17.4%, 34.8% and 19.6%, respectively. There was a strong association between AN and metabolic syndrome. The sensitivity, specificity, positive predictive value and negative predictive value of AN to detect metabolic syndrome were 28.2%, 89.0%, 45.9% and 79.0% for males, and 29.2%, 88.4%, 65.6% and 62.3% for females, respectively.</p> <p>Conclusions</p> <p>AN was common in our study population, and although it did not have a high enough sensitivity to be utilized as a screening test for metabolic syndrome, the presence of AN strongly predicts metabolic syndrome.</p

Climate Change and the Geographic Distribution of Infectious Diseases

Our ability to predict the effects of climate change on the spread of infectious diseases is in its infancy. Numerous, and in some cases conflicting, predictions have been developed, principally based on models of biological processes or mapping of current and historical disease statistics. Current debates on whether climate change, relative to socioeconomic determinants, will be a major influence on human disease distributions are useful to help identify research needs but are probably artificially polarized. We have at least identified many of the critical geophysical constraints, transport opportunities, biotic requirements for some disease systems, and some of the socioeconomic factors that govern the process of migration and establishment of parasites and pathogens. Furthermore, we are beginning to develop a mechanistic understanding of many of these variables at specific sites. Better predictive understanding will emerge in the coming years from analyses regarding how these variables interact with each other

Development of a new version of the Liverpool Malaria Model. II. Calibration and validation for West Africa

<p>Abstract</p> <p>Background</p> <p>In the first part of this study, an extensive literature survey led to the construction of a new version of the <it>Liverpool Malaria Model </it>(LMM). A new set of parameter settings was provided and a new development of the mathematical formulation of important processes related to the vector population was performed within the LMM. In this part of the study, so far undetermined model parameters are calibrated through the use of data from field studies. The latter are also used to validate the new LMM version, which is furthermore compared against the original LMM version.</p> <p>Methods</p> <p>For the calibration and validation of the LMM, numerous entomological and parasitological field observations were gathered for West Africa. Continuous and quality-controlled temperature and precipitation time series were constructed using intermittent raw data from 34 weather stations across West Africa. The meteorological time series served as the LMM data input. The skill of LMM simulations was tested for 830 different sets of parameter settings of the undetermined LMM parameters. The model version with the highest skill score in terms of entomological malaria variables was taken as the final setting of the new LMM version.</p> <p>Results</p> <p>Validation of the new LMM version in West Africa revealed that the simulations compare well with entomological field observations. The new version reproduces realistic transmission rates and simulated malaria seasons are comparable to field observations. Overall the new model version performs much better than the original model. The new model version enables the detection of the epidemic malaria potential at fringes of endemic areas and, more importantly, it is now applicable to the vast area of malaria endemicity in the humid African tropics.</p> <p>Conclusions</p> <p>A review of entomological and parasitological data from West Africa enabled the construction of a new LMM version. This model version represents a significant step forward in the modelling of a weather-driven malaria transmission cycle. The LMM is now more suitable for the use in malaria early warning systems as well as for malaria projections based on climate change scenarios, both in epidemic and endemic malaria areas.</p

Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance.

The development of reliable, mixed-culture biotechnological processes hinges on understanding how microbial ecosystems respond to disturbances. Here we reveal extensive phenotypic plasticity and niche complementarity in oleaginous microbial populations from a biological wastewater treatment plant. We perform meta-omics analyses (metagenomics, metatranscriptomics, metaproteomics and metabolomics) on in situ samples over 14 months at weekly intervals. Based on 1,364 de novo metagenome-assembled genomes, we uncover four distinct fundamental niche types. Throughout the time-series, we observe a major, transient shift in community structure, coinciding with substrate availability changes. Functional omics data reveals extensive variation in gene expression and substrate usage amongst community members. Ex situ bioreactor experiments confirm that responses occur within five hours of a pulse disturbance, demonstrating rapid adaptation by specific populations. Our results show that community resistance and resilience are a function of phenotypic plasticity and niche complementarity, and set the foundation for future ecological engineering efforts