The Influence of Meteorology on the Spread of Influenza: Survival Analysis of an Equine Influenza (A/H3N8) Outbreak

The influences of relative humidity and ambient temperature on the transmission of influenza A viruses have recently been established under controlled laboratory conditions. The interplay of meteorological factors during an actual influenza epidemic is less clear, and research into the contribution of wind to epidemic spread is scarce. By applying geostatistics and survival analysis to data from a large outbreak of equine influenza (A/H3N8), we quantified the association between hazard of infection and air temperature, relative humidity, rainfall, and wind velocity, whilst controlling for premises-level covariates. The pattern of disease spread in space and time was described using extraction mapping and instantaneous hazard curves. Meteorological conditions at each premises location were estimated by kriging daily meteorological data and analysed as time-lagged time-varying predictors using generalised Cox regression. Meteorological covariates time-lagged by three days were strongly associated with hazard of influenza infection, corresponding closely with the incubation period of equine influenza. Hazard of equine influenza infection was higher when relative humidity was <60% and lowest on days when daily maximum air temperature was 20–25°C. Wind speeds >30 km hour−1 from the direction of nearby infected premises were associated with increased hazard of infection. Through combining detailed influenza outbreak and meteorological data, we provide empirical evidence for the underlying environmental mechanisms that influenced the local spread of an outbreak of influenza A. Our analysis supports, and extends, the findings of studies into influenza A transmission conducted under laboratory conditions. The relationships described are of direct importance for managing disease risk during influenza outbreaks in horses, and more generally, advance our understanding of the transmission of influenza A viruses under field conditions

Multiple drug-delivery strategies to enhance the pharmacological and toxicological properties of Mefenamic acid

Objective: To improve the biological and toxicological properties of Mefenamic acid (MA), the galactosylated prodrug of MA named MefeGAL was included in polymeric solid dispersions (PSs) composed of poly(glycerol adipate) (PGA) and Pluronic® F68 (MefeGAL-PS). MefeGAL-PS was compared with polymeric solid formulations of MA (MA-PS) or a mixture of equal ratio of MefeGAL/MA (Mix-PS). Methods: The in vitro and in vivo pharmacological and toxicological profiles of PSs have been investigated. In detail, we evaluated the anti-inflammatory (carrageenan-induced paw edema test), analgesic (acetic acid-induced writhing test) and ulcerogenic activity in mice after oral treatment. Additionally, the antiproliferative activity of PSs was assessed on in vitro models of colorectal and non-small cell lung cancer. Results: When the PSs were resuspended in water, MefeGAL's, MA's and their mixture's apparent solubilities improved due to the interaction with the polymeric formulation. By comparing the in-vivo biological performance of MefeGAL-PS with that of MA, MefeGAL and MA-PS, it was seen that MefeGAL-PS exhibited the same sustained and delayed analgesic and anti-inflammatory profile as MefeGAL but did not cause gastrointestinal irritation. The pharmacological effect of Mix-PS was present from the first hours after administration, lasting about 44 hours with only slight gastric mucosa irritation. In-vitro evaluation indicated that Mix-PS had statistically significant higher cytotoxicity than MA-PS and MefeGAL-PS. Conclusions: These preliminary data are promising evidence that the galactosylated prodrug approach in tandem with a polymer-drug solid dispersion formulation strategy could represent a new drug delivery route to improve the solubility and biological activity of NSAIDs

The crude relationship between hazard of infection and maximum daily wind speed, <i>from all directions</i>, in the largest cluster of the 2007 outbreak of equine influenza outbreak, by time lag.

<p>Estimates are based on hourly wind data from all directions and time-lagged by 1–4 days. Dashed lines represent 95% confidence intervals.</p

Univariable analysis of the association between meteorological covariates (time-changing and time-lagged) and time to infection of premises in the largest cluster (n = 3153), northwest of Sydney, during the 2007 equine influenza outbreak in Australia.

a<p><i>P</i>-values derived from likelihood ratio tests (LRT) comparing univariable to null Cox regression models.</p>b<p>Maximum daily wind speed based on wind from all directions (‘undirected’), making no assumption concerning nearest infected premises assumption.</p

Smoothing of daily meteorological data and estimation of the association with premises-level hazard of infection in the largest cluster of the 2007 outbreak of equine influenza in Australia.

<p>Daily meteorological data provided by Australian Bureau of Meteorology weather stations (white closed circles) were smoothed using kriging, and time-lagged by 1–5 days. (a) Smoothed estimate of relative humidity measured at 3 pm on Day 20 of the outbreak. Small grey dots denote the horse premises. (b) Restricted cubic splines of the crude relationship between hazard of infection and relative humidity (3 pm measurement) at time-lags of 1–5 days over the entire study period. (c) Smoothed daily maximum air temperature on Day 20 and (d) the relationship between daily maximum air temperature and hazard of infection, by time lag.</p

Map of Australia showing the area affected by the 2007 outbreak of equine influenza and the study extent.

<p>(a) From August–December 2007, around 70,000 horses were infected on over 9000 horse premises in two Australian States. (b) This study focused on the largest cluster (n = 3624 horse premises), northwest of Sydney, as defined by a 15 km buffer around the nine earliest infected premises (depicted in yellow) that were contact-traced to events where disease transmission was known to have occurred in the first week of the outbreak. Clinical signs were first observed on 17 August 2007 in a horse in quarantine at Eastern Creek Quarantine Station (red closed circle). The cluster is surrounded by national parks and Sydney urban areas. (For interpretation of the references to colour in this text, the reader is referred to the web version of the article.).</p

Crude nonlinear relationships between hazard of infection and non-meteorological covariates in the largest cluster of the 2007 outbreak of equine influenza in Australia.

<p>(a) The relationship between hazard of infection and premises area, and (b) the relationship between hazard of infection and local human population density (people residing within approximately 1 km of the horse premises). Dashed lines represent 95% confidence intervals.</p

Spatial spread of equine influenza in the largest cluster of the 2007 outbreak in Australia.

<p>Surfaces of log relative risk were estimated in 4-week intervals using adaptive kernel estimation, with upper 95% tolerance contours (solid white lines). With this method the amount of smoothing (bandwidth) is inversely proportional to the density of the population at risk.</p

The crude relationship between hazard of infection and maximum daily wind speed selected from the direction of the <i>k</i> nearest infected premises (time-lagged by 3 days) in the largest cluster of the 2007 outbreak of equine influenza in Australia.

<p>Estimates are based only on hourly wind data from within 45° arcs centred on the direction of the <i>k</i> nearest infected premises, for <i>k</i> = 1,2,3. Arcs may overlap if nearest <i>k</i> infected premises are in the same direction (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035284#pone-0035284-g002" target="_blank">Figure 2c</a> for details). Dashed lines represent 95% confidence intervals.</p

The estimation of premises-level wind speed covariates in a survival analysis of time to infection in the largest cluster of the 2007 outbreak of equine influenza in Australia.

<p>(a) Exponential covariance function (with practical range = 0.25) and its related semivariance function. (b) Hourly wind velocity data from sixteen automated weather stations (open circles) within a 20 km buffer of the cluster's boundary were converted into their East-to-West (‘u’) and North-to-South (‘v’) components, and smoothed using kriging to predict hourly wind speed and direction at each premises (small grey dots). (c) For each premises on each day prior to infection or censoring, the (‘directed’) maximum wind speed originating from within 45( arcs centred on the direction of the nearest 1–3 infected premises was estimated for time lags of 1–5 days.</p